Optimized generation of high resolution breast anthropomorphic software phantoms.

PURPOSE The authors present an efficient method for generating anthropomorphic software breast phantoms with high spatial resolution. Employing the same region growing principles as in their previous algorithm for breast anatomy simulation, the present method has been optimized for computational complexity to allow for fast generation of the large number of phantoms required in virtual clinical trials of breast imaging. METHODS The new breast anatomy simulation method performs a direct calculation of the Cooper's ligaments (i.e., the borders between simulated adipose compartments). The calculation corresponds to quadratic decision boundaries of a maximum a posteriori classifier. The method is multiscale due to the use of octree-based recursive partitioning of the phantom volume. The method also provides user-control of the thickness of the simulated Cooper's ligaments and skin. RESULTS Using the proposed method, the authors have generated phantoms with voxel size in the range of (25-1000 μm)(3)∕voxel. The power regression of the simulation time as a function of the reciprocal voxel size yielded a log-log slope of 1.95 (compared to a slope of 4.53 of our previous region growing algorithm). CONCLUSIONS A new algorithm for computer simulation of breast anatomy has been proposed that allows for fast generation of high resolution anthropomorphic software phantoms.

[1]  Andrew D. A. Maidment,et al.  Comparison of 3D and 2D breast density estimation from synthetic ultrasound tomography images and digital mammograms of anthropomorphic software breast phantoms , 2011, Medical Imaging.

[2]  Aldo Badano,et al.  Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. , 2009, Medical physics.

[3]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[4]  Eric R. Ziegel,et al.  Probability and Statistics for Engineering and the Sciences , 2004, Technometrics.

[5]  Kyle J. Myers,et al.  Monte Carlo package for simulating radiographic images of realistic anthropomorphic phantoms described by triangle meshes , 2007, SPIE Medical Imaging.

[6]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[7]  Donald Meagher,et al.  Geometric modeling using octree encoding , 1982, Comput. Graph. Image Process..

[8]  E. W. Shrigley Medical Physics , 1944, British medical journal.

[9]  Ann-Katherine Carton,et al.  Validation and optimization of digital breast tomosynthesis reconstruction using an anthropomorphic software breast phantom , 2010, Medical Imaging.

[10]  John M Boone,et al.  Methodology for generating a 3D computerized breast phantom from empirical data. , 2009, Medical physics.

[11]  David Ingram,et al.  Simulated Mammography Using Synthetic 3D Breasts , 1998, Digital Mammography / IWDM.

[12]  Andrew D. A. Maidment,et al.  Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm. , 2011, Medical physics.

[13]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.

[14]  Helena Jernström,et al.  Of cup and bra size: Reply to a prospective study of breast size and premenopausal breast cancer incidence , 2006, International journal of cancer.

[15]  Ann-Katherine Carton,et al.  Development of a physical 3D anthropomorphic breast phantom. , 2011, Medical physics.

[16]  Andrew D. A. Maidment,et al.  Digital Breast Tomosynthesis Parenchymal Texture Analysis for Breast Cancer Risk Estimation: A Preliminary Study , 2008, Digital Mammography / IWDM.

[17]  Andrew D. A. Maidment,et al.  Evaluating the Effect of Tomosynthesis Acquisition Parameters on Image Texture: A Study Based on an Anthropomorphic Breast Tissue Software Model , 2008, Digital Mammography / IWDM.

[18]  Zoran Obradovic,et al.  An adaptive partitioning approach for mining discriminant regions in 3D image data , 2008, Journal of Intelligent Information Systems.

[19]  W. Kaiser,et al.  Model-based registration of X-ray mammograms and MR images of the female breast , 2006, IEEE Transactions on Nuclear Science.

[20]  N. Erdoğan,et al.  Effect of age, breast size, menopausal and hormonal status on mammographic skin thickness , 2003, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[21]  Ehsan Samei,et al.  An anthropomorphic breast model for breast imaging simulation and optimization. , 2011, Academic radiology.

[22]  Ann-Katherine Carton,et al.  Development of a 3D high-resolution physical anthropomorphic breast phantom , 2010, Medical Imaging.

[23]  S Suryanarayanan,et al.  Evaluation of an improved algorithm for producing realistic 3D breast software phantoms: application for mammography. , 2010, Medical physics.

[24]  Andrew D. A. Maidment,et al.  Roadmap for efficient parallelization of breast anatomy simulation , 2012, Medical Imaging.

[25]  M E Read,et al.  Breast skin thickness: normal range and causes of thickening shown on film-screen mammography. , 1984, Journal of the Canadian Association of Radiologists.

[26]  Christoph Hoeschen,et al.  A high-resolution voxel phantom of the breast for dose calculations in mammography. , 2005, Radiation protection dosimetry.

[27]  Andrew D. A. Maidment,et al.  Dynamic reconstruction and rendering of 3D tomosynthesis images , 2011, Medical Imaging.

[28]  Robert M. Nishikawa,et al.  An Anthropomorphic Software Breast Phantom for Tomosynthesis Simulation: Power Spectrum Analysis of Phantom Projections , 2010, Digital Mammography / IWDM.

[29]  Björn Carlsson,et al.  The FASEB Journal • FJ Express Full-Length Article Separation of human adipocytes by size: hypertrophic fat , 2022 .

[30]  Spencer Gunn,et al.  Introducing DeBRa: a detailed breast model for radiological studies. , 2009, Physics in medicine and biology.

[31]  Andrew D. A. Maidment,et al.  Development of an anthropomorphic breast software phantom based on region growing algorithm , 2008, SPIE Medical Imaging.

[32]  Andrew D. A. Maidment,et al.  Mammogram synthesis using a 3D simulation. I. Breast tissue model and image acquisition simulation. , 2002, Medical physics.

[33]  R M Nishikawa,et al.  Task-based assessment of breast tomosynthesis: effect of acquisition parameters and quantum noise. , 2010, Medical physics.

[34]  J. Michael O'Connor,et al.  Comparison of Two Methods to Develop Breast Models for Simulation of Breast Tomosynthesis and CT , 2008, Digital Mammography / IWDM.

[35]  David G. Stork,et al.  Pattern Classification , 1973 .

[36]  Andrew D. A. Maidment,et al.  Analysis of Geometric Accuracy in Digital Breast Tomosynthesis Reconstruction , 2010, Digital Mammography / IWDM.

[37]  K Bliznakova,et al.  A three-dimensional breast software phantom for mammography simulation. , 2003, Physics in medicine and biology.

[38]  I. Davidsohn Medical Research , 1923, Nature.

[39]  C. D'Orsi,et al.  Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. , 2010, Journal of the American College of Radiology : JACR.

[40]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[41]  L. Kochian Author to whom correspondence should be addressed , 2006 .

[42]  Robert M. Nishikawa,et al.  TH‐D‐201B‐08: An Anthropomorphic Software Breast Phantom for Tomosynthesis Simulation: Power Spectrum Analysis of Phantom Reconstructions , 2010 .