Reiser I, Nishikawa R M, Giger M L, Wu T, Rafferty E A, Moore R, Kopans D B
Department of Radiology, The University of Chicago, Chicago, Illinois 60637, USA.
Med Phys. 2006 Feb;33(2):482-91. doi: 10.1118/1.2163390.
Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three-dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen-film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three-dimensional (3-D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number.of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.
数字乳腺断层合成（DBT）最近已成为乳腺成像中一种新的且有前景的三维模态。在DBT中，乳腺体积是从11张投影图像重建而来的，这些图像是在50度弧上等间隔的源角度拍摄的。这种模态的重建算法尚未完全优化。由于在重建的乳腺体积中进行计算机化病变检测会受到重建技术的影响，我们正在开发一种新颖的肿块检测算法，该算法直接对原始投影图像集进行操作。肿块检测分三个阶段进行。首先，使用最初为屏-片乳腺摄影开发的肿块检测算法，分别为每个投影图像获取病变候选区域。其次，将病变候选区域的位置反向投影到乳腺体积中。在这个特征体积中，体素强度是检测频率（例如，检测到给定病变候选区域的投影数量）和检测到给定病变的角度范围的综合度量。第三，将病变候选区域的三维（3-D）位置重新投影到投影图像后提取特征。使用线性判别分析对特征进行组合。用于测试该算法的数据库由21例有肿块病例（13例恶性，8例良性）和15例无肿块病变病例组成。基于这个数据库，该算法在每个乳腺体积1.5个假阳性的情况下，灵敏度达到了90%。由于该数据集用于开发、训练和测试，并且算法参数的数量与患者病例数量大致相同，所以算法性能存在正向偏差。我们的结果表明，尽管DBT投影图像中的噪声水平较高，但在这些图像序列中进行计算机化肿块检测可能是有效的。
