Optimization of Dual Energy Contrast Enhanced Breast Tomosynthesis for Improved Mammographic Lesion Detection

and Diagnosis

R. Saunders,1 E. Samei,1 C. Badea,2 H. Yuan,4 K. Ghaghada,2 Y. Qi,2 L.W. Hedlund,2 S. Mukundan3

1Duke Advanced Imaging Labs Department of Radiology

2 Center for In Vivo Microscopy Department of Radiology

3 Department of Radiology 4 Department of Radiation Oncology

Duke University Medical Center Durham, NC 27705

ABSTRACT Dual-energy contrast-enhanced breast tomosynthesis has been proposed as a technique to improve the detection of early-stage cancer in young, high-risk women. This study focused on optimizing this technique using computer simulations. The computer simulation used analytical calculations to optimize the signal difference to noise ratio (SdNR) of resulting images from such a technique at constant dose. The optimization included the optimal radiographic technique, optimal distribution of dose between the two single-energy projection images, and the optimal weighting factor for the dual energy subtraction. Importantly, the SdNR included both anatomical and quantum noise sources, as dual energy imaging reduces anatomical noise at the expense of increases in quantum noise. Assuming a tungsten anode, the maximum SdNR at constant dose was achieved for a high energy beam at 49 kVp with 92.5 µm copper filtration and a low energy beam at 49 kVp with 95 µm tin filtration. These analytical calculations were followed by Monte Carlo simulations that included the effects of scattered radiation and detector properties. Finally, the feasibility of this technique was tested in a small animal imaging experiment using a novel iodinated liposomal contrast agent. The results illustrated the utility of dual energy imaging and determined the optimal acquisition parameters for this technique. This work was supported in part by grants from the Komen Foundation (PDF55806), the Cancer Research and Prevention Foundation, and the NIH (NCI R21 CA124584-01). CIVM is a NCRR/NCI National Resource under P41-05959/U24-CA092656.

Keywords: Mammography, Tomosynthesis, Dual-Energy, Contrast Imaging

1. INTRODUCTION Several medical organizations advise women at high risk of breast cancer to start regular mammographic screening at a young age and to augment standard mammography with breast MRI.1-4 While these recommendations should increase the likelihood of detecting early-stage cancers, they continue to face problems of missed cancers and false positives. Missed cancers may occur as mammography has difficulty detecting cancers in younger women with dense breasts,5 while the number of false positives remains a concern with breast MRI.6

Medical Imaging 2008: Physics of Medical Imaging, edited by Jiang Hsieh, Ehsan Samei,Proc. of SPIE Vol. 6913, 69130Y, (2008) · 1605-7422/08/$18 · doi: 10.1117/12.772042

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Dual energy contrast enhanced breast tomosynthesis offers an alternative for these women. This technology has promise in providing a lower cost and higher throughput alternative to MRI while providing the functional three-dimensional information missing from standard mammography. Previous research has shown the value of contrast-enhanced mammography,7-9 while more recent research has added a dual-energy component to this contrast imaging.10-12 However, there is no consensus on the optimal radiographic technique for this new imaging method. The purpose of this study was to optimize the radiographic technique for dual energy contrast enhanced tomosynthesis to maximize lesion detection. This was accomplished in three stages. First, analytical techniques were used to examine signal difference to noise ratio (SdNR) of projection images imaged with a wide variety of beam energies and beam profiles. Next, a Monte Carlo model examined photon transport through a voxelized breast phantom for the most promising beam energies identified by the analytical model and examined lesion conspicuity in tomosynthesis projections. Finally, the method was validated by a small animal imaging experiment. The results of these three stages identified the most promising radiographic techniques and showed the potential of this breast imaging method.

2. ANALYTICAL MODELING OF PROJECTION IMAGING The first stage used an analytical model to analyze the SdNR in projection images. The model began by creating x-ray photons according to a given beam profile. These x-ray photons were then tracked through a 4 cm voxelized breast phantom with anatomical structure modeled by a three dimensional power law spectrum.13, 14 The phantom included a subtle iodinated lesion, which was simulated as an embedded three-dimensional Gaussian with a maximum iodine fraction (by weight) of 0.1%. The x-ray attenuation coefficients of the phantom were based on previous measurements,15 while the absorbed glandular dose was based on simulation results.16 After traveling through the phantom, the photons were finally absorbed by a perfect energy-integrating detector, such that the final result of this simulation was a two-dimensional projection image of the breast. The single-energy images were combined using a weighted log subtraction to form a dual energy subtracted image IDE as

( ) ( ) ( )log logDE T H H L T HI I w Iδ δ δ δ⎡ ⎤ ⎡ ⎤= − −⎣ ⎦ ⎣ ⎦ , (1) where w corresponds to the weighting factor, δT refers to the total glandular dose for the procedure, δH is the dose allocated to the high energy image, and IH and IL represent the high and low energy images, respectively. For a given total dose, the subtraction process has two free parameters (w and δH). The weight w was chosen to minimize the anatomical noise in the dual energy subtracted image while δH was selected to minimize the quantum noise in that image. The dual energy subtracted image was analyzed to measure the conspicuity of the iodinated signal. Conspicuity was represented as the SdNR, where the signal difference was defined as the average difference between the iodinated lesion and anatomical background in the central lesion area while the noise was the standard deviation of the background in the area surrounding the lesion. Importantly, the noise included both anatomical and quantum noise sources, as both will impede lesion detection in clinical images. The model first simulated x-ray beams with Gaussian profiles ζ(ε),

( ) ( )2 20 2

0 e ε ε σζ ε ζ − −= ⋅ , (2) where ζ0 represents the maximum beam intensity, ε refers to the photon energy, ε0 is the mean photon energy, and σ corresponds to the beam width. Once optimal mathematical beam profiles were identified, the model then examined clinical mammographic beams. The model analyzed brehmmstrahlung from a tungsten

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N N

2' 2'a, a,C CUi Ui

Energy1 (key) Energy1 (key) Energy1 (key)

N (N

2' 2'a, a)C CUi Ui

20

Energy1 (key) Energy1 (key) Energy1 (key)

tube with different types and thicknesses of added filtration. The tungsten spectra were generated from published simulations,17 while the attenuation characteristics for the added filtration were drawn from existing databases.15 The model examined beam energies ranging from 18-49 kVp, eight filter materials (aluminum, platinum, silver, tin, copper, iridium, rhodium, and cerium), and filter thicknesses including half-value layer, quarter-value layer, twentieth-value layer, and fiftieth-value layer at the applied kVp. The analysis did not exceed fiftieth-value layer to maintain attenuation levels consistent with those of added filtration in current mammography systems. Analytical Results Figure 1 illustrates the relationship between beam energy and SdNR for different total dose conditions. The SdNR is plotted for three doses: tomosynthesis projection dose (0.17 mGy), two view mammogram dose (4.3 mGy),18, and high dose (1 Gy). The optimal beam energies, or the beam energy combinations that maximized SdNR, changed as a function of dose. As dose increased, the quantum noise component decreased in importance while anatomical noise played a more important role. For dose corresponding to a tomosynthesis projection, the maximum SdNR was achieved with Gaussian beams having mean energy of 21 keV and 41 keV. Figure 2 illustrates the relationship between beam energy and SdNR for different beam widths at tomosynthesis dose. The SdNR is plotted for three beam widths: 3 keV, 5 keV, and 10 keV. As the beam width increases, the optimal beam energies move further away from one another to increase their spectral separation.

Figure 1. SdNR of the dual energy subtracted image as a function of mean beam energy for varying total dose. This graph illustrates the SdNR for cases with total absorbed dose similar to a tomosynthesis

projection (left), two-view mammogram (center), and high dose (right). All three cases are for a fixed beam width (σ = 3 keV).

Figure 2. SdNR of the dual energy subtracted image as a function of mean beam energy for varying beam widths. This graph illustrates the SdNR for cases with beam widths of 3 keV (left), 5 keV (center), and 10 keV

(right). All three cases are for a tomosynthesis projection dose.

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35 40

Energy1 (kVp)

Al Cu Rh Ag Sn Ce

Filter1qvl

Cu Thickness

Figure 3 shows the SdNR for realistic tungsten beams where each beam was normalized to result in tomosynthesis dose to the breast. This figure indicates that the maximum SdNR occurred when the low and high energy images were acquired at 49 kVp. For this beam energy, the combinations of tin with platinum, iridium, and copper produced the best results, with the copper-tin pair giving the maximum SdNR. Finally, the optimal technique occurred with a high energy tungsten beam at 49 kVp with a fiftieth-value layer of copper (92.5 µm) and a low-energy tungsten beam at 49 kVp with a fiftieth-value layer of tin (95 µm).

Figure 3. Maximum intensity projection of the signal difference to noise ratio over all other conditions. The figure shows the maximum SdNR achieved for different kVps (left), for different filter materials at the optimal

kVp (center), and different filter thicknesses for the optimal kVp and filter material (right). The filter thicknesses are indicated in value layers with half-value layer (hvl), quarter-value layer (qvl), twentieth-value

layer (tvl), and fiftieth-value layer (fvl).

3. MONTE CARLO MODELING OF TOMOSYNTHESIS ACQUISITION Monte Carlo Program The analytical model was expanded to include contrast degradation from scatter, realistic beams, detector effects, and tomosynthesis acquisition using the Penelope Monte Carlo code.19 The Penelope package has been successfully applied to medical imaging in a number of previous studies.20-23 Simulations were based on a model of a direct flat-panel breast tomosynthesis system, as shown in Figure 4 and summarized in Table I, using custom tracking code for voxelized phantoms. This model consisted of an anode, a voxelized breast phantom, and a selenium detector. To model a tomosynthesis system, the x-ray anode was rotated to generate 25 projection images over a 45° angular arc. The anode rotated around a center of rotation located 59 cm from the anode position.

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Side View Top View Magnified Detector View

65 Cr,,

10 cm

20 cm

Anode

X-Ray Beam

Ljeteotor

Figure 4. Schematic of simulated imaging system. The breast was composed of 50% glandular/50%

adipose tissue.

Table I. Parameter values for each element of the simulation.

Simulation Element Parameter Value Detector Pixel Size 85 µm

Detector Material Selenium (250 µm) Cross-Sectional Area 10 cm x 20 cm Backing Material Pyrex glass (3 mm)

X-Ray Tube Anode Tungsten Focal Spot Size 0.3 mm

Prior publications provided the elemental compositions of the phantom materials: one for adipose, skin, ductal tissue, pyrex, and selenium;24 another for glandular tissue and malignant masses infused with 1% iodine;25 and another for the microcalcifications.26 These are summarized in Table II. Attenuation properties for these materials were supplied by Penelope’s databases.

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Table II. Elemental composition of phantom materials.

Material Density (g/cm3)

Elements (Fraction by Weight)

Adipose (ICRP)24 0.92 H: 0.12 C: 0.64

N: 0.0080

O: 0.23 Na: 5.0E-4 Mg: 2.0E-5

P: 1.6E-4 S: 7.3E-4 Cl: 0.0012

K: 3.2E-4 Ca: 2.0E-5 Fe: 2.0E-5 Zn: 2.0E-5

Skin (ICRP)24 1.1 H: 0.10 C: 0.23

N: 0. 046

O: 0.62 Na: 7.0E-5 Mg: 6.0E-5

P: 3.3E-4 Cl: 2.7E-3 K: 8.5E-4

Ca: 1.5E-4 Fe: 1.0E-5 Zn: 1.0E-5

Ductal Tissue (Soft Tissue ICRP)24 1.0

H: 0.10 C: 0.23

N: 0. 025

O: 0.63 Na: 0.0011 Mg: 1.3E-4

P: 0.0013 S: 0.0020 Cl: 0.0013

K: 0.0020 Ca: 2.3E-4 Fe: 5.0E-5 Zn: 3.0E-5

Glandular Tissue25 0.93

H: 0.13 C: 0.22

N: 0. 026 O: 0.61

Cl: 0.0020 Na: 0.0019 K: 8.9E-4 Fe: 9.8E-5

Ca: 8.3E-5 Zn: 1.2E-5 Br: 7.1E-6 Al: 6.7E-6

Rb: 6.0E-6 Mn: 3.9E-7 Co: 2.1E-7 Cs: 3.3E-9

Malignant Mass infused with 1%

iodine25 1.058

H: 0.12 C: 0.23

N: 0. 025 O: 0.61

Cl: 0.0021 Na: 0.0020 K: 0.0019 Fe: 7.8E-5

Ca: 1.1E-4 Zn: 1.1E-5 Br: 6.5E-6 Al: 5.6E-6

Rb: 5.8E-6 Mn: 3.1E-7 Co: 2.0E-7 Cs: 3.6E-9

I: 0.01

Microcalcifications26 3.3 Ca10(PO4)6(OH)2 *

Selenium Detector 4.5 Se (1.0)

Pyrex24 2.23 B: 0.040 O: 0.54 Na: 0.028 Al: 0.012 Si: 0.38

K: 0.0033 * Stoichiometric formula instead of weight fractions Voxelized Breast Phantom We used a new implementation of a previously developed breast simulation program27 to create realistic breast phantoms in a voxelized format, consisting of a skin layer, a layer of subcutaneous adipose tissues, a ductal network, and an inner volume with adipose and glandular tissues (Figure 5). The phantom included random components such that the layout of adipose and glandular tissue and the ductal network differed for each realizations of the phantom. The phantom also included breast masses and microcalcifications. The breast masses were created using a stochastic growth model to approximate lesion infiltration, while the microcalcifications were located along a section of the ductal tree. The model allowed control over the cross-sectional size and compressed breast thickness of the phantom. For this work, we produced phantoms with an 8 cm cross-sectional radius and compressed breast thickness of 4 cm. Based on previous research on the relationship between glandular fraction and compressed breast thickness,28-30 we chose an average glandular fraction of 60% for the 4 cm breast.

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Skin Layer-Adipose

Layer

Glandular and AdiposeTissues

Figure 5. Major components of breast phantom.

Monte Carlo Results Figure 6 shows tomosynthesis projections at low energy, high energy, and for the dual-energy subtracted case for the optimal radiographic technique. These projections have greater noise than the analytical results because they include scattered radiation and also include detector noise. However, even with the increased noise, the dual-energy subtracted image still is able to show the iodinated lesions in the breasts. Figure 7 shows the same projections for a suboptimal technique. The lesion conspicuity is severely reduced compared to the optimal technique. This suggests the optimal technique will produce superior tomosynthesis reconstructions than the suboptimal condition.

Figure 6. Tomosynthesis projections obtained from Monte Carlo model for the optimal conditions. All were acquired with tungsten beams at 49 kVp; the high energy image used an fvl of copper filtration while the low energy image used an fvl of tin. These three include the low-energy case (left), high-energy case (middle),

and dual-energy subtracted case (right). The iodinated lesions are minimally visible in the single-energy projections but are very conspicuous in the dual-energy image.

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I

Figure 7. Tomosynthesis projections obtained from Monte Carlo model for a set of suboptimal conditions. These three include the low-energy case (left), high-energy case (middle), and dual-energy subtracted case

(right). All were acquired with tungsten beams at 49 kVp; the high energy image used an hvl of copper filtration while the low energy image used an hvl of tin. The iodinated lesions are less visible in the dual

energy image compared to the optimal conditions.

4. SMALL ANIMAL IMAGING This method was tested in a proof of concept small animal experiment. Female Fischer 344 rats (National Cancer Institute, Frederick Cancer Center) with mammary tumors (R3230AC) were imaged with micro-CT under a protocol approved by the Duke Institutional Animal Care and Use Committee. A novel iodinated liposomal contrast agent was used to illustrate blood pool volume and thereby to show angiogenesis in the tumor.31 The projection images were utilized for micro-CT reconstruction using Cobra EXXIM software package, which relied on a Feldkamp algorithm with Parker weighting (EXXIM Computing Corp, Livermore, CA).

Small Animal Image Results Figure 8 demonstrates the feasibility of multi-energy tomographic imaging with a CT slice of a rat injected with the novel liposomal iodinated contrast agent. The projection image shows the tumor location, but the vasculature is not easily visible. The dual energy image processing and the image reconstruction enhanced the iodine contrast allowing for visualization of vasculature in the mammary tumor.

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V. __ .1*.at,

Figure 8. Projection image, left, of rat with R3230AC mammary carcinoma and iodinated liposomal contrast agent. The arrow indicates the site of the tumor. Right, magnified view of maximum intensity projection of

reconstruction of tumor area. The vasculature is clearly visible in the reconstruction, illustrating the angiogenesis in the tumor.

5. DISCUSSION AND CONCLUSIONS Dual-energy contrast-enhanced breast tomosynthesis was very recently introduced and therefore work remains in optimizing this technology. This paper contributes to this field in three ways. First, this study performed a full Monte Carlo simulation of the imager in order to fully account for scatter effects. Second, the optimization was aimed toward quantitative imaging of the lesion. Third, the optimization was verified in a small animal imaging experiment with novel liposomal contrast agents. These three experiments show the potential of this method and highlight areas for future work. The theoretical optimization included both anatomical and quantum noise sources. This was critical as a dual energy subtracted image will always have higher quantum noise than a single energy image, but the overall noise in a dual energy image declines because of the reduction in anatomical noise. In addition, both noise sources impede lesion detection, with some prior studies indicating that anatomic noise can have a greater impact on detection than quantum noise.32 This suggests that the figures of merits for future breast dual-energy optimizations must account for anatomical noise in order to provide proper results. Dual energy contrast-enhanced tomosynthesis shows promise in enhancing subtle lesions and revealing functional information about suspicious regions for high-risk women. This study used computational simulation experiments to optimize this technique to maximize the detectability of the iodinated lesions in the dual energy subtracted image. The feasibility of the dual energy technique was verified in a small animal experiment using a new liposomal contrast agent.

ACKNOWLEDGEMENTS The authors wish to thank Brian Harrawood for computational support as well as Joseph Lo and Swatee Singh for their advice on tomosynthesis acquisition and reconstruction. This work was supported in part by

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grants from the Komen Foundation (PDF55806), the Cancer Research and Prevention Foundation, and the NIH (NCI R21 CA124584-01). CIVM is a NCRR/NCI National Resource under P41-05959/U24-CA092656.

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