GUSTAVO MACHADO SANTAELLA O impacto na qualidade ...

UNIVERSIDADE ESTADUAL DE CAMPINAS

Faculdade de Odontologia de Piracicaba

GUSTAVO MACHADO SANTAELLA

O impacto na qualidade de imagem e interpretabilidade com artefatos de movimento em

exames de tomografia computadorizada de feixe cônico adquiridos com geometria total e

parcial de exposição

PIRACICABA

2019

GUSTAVO MACHADO SANTAELLA

O impacto na qualidade de imagem e interpretabilidade com artefatos de movimento em

exames de tomografia computadorizada de feixe cônico adquiridos com geometria total e

parcial de exposição

Tese apresentada à Faculdade de Odontologia

de Piracicaba da Universidade Estadual de

Campinas como parte dos requisitos exigidos

para a obtenção do título de Doutor em

Radiologia Odontológica, na Área de

Radiologia Odontológica

Orientador: Prof. Dr. Pedro Luiz Rosalen

Este trabalho corresponde à versão final

da tese defendida pelo aluno Gustavo

Machado Santaella, e orientada pelo

Prof. Dr. Pedro Luiz Rosalen.

PIRACICABA

2019

Identificação e informações acadêmicas e profissionais do aluno - ORCID: 0000-0002-0884-2443 - Currículo Lattes: http://lattes.cnpq.br/4989918323268952

AGRADECIMENTOS

O presente trabalho foi realizado com apoio da Coordenação de Aperfeiçoamento

de Pessoal de Nível Superior – Brasil (CAPES) - Código de Financiamento 001.

Agradeço ao meu orientador Prof. Dr. Pedro Luiz Rosalen, que topou o desafio de

orientar um aluno de uma outra área e o fez com maestria, dando liberdade e muito incentivo

para que o trabalho fosse executado da forma como foi.

Aos professores Dr. Rubens Spin-Neto e Ann Wenzel, Dr. Odont., e à

Universidade de Aarhus, na Dinamarca, por me receberem tão bem e, principalmente, por

ajudarem e cederem a metodologia desenvolvida por eles para que esse trabalho pudesse ser

desenvolvido. Com certeza a execução desse não seria possível sem essa colaboração.

À Universidade Estadual de Campinas, na pessoa do Magnífico Reitor Prof. Dr.

Marcelo Knobel.

À Faculdade de Odontologia de Piracicaba, na pessoa do Senhor Diretor Prof. Dr.

Francisco Haiter Neto.

Ao Programa de Pós-Graduação em Radiologia Odontológica da FOP-UNICAMP,

na pessoa da Senhora Coordenadora Profa. Dra. Deborah Queiroz de Freitas França.

Aos meus pais, José Francisco e Maria de Fátima, por tudo que me

proporcionaram nessa caminhada. Dificilmente essa jornada seria feita sem o apoio

emocional e financeiro que tenho recebido, e serei eternamente grato por tudo.

Aos meus avós paternos e padrinhos Miguel e Maria de Lourdes que foram a

minha família mais próxima nos últimos anos, sempre de portas abertas para me receber.

Muito obrigado!

Aos meus avós maternos Sebastiana e José (que Deus o tenha), por todo o amor

e apoio recebido mesmo à distância.

Ao meu irmão Thiago pela amizade e companheirismo.

À Danieli Moura Brasil por todos os ótimos momentos vividos e compartilhados

nos últimos anos, pelo carinho recebido, e por todos nossos planos compartilhados, tanto os

já realizados quanto os que estão ainda a serem alcançados.

Ao professor Dr. Francisco Haiter Neto, por estar sempre disposto a ajudar e pelo

contato inicial com os professores da Dinamarca, que levou à realização deste trabalho.

Aos professores Dra. Deborah Queiroz de Freitas França e Dr. Matheus Lima de

Oliveira, por todo o esforço que fazem pelo programa e pelos alunos para que possamos

crescer juntos. Com certeza seremos sempre gratos por tudo que vocês nos proporcionam.

Aos professores Dr. Frab Norberto Boscolo e Dra. Solange Maria de Almeida

Boscolo pelos valiosos ensinamentos compartilhados.

Aos professores Dra. Anne Caroline Costa Oenning, Dr. Claudio Costa, Dra.

Deborah Queiroz de Freitas França e Dr. Luiz Roberto Coutinho Manhães Junior pelas

contribuições dadas a esse trabalho no exame de defesa de tese de doutorado.

Aos professores Dr. Matheus Lima de Oliveira, Dr. Yuri Martins Costa e Dr. Yuri

Nejaim pelas considerações e colaborações dadas a este trabalho no exame de qualificação.

Aos funcionários da Área de Radiologia Odontológica, Luciane Sattolo, Fernando

Andrade, Waldeck Moreira e Sarah do Amaral Bacchim pela dedicação ao trabalho na

faculdade.

À grande amiga e colega Polyane Mazucatto Queiroz, vulgo “dupla de doutorado”,

por todo o incentivo, apoio e troca de ideias nessa busca pelo título de Doutor. A ela devo

muito do que conquistei cientificamente.

Aos amigos de turma de mestrado e doutorado Eliana Dantas Da Costa, Leonardo

Vieira Peroni, Luciana Jácome Lopes e Mayra Cristina Yamasaki por todo o companheirismo

nessa caminhada.

A todos os amigos do programa de Radiologia Odontológica e colegas de pós-

graduação da FOP-UNICAMP, pelo tempo convivido e aprendizado compartilhado. Com

certeza, de uma maneira ou de outra, vocês ajudaram muito nessa jornada.

RESUMO

A tomografia computadorizada de feixe cônico (TCFC) se baseia no uso de um aparelho

contendo uma fonte e um detector de raios X. A forma como este feixe incide no detector

define se a exposição é alinhada com o campo de visão (FOV) e o detector, ou se o detector é

deslocado para um dos lados, configurando uma geometria parcial de exposição. É também

bem descrito na literatura que movimentos realizados pelo paciente durante a aquisição

destas imagens podem influenciar negativamente na imagem final, resultando na formação

de artefatos. Além disso, novos aparelhos possuem algoritmos para correção destes artefatos

de movimento. Desta forma, este estudo objetivou avaliar a aquisição de imagens com

detector alinhado e deslocado e a influência na qualidade e na interpretabilidade de imagens

de TCFC com artefatos de movimento. Além disso, testou-se a eficácia de dois métodos de

redução de artefatos de movimento nas diferentes geometrias de exposição. Para isso, foram

utilizados um fantoma de cera utilidade, um crânio humano afixado em um robô programado

para executar diferentes movimentos, e diversos equipamentos de TCFC com protocolos de

geometria total e parcial de exposição, como o Cranex 3Dx, Ortophos SL, Picasso Trio, Promax

3D Mid, Scanora 3D e X1. Dois desses equipamentos apresentavam ferramentas de redução

de artefatos de movimento (Promax 3D Mid e X1). As imagens técnicas do fantoma foram

avaliadas no software ImageJ por um único avaliador, onde foram obtidos a média e o desvio

padrão dos valores de voxel de treze regiões de interesse em diferentes posições dentro do

volume. As imagens clínicas com o crânio foram aleatorizadas e avaliadas por 3 avaliadores

experientes no software OnDemand3D, onde foram descritas a presença de artefatos de

movimento, perda de nitidez e interpretabilidade dessas imagens em três regiões de

interesse. Quando comparados com protocolos com o detector alinhado, as imagens

adquiridas por protocolos de TCFC com geometria parcial apresentaram variações na

distribuição dos valores de voxel dentro do campo de visão, e os artefatos de movimento

foram percebidos apenas parcialmente no campo visão, afetando principalmente as regiões

sendo adquiridas no momento da movimentação. As ferramentas de redução de artefatos de

movimentos testadas foram eficazes na interpretabilidade em 97,2% dos casos para

protocolos de detector alinhado, porém para detectores deslocados essa eficácia foi menor

(42,6%). Desta forma, a aquisição de imagens de TCFC utilizando uma geometria de exposição

parcial pode alterar a distribuição dos valores de voxel dentro do FOV e afeta diretamente a

forma como os artefatos de movimento se apresentam dentro da imagem e sua

interpretabilidade em tarefas diagnósticas. Além disso, ela compromete a eficácia da

ferramenta de compensação de artefatos de movimentos presente em um dos aparelhos

testados (Promax 3D Mid).

Palavras-chave: tomografia computadorizada de feixe cônico, artefatos, intensificação de

imagem radiográfica

ABSTRACT

Cone Beam Computed Tomography (CBCT) is based on the use of a unit containing an X-ray

source and a detector. The way the beam is exposed defines whether the exposure is aligned

with the field of view (FOV) and the detector, or if the detector is offset to one side by

configuring a partial exposure geometry. It is also well described in the literature that

movements performed by the patient during the acquisition of these images can negatively

influence the final image, resulting in the formation of artifacts. In addition, new devices have

algorithms for correction of these movement artifacts. In this way, this study aimed to

evaluate the acquisition of images with aligned and lateral-offset detectors and the influence

on the quality and the interpretability of CBCT images with motion artefacts. In addition, the

effectiveness of methods of reducing movement artefacts in different exposure geometries

was tested. To do this, we used a utility wax phantom, and a human skull affixed in a robot

programmed to perform different movements, and several CBCT equipment with aligned and

partial geometry exposure protocols, such as Cranex 3Dx, Ortophos SL, Picasso Trio, Promax

3D Mid, Scanora 3D and X1. Two of these devices featured motion artefacts reduction tools

(Promax 3D Mid and X1). The phantom images were evaluated in the ImageJ software by a

single evaluator, where the mean and standard deviation of the voxel values of thirteen

regions of interest were obtained at different positions within the volume. The clinical images

with the skull were randomized and evaluated by 3 experienced evaluators in the software

OnDemand3D, where they were described the presence of movement artifacts, loss of

sharpness and interpretability of these images in three regions of interest. When compared

with protocols with the aligned detector, the images acquired by protocols with an offset

detector showed variations in the distribution of voxel values within the field of view, and the

motion artefacts were only partially observed in the FOV, affecting mainly the regions being

acquired at the moment of the movement. The artefact reduction tools tested were effective

in interpretability in 97.2% of cases for aligned detector protocols, but for offset detectors this

efficacy was lower (42.6%). Thus, the acquisition of CBCT images using a partial exposure

geometry can alter the distribution of voxel values within the FOV and directly affects the way

motion artefacts appear within the image and their interpretability in diagnostic tasks. In

addition, it compromises the effectiveness of the motion artefact compensation tool present

in one of the tested devices (Promax 3D Mid).

Keywords: cone beam computed tomography, artifacts, radiographic image enhancement.

SUMÁRIO

1 INTRODUÇÃO ...............................................................................................................................................10

2 ARTIGOS.......................................................................................................................................................13

2.1 ARTIGO: QUANTITATIVE ASSESSMENT OF CBCT IMAGE QUALITY VARIATION RELATED TO CBCT-DETECTOR LATERAL-OFFSET

POSITION .......................................................................................................................................................13

2.2 ARTIGO: THE IMPACT OF MOVEMENT ON IMAGE QUALITY AND INTERPRETABILITY IN CBCT DEVICES WITH ALIGNED AND

LATERAL-OFFSET DETECTORS ...............................................................................................................................27

3 DISCUSSÃO ..................................................................................................................................................46

4 CONCLUSÃO .................................................................................................................................................48

REFERÊNCIAS* .................................................................................................................................................49

ANEXOS ..........................................................................................................................................................52

ANEXO 1 – CARTA DE ISENÇÃO DE NECESSIDADE DE APROVAÇÃO EM COMITÊ DE ÉTICA (DINAMARCA) ...................................52

ANEXO 2 – CARTA DE ISENÇÃO DE NECESSIDADE DE APROVAÇÃO EM COMITÊ DE ÉTICA (BRASIL) ..........................................53

ANEXO 3 – VERIFICAÇÃO DE ORIGINALIDADE E PREVENÇÃO DE PLÁGIO ...........................................................................54

ANEXO 4 – COMPROVANTE DE SUBMISSÃO DO ARTIGO PARA REVISTA CIENTÍFICA .............................................................55

10

1 INTRODUÇÃO

A tomografia computadorizada de feixe cônico (TCFC), desenvolvida na década de

90 (Mozzo et al., 1998; Arai et al., 1999), se baseia no uso de um aparelho contendo uma fonte

de raios X, que emite um feixe em formato cônico ou piramidal, e um detector de raios X. O

paciente é posicionado entre essas duas partes, que rotacionam ao seu redor. Essa rotação

pode ser total (360o) ou parcial (~180o - 210o) dependendo do aparelho e do protocolo de

aquisição. O feixe resultante ao atingir o detector é convertido em diversas imagens base, que

são imagens radiográficas bidimensionais. Estas, por sua vez, passam por um algoritmo e são

reconstruídas em imagens axiais do paciente, para que possam ser avaliadas posteriormente

em um software (Scarfe e Farman, 2008; Pauwels et al., 2015a).

As imagens digitais são formadas por pixels. Em imagens radiográficas, um pixel

pode representar apenas uma tonalidade de cinza. Um fator importante em imagens digitais

é a quantidade de tons de cinza que uma imagem pode apresentar, isto é, a quantidade de

pixels com diferentes tons de cinza cada um. Por isso, diz-se que quanto mais tons de cinza

disponíveis em uma imagem, representada pela profundidade de bits desta, diz-se que tem

uma maior resolução de contraste. E quanto menor o tamanho de pixel da imagem, maior a

sua resolução espacial, e com isso maior a capacidade de representação de detalhes de uma

imagem. Em TCFC, após o processo de reconstrução das imagens, estas imagens axiais

bidimensionais são sobrepostas por um software para serem avaliadas tridimensionalmente,

e por isso passam a ser chamadas de voxel (Pauwels et al., 2012; Scarfe et al., 2017).

Antes da aquisição das imagens, um fator importante a ser determinado é o

tamanho do FOV (field of view – campo de visão) que será utilizado. A área de interesse do

exame deve estar contida dentro das imagens adquiridas. Para isso, pode-se optar por um

volume maior, contendo toda a região de região de interesse, ou mais de um volume com FOV

menores (Pauwels et al., 2015a). Para aquisição de volumes maiores, o tamanho do detector

pode ser um fator limitante, pois é necessário que toda informação a ser reconstruída esteja

contida nas projeções base para que seja possível a reconstrução. Porém, como uma forma

de se obter FOV maiores com a utilização de receptores menores, o equipamento pode ter

um deslocamento do feixe de projeção e do detector para fora do eixo central do FOV (Figuras

1 e 2). Desta forma, durante uma aquisição com 360o de rotação, o centro do FOV é irradiado

11

durante toda a rotação, enquanto que as periferias por apenas metade da rotação (Scarfe e

Farman, 2008; Molteni, 2013; White e Pharoah, 2013).

Figura 1. Esquema de aquisição em projeção total (esquerda) e parcial (direita) com um detector de mesmo tamanho. (Fonte: Scarfe e Farman, 2008)

Figura 2. Aquisição em geometria parcial para o equipamento NewTom VGi. (Fonte: Molteni, 2013)

O processo de reconstrução das imagens base em axiais vem de um algoritmo

conhecido como retroprojeção filtrada, que foi adaptado para imagens obtidas por meio de

feixe cônico por Feldkamp, Davis e Kress (Feldkamp et al., 1984; Schulze et al., 2011). Essa

fórmula exige que o objeto a ser reconstruído se mantenha estático durante toda aquisição

das imagens, para que os artefatos na imagem reconstruída sejam reduzidos (Schulze et al.,

2011).

Artefatos são alterações na imagem que não representam corretamente o objeto

escaneado. São resultado de discrepâncias entre o processo de aquisição das imagens base e

o algoritmo utilizado para reconstrução matemática. Dentre os diferentes tipos de artefatos

observados nas imagens de TCFC, temos os de ausência de sinal, endurecimento do feixe de

12

radiação, efeito do volume parcial, subamostragem, em anel e os de movimento (Barrett e

Keat, 2004; Schulze et al., 2011).

Os artefatos de movimento são causados por um movimento do paciente durante

a aquisição das imagens base, o que leva a um erro geométrico de exposição. Desta forma,

viola-se o princípio da retroprojeção filtrada, onde o objeto deve permanecer completamente

estático e alinhado entre as múltiplas projeções base. Com isso há uma perda de nitidez, ou

uma informação duplicada nas imagens reconstruídas (Schulze et al., 2011; Spin-Neto et al.,

2013).

Alguns métodos foram desenvolvidos para detectar movimentos durante a

aquisição. Estes podem ser pela utilização de câmeras de precisão que percebem

movimentações, ou por dispositivos com acelerômetro fixados ao paciente (Spin-Neto et al.,

2017b), ou pela detecção dos movimentos a partir da avaliação das imagens base (Schulze et

al., 2015). Com isso, alguns algoritmos conseguem implementar correções para estes

movimentos durante a reconstrução dessas imagens, resultando em imagens com qualidade

adequada para interpretação em algumas tarefas diagnósticas (Spin-Neto et al., 2018b).

Sabendo da prevalência desses tipos de artefatos, o quanto comprometem a

imagem e da relação direta desses com fatores geométricos de aquisição e reconstrução da

imagem tomográfica, é necessário avaliar aspectos que possam afetar na formação desses

artefatos em imagens de diferentes equipamentos de TCFC. Os objetivos do presente estudo

são avaliar a influência da aquisição de imagens de TCFC em geometria parcial ou total na

qualidade e na interpretabilidade de imagens adquiridas com diferentes tipos de

movimentação em momentos distintos durante o exame. Além disso, testou-se a eficácia de

métodos de redução de artefatos de movimento nas diferentes geometrias de exposição.

13

2 ARTIGOS

2.1 Artigo: Quantitative assessment of CBCT image quality variation related to CBCT-

detector lateral-offset position

Authors: Gustavo Machado Santaella, Pedro Luiz Rosalen, Polyane Mazucatto Queiroz,

Francisco Haiter-Neto, Ann Wenzel, Rubens Spin-Neto

Abstract

Objectives: To assess the effect of CBCT detector position (aligned or lateral-offset) on image

quality parameters, by the mean voxel value difference/MVVD and standard deviation of voxel

value/SDVV.

Methods: Forty CBCT volumes of a cylindrical utility wax phantom centralized in the field-of-

view (FOV) were acquired in six units with aligned and offset detectors: Cranex 3Dx (CRA),

Ortophos SL (ORT), Picasso Trio (PIC), Promax 3D Mid (PRO), Scanora 3D (SCA), and X1. Eight

image-acquisition protocols were selected to provide four protocols with the detector aligned

(CRA, ORT, PRO, X1), and four protocols with the offset detector (CRA, PIC, PRO, SCA). In all

volumes, thirteen regions-of-interest (ROIs) inside the FOV were evaluated and MVVD (the

percentage of difference to the central ROI) and SDVV were obtained.

Results: MVVD for units with aligned detectors ranged from -32.8% to 22.8%, and from -20.7%

to 69.5% for units with offset detectors. SDVV for most aligned detectors was lower near the

FOV centre, while for the units with offset detectors it was lower for the peripheral ROIs,

except for one unit (PIC).

Conclusion: The use of an offset detector to acquire CBCT images lead to increased MVVD

ranges and modified SDVV distribution inside the FOV compared to the use of an aligned

detector.

Keywords: cone beam computed tomography, artefacts, radiographic image enhancement

Submitted to Dentomaxillofacial Radiology.

14

Introduction

Cone beam computed tomography (CBCT) volumes are acquired using an X-ray source and a

detector that rotates around the patient, acquiring multiple two-dimensional images, often

called basis images (or raw projections), that are posteriorly reconstructed into a three-

dimensional volume.1–3 Due to the divergence of the X-rays, the cone- or pyramidal-shaped

beam1 emitted by the source hits the detector (i.e. image receptor) with different angulations.

The central beam is perpendicular (90º) to the detector and increases the accuracy of the

method used for image reconstruction,4 while the peripheral beams have more acute angles,

which cause variations in the reconstructed image and may degrade image quality (i.e.

introduction of noise and artefacts).1,5,6

Multiple studies have been conducted considering the variations in voxel values

depending on the position of the patient/object in the field-of–view (FOV).7–11 However, little

attention has been given on how the basis images are acquired, focusing mainly on the final

reconstructed volume. This can be related to the fact that data available for these projections

is rarely provided by the manufacturers, and to difficulties accessing such images. One can

speculate that the central region in the volume (i.e. its midplane), is reconstructed based on

the region where the central X-ray beam hits the detector perpendicularly, while the edges of

the volume are related to the peripheral beams. But this is not always the case. Some

manufacturers do use a detector aligned with the X-ray source, in which the central beam is

aligned with the central part of the detector and with the midplane of the FOV. This results in

basis images in which the entire FOV is seen, in all images. In a 15x13 cm FOV, for example,

the detector must have dimensions larger than those, considering the divergence of the X-ray

beam and the distance between the patient and the detector. There is a limiting factor,

however, mostly related to the costs of the unit, and the image detector is not always large

enough to fit the entire FOV, both in width and height.

Two methods have been developed to acquire FOVs that are actually larger than the

detector. One is volume stitching,2,12 in which multiple cylindrical volumes are acquired,

reconstructed, and then stitched together, and the other is the use of offset detectors.1,6 In

this method, during the acquisition of the basis images, the detector is horizontally offset, and

not centrally aligned with the FOV (Figure 1). Only the central part of the FOV is present in all

basis images, and this area is used by the algorithm to blend the images during reconstruction,

15

while the peripheral regions of the FOV are present in just some projections.6 When a lateral

offset detector is used, the resulting volume is a single cylinder, not based on stitched

volumes.

Figure 1. Schematic representation of aligned (A) and lateral-offset (B and C) detector setups. In (A) the central X-ray beam passes through the centre of the FOV and hits the central part of the detector. In (B), it does not pass the centre of the FOV, but still hits the central part of the detector. In (C) it passes the centre of the FOV but hits the edge of the detector.

The aim of the present study was to assess two image quality parameters (mean voxel

value difference/MVVD and standard deviation of voxel values/SDVV), in multiple regions-of-

interest (ROIs) of CBCT images acquired using units with aligned and laterally offset detectors.

Methods and materials

Phantom

A cylindrical phantom developed and described in a previous study8 was used. The phantom

(depicted in figure 2) was made of utility wax, had 98 mm in diameter and a height of 50 mm,

and a cylindrical metal alloy (aluminium-copper) sample (5 mm in diameter and 5 mm in

height) at the centre.

16

Figure 2. Utility wax phantom used to acquire the volumes.

Image acquisition

Six CBCT units were used: Cranex 3Dx (CRA, Soredex Oy, Finland), Ortophos SL (ORT, Sirona

Dental Systems GmbH, Germany), Picasso Trio (PIC, Vatech, South Korea), Promax 3D Mid

(PRO, Planmeca Oy, Finland), Scanora 3D (SCA, Soredex Oy, Finland), and X1 (3Shape,

Denmark). Eight image-acquisition protocols were selected, providing four protocols with an

aligned detector, and four with an offset detector. CRA and PRO were able to acquire both

aligned and offset setups. Image protocols are presented in Table 1.

Table 1. CBCT units and protocol used for volume acquisition

Name Field-of-view

(cm) Detector position (offset

type) Voxel size

(mm) kVp mA

Cranex 3Dx (CRA) 8 x 8 Aligned 0.20 89.8 6.0

Cranex 3Dx (CRA) 15 x 8 Offset (2 partial rotations) 0.25 89.8 5.0

Orthophos SL (ORT) 11 x 10 Aligned 0.16 85.0 6.0

Picasso Trio (PIC) 12 x 8.5 Offset (360°) 0.20 80.0 3.7

ProMax 3D Mid (PRO) 10 x 10 Aligned 0.15 90.0 10.0

ProMax 3D Mid (PRO) 16 x 10 Offset (360°) 0.20 90.0 10.0

Scanora 3D (SCA) 10 x 8 Offset (360°) 0.30 90.0 13.0

X1 8 x 8 Aligned 0.15 90.0 12.0

To acquire the images, the phantom was positioned centralized in the FOV, and the

position was not changed between acquisitions. If available in the unit, tools for metal artefact

correction were de-activated. The X1 required the use of the motion tracking device,13 and

17

therefore it was set-up above the phantom and the X-ray beam trajectory. The motion

correction tool was active in X1, since it was a requirement for image acquisition.

For each unit and for each protocol, five image volumes were acquired. The basis

images of each acquisition were obtained to confirm the centralized position of the phantom

within the FOV and the position of the detector in relation to the FOV (Figure 3).

Figure 3. Single basis image of each protocol used to show the alignment or offset of images acquired before the reconstruction.

Data management

The acquired CBCT volumes were exported as “digital imaging and communications in

medicine” (DICOM) multi-files. Three axial sections of each volume were selected. The central

axial section of the metal cylinder included in the phantom was used for ROI determination,

and two sections, one 5 mm above (“upper”) and one 5 mm below (“lower”) the metal cylinder

were selected for further evaluation.

To select the ROIs for evaluation, in the central axial section showing the metal sample,

a central quadrangular ROI (6x6 mm) was chosen around the sample. Based on this ROI, 12

other ROIs were selected in four directions around the metal sample (left, right, anterior, and

posterior) and in three distances from the central ROI (touching the central ROI – Near; 15

mm from the central ROI – Middle; and 30 mm from the central ROI – Far) for the upper and

lower axial section as shown in Figure 4. The ROIs were grouped into latero-lateral (LL) and

antero-posterior (AP) samples.

18

Figure 4. Axial sections of PIC in the centre of the metal sample used for ROI selection, and above the metal sample used for evaluation.

To calculate MVVD and SDVV, the mean and the standard deviation of voxel values for

each ROI was measured using ImageJ 1.52e (U.S. National Institutes of Health, Bethesda,

Maryland, USA), in the “upper” and the “lower” sections. The values were then grouped

according to region for each unit: LL Far, LL Middle, LL Near, Central, AP Near, AP Middle, and

AP Far. For each of these seven groups, there were 20 values (2 ROIs x 2 sections x 5 volumes)

per unit, while there were 10 values for the central (1 ROI x 2 sections x 5 volumes).

MVVD was calculated comparing the mean voxel value of each group to the mean

voxel value of the central ROI, while SDVV was the mean standard deviation value for each

group. Descriptive statistics and graphics depicting MVVD and SDVV values were performed

using Prism 7.05 (GraphPad Software, La Jolla, California, USA). The mean voxel values and

SDVV were compared with the central ROI using ANOVA One-Way with Dunnet as post-hoc (α

= 5%).

Results

The mean voxel values and SDVV obtained are presented in Table 2. The central ROI showed,

for all units and protocols, a relatively large range for the mean voxel values, also with large

standard deviation. For example, for SCA it was the ROI with the highest standard deviation.

MVVD and SDVV for aligned and offset detectors are presented in Figures 5-8, showing

the ROIs that had statistically significant differences with the central ROI. MVVD observed for

units with aligned detectors ranged from -32.8% to 22.8%, while for the units with offset

detectors it ranged from -20.7% to 69.5%. For units with aligned detectors, ROIs, which were

19

farther from the central region, were those with the largest MVVD. For units with offset

detectors the same was seen for CRA and PIC, but not for PRO and SCA.

SDVV for aligned detectors was lower in the ROI near the FOV centre, except for PRO.

For the units with offset detectors, SDVV was lower for the ROIs farther from the centre,

except for PIC.

21

Figure 5. Percentage of mean voxel value difference (MVVD) for imaging protocols based on the use of aligned detectors.

Figure 6. Percentage of mean voxel value difference (MVVD) for imaging protocols based on the use of latera-offset detectors.

Figure 7. Standard deviation of voxel value (SDVV) for imaging protocols based on the use of aligned detectors.

Figure 8. Standard deviation of voxel value (SDVV) for imaging protocols based on the use of lateral-offset detectors.

Discussion

This study focused on presenting a feature that some dental CBCT units use to acquire volumes

which are larger in diameter than the imaging detector used: changing the location of the

centre, allowing it to be laterally offset from the mid-plane of the FOV. The use of a laterally-

22

positioned detector in dental CBCT units can be implemented in two manners.1,4,6 In some

units, the detector is always offset to the same side, and a full rotation (i.e. 360º) around the

patient is needed. Some units with this trajectory are the Picasso Trio (Vatech, South Korea)

and Scanora 3D (Soredex Oy, Finland) for all the FOV sizes, and Promax 3D Mid (Planmeca Oy,

Finland) for larger FOVs. We now present a second variation in dental CBCT units, used by

some manufacturers, where the machine does a first clockwise partial rotation (180º + the

beam angle)2 with the detector offset to one side, then the entire C-arm (with the X-ray source

and the detector) moves leaving the detector offset to the opposite side, and rotates back

counter clockwise to the initial position. Some units with this trajectory are the Cranex 3Dx

(Soredex Oy, Finland) and the OP300 Maxio (Instrumentarium Dental, Finland) in larger FOVs.

We analysed images of a utility wax phantom to evaluate some technical characteristics of

image quality among different units (i.e. mean and standard deviation of voxel values within

an image volume).

The detector being positioned with a lateral offset might affect the resulting image as

the number of projections available for reconstructing an image is different depending on the

region of interest covered in each offset acquisition. In other words, the central region of the

FOV is covered during the full examination, while peripheral regions are present only in some

basis images, during some specific periods of the partial rotations. Therefore, the number of

projections available for reconstruction is different among different regions of the volume.

The central cylindrical part of the volume is seen in all projections, to be able to blend the

projections before reconstruction, essentially almost doubling the number of images of this

region compared to the others, in both types of laterally offset detectors. The area between

these two parts can show some ring artefacts in the reconstructed images.6

Adding to that, the central X-ray beam may be aligned with the centre of the detector,

and not with centre of the FOV. With this, using laterally offset detectors, the midplane of the

projections, the region where the central (and most perpendicular) X-ray beam hits the

detector, might not represent the midplane of the reconstructed volume. And it is known that

the overall image quality (i.e. noise and resolution) is reduced with increasing cone angles due

to the algorithm’s quality being guaranteed only for the central plane, and the quality

degrading as a function to the distance from it.5,14

23

The central region of the tested phantom was used as reference for statistical

comparisons and for MVVD calculation based on this assumption that it corresponds to the

midplane section where the photons hit the detector perpendicularly, and therefore should

present more ideal image quality values than regions farther from it.4,11 At least for SDVV,

which in this study was used as a method to evaluate the homogeneity of the image,15 it is

expected that the central region presents lower values than the others due to the acuter cone

angles in other regions.

These two changes related to laterally offset detectors (the different number of

projections within regions of the FOV, and the offset of the central beam in relation to the

mid-plane of the FOV), were observed for the units CRA 15x8 and PRO 16x10, where the ROI,

which was further from the FOV centre, had lower SDVV values than those of ROI near the

centre, while the central region, that had more regions available for reconstruction, had a

SDVV lower than those of ROI near the centre. This can be explained by the fact that the entire

C-arm moves to the side when acquiring images with the offset detector in these units,

therefore it is understandable that the central beam is still aligned with the central area of the

detector, which is not centrally aligned to the FOV. Although only PRO 16x10 presented

statistically significant differences in the aforementioned ROI, it is important to note the

change in SDVV distribution observed between aligned and laterally offset detector positions

for CRA, where the CRA 8x8 protocol presented statistically significant higher SDVV for the ROI

far from the centre.

The other units with an aligned detector setup also had the highest SDVV for the ROIs

further from the centre, except for PRO 10x10. This behaviour was also observed for PIC, that

has an offset detector, but a standard deviation in the evaluated ROI that resembles the ones

observed in the aligned detector units. One can speculate that, even though the detector is

offset for all FOV, the central beam is aligned with edge of the detector, making it aligned with

the midplane of the reconstructed volume as well.

Different studies have evaluated the variations in mean pixel intensities and noise in

different positions of an object inside the field of view.7–10 Oliveira et al. (2013)7 observed

variations in CT numbers in ROI located in different teeth regions using a NewTom 3G and a

5G with different FOV. No information was provided on the geometry of projections. Molteni

et al. (2013)4 listed the NewTom VGi as a unit that has an offset detector, but that can not be

24

transferred with certainty to the other units by this brand. As our results showed no

consistency among MVVD between aligned and offset detectors, viewing the basis images is

needed to define which type of projection was used. Some of the MVVD observed in our study

were higher than the 10% suggested by SEDENTEXCT,16 when considering the variation in

image density values compared to a baseline image. Interesting to notice, the differences

found in the present study are higher than the acceptable values within the same acquisitions.

The results found by Machado et al. (2018)10 evaluating the artefact formation in

implants using the voxel value SD are consistent with the ones of the present study for most

aligned detectors, if considered that the anterior region is usually closer to the edges of the

field of view than the posterior regions in dental scans.9 No information is given on the

projection geometry, but the device used has a 20 x 25 cm amorphous silicon flat panel

detector,17 large enough to cover in all projections the entire FOV used. Another study8

observed similar results than ours for PIC, even though different methods of positioning the

phantom were used. Higher SDVV were observed around the metal object when closer to the

edge of the phantom. One of the studies found in dental CBCT units also evaluated the basis

images.9 The aligned detector of the unit used in the study presented similar SDVV increase in

the edges of the field of view, as observed in our study for aligned detectors.

An important consideration is related to the radiation dose the patient is exposed on

these different geometries. Even though it was not evaluated in this study, compared to a full

rotation with aligned detectors, a dose reduction is expected in acquisitions with an offset

detector, as only the central region will be exposed the entire time. However, in acquisitions

with partial rotation, the central region will probably be exposed twice more than in aligned

geometries. Further studies should focus on the influence of these different image acquisition

geometries on different diagnostic tasks, and how the patient is affected considering radiation

burden and diagnostic outcome.

Conclusion

The use of an offset detector to acquire CBCT images lead to increased MVVD ranges and

modified the SDVV distribution inside the FOV in some units compared to the use of an aligned

detector.

25

References

1. Scarfe WC, Farman AG. What is Cone-Beam CT and How Does it Work? Dent Clin

North Am 2008; 52: 707–30. doi: https://doi.org/10.1016/j.cden.2008.05.005

2. Pauwels R, Araki K, Siewerdsen JH, Thongvigitmanee SS. Technical aspects of dental

CBCT: state of the art. Dentomaxillofacial Radiol 2015; 44: 20140224. doi:

https://doi.org/10.1259/dmfr.20140224

3. Scarfe W, Azevedo B, Toghyani S, Farman A. Cone Beam Computed Tomographic

imaging in orthodontics. Aust Dent J 2017; 62: 33–50. doi:

https://doi.org/10.1111/adj.12479

4. Molteni R. Prospects and challenges of rendering tissue density in Hounsfield units for

cone beam computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol 2013;

116: 105–19. doi: https://doi.org/10.1016/j.oooo.2013.04.013

5. Feldkamp LA, Davis LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A 1984;

1: 612. doi: https://doi.org/10.1364/JOSAA.1.000612

6. Schulze R, Heil U, Groß D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al.

Artefacts in CBCT: A review. Dentomaxillofacial Radiol 2011; 40: 265–73. doi:

https://doi.org/10.1259/dmfr/30642039

7. Oliveira ML, Tosoni GM, Lindsey DH, Mendoza K, Tetradis S, Mallya SM. Influence of

anatomical location on CT numbers in cone beam computed tomography. Oral Surg

Oral Med Oral Pathol Oral Radiol 2013; 115: 558–64. doi:

https://doi.org/10.1016/j.oooo.2013.01.021

8. Queiroz PM, Santaella GM, da Paz TDJ, Freitas DQ. Evaluation of a metal artefact

reduction tool on different positions of a metal object in the FOV. Dentomaxillofacial

Radiol 2017; 46: 20160366. doi: https://doi.org/10.1259/dmfr.20160366

9. Pauwels R, Jacobs R, Bogaerts R, Bosmans H, Panmekiate S. Reduction of scatter-

induced image noise in cone beam computed tomography: Effect of field of view size

and position. Oral Surg Oral Med Oral Pathol Oral Radiol 2016; 121: 188–95. doi:


26

10. Machado AH, Fardim KAC, de Souza CF, Sotto-Maior BS, Assis NMSP, Devito KL. Effect

of anatomical region on the formation of metal artefacts produced by dental implants

in cone beam computed tomographic images. Dentomaxillofacial Radiol 2018; 47:

20170281. doi: https://doi.org/10.1259/dmfr.20170281

11. Hwang JJ, Park H, Jeong H-G, Han S-S. Change in Image Quality According to the 3D

Locations of a CBCT Phantom. Bencharit S, organizador. PLoS One 2016; 11: e0153884.

doi: https://doi.org/10.1371/journal.pone.0153884

12. Ozemre MO, Gulsahi A. Comparison of the accuracy of full head cone beam CT images

obtained using a large field of view and stitched images. Dentomaxillofacial Radiol

2018; 20170454. doi: https://doi.org/10.1259/dmfr.20170454

13. Spin-Neto R, Matzen LH, Schropp LW, Sørensen TS, Wenzel A. An ex vivo study of

automated motion artefact correction and the impact on cone beam CT image quality

and interpretability. Dentomaxillofacial Radiol 2018; 47: 20180013. doi:


14. Kalender WA, Kyriakou Y. Flat-detector computed tomography (FD-CT). Eur Radiol

2007; 17: 2767–79. doi: https://doi.org/10.1007/s00330-007-0651-9

15. Pauwels R, Stamatakis H, Manousaridis G, Walker A, Michielsen K, Bosmans H, et al.

Development and applicability of a quality control phantom for dental cone-beam CT.

J Appl Clin Med Phys 2011; 12: 3478. doi: https://doi.org/10.1120/jacmp.v12i4.3478

16. SEDENTEXCT. Radiation protection no. 172: cone beam CT for dental and maxillofacial

radiology (evidence-based guidelines). 2012.

17. Davies J, Johnson B, Drage N. Effective doses from cone beam CT investigation of the

jaws. Dentomaxillofacial Radiol 2012; 41: 30–6. doi:


27

2.2 Artigo: The impact of movement on image quality and interpretability in CBCT devices

with aligned and lateral-offset detectors

Authors: Gustavo Machado Santaella, Pedro Luiz Rosalen, Francisco Haiter-Neto, Ann

Wenzel, Rubens Spin-Neto

Abstract

Objectives: To evaluate the formation of motion artefacts when using a lateral-offset detector

on image quality and interpretability of simulated diagnostic tasks and the effect of two

motion correction algorithms in reducing these artefacts.

Methods: A human skull with three different conditions, 2 implant planning regions and 1

furcation problem, was mounted on a robot simulating intense movement patterns

(anteroposterior translation, lifting, anteroposterior translation + lifting, lateral rotation,

tremor for 2s and continuous tremor). Four CBCT units were used: Cranex 3Dx (CRA), Ortophos

SL (ORT), Promax 3D Mid (PRO), and X1. Protocols with aligned (CRA, ORT, PRO and X1) and

lateral-offset (CRA and PRO) detectors, and three protocols with motion correction were

tested (PRO and X1). Movements were executed in 3 different timings for units with a lateral-

offset detector and 1 timing for units with an aligned detector. In total, 98 volumes were

acquired and evaluated. Images were scored by three blinded evaluators for the presence of

motion stripes artefacts, overall unsharpness, and interpretability. Cohen’s kappa coefficient

was used to score interrater agreement, and the results were summarized and described as

percentages.

Results: Interobserver agreement was good for all evaluated aspects (0.67 to 0.70 on

average). Regarding aligned detectors, images were considered not-interpretable in all tasks

for most protocols without motion correction, and the motion reduction algorithm of X1 and

PRO greatly enhanced interpretability for most protocols. Protocols with a lateral-offset

detector presented differences in interpretability of the different task regions depending on

which moment the movement happened. Task 2 interpretability was most compromised in

timings 2 and 3, task 3 in timing 1 for PRO and CRA and 3 for CRA, and task 1 in timings 1 and

3 for CRA and 1 and 2 for PRO. The motion artefact compensation of PRO was less effective as

its aligned counterpart.

28

Conclusion: A lateral-offset detector resulted in motion artefacts being formed in different

regions of the FOV, depending on the timing of the movements, which might result in images

not having to be reacquired if the region of interest was not affected. The motion correction

algorithms greatly enhanced image quality and interpretability for aligned detector units but

were less effective for images acquired with a lateral-offset detector.

Keywords: cone beam computed tomography, patient movement; motion artifacts;

radiographic image enhancement.

To be submitted to Dentomaxillofacial Radiology.

29

Introduction

Cone beam computed tomography images in dental exams are deeply affected by movements

during the acquisition, which can result in motion artefacts in the reconstructed volumes.

These are more prone to happen due to the sometimes long acquisition times of CBCT,

especially in children and elderly patients with systemic alterations, such as Parkinson’s

disease.1

The problem lies in the characteristics of the reconstruction algorithm, usually being a

variation of the filtered backprojection for CBCT proposed by Feldkamp et al. (1984)2 which

assumes a complete stationary geometry in all the basis images. But with the movement

between the projections, the intensities representing the same area are backprojected into

different positions, resulting in multiple inconsistencies, or artefacts, that may be present in

the reconstructed images.3 Artefacts are changes in the image that do not correctly represent

the scanned object. They are the result of discrepancies between the acquisition process of

the basis images and the algorithm used for reconstruction.3,4 In case of motion artefacts,

these can be the presence of double contours or overall unsharpness, for example.5

Using a lateral-offset detector in relation to the source-to-rotation-centre axis is a

method used by some units’ manufacturers to acquire volumes larger in diameter with a

smaller detector when compared to units with an aligned detector, mainly related to the

higher cost of larger sized detectors.3,6,7 This way, a single volume is acquired, where the

centre of the volume is acquired in all projections, while the edges are viewed in only some of

the basis images. Also, movements known to cause motion artefacts in the images affect

different regions of the FOV depending on which region was being acquired.7

Methods of detecting the movements during the acquisition have been proposed,

either by using a head tracking device8,9 or by detecting the movements in the basis images.10

Then, a correction can be applied in some units through an iterative reconstruction that

adjusts for the movements during the backprojection, reducing the motion artefacts visible in

the final image.8 But the algorithms available were tested in a setup with an aligned detector.

With this, the aims of this study were to evaluate the influence on some clinical tasks

evaluation of acquiring CBCT images with a lateral-offset detector in the formation of artefacts

30

with different types of movement at different moments during the examination and the effect

of two motion correction algorithms in reducing these artefacts.

Methods and materials

CBCT units

Four CBCT units were selected to be included in this study: Cranex 3Dx (CRA, Soredex Oy,

Finland), Ortophos SL (ORT, Sirona Dental Systems GmbH, Germany), Promax 3D Mid (PRO,

Planmeca Oy, Finland) and X1 (3Shape, Denmark). All units had in common the fact that

images are usually acquired with the patient standing up, to better reproduce the movement

protocols of the robot used among the different devices.

In total, the devices were separated into eight protocols, described in Table 1. The

parameters were selected as the default mA and kVp for each device, and the smallest voxel

size available for that field-of-view (FOV). Two devices had offset detector protocols (Cranex

3Dx and Promax 3D Mid), while the other two were all with aligned detectors. Both devices

with offset detector had a similar approach, in which they have FOVs with an aligned detector

(up to 8x8 in CRA and 10x10 in PRO), and FOVs larger than those used an offset detector to

acquire the images. CRA did two partial-rotations, with the detector offset to the right side

first, then to the left side in the second rotation, while PRO did a 360o rotation with the

detector offset to the right side.

Table 1. Units and acquisition protocols used in this study.

Unit Field-of-view (cm)

Offset detector Voxel size (mm)

kVp mA Motion Artefact Correction Aligned detector

Soredex Cranex 3Dx 8 x 8 No 0.20 89.8 6 No

3Shape X1 8 x 8 No 0.15 90.0 12 Yes (head tracker)

Sirona Orthophos SL 8 x 8 No 0.16 85.0 6 No

Planmeca ProMax 3D Mid 10 x 10 No 0.15 90.0 10 No

Planmeca ProMax 3D Mid 10 x 10 No 0.15 90.0 10 Yes (CALM)

Offset detector

Soredex Cranex 3Dx 15 x 8 2x rotations 0.25 89.8 5 No

Planmeca ProMax 3D Mid 16 x 10 360º 0.20 90.0 10 No

Planmeca ProMax 3D Mid 16 x 10 360º 0.20 90.0 10 Yes (CALM)

Two of the devices tested had movement artefact correction methods (PRO and X1).

In PRO it was possible to disable the compensation and, therefore, images were reconstructed

31

and evaluated with it turned ON and OFF. X1 requires the use of the tracking device and does

not allow images to be acquired without it, so only volumes with motion correction activated

were obtained.

Experimental setup and robot programming

A human skull embedded in wax to simulate soft-tissues11 and partially dentate was used in

this study. Three regions of interest with different conditions were present in the skull and

were selected due to their position in the FOV. They were edentulous anterior maxilla, defined

as a region evaluated for an implant or graft placement, and mandible left and right sides, in

which there was an edentulous region for implant placement and a furcation lesion in the first

molar, respectively.

The skull was mounted on a robot (UR10, Universal Robots, Odense, Denmark) that

executes pre-defined movements with precise control of angular position, velocity and

acceleration.8 Six different movement types (described in Table 2) were selected based on a

previous study8 as movements considered of high intensity, to better identify the regions

affected, with a non-returning to the initial position pattern, a distance of 3 mm and a speed

of 5 mm/s. Two movements were categorized as uniplanar or non-complex (head

anteroposterior translation and lifting) and four as multiplanar or complex (head

anteroposterior translation + lifting, lateral rotation, 2s tremor, and continuous tremor). In

the devices with an aligned detector geometry, the movements were executed only when the

X-ray source was behind the skull, while in devices with an offset geometry, the movements

were executed in 3 different timings: timing 1 (T1 - when the source was behind the skull),

timing 2 (T2 - source in front of skull for PRO, due to the 360o rotation, and behind the skull in

the second rotation for CRA) and timing 3 (T3 - movements executed both in timings, first to

the final motion position, than back to the initial position the second time). All movement

timings were adjusted for each unit and programmed in the robot to assure that the

movements were consistent among the acquisitions. The basis images showing the moment

the movements started in all units are seen in Figure 1. The continuous tremor was an

exception as it run during the full acquisition for aligned detectors, or during the entire first,

last and both timings for offset detector units.

32

Table 2. Movements executed by the robot.

Motion pattern

1 Head antero-posterior translation

2 Head lifting

3 Head antero-posterior translation + head lifting (2 separate movements)

4 Head lateral rotation

5 Tremor lasting 2s

6 Continuous tremor (6 Hz)

0 Still – no movement (control)

Figure 1. Basis images showing the timing where the movements started in most protocols for all the units.

Image acquisition

In total, 98 images were acquired and evaluated (Table 3). The FOV chosen were based on:

first, if the device had offset and aligned projections, to keep the same height, and second to

choose a similar FOV for all devices, the exception being PRO, in which the smallest height for

offset acquisitions is 16x10, so a FOV of 10x10 for aligned acquisitions was chosen, following

criteria #1. PRO works with a compensation of movement artefacts based on an iterative

reconstruction of the projections and does not need an external apparatus to work, and then

images were acquired only once and reconstructed with and without the motion

compensation, resulting in 4 sets of volumes (2 for aligned, and 2 for offset). X1, on the other

hand, needs the tracking device for motion correction,8 and cannot be used without it, so only

one group of volumes was obtained.

33

Table 3. Number of volumes acquired per protocol and timing.

Units Timing Number of volumes

Aligned detector

CRA 1 7

ORT 1 7

PROwo 1 7

PROwi 1 7

X1 1 7

Offset detector

CRA

1 7

2 7

3 7

PROwo 1 7

2 7

3 7

PROwi

1 7

2 7

3 7

Total 98

Image evaluation

All the obtained volumes were anonymized and blinded to the evaluators. OnDemand3D was

used to evaluate the images, in a low light room and using a large screen size (60’) and FullHD

resolution (1920x1080 pixels) monitor.

Three different tasks of clinical relevance in dental exams were evaluated. Task 1 was

an edentulous region for implant and bone graft planning in the maxilla, located in the anterior

part of the FOV, and focused on the contours of the bone tissue and visibility of the

nasopalatine canal. Task 2 was for implant planning in an edentulous region in the mandible

located in the left side of the FOV, focusing also on the contours of the bone tissue and

visibility of the mandibular canal. And task 3 was a diagnosis for a furcation problem in the

lower first molar on the right side of the FOV, where the correct limits of bone contours and

soft-tissue simulator should be identifiable. This way, the tasks combined formed a triangle,

with its vertices in each task, with most movements happening at the exact time the rotation

centre intersected the task 1 region (X-ray source behind the skull).

Three evaluators with experience in movement artefacts evaluation based on previous

studies did the images screening together, but scored separately for presence of movement

stripes (0 = No stripes or enamel stripes / 1 = Movement stripes), unsharpness (0 = None or

mild - Bony and dental contours are easily discernible / 1 = Moderate to severe - bony and

34

dental contours are not discernible, sometimes with double contours) and image

interpretability (0 = Interpretable / 1 = Not interpretable) for each diagnostical task. The

evaluators were blinded entirely to the acquisition protocol used. The regions chosen did not

have any metal or other high-density materials that could generate other types of artefacts.

After opening the volumes, images of the first task region were observed in all three main

reconstruction planes (Axial, Sagittal, and Coronal), while the observers gave an overall score

for the three evaluated aspects. Then, they readjusted the planes to evaluate task 2 and then

task 3. This was repeated for every volume, with a limit of 15 volumes per session to avoid

observer fatigue.

Data treatment

The scores of the three evaluators were tabulated and evaluated in a commercially available

software (IBM Corp., New York, NY; formerly SPSS Inc., Chicago, IL). Interobserver agreement

was measured by Cohen’s Kappa coefficient. The findings for all observers were summarized

and a cross-table was made showing the consensus of the findings between the presence of

movement stripes/presence of unsharpness/not interpretable images and the CBCT

unit/movement characteristics (i.e. movement type, detector offset, and timing) for each task.

Data were reported as percentage and agreement values.

Results

The interobserver agreements in the acquired volumes were good for all the evaluated

aspects,12 such as the presence of movement stripe artefacts (0.68 on average, 0.60 – 0.82

range), overall unsharpness (0.67 on average, 0.63 – 0.72 range) and image interpretability

(0.70 on average, 0.63 – 0.78 range).

All the images from the still groups were scored as interpretable and absent of

movement artefacts stripes or overall unsharpness, for all evaluators and protocols.

Therefore, only the images which had movements registered during the acquisition are

considered in the following results.

The task 1, which was for implant and bone graft planning, located in the anterior part

of the FOV, in acquisitions with an aligned detector, was scored for presence of movement

artefact stripes and overall unsharpness and non-interpretable images, respectively, in 100 –

100 – 88.9% for CRA, 100 – 94.4 – 88.9% for ORT and 94.4 – 88.9 – 83.3% for PROWO for the

35

images without movement artefacts correction, and 88.9 – 44.4 – 33.3% for PROWI and 5.6 –

0 – 0% for X1 for the images with correction. The offset detector groups were scored for

movement timing 1 in 100 – 94.4 – 94.4% for CRA, 88.9 – 94.4 – 77.8% for PROWO and 100 –

94.4 – 61.1% for PROWi. For timing 2 in 66.7 – 33.3 – 16.7% for CRA, 66.7 – 61.1 – 61.1% for

PROWO and 94.4 – 77.8 – 55.6% for PROWi, and for timing 3 100 – 94.4 – 88.9% for CRA, 83.3 –

22.2 – 22.2% for PROWO and 77.8 – 38.89 – 33.3% for PROWI.

Regarding the task 2, which was for implant planning in a edentulous region, located

in the left side of the FOV, in acquisitions with an aligned detector, it was scored for presence

of movement artefact stripes and overall unsharpness and non-interpretable images,

respectively, in 88.9 – 77.8 – 55.6% for CRA, 83.3 – 83.3 – 61.1% for ORT and 94.4 – 72.2 –

55.6% for PROWO for the images without movement artefacts correction, and 61.1 – 44.4 –

16.67% for PROWI and 11.1 – 0 – 0% for X1. The offset detector groups were scored for

movement timing 1 in 66.7 – 22.2 – 5.6% for CRA, 61.1 – 44.4 – 22.2% for PROWO and 72.2 –

38.9 – 11.1% for PROWI. For timing 2 in 77.8 – 50 – 22.2% for CRA, 83.3 – 77.8 – 77.8% for

PROWO and 94.4 – 66.7 – 50% for PROWI, and for timing 3, 100 – 83.3 – 72.2% for CRA, 88.9 –

61.1 – 61.1% for PROWO and 83.3 – 55.6 – 16.7% for PROWI.

Task 3 was a diagnosis for a furcation problem in the lower first molar on the right side

of the FOV, and in acquisitions with an aligned detector it was scored for presence of

movement artefact stripes and overall unsharpness and non-interpretable images,

respectively, in 94.4 – 94.4 – 83.3% for CRA, 88.9 – 83.3 – 55.6% for ORT and 100 – 94.4 –

66.7% for PROWO for the images without movement artefacts correction, and 83.3 – 50 –

16.67% for PROWI and 11.1 – 0 – 0% for X1. The offset detector groups were scored for

movement timing 1 in 100 – 100 – 77.8% for CRA, 100 – 94.4 – 66.7% for PROWO and 100 –

83.3 – 61.1% for PROWI. For timing 2 in 5.6 – 11.1 – 0% for CRA, 83.3 – 77.8 – 55.6% for PROWO

and 94.4 – 72.2 – 61.1% for PROWI, and for timing 3, 100 – 94.4 – 72.2% for CRA, 88.9 – 33.3 –

27.8% for PROWO and 61.1 – 38.9 – 22.2% for PROWI.

The scores regarding the different tasks and different detector positions are presented

in Tables 4 and 5, while Figures 2, 3 and 4 show examples of the images.

37

Table 5. The observers’ consensus scores regarding the presence of movement stripes (filled circle, present; empty circle, absent), overall unsharpness (filled circle, present; empty circle, absent), and image interpretability (filled circle, non-interpretable; empty circle, interpretable), according to movement pattern and tasks for protocols with an offset detector.

Task 1 – Anterior region

Type Timing Movement stripes Unsharpness Non-interpretable

CRA PROwo PROwi CRA PROwo PROwi CRA PROwo PROwi

AP translation 1 ● ● ● ● ● ● ● ● ● 2 ○ ○ ● ○ ○ ● ○ ○ ○ 3 ● ● ○ ● ○ ○ ● ○ ○

Head lifting 1 ● ● ● ● ● ● ● ● ● 2 ○ ○ ● ○ ○ ● ○ ○ ● 3 ● ● ● ● ○ ○ ● ○ ○

AP translation + Head lifting

1 ● ● ● ● ● ● ● ● ● 2 ● ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ● ● ○ ●

Lateral rotation

1 ● ● ● ● ● ● ● ● ○ 2 ● ● ● ● ● ● ○ ● ○ 3 ● ● ● ● ○ ○ ● ○ ○

Tremor 1 ● ● ● ● ● ● ● ● ● 2 ● ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ○ ● ○ ○

Continuous tremor

1 ● ● ● ● ● ● ● ● ○ 2 ● ● ● ● ● ○ ● ● ○ 3 ● ● ● ● ● ● ● ● ●

Still

1 ○ ○ ○ ○ ○ ○ ○ ○ ○

2 ○ ○ ○ ○ ○ ○ ○ ○ ○

3 ○ ○ ○ ○ ○ ○ ○ ○ ○

Task 2 – Left side

Type Timing Movement stripes Unsharpness Non-interpretable

CRA PROwo PROwi CRA PROwo PROwi CRA PROwo PROwi

AP translation 1 ● ● ● ○ ● ● ○ ○ ○ 2 ○ ○ ● ○ ○ ○ ○ ○ ○ 3 ● ● ● ○ ○ ○ ○ ○ ○

Head lifting 1 ● ● ● ○ ● ● ○ ● ● 2 ● ● ● ● ● ● ○ ● ● 3 ● ● ● ● ● ○ ● ● ○


1 ● ● ● ● ● ○ ○ ○ ○ 2 ● ● ● ● ● ● ○ ● ● 3 ● ● ● ● ○ ● ● ○ ○

Lateral rotation

1 ● ● ● ● ● ○ ○ ○ ○ 2 ● ● ● ● ● ● ○ ● ○ 3 ● ● ● ● ● ● ○ ● ○

Tremor 1 ● ● ● ○ ○ ○ ○ ○ ○ 2 ● ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ● ● ● ● ○

Continuous tremor

1 ○ ○ ● ○ ○ ● ○ ○ ○ 2 ● ● ● ● ● ○ ● ● ○ 3 ● ● ● ● ● ● ● ● ●

Still

1 ○ ○ ○ ○ ○ ○ ○ ○ ○

2 ○ ○ ○ ○ ○ ○ ○ ○ ○

3 ○ ○ ○ ○ ○ ○ ○ ○ ○

38

Table 5 (continued). The observers’ consensus scores regarding the presence of movement stripes (filled circle, present; empty circle, absent), overall unsharpness (filled circle, present; empty circle, absent), and image interpretability (filled circle, non-interpretable; empty circle, interpretable), according to movement pattern and tasks for protocols with an offset detector.

Task 3 – Right side

Type Timin

g

Movement stripes Unsharpness Non-interpretable

CRA PROw

o PROwi CRA

PROw

o PROwi CRA

PROw

o PROwi

AP translation 1 ● ● ● ● ● ● ○ ○ ● 2 ○ ○ ● ○ ○ ● ○ ○ ● 3 ● ● ○ ● ○ ○ ○ ○ ○

Head lifting 1 ● ● ● ● ● ● ● ● ● 2 ○ ● ● ○ ● ● ○ ● ● 3 ● ● ○ ● ● ○ ● ● ○


1 ● ● ● ● ● ● ● ● ● 2 ○ ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ● ● ○ ○

Lateral rotation 1 ● ● ● ● ● ○ ● ○ ○ 2 ○ ● ● ○ ● ● ○ ○ ○ 3 ● ● ○ ● ○ ○ ○ ○ ○

Tremor 1 ● ● ● ● ● ● ● ● ○ 2 ○ ● ● ○ ● ● ○ ● ● 3 ● ● ● ● ○ ○ ● ○ ○

Continuous tremor

1 ● ● ● ● ● ● ● ● ● 2 ○ ● ● ● ● ○ ○ ○ ○ 3 ● ● ● ● ● ● ● ● ●

Still

1 ○ ○ ○ ○ ○ ○ ○ ○ ○

2 ○ ○ ○ ○ ○ ○ ○ ○ ○

3 ○ ○ ○ ○ ○ ○ ○ ○ ○

40

Discussion

The presence of motion artefacts in dental CBCT images is a well-documented

challenge.1,3,5,7,13,14 The causes for such problem are established as discrepancies in the

acquired images, when a patient moves during the exam, changing the position of the

intensities used by the algorithm for the image reconstruction,3 causing stripe like artefacts to

show, and the structures to appear either blurry (i.e. overall unsharpness) or with double

contours.1,15

To this date, two studies evaluated automated methods of detecting movements during the

acquisition in dental CBCT units.9,10 One used a head tracking device detected by cameras

positioned above the patient to precisely detect movement in all axis,9 while the other

presented an algorithm to detect the movements in the basis projections of a unit that does

not have any extra apparatus for motion evaluation.10 A unit with the head tracking device

and a motion correction algorithm was tested later, showing excellent results compared to

other devices and protocols without the correction, both with uniplanar and multiplanar

movements.8 The present study tested two units that had algorithms to reduce motion

artefacts, one with the head tracking device (X1) and the other based on movements detected

in the basis images (PRO). Due to the nature of the algorithms applied during the

reconstruction, where X1 had the exact information of the movements acquired by the head

tracker, while PRO obtain this information less precisely by analysing the basis images, the

authors chose to name the algorithms as motion correction for X1 and motion compensation

for PRO.

The detector offset method used by some units to acquire volumes with a diameter

larger than the width of the detector used is described in some other studies,3,6,16,17 but

without many considerations on how it affects the evaluation of different diagnostic tasks. A

previous study6 described the two types of detector offset used by the units in this study: the

full 360º rotation with the detector offset to the right applied by PRO and the double partial

rotation of CRA, with the detector offset to the right side in the first rotation, and to the left

side in the second rotation. An image quality evaluation was done, but in a utility wax

phantom, not directly related to clinical evaluations.

41

One study7 used a unit that is said to have an offset-axis geometry of acquisition

(NewTom™ 5G; QR srl, Verona, Italy)16 to evaluate different movements and the resulting

motion artefacts. The angular exposition of 180º used in one of the protocols by the authors,

which corresponds to the X-ray source behind the skull employed in the present study,

showed a similar amount of artefacts formed in the right and left arches. In the present study,

this result was observed in the units that had an aligned detector, while the ones with offset

presented differences between left and right sides for most protocols (Figure 5). An

explanation for this would be if the unit used by the authors is similar to the ones used in the

present study, where depending on the FOV size an aligned or offset protocol is used. The

detector size listed (20 x 25 cm) is large enough to accommodate the FOV used (12 x 8 cm). In

our study, we evaluated the basis images to be sure that the lateral-offset protocol was being

used and that the movements happened in the planned timing.

Figure 5. Axial and coronal images of the “continuous tremor” group of the CRA protocol with the offset detector in the three timings tested.

The movements protocols used in the present study are considered intense, and most

are multiplanar, adapted from previous studies to provide images with more artefacts. The

only distance used was 3 mm, which highly increases the risk of obtaining images considered

42

“not interpretable.14 The other settings such as fast movement speed (5 mm s-1) and to not

return to the initial position were based on a previous study that evaluated the motion

correction algorithm.8 They were chosen based on the fact that the main variable studied was

the offset detector acquisition and the effectiveness of the motion artefact

correction/compensation under these protocols. Therefore, protocols that resulted in intense

artefacts were needed, especially the continuous tremor, which is the most aggressive.

Our results show that the resulting motion artefacts are directly related to the

acquisition geometry. The presence of movement stripes, overall unsharpness, and the

interpretability were evaluated. Even though the first two aspects are important, it’s the

interpretability that may define if an image needs to be reacquired or not. When evaluating

the units with aligned detectors, most of the multiplanar movements at timing 1 resulted in

non-interpretable images for the three evaluated tasks in the three protocols without motion

correction (CRA, ORT, and PROwo). A different behaviour was observed for units with offset

detectors, in which the timings affected the evaluated tasks more inconsistently. In our

hypothesis, timing 1 would affect more the anterior and right regions of the FOV, while timing

2 would affect more the anterior and left regions, and timing 3 on all the regions. This was

true for most cases and observed more for CRA than for PROwo, which might be due to the

different type of detector lateral-offset and acquisition protocol between these units.

Therefore, even though the movements were the same, in protocols with a lateral-offset

detector, there is a chance of intense movements not affecting the region of interest

depending on its location and the timing that the movement happened during acquisition.

But it’s important to note that, in the units tested, using an offset protocol meant an

increased acquisition time, sometimes even twice as much such as in CRA, which required 2

partial rotations for these protocols. This was also observed in PRO, which required a partial

rotation for the aligned protocol, and a full rotation for the offset protocol. And it’s known

that the chances of movements happening during a CBCT examination are directly related to

the total time required for the acquisition.1 Also, even though the radiation dose was not

measured in this study, it should be noted that the offset protocols in this study required a

larger FOV (CRA and PRO), and therefore exposed a larger area than its aligned counterparts.

In addition, there is a central area in the volume that is exposed during the full rotation for

43

PRO and in both rotations for CRA, which might slight increased the total dose compared to a

protocol that has an aligned detector and a partial rotation.

Considering the automated motion artefacts reduction algorithms in the aligned

detector protocols, X1 had the images of all protocols considered as interpretable, the same

as observed by Spin-Neto et al. (2018).8 PROwi had similar results in the present study, except

for the continuous tremor in Task 1. But this was the most intense type of movement, not

commonly seen in patients, and used in this study mostly to accentuate the lateral-offset

geometry effects, therefore PROwi had an excellent result as well in providing interpretable

images for protocols with aligned detectors. Considering the other evaluated aspects, X1 had

no images with unsharpness and only one movement that resulted in stripes, while PROwi was

less effective on those aspects, with most protocols showing both stripes and overall

unsharpness, even though the images were still interpretable.

When compared to the lateral-offset detector protocols, the PROwi motion

compensation was less effective than its aligned counterpart, sometimes even making non-

interpretable images that were otherwise considered interpretable with the tool turned off,

such as in Task 1 with head lifting and head lifting + ap translation for timings 2 and 3,

respectively, and in Task 3 for AP translation movement in timings 1 and 2. X1 did not have a

protocol with a lateral-offset detector and therefore its motion correction algorithm could not

be tested with this acquisition geometry.

The results in the present study suggest that the acquisition geometry of CBCT have

great influence in the regions that will be affected by artefacts generated by movements

during the acquisition, and on the efficacy of some motion correction algorithms.

Conclusion

The use of an offset detector to acquire CBCT images results in motion artefacts being formed

in different regions of the FOV, depending on which moment of the rotation the movements

occurred, which might result in images not having to be reacquired if the region of interest

was not affected. The motion correction algorithms tested greatly enhanced image quality

and interpretability for aligned detector units but were less effective for images acquired with

a lateral-offset detector.

44

References

1. Spin-Neto R, Wenzel A. Patient movement and motion artefacts in cone beam

computed tomography of the dentomaxillofacial region: A systematic literature

review. Oral Surg Oral Med Oral Pathol Oral Radiol 2016; 121: 425–33. doi:


2. Feldkamp LA, Davis LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A 1984;

1: 612. doi: https://doi.org/10.1364/JOSAA.1.000612

3. Schulze R, Heil U, Groß D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al.

Artefacts in CBCT: A review. Dentomaxillofacial Radiol 2011; 40: 265–73. doi:


4. Barrett JF, Keat N. Artifacts in CT: Recognition and Avoidance. RadioGraphics 2004; 24:

1679–91. doi: https://doi.org/10.1148/rg.246045065

5. Spin-Neto R, Mudrak J, Matzen L, Christensen J, Gotfredsen E, Wenzel A. Cone beam

CT image artefacts related to head motion simulated by a robot skull: visual

characteristics and impact on image quality. Dentomaxillofacial Radiol 2013; 42:

32310645. doi: https://doi.org/10.1259/dmfr/32310645

6. Santaella GM, Queiroz PM, Haiter-Neto F, Wenzel A, Spin-Neto R, Rosalen PL.

Quantitative assessment of CBCT image quality variation related to CBCT-detector

lateral offset position. To-be-published.

7. Nardi C, Molteni R, Lorini C, Taliani GG, Matteuzzi B, Mazzoni E, et al. Motion artefacts

in cone beam CT: an in vitro study about the effects on the images. Br J Radiol 2016;

89: 20150687. doi: https://doi.org/10.1259/bjr.20150687

8. Spin-Neto R, Matzen LH, Schropp LW, Sørensen TS, Wenzel A. An ex vivo study of

automated motion artefact correction and the impact on cone beam CT image quality

and interpretability. Dentomaxillofacial Radiol 2018; 47: 20180013. doi:


9. Spin-Neto R, Matzen LH, Schropp L, Sørensen TS, Gotfredsen E, Wenzel A. Accuracy of

video observation and a three-dimensional head tracking system for detecting and

45

quantifying robot-simulated head movements in cone beam computed tomography.

Oral Surg Oral Med Oral Pathol Oral Radiol 2017; 123: 721–8. doi:


10. Schulze RKW, Michel M, Schwanecke U. Automated detection of patient movement

during a CBCT scan based on the projection data. Oral Surg Oral Med Oral Pathol Oral

Radiol 2015; 119: 468–72. doi: https://doi.org/10.1016/j.oooo.2014.12.008

11. Lopes PA, Santaella GM, Lima CAS, Vasconcelos K de F, Groppo FC. Evaluation of soft

tissues simulant materials in cone beam computed tomography. Dentomaxillofacial

Radiol 2018; 48: 20180072. doi: https://doi.org/10.1259/dmfr.20180072

12. Cicchetti D V. Guidelines, criteria, and rules of thumb for evaluating normed and

standardized assessment instruments in psychology. Psychol Assess 1994; 6: 284–90.

doi: https://doi.org/10.1037/1040-3590.6.4.284

13. Spin-Neto R, Matzen LH, Schropp L, Gotfredsen E, Wenzel A. Detection of patient

movement during CBCT examination using video observation compared with an

accelerometer-gyroscope tracking system. Dentomaxillofacial Radiol 2017; 46:

20160289. doi: https://doi.org/10.1259/dmfr.20160289

14. Spin-Neto R, Costa C, Salgado DM, Zambrana NR, Gotfredsen E, Wenzel A. Patient

movement characteristics and the impact on CBCT image quality and interpretability.

Dentomaxillofacial Radiol 2018; 47: 20170216. doi:


15. Lee K-M, Song J-M, Cho J-H, Hwang H-S. Influence of Head Motion on the Accuracy of

3D Reconstruction with Cone-Beam CT: Landmark Identification Errors in Maxillofacial

Surface Model. Bencharit S, organizador. PLoS One 2016; 11: e0153210. doi:

https://doi.org/10.1371/journal.pone.0153210

16. Molteni R. Prospects and challenges of rendering tissue density in Hounsfield units for

cone beam computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol 2013;

116: 105–19. doi: https://doi.org/10.1016/j.oooo.2013.04.013

17. Scarfe WC, Farman AG. What is Cone-Beam CT and How Does it Work? Dent Clin

North Am 2008; 52: 707–30. doi: https://doi.org/10.1016/j.cden.2008.05.005

46

3 DISCUSSÃO

Diversos estudos avaliaram a média e a variação dos valores de voxel em imagens de

TCFC. O posicionamento do objeto no FOV (Oliveira et al., 2013; Pauwels et al., 2016; Queiroz

et al., 2017), a variação de parâmetros energéticos (Oliveira et al., 2014; Pauwels et al.,

2015b), o número de imagens usadas para reconstrução (Queiroz et al., 2018b) e o tamanho

do voxel (Queiroz et al., 2018a), entre outros fatores, já foram avaliadas como diretamente

relacionadas à alterações nesses valores.

Mas pouca ou quase nenhuma consideração foi dada à geometria de exposição do

feixe de raios X para aquisição das imagens de TCFC. Uma busca na literatura por “detector

offset”, ou detector deslocado, revela alguns estudos, principalmente de revisão de literatura,

onde é citado o método de aquisição por geometria parcial em equipamentos de TCFC (Scarfe

e Farman, 2008; Schulze et al., 2011; Molteni, 2013). Mas nenhum estudo foi encontrado

testando a influência na qualidade de imagem desta forma de aquisição em aparelhos de TCFC

odontológicos. Isso porque muitas vezes o acesso às imagens base e a informação sobre como

é feita essa aquisição não é revelado abertamente pelos fabricantes, e o acesso à essas

imagens base é por vezes dificultado. No presente estudo, foi conseguido o acesso às

projeções base de todos os aparelhos estudados, e desta forma foi possível confirmar de

maneira precisa o modo de aquisição de cada protocolo, tanto pela avaliação destas imagens,

quanto por avaliação dos equipamentos e de suas partes.

Com isso, foram realizados dois estudos, sendo o primeiro focando em aspectos

técnicos da qualidade de imagem, com a avaliação da variação do valor médio e do desvio

padrão dos valores de voxel. Para isso foi utilizado um fantoma de cera utilidade já preparado

para outros estudos de qualidade de imagem (Queiroz et al., 2017, 2018a). Observou-se então

uma variação no comportamento destes aspectos dentro do FOV avaliado, principalmente do

desvio-padrão dos valores de voxel, quando comparados aos detectores alinhados. Neste

primeiro estudo foi objetivado também explicar de forma aprofundada essa forma de

aquisição de imagem, para que que o estudo clínico não ficasse demasiadamente longo.

No segundo estudo, mais voltado para aspectos clínicos de diagnóstico de imagem, foi

utilizada uma metodologia de formação de artefatos de movimento em imagens de TCFC, já

amplamente testada em estudos prévios (Spin-Neto et al., 2013, 2018b). Porém, desta vez

47

estudada com a utilização de detectores deslocados, que foi avaliado também em um outro

estudo anterior com artefatos de movimento (Nardi et al., 2016), mas com metodologia

diferente da aplicada no presente estudo, o que pode explicar os resultados diferentes

encontrados entre os estudos. A linha de pesquisa de artefatos de movimentos em TCFC é

amplamente estudada por alguns dos autores do presente estudo (Spin-Neto et al., 2014,

2015, 2016, 2017a; b, 2018a; Spin-Neto e Wenzel, 2016), e com isso teve-se o objetivo de aliar

esta linha de pesquisa, com a de geometria parcial de exposição pouco explorada pela

literatura. Nossos resultados mostraram que a exposição parcial está diretamente relacionada

com as áreas que serão afetadas pelos artefatos de movimento, dependo do momento de

aquisição em que ocorre esta movimentação. Além disso, foi avaliada uma nova ferramenta

de compensação de artefatos de movimento ainda não estudada, e que se mostrou quase tão

eficaz quanto outra ferramenta já avaliada (Spin-Neto et al., 2018b) para detectores

alinhados, mas com menor eficácia na qualidade da imagem para o diagnóstico em protocolos

que foram utilizados detectores deslocados.

Contudo, foram avaliados apenas dois aspectos em que esta geometria pode

influenciar na qualidade de imagem, não sendo encontrados estudos avaliando diversas

outras tarefas diagnósticas, nem a influência da dose de radiação à qual o paciente é exposto

entre os diferentes protocolos, fator de extrema importância atualmente. Desta forma, novos

estudos são sugeridos para se estudar e entender ainda mais este método de aquisição, que

vem sendo adotado frequentemente pelos fabricantes de aparelhos de TCFC.

48

4 CONCLUSÃO

A aquisição de imagens de TCFC utilizando uma geometria de exposição parcial pode

alterar a distribuição dos valores de voxel dentro do FOV, comparado a detectores alinhados,

e afeta diretamente a forma como os artefatos de movimento se apresentam dentro da

imagem, dependendo do momento em que ocorre a movimentação. Além disso, compromete

a eficácia de ferramentas de compensação de artefatos de movimentos presente em um dos

aparelhos testados (Promax 3D Mid).

49

* De acordo com as normas da UNICAMP/FOP, baseadas na padronização do International Committee of Medical Journal Editors - Vancouver Group. Abreviatura dos periódicos em conformidade com o PubMed.

REFERÊNCIAS*

Arai Y, Tammisalo E, Iwai K, Hashimoto K, Shinoda K. Development of a compact computed

tomographic apparatus for dental use. Dentomaxillofacial Radiol. 1999;28(4):245–8.

Barrett JF, Keat N. Artifacts in CT: Recognition and Avoidance. RadioGraphics. novembro de

2004;24(6):1679–91.

Feldkamp LA, Davis LC, Kress JW. Practical cone-beam algorithm. J Opt Soc Am A. 1 de junho

de 1984;1(6):612.

Molteni R. Prospects and challenges of rendering tissue density in Hounsfield units for cone

beam computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol. Elsevier Inc.;

julho de 2013;116(1):105–19.

Mozzo P, Procacci C, Tacconi A, Martini PT, Andreis IA, Tinazzi Martini P, et al. A new

volumetric CT machine for dental imaging based on the cone-beam technique:

preliminary results. Eur Radiol. 23 de novembro de 1998;8(9):1558–64.

Nardi C, Molteni R, Lorini C, Taliani GG, Matteuzzi B, Mazzoni E, et al. Motion artefacts in

cone beam CT: an in vitro study about the effects on the images. Br J Radiol. fevereiro

de 2016;89(1058):20150687.

Oliveira ML, Freitas DQ, Ambrosano GMB, Haiter-Neto F. Influence of exposure factors on

the variability of CBCT voxel values: a phantom study. Dentomaxillofacial Radiol.

setembro de 2014;43(6):20140128.

Oliveira ML, Tosoni GM, Lindsey DH, Mendoza K, Tetradis S, Mallya SM. Influence of

anatomical location on CT numbers in cone beam computed tomography. Oral Surg

Oral Med Oral Pathol Oral Radiol. Elsevier; abril de 2013;115(4):558–64.

Pauwels R, Araki K, Siewerdsen JH, Thongvigitmanee SS. Technical aspects of dental CBCT:

state of the art. Dentomaxillofacial Radiol. 2015a;44(1):20140224.

Pauwels R, Beinsberger J, Stamatakis H, Tsiklakis K, Walker A, Bosmans H, et al. Comparison

of spatial and contrast resolution for cone-beam computed tomography scanners. Oral

Surg Oral Med Oral Pathol Oral Radiol. 2012;114(1):127–35.

Pauwels R, Jacobs R, Bogaerts R, Bosmans H, Panmekiate S. Reduction of scatter-induced

50


image noise in cone beam computed tomography: Effect of field of view size and

position. Oral Surg Oral Med Oral Pathol Oral Radiol. Elsevier Inc.; 2016;121(2):188–95.

Pauwels R, Seynaeve L, Henriques JCG, de Oliveira-Santos C, Souza PC, Westphalen FH, et al.

Optimization of dental CBCT exposures through mAs reduction. Dentomaxillofacial

Radiol. 2015b;44(9):20150108.

Queiroz PM, Santaella GM, Capelozza ALA, Rosalen PL, Freitas DQ, Haiter-Neto F. Zoom

Reconstruction Tool: Evaluation of Image Quality and Influence on the Diagnosis of Root

Fracture. J Endod. 3 de abril de 2018a;44(4):621–5.

Queiroz PM, Santaella GM, Groppo FC, Freitas DQ. Metal artifact production and reduction

in CBCT with different numbers of basis images. Imaging Sci Dent. março de

2018b;48(1):41–4.

Queiroz PM, Santaella GM, da Paz TDJ, Freitas DQ. Evaluation of a metal artefact reduction

tool on different positions of a metal object in the FOV. Dentomaxillofacial Radiol.

março de 2017;46(3):20160366.

Scarfe W, Azevedo B, Toghyani S, Farman A. Cone Beam Computed Tomographic imaging in

orthodontics. Aust Dent J. 2017;62:33–50.

Scarfe WC, Farman AG. What is Cone-Beam CT and How Does it Work? Dent Clin North Am.

outubro de 2008;52(4):707–30.

Schulze R, Heil U, Groß D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al. Artefacts in

CBCT: A review. Dentomaxillofacial Radiol. 2011;40(5):265–73.

Schulze RKW, Michel M, Schwanecke U. Automated detection of patient movement during a

CBCT scan based on the projection data. Oral Surg Oral Med Oral Pathol Oral Radiol.

Elsevier Inc.; abril de 2015;119(4):468–72.

Spin-Neto R, Costa C, Salgado DM, Zambrana NR, Gotfredsen E, Wenzel A. Patient

movement characteristics and the impact on CBCT image quality and interpretability.

Dentomaxillofacial Radiol. 20 de janeiro de 2018a;47(1):20170216.

Spin-Neto R, Matzen LH, Schropp L, Gotfredsen E, Wenzel A. Factors affecting patient

movement and re-exposure in cone beam computed tomography examination. Oral

51


Surg Oral Med Oral Pathol Oral Radiol. Elsevier Inc.; maio de 2015;119(5):572–8.

Spin-Neto R, Matzen LH, Schropp L, Gotfredsen E, Wenzel A. Movement characteristics in

young patients and the impact on CBCT image quality. Dentomaxillofacial Radiol.

2016;45(4):20150426.

Spin-Neto R, Matzen LH, Schropp L, Gotfredsen E, Wenzel A. Detection of patient movement

during CBCT examination using video observation compared with an accelerometer-

gyroscope tracking system. Dentomaxillofacial Radiol. fevereiro de

2017a;46(2):20160289.

Spin-Neto R, Matzen LH, Schropp L, Liedke GS, Gotfredsen E, Wenzel A. Radiographic

observers’ ability to recognize patient movement during cone beam CT.

Dentomaxillofacial Radiol. maio de 2014;43(4):20130449.

Spin-Neto R, Matzen LH, Schropp L, Sørensen TS, Gotfredsen E, Wenzel A. Accuracy of video

observation and a three-dimensional head tracking system for detecting and

quantifying robot-simulated head movements in cone beam computed tomography.

Oral Surg Oral Med Oral Pathol Oral Radiol. Elsevier Inc.; junho de 2017b;123(6):721–8.

Spin-Neto R, Matzen LH, Schropp LW, Sørensen TS, Wenzel A. An ex vivo study of automated

motion artefact correction and the impact on cone beam CT image quality and

interpretability. Dentomaxillofacial Radiol. 22 de março de 2018b;47(5):20180013.

Spin-Neto R, Mudrak J, Matzen L, Christensen J, Gotfredsen E, Wenzel A. Cone beam CT

image artefacts related to head motion simulated by a robot skull: visual characteristics

and impact on image quality. Dentomaxillofacial Radiol. fevereiro de

2013;42(2):32310645.

Spin-Neto R, Wenzel A. Patient movement and motion artefacts in cone beam computed

tomography of the dentomaxillofacial region: A systematic literature review. Oral Surg

Oral Med Oral Pathol Oral Radiol. Elsevier Inc.; 2016;121(4):425–33.

White SC, Pharoah MJ. Oral Radiology: Principles and Interpretation. 7o ed. St Louis, MO:

Mosby; 7 edition (May 1, 2014); 2013.

52

ANEXOS

Anexo 1 – Carta de isenção de necessidade de aprovação em comitê de ética (Dinamarca)

53

Anexo 2 – Carta de isenção de necessidade de aprovação em comitê de ética (Brasil)

54

Anexo 3 – Verificação de originalidade e prevenção de plágio

55

Anexo 4 – Comprovante de submissão do artigo para revista científica

GUSTAVO MACHADO SANTAELLA O impacto na qualidade ...

Documents

Transcript of GUSTAVO MACHADO SANTAELLA O impacto na qualidade ...