Simulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images

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1 Simulation of Mammograms & Tomosynthesis imaging with Cone Beam Breast CT images Tao Han, Chris C. Shaw, Lingyun Chen, Chao-jen Lai, Xinming Liu, Tianpeng Wang Digital Imaging Research Laboratory (DIRL), Department of Imaging Physics, The University of Texas M. D. Anderson Cancer Center, 8014A El Rio, Houston, TX, ABSTRACT The use of mammography techniques for the screening and diagnosis of breast cancers has been limited by the overlapping of cancer symptoms with normal tissue structures. To overcome this problem, two methods have been developed and actively investigated recently: digital tomosynthesis mammography and cone beam breast CT. Comparison study with these three techniques will be helpful to understand their difference and further might be supervise the direction of breast imaging. This paper describes and discusses about a technique using a general-purpose PC cluster to develop a parallel computer simulation model to simulate mammograms and tomosynthesis imaging with cone beam CT images of a mastectomy breast specimen. The breast model used in simulating mammography and tomosynthesis was developed by re-scaling the CT numbers of cone beam CT images from 80kVp to 20 kev. The compression of breast was simulated by deformation of the breast model. Re-projection software with parallel computation was developed and used to compute projection images of this simulated compressed breast for a stationary detector and a linearly shifted x-ray source. The resulting images were then used to reconstruct tomosynthesis mammograms using shift-and-add algorithms. It was found that MCs in cone beam CT images were not visible in regular mammograms but faintly visible in tomosynthesis images. The scatter signal and noise property needs to be simulated and incorporated in the future. Keywords: Simulation, Mammogram, Tomosynthesis, Cone beam CT 1. INTRODUCTION According to American Cancer Society (ACS) report, breast cancer remains the second leading cause for cancer death among women in the United States. Approximately 40,000 people may die from breast cancer each year and the chance of a woman having invasive breast cancer some time during her life is about 1 in 8 1. The wide use of mammography, which can detect the breast cancer in their early stage, has helped to reduce the death rates both in screening and diagnostic imaging. But mammography has some significant limitations. During the mammography examination, the breast need to be compressed which is very uncomfortable to the patient. The compression also causes distortion of the breast tissue structures. Two view mammograms, which show attenuation information integrated along the x-ray paths, are intrinsically two-dimensional in nature and are subject to the problem of overlapping structures. Breast cancer symptoms may be hidden in the overlapping breast tissue structures without being detected. Currently, two methods have been developed to overcome these limitations: the digital tomosynthesis and the cone beam breast CT techniques. Breast Tomosynthesis is a 3D slice images technique developed from the conventional mammography. It can separate the overlapping tissue on arbitrary number of in-focus slices. Breast tomosynthesis takes multiple X-ray pictures of each breast from many angles. The breast is positioned the same way as a conventional mammogram, also it needs the breast to be compressed, but sometime might be less pressure. The X-ray tube moves in an arc around the breast while images are taken during its shifting. These sequence of projection images are used to reconstructed to get the 3-dimensional tomosynthesis images. Medical Imaging 2008: Physics of Medical Imaging, edited by Jiang Hsieh, Ehsan Samei, Proc. of SPIE Vol. 6913, , (2008) /08/$18 doi: / SPIE Digital Library -- Subscriber Archive Copy Proc. of SPIE Vol

2 Cone beam breast CT has became feasible since the development and commercialization of flat panel detector. Comparing with traditional CT, cone beam breast CT uses an area flat panel detector and need only one rotation. The breast was suspended from a hole of the patient table without compression, which is much more comfortable for the patient. The x-ray source energy ranged from 80kvp to 120kvp. X-ray generator and detector were mounted together and rotate around the breast. During the rotation, projection images were obtained and sent to computer for reconstruction. During the examination only the breast was exposed to the x-rays. Cone beam breast CT can provide true 3D image with almost isotropic spatial resolution, which may not only overcome the overlapping issue, but also will largely decrease the structure noise. Although these two techniques have been developed for several years by separated groups 2-5, the difference of them is still need to be investigated and evaluated. The comparison study will help us to understand their advantage and disadvantage and thus might be useful to supervise breast imaging in the future. In this paper, we develop a ray-tracing method to simulate the mammography and tomosynthesis imaging with cone beam CT images of mastectomy specimen, and implement this method into a general used PC cluster. We also evaluate the micro-calcification detection ability of the simulated mammograms and tomosynthesis images. The scatter signal and noise property will be incorporated in the future work. 2.1 Cone beam breast CT image acquisition 2. MATERIALS AND METHODS A stationary gantry, rotating phantom bench top system was constructed (figure 1) to acquire cone beam CT images of mastectomy specimen. It consisted of a radiographic x-ray tube with 0.6mm focal spot size, a Varian Paxscan 4030CB flat panel detector with mm pixel size and a step motor driven rotating table. Breast specimen from mastectomy was placed in a breast shape holder and put on the rotating table. 300 projection images were acquired under 80 kvp during one rotation. FDK algorithm with a normal ramp filter was used for cone beam CT reconstruction 6. Fig 1. Bench top cone beam CT system (a) Bench table (b) X-ray tube with collimator (c) Plat panel detector (d) Rotating table (e) Mastectomy specimen holder Proc. of SPIE Vol

3 2.2 Ray-tracing algorithm with parallel computation The x-ray projection image is physically based on the linear attenuation coefficient of X-ray of object. If we do not consider the scatter effect, this process can be simulated by ray tracing algorithm. From the source position, a set of rays are emitted to detector pixels. Each x-ray path is divided into many sampling intervals which are embedded in an array of voxels, and their µ values are summed up to get the attenuation information of this ray. Figure 3 shows the diagram of the ray tracing algorithm. In order to implement to a 48-cpu pc cluster to improve the computational performance, cone beam CT images need to load to every slave CPU separately, which will cost the most computing time. So every slave CPU load different parts of cone beam CT data will be one of the requirements for the task separation. But this also resulted in another problem that the sampling points outside the partial data geometry need to be withdrawn. It can be resolved by identifying the entrance and exit points between the ray and the data geometry. Consider that cone beam CT data is a cubic data set that contains 6 planes. So there will be 6 cross points with ray path. The maximum length of 3 points faced to source is the entrance point, while the minimum length of 3 points back to source is the exit point. The sampling points between these two points will be summed up while others will jump out the computation loop. We have test the performance of this technique and found it can greatly improve the computation speed. For a 900x900x640 phantom, one projection image with 1000x800 pixels will improve from 5 minutes to 21 seconds. Detector CBCT Data Sampling point X-Ray Tube Fig. 2. The diagram of ray tracing algorithm. The ray path was separate to small sampling point and only those in object will be calculate. Using this ray-tracing method, we could incorporate focal spot blurring, detector blurring, X-ray spectrum, quantum noise and system noise and investigate their effects on the image quality. These studies will be parts of our future work. 2.3 Breast modeling with cone beam breast CT images Mammography and tomosynthesis images are usually taken at kvp with the breast compressed. In order to construct a model that satisfies these conditions, the cone beam CT images of mastectomy specimen need to be converted to those for mammographic x-rays. Assuming the X-rays are monoenergetic at 20 kev, and the shape of breast need to deformed to simulate a compressed breast. Because of beam hardening and x-ray scattering, the reconstructed cone beam CT images are subject to a Cupping Proc. of SPIE Vol

4 effect with which the CT numbers towards the center tend to be lower than those towards the edge. Thus, prior to further modification, this Cupping effect needs to be corrected for. A technique reported by Altunbas et al was used to remove this effect 7. The breast tissue is mostly composed of adipose and fibroglandular tissue. In this study, the linear attenuation coefficients of the latter were approximated by those of the muscle. The 80 kvp x-rays were approximated by 60 kev x-rays while the mammographic x-rays were approximated by monogenetic x- rays at 20 kev. Thus, the CT image data generated with 80 kvp x-rays may be converted into those for mammographic x-rays by using a simple rescaling method. In CT, the reconstructed linear attenuation coefficients were converted into CT number using the water coefficients as the reference. To convert 60 kev data to 20 kev data, a muscle based CT number was computed using the muscle coefficients as the reference instead. The muscle based CT numbers for the glandular tissue will not vary with respect to the kvp, thus allowing us to focus on the CT numbers for the adipose tissue. The two plots on the right side of Fig. 3 show the energy dependence of the water based CT numbers and that of the muscle based CT numbers. The rescaling process is shown on the left side of Fig. 3. Notice that with the muscle based CT numbers, the glandular component of breast tissue is independent of the photon energy. The 60 kev CT numbers for the adipose tissue are multiplied by 2.9 to obtain the 20 kev CT numbers. This makes the contrast between the adipose and glandular tissues increase according to the known curves. 101 CT# at 60 key in Water base CT#m =11 a_ n - CT# at 60 kevin Musde base SJk 60& _1L 60 J1, U u IL 0 Muscle Key CT# vesus key in water base CT# at 20 key in Muscle base 0 = 2.9*CTWbU SD 90 IOU I 0 CT# at 20 kevin Water base -310 / = ooo // (C -510 ;:: ::i::::i::: ::::i:::tti:i::i Muscle -.-Aj' Keg CT# vesus key in Muscle base Fig. 3 Re-scale CT# of cone beam CT images acquired under 80kVp to 20 kev. To simulate breast compression, which is generally required in mammography and tomosynthesis imaging, a deformation process is applied to the CT image based breast model. To simplify the process but maintain the spatial resolution of images, we shrunk the 3-D breast images in the horizontal direction (Fig. 4a, 4b) leaving the dimensions in the vertical direction and resulting in a compressed breast with uniform thickness Proc. of SPIE Vol

5 in the horizontal direction (Fig. 4a, 4b). Fig. 4 shows the deformation of cone beam CT images. Fig. 4 (a) is the side view of reconstructed cone beam CT image of a mastectomy specimen. The dark area is the adipose tissue and the write area is the glandular tissue. The bright spot in the yellow circle is one of microcalcifications. Front view of this cone beam CT image is showed in Fig. 4 (b). The micro-calcification in yellow circle is the same one as the side view. Fig. 4 (c) shows a front view image of the deformed cone beam CT image data which may be used to model the compressed breast for simulation of mammography and tomosynthesis imaging. (a) (b) Fig. 4 Cone beam CT image of a mastectomy breast specimen. (a) Side view (b) Front view, (b) Deformed to simulate a compressed breast. (c) 2.4 Mammography and tomosynthesis simulation The ray-tracing technique is very flexible in simulating the primary image signals for mammography and tomosyntheis imaging. In this paper the simulated mammography geometry is: the distance from the source to the breast center is 65 cm; 1.2 cm air gap exists between the breast and the detector; and breast is compressed from 14 cm to 8 cm. The same compression ratio is applied to tomosynthesis imaging. In order to simulate tomosynthesis, the sources move along the horizontal direction and 21 projection images were Proc. of SPIE Vol

6 taken in every 5 degree during the shifting. In addition, the x-ray source was assumed to be a point source and monoenergetic both in mammography and tomosyntheis imaging. The conventional tomosynthesis reconstruction algorithm is shift-and-add. This approach considers the fact that objects at different heights will experience different degrees of parallax as the tube moves, and thus be projected at different positions in detector. So it is possible to shift and add images such that the structures in some plane are all made to line up exactly and thus be in focus, while structures in other planes are distributed over the image and thus appear blurred. So the projection images taken at different angles can be reconstructed to generate a tomosynthesis mage in which certain plane is enhanced while other planes are blurred. This shift-and-add approach has obvious artifact but in physics it can demonstrate our simulation method is valid or not. Because it is an initial step of our work, we use this reconstruction method to construct the tomosynthesis images. 3. RESULTS Figure 5 (a) shows the simulated mammogram. It was observed that the MCs visible in the breast CT images were invisible in the simulated mammograms (Figure 2 (a)). This may be because the rescaling process was based on the assumption that the breast contains only adipose and glandular tissue, so the same rescaling factor was applied to the voxels containing micro-calcifications, which should have been scaled with a larger factor. Segmentation of the breast into the adipose, glandular and micro-calcification regions may help avoid this problem and produce better simulation. However, the segmentation process itself may also have errors which may alter the breast structure. The comparison of these two methods will be studied in our future work. Fig. 5 Simulated mammogram. The area might contain micro-calcification is enlarged in right hand side. The MC circled in Figs. 2(a) is not visible. No other MCs are visible. The second image in Figure 5 shows a simulated tomosynthesis image focused on the slice contain the micro-calcification circled in Fig. 4(a). This image was reconstructed from 21 projection images at different angles using shift-and-add algorithm. It is noticed that the whole image looks more blurred than mammogram, because the out-of focus planes are blurred as background, but for the micro-calcification invisible in mammogram, is faintly visible in tomosynthesis images. It means that the plane of the microcalcification was enhanced and focused. This result can demonstrate that our simulation method is valid in simulating the geometry of mammography and tomosynthesis. We will use other advanced reconstruction algorithms to investigate the performance of tomosynthesis image comparing with mammography and cone beam CT in the future. Proc. of SPIE Vol

7 Fig. 6 Simulated tomosynthesis image. The area might contain micro-calcification is also enlarged in right hand side. The MC circled in Figs. 2(a) is faintly visible. 4. CONCLUSIONS In this paper we have successively used cone beam CT images of mastectomy breast specimens to generate a 3-D model of compressed breast. We have also successfully implemented re-projection software with parallel computation to simulate digital mammograms and tomosynthesis imaging. It was found that the micro-calcifications clearly visible in cone beam CT images were not visible in regular mammograms but faintly visible in tomosynthesis images. Scatter and noise effect need to be incorporate into the simulating image in the future work. 5. ACKNOWLEDGEMENT This work was supported in Supported by grants CA and CA from National Cancer Institute and a grant EB00117 from National Institute of Biomedical Imaging, and a subcontract from National Institute of Standards and Technology-Advanced Technology Program. 6. REFERENCE 1. American Cancer Society Breast Cancer Facts & Figures Atlanta, GA. 2.Dobbins JT 3rd, Godfrey DJ, Digital x-ray tomosynthesis: current state of the art and clinical potential, Phys Med Biol, (19): p, Wu T, Stewart A, Stanton M, etc., Tomographic mammography using a limited number of low-dose cone beam projection images. Med Phys., (3): p Boone J. M., Nelson T. R., Lindfors K. K., Seibert J. A, Dedicated breast CT: radiation dose and image quality evaluation. Radiology, (3): p Chen, B. and R. Ning, Cone-beam volume CT breast imaging: Feasibility study. Med. Phys, (5): p Avinash, K.C. and S. Malcolm, Principles of computerized tomographic imaging Altunbas MC, Shaw CC, Chen L, Lai C, Liu X, Han T, Wang T, A post-reconstruction method to correct cupping artifacts in cone beam breast computed tomography. Med Phys, (7): p Proc. of SPIE Vol

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