Digital Imaging Research Laboratory, Department of Imaging Physics, UT M.D. Anderson Cancer Center Houston, Texas 77030, USA.
Med Phys. 2007 Jul;34(7):3109-18. doi: 10.1118/1.2748106.
In cone beam breast computed tomography (CT), scattered radiation leads to nonuniform biasing of CT numbers known as a cupping artifact. Besides being visual distractions, cupping artifacts appear as background nonuniformities, which impair efficient gray scale windowing and pose a problem in threshold based volume visualization/segmentation. To overcome this problem, we have developed a background nonuniformity correction method specifically designed for cone beam breast CT. With this technique, the cupping artifact is modeled as an additive background signal profile in the reconstructed breast images. Due to the largely circularly symmetric shape of a typical breast, the additive background signal profile was also assumed to be circularly symmetric. The radial variation of the background signals was estimated by measuring the spatial variation of adipose tissue signals in front view breast images. To extract adipose tissue signals in an automated manner, a signal sampling scheme in polar coordinates and a background trend fitting algorithm were implemented. The background fits compared with targeted adipose tissue signal value (constant throughout the breast volume) to get an additive correction value for each tissue voxel. To test the accuracy, we applied the technique to cone beam CT images of mastectomy specimens. After correction, the images demonstrated significantly improved signal uniformity in both front and side view slices. The reduction of both intraslice and interslice variations in adipose tissue CT numbers supported our observations.
在锥形束乳腺计算机断层扫描（CT）中，散射辐射会导致 CT 数的非均匀偏差，这种现象被称为杯状伪影。除了视觉干扰外，杯状伪影还表现为背景不均匀性，这会影响有效的灰度窗口设置，并在基于阈值的体积可视化/分割中造成问题。为了克服这个问题，我们专门为锥形束乳腺 CT 开发了一种背景不均匀性校正方法。使用该技术，将杯状伪影建模为重建乳腺图像中的附加背景信号轮廓。由于典型乳房的形状在很大程度上是圆对称的，因此也假设附加背景信号轮廓是圆对称的。通过测量前视图乳腺图像中脂肪组织信号的空间变化来估计背景信号的径向变化。为了以自动方式提取脂肪组织信号，在极坐标中实现了信号采样方案和背景趋势拟合算法。将背景拟合与靶向脂肪组织信号值（在整个乳房体积中保持不变）进行比较，以获得每个组织体素的附加校正值。为了测试准确性，我们将该技术应用于乳房切除术标本的锥形束 CT 图像。校正后，前视图和侧视图切片中的图像显示出明显改善的信号均匀性。脂肪组织 CT 值的切片内和切片间变化减少也支持了我们的观察。
