Erickson David W, Wells Jered R, Sturgeon Gregory M, Samei Ehsan, Dobbins James T, Segars W Paul, Lo Joseph Y
Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705 and Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705.
Clinical Imaging Physics Group and Carl E. Ravin Advanced Imaging Laboratories, Duke University Medical Center, Durham, North Carolina 27705 and Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705.
Med Phys. 2016 Jan;43(1):23. doi: 10.1118/1.4937597.
To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data.
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.
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.
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.
基于专用乳腺计算机断层扫描（bCT）数据创建一个具有高度真实感且解剖结构可变的三维虚拟乳腺模型数据库。
使用一种组织分类和分割算法，基于来自正常人类受试者的230多个专用bCT数据集创建逼真且详细的三维计算乳腺模型。使用一种粗略的三类模糊C均值分割算法识别乳腺体积，该算法考虑并消除了乳腺周边的运动模糊。通过应用重建后的三维双边滤波器降低bCT数据中的噪声。然后应用三维脂肪不均匀性（偏置场）校正，接着使用三维偏置校正模糊C均值算法进行腺体分割。定义了多个组织类别，包括皮肤、脂肪以及几种不同的腺体密度分数。分割后，生成一个皮肤掩码，保留了皮肤内部相互交错的皮肤、脂肪和腺体边界。最后，使用表面建模方法生成数字模型，这些方法与数字人体模型的XCAT套件互补。
由于伪影剔除了一些数据集后，创建了224个虚拟乳腺模型，这些模型模拟了实际人类受试者复杂的乳腺实质。含皮肤的乳腺体积密度范围为5.5%至66.3%，平均值为25.3%±13.2%。乳腺体积范围为25.0至2099.6毫升，平均值为716.3±386.5毫升。选择了三个乳腺模型进行数字压缩成像（使用有限元建模）和简单光线追踪，结果表明它们有潜力生成逼真的模拟乳腺X线照片。
这项工作基于体内bCT数据为成像研究提供了一组新的224个乳腺模型。与之前仅基于少数原型病例的研究相比，该数据集提供了丰富的新病例来源，涵盖了广泛的乳腺类型、体积、密度和实质模式。
