Department of Medical Imaging, Radboud University Medical Center, Nijmegen, 6500 HB, The Netherlands.
Department of Radiology, The University of Chicago, Chicago, IL, 60637, USA.
Med Phys. 2020 Oct;47(10):4906-4916. doi: 10.1002/mp.14400. Epub 2020 Aug 16.
To develop and test the feasibility of a two-pass iterative reconstruction algorithm with material decomposition designed to obtain quantitative iodine measurements in digital breast tomosynthesis.
Contrast-enhanced mammography has shown promise as a cost-effective alternative to magnetic resonance imaging for imaging breast cancer, especially in dense breasts. However, one limitation is the poor quantification of iodine contrast since the true three-dimensional lesion shape cannot be inferred from the two-dimensional (2D) projection. Use of limited angle tomography can potentially overcome this limitation by segmenting the iodine map generated by the first-pass reconstruction using a convolutional neural network, and using this segmentation to restrict the iodine distribution in the second pass of the reconstruction. To evaluate the performance of the algorithms, a set of 2D digital breast phantoms containing targets with varying iodine concentration was used. In each breast phantom, a single simulated lesion with a random size (4 to 8 mm) was placed in a random location within each phantom, with the iodine distribution defined as either homogeneous or rim-enhanced and blood iodine concentration set between 1.4 and 5.6 mg/mL. Limited angle projection data of these phantoms were simulated for wide and narrow angle geometries, and the proposed reconstruction and segmentation algorithms were applied.
The median Dice similarity coefficient of the segmented masks was 0.975 for the wide angle data and 0.926 for the narrow angle data. Using these segmentations during the second reconstruction pass resulted in an improvement in the concentration estimates (mean estimated-to-true concentration ratio, before and after second pass: 48% to 73% for wide angle; 30% to 73% for narrow angle), and a reduction in the coefficient of variation of the estimates (55% to 27% for wide angle; 54% to 35% for narrow angle).
We demonstrate that the proposed two-pass reconstruction can potentially improve accuracy and precision of iodine quantification in contrast-enhanced tomosynthesis.
开发并测试一种具有材料分解功能的双通迭代重建算法的可行性,旨在对数字乳腺断层合成术进行碘定量测量。
对比增强乳房 X 线摄影术已显示出作为磁共振成像的一种具有成本效益的替代方法用于乳腺癌成像的潜力,尤其是在致密乳房中。然而,一个限制是碘对比剂的定量效果不佳,因为无法从二维(2D)投影推断出真实的三维病变形状。使用有限角度断层摄影术可以通过使用卷积神经网络对第一通过重建生成的碘图进行分段,并在重建的第二通过中使用该分段来限制碘的分布,从而潜在地克服此限制。为了评估算法的性能,使用一组包含具有不同碘浓度的目标的 2D 数字乳腺体模。在每个乳腺体模中,在每个体模中的随机位置放置一个具有随机大小(4 至 8mm)的单个模拟病变,碘分布定义为均匀或边缘增强,血液碘浓度设置在 1.4 至 5.6mg/mL 之间。模拟了这些体模的宽角和窄角几何形状的有限角度投影数据,并应用了所提出的重建和分割算法。
广角数据的分割掩模的中位数 Dice 相似系数为 0.975,窄角数据的中位数 Dice 相似系数为 0.926。在第二重建通过中使用这些分割结果导致浓度估计值的改善(广角之前和之后的第二通过的估计与真实浓度比:48%至 73%;窄角:30%至 73%),并且估计值的变异系数降低(广角:55%至 27%;窄角:54%至 35%)。
我们证明了所提出的双通重建方法有可能提高对比度增强断层合成术的碘定量准确性和精度。