REGULAR PAPER

Ali S. Saad • Achraf Al Faraj

3D Visualization of iron oxide nanoparticles in MRIof inflammatory model

Received: 12 April 2014 / Revised: 28 October 2014 / Accepted: 1 December 2014� The Visualization Society of Japan 2014

Abstract Nanomedicine is becoming an extremely promising research area for healthcare. The visualiza-tion and quantification of nanoparticles (NPs) inside the organs of interest pose a significant challenge andtherefore, novel image processing approaches are required for a better diagnosis. The purpose of this workwas to develop a novel approach for better visualization and quantification of iron oxide NPs in threedimension (3D) high-resolution magnetic resonance (MR) images of an inflammatory model. The proposedprocedure focuses on the extraction of NPs from the background surrounding it. It is applied on 2D and 3Dimages and is based on pre-processing and segmentation by automatic threshold to visualize the NPs insidethe mouse calf using a control set of images of the same calf before injecting the NPs. The resultingvisualization of the 3D distribution of iron oxide NPs inside the inflamed area of the calf has a potential inthe advancement of NPs application in nanomedicine therapy and diagnosis.

Keywords MRI image processing � Visualization � Segmentation � Iron oxide nanoparticles � Extraction �Quantification

1 Introduction

Computerized visualization and tracking of biological cells, in different imaging modalities, are becomingessential as the image processing surpasses human capacity, especially, when complex image processingtasks are required, such as spatio-temporal tracking, counting and classifying huge numbers of differentcells. Automated cell identification and spatio-temporal tracking are now widely used (Li et al. 2007;Padfield et al. 2009); however, it is still not an easy task to implement and requires dedicated computationalalgorithms. Segmentations by threshold approaches are used to identify cell outlines and are often complex,reflecting the intrinsic problem of poor contrast between cells and their surrounding environment (Sezgin2004). Actually, in order to enhance the contrast and separate cells from tissues, a contrast agent has to beintroduced into cells. When the contrast agent has magnetic properties, the tracking of these cells can beachieved in vivo, noninvasively by molecular magnetic resonance imaging (MRI), a relatively recentapproach for in vivo monitoring of biological processes at the cellular level (Cromer Berman et al. 2011;Arbab et al. 2009). Among the different types of NPs used for diagnosis and therapy, super paramagnetic

A. S. Saad (&)Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, P.O. Box 10219,Riyadh 11433, Saudi ArabiaE-mail: alisaad@ksu.edu.sa

A. Al FarajDepartment of Radiological Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box 10219,Riyadh 11433, Saudi Arabia

J VisDOI 10.1007/s12650-014-0259-5

iron oxide (SPIO) NPs possess unique characteristics that make them promising for several biomedicalapplications such as MRI, hyperthermia treatment (Laurent et al. 2011), tissue repair (Gupta et al. 2007), andas drug delivery systems (DDSs) (Mok and Zhang 2013; Wahajuddin 2012). Due to their biocompatibility,appropriate surface architecture, and easy conjugation with targeting ligands, SPIOs are used as intracellularcontrast agents. MRI became one of the most commonly used imaging modalities for in vivo tracking ofdifferent cell types, because it is noninvasive, generates high-resolution images, and does not rely onradioactive isotopes, which may be an important advantage for longitudinal follow-up studies (Arbab andFrank 2008; Bulte and Kraitchman 2004; Modo et al. 2005).

The use of SPIOs as intracellular contrast agents has been optimized, to endow specific cells withsufficient magnetization to become detectable by MRI. The detection of SPIO-labeled cells is affected by anumber of factors, including field strength, signal-to-noise ratio, pulse sequence, acquisition parameters, etc.A number of studies have confirmed the detection of a few hundreds cells with SPIO labeling (Magnitskyet al. 2005; Dahnke and Schaeffter 2005). The variance in the number of detectable cells in those studiescould be attributed to the differences in hardware, resolution of acquired images and uptake of SPIO NPs bycells. Some studies have demonstrated the feasibility to detect a small number of cells or even a single cell(Kircher et al. 2003). Foster-Gareau et al. (2003) showed that single cells labeled with SPIO NPs could beimaged with a 1.5 T clinical scanner. High-resolution MRI with cryogenic probes has allowed imaging anddetecting of a single cell as localized black spots in the region of interest (Smirnov et al. 2006).

Quantification of the approximate number of magnetically labeled cells within a region may provide aneffective and efficient method to monitor and optimize cellular therapies (Liu and Frank 2009). It should benoted that MRI quantification of SPIO-labeled cells is an indirect technique. As such, signal change is due tothe concentration of SPIO NPs and not the total number of cells (Walczak et al. 2007). Some recent studiesusing microscopic images have been developed to quantify NPs inside cells in order to monitor drugdelivery (Wiekhorst et al. 2006; Tonkin et al. 2012). However, such methods are in 2D and could notmonitor in vivo NPs distribution and are not valid for MR images.

Visualization of the three-dimensional (3D) distribution of SPIO is an important factor for success ofdiagnosis and therapy. However, automated reliable quantification of the number of SPIO-labeled cells andvisualization of the 3D distribution in the region of interest, imaged by high-resolution MRI, remain achallenging problem. In this study, a new image processing approach for 3D visualization and quantificationof SPIO labeled macrophages based on high-resolution MR images of inflammation-bearing mice calfmuscle was proposed. Pre-processing of the MR images was performed in order to enhance the contrast andreduce the noise. Segmentation by automatic threshold and a method to extract SPIO particles weredeveloped to visualize the 3D distribution of NPs and improve the accuracy of quantifying the number ofSPIO labeled cells in the region of interest.

2 Material and method

2.1 Experimental protocol

Bone marrow derived macrophages (BMDM) issued from a donor C57BL/6J mice were obtained anddifferentiated ex vivo into nonpolarized M0, pro-inflammatory M1 and immunomodulator M2 macrophagesubsets and labeled with citrate-coated anionic iron oxide NPs. The labeling and inflammation processeswere performed as described in Al Faraj et al. (2013).

MR imaging protocol was then performed on a 40-cm bore size, actively-shielded, 4.7 Tesla Brukermagnet interfaced to ParaVision software (Bruker Biospin GmbH, Rheinstetten, Germany) for preclinicalMR imaging research. High resolution MRI (HR-MRI) was achieved by means of the cryogenic surfaceprobe (CryoProbeTM, Bruker). 3D susceptibility weighted gradient echo sequence (TR/TE = 20/5 ms, flipangle = 25�, 2 averages, and a pixel resolution of 50 9 50 9 50 lm3 for a total acquisition time of 10 minwas used over the same part of the muscle for the different animals.

ImageJ free Software was used for processing and analysis of the images. High-resolution MRI 3Dacquisition was performed as a stack of 100 slices to encompass the volume of interest in the mouse calf.Figure 1 shows a representative slice image of mouse calf with 50 lm 3D resolution in control mouse vs.SPIO labeled macrophages that have home to the site of inflammation and appear as black dots in the HRMR image.

A. S. Saad, A. Al Faraj

2.2 Image processing and quantification

The block diagram which describes the process, by which SPIO labeled macrophages in the mouse calf wasdetected, quantified and then visualized, is presented in Fig. 2. First, a sub-stack from the whole set ofimages containing the area of interest (e.g., 30–50 images) was chosen in the region of interest (inflam-matory site).

2.2.1 Background subtraction

Background subtraction is often used for motion detection in movies. In our case we have still images alltechniques related to moving objects such as temporal median filter, Mixture of Gaussians, Kernel densityestimation (KDE), Sequential KD approximation, and Eigen background are not suitable to the still MRIdata. Background subtraction was performed on all images in a stack of 50 images, using the sameprocessing conditions. The ‘‘Rolling Ball’’ (Sternberg’s 1983) algorithm was used here because it removessmooth continuous backgrounds from images, which it is most suitable for MRI images. A local backgroundvalue is determined for every pixel by averaging over a very large ball around the pixel. This value ishereafter subtracted from the original image, removing large spatial variations of the background intensities.The ‘‘Rolling Ball’’ radius for 8-bit or RGB images should be at least as large as the radius of the largestobject in the image that is not part of the background. Larger values are also suitable unless the backgroundof the image is too uneven. For images with pixel values having very different ranges, typical values of theradius are selected between 0.2 and 5 for 16-bit images.

2.2.2 Contrast enhancement

To improve the visual quality, the stack of images was inverted so that the NPs appear as white dots orclusters of dots. Next, to better separate the NPs from other objects in the image, contrast enhancement wasapplied to the stack by using histogram equalization technique (Ahmed et al. 2012).

2.2.3 Segmentation by threshold

A normalization process was applied to ensure that all pixel values were scaled to range from 0 to 1. Thenormalization step is necessary to make this technique applies to images acquired with different fieldstrength, pulse sequence, coil and imaging parameters. A median filter with a radius value of a few pixels is

Fig. 1 Representative MR Images of mouse calf acquired at a 3D resolution of 50 lm of a control (left) vs. SPIO labeledmacrophages group which revealed the presence of void signal dots (indicated by blue arrows)

3D Visualization of iron oxide nanoparticles

applied to the each image in the stack of images. The next step is the segmentation by automatic localthreshold, which was suitable for the processed data because it does not introduce the bias of manualselection and make the fully independent from the imaging parameters. More than 17 automatic thresholdmethods existing in ImageJ were tested as a pre-selection study. In order to choose the best segmentation acomparison with a control set of images was performed. The control set of images does not contain NPs. Themethod for automatic threshold (Lie 1995), is based on a local histogram of the image. It was chosen todetect the NPs in the image, because it provides the minimum number of noise particles in the controlimages.

2.2.4 Morphological operations

After segmentation, the image became binary with only two levels, the black background and white objects,including the targeted NPs. Morphological operations on the binary image were required to clear all theoutlying pixels that were considered noise. An opening that consisted of an erosion of radius of two pixelswas applied, followed by a dilation of the same radius, to maintain the structure of the image at the samedimension as the original. In order to show mainly the NPs in the resulted stack of images, the mouse legstructures existing in the images like bone and muscles should be removed. For this purpose ‘‘AnalyzeParticles’’ process in ImageJ was used, with parameters selected in order to provide images containing onlythe linear structure, without NPs, with the assumption that NP or aggregation of it does not form linear

Fig. 2 Block diagram representing the steps of the proposed image processing and quantification procedure

A. S. Saad, A. Al Faraj

shapes. On the other hand, some non-particles may form spherical or elliptical shapes which will beconsidered as NPs. In our procedure, a control stack not containing NPs served as reference images to set upthe parameters of the particle analysis procedure in order to minimize the appearance of this case. Then asimple subtraction between the input image and the resulting image of the particles analysis provides theimage of NPs distribution.

2.2.5 Quantification of NPs

Finally, we quantified white dots with a range of pixels corresponding to NP sizes or an agglomeration ofthem. A control stack without NPs was used for comparison with other images containing NPs. Particleanalysis was used to automatically count the average number of particles in the stack of images. A 3Dvisualization was established using 3D plugins in ImageJ.

3 Results and discussion

The method was tested on 3D model of the calf using MRI imaging system, but the processing is applied oneach 2D image, it has about 50 images per one 3D model. Figure 3a shows image after backgroundsubtraction and inversion of the grey levels in order to shows the SPIO labeled macrophages in white color.

Fig. 3 a Image of the mouse calf after background subtraction and inversion of the grey levels. b After segmentation,c without nanoparticles and d 2D distribution of SPIO labeled macrophages inside the region of interest in a single slice

3D Visualization of iron oxide nanoparticles

Figure 3b shows the segmentation by threshold of the image in Fig. 3a. The image is in binary format, wecan see the structure of the mouse calf. Figure 3c shows an image with only leg structure without NPs.‘‘Particle Analyze’’ function in ImageJ was applied on image of Fig. 3b in order to eliminate the sphericaland elliptical small particles and generate the mouse calf structure. In order to extract the SPIO labeledmacrophages, a subtraction between image in Fig. 3b and image in Fig. 3c was performed to produce theimage in Fig. 3d, which reveals their distribution in a slice of the inflamed area.

For this purpose ‘‘Analyze Particles’’ process in ImageJ was used, with parameters selected in order toprovide images containing only the linear structure, without NPs. The assumption that NP or aggregation ofit does not form linear shapes has been adopted. In reality it may happen but the frequency of occurrence isvery low, for this reason it has been neglected. On the other hand some non-particles may form spherical orelliptical shapes and will be considered as NPs. In order to minimize this aspect, a control stack of MRIimages of the same inflamed calf before injecting the NPs served as reference to select the parameters of the‘‘particle analysis’’ procedure. After the application of the ‘‘particle analysis’’ procedure to the referencestack, an estimation of the average number (percentage) of the circular and elliptical shapes left in thereference stack was about 1 % of the total average number of the counted NPs per slice.

Figure 4a shows a 3D view of the whole leg, the green circle surrounding the area where the sphericalshapes represents the SPIO NPs. Figure 4b is a zoom of the area surrounded by yellow rectangle in 4a,where we can see several spherical shapes representing the 3D locations of the NPs pointed by yellowarrows heads.

Figure 5a, b shows two different views of the 3D visualization of the same mouse calf after applying ournew approach, where only the 3D bio-distribution of the SPIO labeled macrophages, in green color, isshown inside the inflamed area of the mouse calf.

This approach is the first targeting the visualization and semi-automatic quantification of SPIO labeledmacrophages inside the inflamed mouse calf using noninvasive high resolution 3D MR imaging, whichallows assessing the volume of the inflamed site and detecting the homing of macrophages for an early andbetter diagnosis. Furthermore, the estimation of the quantity of SPIO NPs inside the calf is straightforward.

A simple count of the green voxels in the 3D cube of data, in Fig. 5, provides the number of voxels filledby SPIO NPs. If we want to know the number of SPIO NPs inside the inflamed area, which is proportional tothe number of green voxels in Fig. 5, we need first to know how many single SPIO NP can fit in a voxel in

Fig. 4 a Shows a 3D view of the whole leg, the green circle surrounding the area where the spherical shapes represent theSPIO NPs and b is a zoom of the area surrounded by yellow rectangle in a

A. S. Saad, A. Al Faraj

the MRI 3D image. Then a simple multiplication of the total number of green voxels by the number of SPIONP occupying a voxel provides an estimation of the quantity of SPIO NPs in the area of interest.

Visualization of the 3D distribution of NPs will help to locate the inflamed area and measure itsdimension. In our data the inflamed area was generated in the calf as described in the method (Al Faraj et al.2013), the location of the area is known, the visualization show the location and the volume of the inflamedarea. The result obtained from the visualization provides the same location where the inflammation wasinjected. The green dots in Fig. 5 represent the locations of SPIO NPs; the whole set of green dots representa sampled version of the volume of the inflamed area. The volume of the inflamed area could be visuallyseen by Doctors from the distribution of the green dots and could be approximated quantitatively by usingdedicated image processing software. This visualization improves diagnosis and therapy of the inflamed siteand the accuracy of drug delivery by estimating the amount of drug received by the inflamed region andother therapy procedures using nanomedicine.

4 Conclusions

The proposed procedure extracts SPIO labeled macrophages from the tissues of the mouse calf and showsthe iron oxide NPs alone in 3D space. The ability to see the 3D distribution of the NPs inside the region ofinterest helps for optimal targeted NPs diagnosis and therapy for many applications in nanomedicine. Thisapproach of 3D visualization of NPs will certainly help researchers in the field of nanomedicine especiallyin cancer, to better optimize their diagnosis and treatment strategies by assuring the delivery of drugattached to iron oxide to the tumor using MRI imaging system.

Acknowledgments This work was supported by National Science Technology and Innovation plan NSTIP strategic tech-nologies programs, project number 11-MED1773, in the Kingdom of Saudi Arabia. We thank Prof. Kelechi Ogbuehi forEnglish editing.

References

Ahmed MM, Zain JM, Ahmed MM (2012) A study on the validation of histogram equalization as a contrast enhancementtechnique, International Conference ACSAT

Fig. 5 3D longitudinal (a) and axial (b) view of the 3D biodistribution of SPIO labeled macrophages inside the whole mousecalf

3D Visualization of iron oxide nanoparticles

Al Faraj A, Luciani N, Kolosnjaj-Tabi J, Mattar E, Clement O, Wilhelm C, Gazeau F (2013) Real-time high-resolutionmagnetic resonance tracking of macrophage subpopulations in a murine inflammation model: a pilot study with acommercially available cryogenic probe. Contrast Media Mol Imaging 8(2):193–203

Arbab AS, Frank JA (2008) Cellular MRI and its role in stem cell therapy. Regenerative Med 3(2):199–215Arbab AS, Janic B, Haller J, Pawelczyk E, Liu W, Frank JA (2009) In vivo cellular imaging for translational medical research.

Curr Med Imaging Rev 5(1):19–38Bulte JW, Kraitchman DL (2004) Iron oxide MR contrast agents for molecular and cellular imaging. NMR Biomed

17(7):484–499Cromer Berman SM, Walczak P, Bulte JWM (2011) Tracking stem cells using magnetic nanoparticles. Wiley Interdiscip Rev

Nanomed Nanobiotechnol 3(4):343–355Dahnke H, Schaeffter T (2005) Limits of detection of SPIO at 3.0 T using T2 relaxometry. Magn Reson Med 53(5):1202–1206Foster-Gareau P, Heyn C, Alejski A, Rutt BK (2003) Imaging single mammalian cells with a 1.5 T clinical MRI scanner. Magn

Reson Med 49(5):968–971Gupta AK, Naregalkar RR, Vaidya VD, Gupta M (2007) Recent advances on surface engineering of magnetic iron oxide

nanoparticles and their biomedical applications. Nanomedicine (Lond) 2(1):23–39Kircher MF, Allport JR, Graves EE, Love V, Josephson L, Lichtman AH, Weissleder R (2003) In vivo high resolution three-

dimensional imaging of antigen-specific cytotoxic T-lymphocyte trafficking to tumors. Cancer Res 63(20):6838–6846Laurent S, Dutz S, Hafeli UO, Mahmoudi M (2011) Magnetic fluid hyperthermia: focus on superparamagnetic iron oxide

nanoparticles. Adv Colloid Interface Sci 166(1–2):8–23Li K, Chen M, Kanade T (2007) Cell population tracking and lineage construction with spatiotemporal context. Med Image

Comput Comput Assist Interv 10(Pt 2):295–302Lie WN (1995) Automatic target segmentation by locally adaptive image thresholding image processing. IEEE Trans

4(7):713–719Liu W, Frank JA (2009) Detection and quantification of magnetically labeled cells by cellular MRI. Eur J Radiol

70(2):258–264Magnitsky S, Watson DJ, Walton RM, Pickup S, Bulte JW, Wolfe JH, Poptani H (2005) In vivo and ex vivo MRI detection of

localized and disseminated neural stem cell grafts in the mouse brain. Neuroimage 26(3):744–754Modo M, Hoehn M, Bulte JW (2005) Cellular MR imaging. Mol Imaging 4(3):143–164Mok H, Zhang M (2013) Superparamagnetic iron oxide nanoparticle-based delivery systems for biotherapeutics. Expert Opin

Drug Deliv 10(1):73–87Padfield D, Rittscher J, Thomas N, Roysam B (2009) Spatio-temporal cell cycle phase analysis using level sets and fast

marching methods. Med Image Anal 13(1):143–155Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. Cns Neurol

Disord Dr 3(13):146–165Smirnov P, Lavergne E, Gazeau F, Lewin M, Boissonnas A, Doan BT, Gillet B, Combadiere C, Combadiere B, Clement O

(2006) In vivo cellular imaging of lymphocyte trafficking by MRI: a tumor model approach to cell-based anticancertherapy. Magn Reson Med 56(3):498–508

Sternberg’s Stanley (1983) Biomedical image processing. IEEE Comput 31(1):67–98Tonkin JA, Rees P, Brown MR, Errington RJ, Smith PJ, Chappell SC, Summers HD (2012) Automated cell identification and

tracking using nanoparticle moving-light-displays. PLoS One 7(7):e40835Wahajuddin Arora S (2012) Superparamagnetic iron oxide nanoparticles: magnetic nanoplatforms as drug carriers. Int J

Nanomedicine 7:3445–3471Walczak P, Kedziorek DA, Gilad AA, Barnett BP, Bulte JW (2007) Applicability and limitations of MR tracking of neural

stem cells with asymmetric cell division and rapid turnover: the case of the shiverer dysmyelinated mouse brain. MagnReson Med 58(2):261–269

Wiekhorst F, Seliger C, Jurgons R, Steinhoff U, Eberbeck D, Trahms L, Alexiou C (2006) Quantification of magneticnanoparticles by magnetorelaxometry and comparison to histology after magnetic drug targeting. J Nanosci Nanotechnol6(9–10):3222–3225

A. S. Saad, A. Al Faraj