A high-resolution voxel phantom of the breast for dose calculations in mammography

Hoeschen, Christoph; Fill, U.A.; Zankl, M.; Panzer, W.; Regulla, D.F.; Döhring, W.

doi:10.1093/rpd/nch558

Cited by 36 publications

(27 citation statements)

References 7 publications

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“…[19][20][21][22][23][24][25] We needed three specific features in a software phantom: (1) the ability to generate random breast parenchymal patterns, (2) projection image statistics that match mammogram statistics, and (3) the ability to generate a large batch of phantoms relatively quickly. Bakic's voxelized breast phantom model was chosen to meet these requirements.…”

Section: Iia the Digital Phantom Ensemblementioning

confidence: 99%

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

et al. 2013

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Purpose: Digital breast tomosynthesis (DBT) is a promising breast cancer screening tool that has already begun making inroads into clinical practice. However, there is ongoing debate over how to quantitatively evaluate and optimize these systems, because different definitions of image quality can lead to different optimal design strategies. Powerful and accurate tools are desired to extend our understanding of DBT system optimization and validate published design principles. Methods: The authors developed a virtual trial framework for task-specific DBT assessment that uses digital phantoms, open-source x-ray transport codes, and a projection-space, spatial-domain observer model for quantitative system evaluation. The authors considered evaluation of reconstruction algorithms as a separate problem and focused on the information content in the raw, unfiltered projection images. Specifically, the authors investigated the effects of scan angle and number of angular projections on detectability of a small (3 mm diameter) signal embedded in randomly-varying anatomical backgrounds. Detectability was measured by the area under the receiver-operating characteristic curve (AUC). Experiments were repeated for three test cases where the detectability-limiting factor was anatomical variability, quantum noise, or electronic noise. The authors also juxtaposed the virtual trial framework with other published studies to illustrate its advantages and disadvantages. Results: The large number of variables in a virtual DBT study make it difficult to directly compare different authors' results, so each result must be interpreted within the context of the specific virtual trial framework. The following results apply to 25% density phantoms with 5.15 cm compressed thickness and 500 μm 3 voxels (larger 500 μm 2 detector pixels were used to avoid voxel-edge artifacts): 1. For raw, unfiltered projection images in the anatomical-variability-limited regime, AUC appeared to remain constant or increase slightly with scan angle. 2. In the same regime, when the authors fixed the scan angle, AUC increased asymptotically with the number of projections. The threshold number of projections for asymptotic AUC performance depended on the scan angle. In the quantum-and electronic-noise dominant regimes, AUC behaviors as a function of scan angle and number of projections sometimes differed from the anatomy-limited regime. For example, with a fixed scan angle, AUC generally decreased with the number of projections in the electronic-noise dominant regime. These results are intended to demonstrate the capabilities of the virtual trial framework, not to be used as optimization rules for DBT. Conclusions:The authors have demonstrated a novel simulation framework and tools for evaluating DBT systems in an objective, task-specific manner. This framework facilitates further investigation of image quality tradeoffs in DBT.

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Section: Iia the Digital Phantom Ensemblementioning

confidence: 99%

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

et al. 2013

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“…These types of phantoms have been largely based on dedicated bCT data from mastectomy specimens. 20,21 However, these specimens do not adequately represent intact breasts. Currently, only a small number of voxelized phantoms using this method exist due to limited access to specimens.…”

Section: Introductionmentioning

confidence: 99%

Population of 224 realistic human subject-based computational breast phantoms

et al. 2015

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Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data. Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230+ dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms. Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms. Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

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“…There have been several computerized 3D breast phantoms created; the majority were based either on the voxelization of real subject data or mathematical models based on geometric primitives [73][74][75][76][77][78][79][80] slices that were positioned normal to the compression plates. These slices were then stacked together again to form the total compressed breast model.…”

Section: Mathematically Based Breast Phantomsmentioning

confidence: 99%

Three-dimensional computer generated breast phantom based on empirical data

Segars

et al. 2008

SPIE Proceedings

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Breast imaging has improved the early detection of breast cancer thereby decreasing the mortality rate; however, thousands of women are wrongly diagnosed each year.Improving the sensitivity and specificity of breast cancer imaging is an important area of research and development. One of the major hurdles in imaging research arises from the difficulty in accruing human subject data because of cost, time, or patient risk considerations. Consequently, computerized phantoms are an important research tool that can help in developing new imaging techniques and devices. They can simulate a potentially unlimited amount of patient anatomies and provide a known truth with which to quantitatively evaluate, compare, and improve new imaging technologies in a costeffective and efficient method. It is essential for computerized phantoms to be anatomically realistic and produce realistic imaging data such that results from studies utilizing the phantoms are indicative of what would occur in human subjects.The purpose of this dissertation is to develop a three-dimensional computer generated breast phantom that is based on empirical data that can be used in breast imaging research. Currently available breast phantoms are either voxelized phantoms with fixed anatomy or flexible mathematical phantoms based on geometric primitives such as spheres and cylinders. In this work, we present the method to generate a suite of hybrid breast phantoms that combine the realism of a voxelized phantom with the flexibility to easily model anatomical variations by incorporating a mathematical basis.The first step in phantom generation was to acquire and process the imaging data.We received dedicated breast computed tomography imaging data of pendant v uncompressed breasts of human subjects from our collaborators at UC Davis. We implemented pre-and post-reconstruction algorithms to reduce the noise and scatter inherent in the images from the low-dose acquisition of the data and the cone-beam geometry of the CT system, respectively. Following image processing, we developed a custom volumetric segmentation algorithm to differentiate the breast tissues and maintain the high-resolution detail available in the imaging data. Derived from real human data, this step produced an anatomically realistic basis for the breast phantom.Following segmentation, a subdivision surface model of the breast tissue was generated. This step introduced flexibility to the empirically based phantom by using a mathematical description for the breast tissue surfaces as subdivision surface models can be altered using affine or other transformations. This phantom can be used for imaging studies using an uncompressed geometry or it can be used to generate a finite element mesh of the breast to be used for a compression model. Simulated compression of the breast phantom was achieved by applying finite element methods that can realistically deform the phantom. The material properties of the different types of breast tissue were incorporated into this model. Also, a comprehe...

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A high-resolution voxel phantom of the breast for dose calculations in mammography

Cited by 36 publications

References 7 publications

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

A virtual trial framework for quantifying the detectability of masses in breast tomosynthesis projection data

Population of 224 realistic human subject-based computational breast phantoms

Three-dimensional computer generated breast phantom based on empirical data

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