Lai C, Shen Y, Zhong Y, Whitman G, Yang W, Wang T, Shaw C
UT MD Anderson Cancer Center, Houston, TX.
Med Phys. 2012 Jun;39(6Part24):3915. doi: 10.1118/1.4735980.
To develop and validate algorithms for extraction and insertion of three-dimensional (3D) profiles of tumor masses in cone beam breast CT (CBBCT) images to create simulated abnormality for evaluation with observer performance study.
A bench-top experimental CBBCT scanner was constructed and used to image mastectomy breast specimens with IRB approval. 5 sets of CBBCT images with confirmed tumor masses and 2 sets of normal CBBCT images were selected and used for this study. All CBBCT images were first corrected for cupping artifacts. The corrected images were then processed to reduce their noise levels and form the denoised images. The corrected and denoised CBBCT images for normal breasts were then segmented into adipose and glandular voxels. The images for abnormal breasts were reviewed by mammographers with the help of clinical images and reports to delineate the tumor masses and form 3D tumor maps. Using these maps, the 3D tumor profiles were rescaled to average glandular signals around the insertion locations in normal images. The boundaries of the 3D rescaled tumor profiles were smoothed to avoid sharp edge. The resultant 3D tumor profiles were inserted onto cupping artifact corrected normal CBBCT images by replacing the signals in adipose voxels near the insertion locations. The cupping artifacts were then added back to generate the simulated abnormal CBBCT images. These images were then visually compared with actual abnormal images for their degree of realism.
Based on visual comparison, the simulated abnormal CBBCT images showed no significant difference in realism from actual abnormal CBBCT images.
We have successfully demonstrated the technique to extract 3D tumor profiles from abnormal CBBCT images and insert them onto normal CBBCT images to form simulated abnormal images for use in observer performance study. This work was supported in part by grants CA104759, CA13852 and CA124585 from the NIH-NCI, a research grant EB00117 from the NIH-NIBIB, and a subcontract from NIST-ATP.
开发并验证用于在锥束乳腺CT（CBBCT）图像中提取和插入肿瘤块三维（3D）轮廓的算法，以创建模拟异常用于观察者性能研究评估。
构建了一台台式实验CBBCT扫描仪，并在获得机构审查委员会（IRB）批准后用于对乳房切除标本进行成像。选择了5组有确诊肿瘤块的CBBCT图像和2组正常CBBCT图像用于本研究。所有CBBCT图像首先进行了杯状伪影校正。然后对校正后的图像进行处理以降低其噪声水平，形成去噪图像。接着将正常乳房的校正和去噪后的CBBCT图像分割为脂肪和腺体体素。异常乳房的图像由乳腺放射科医生借助临床图像和报告进行审查，以勾勒出肿瘤块并形成3D肿瘤图谱。利用这些图谱，将3D肿瘤轮廓重新缩放至正常图像中插入位置周围的平均腺体信号。对3D重新缩放后的肿瘤轮廓边界进行平滑处理以避免尖锐边缘。通过替换插入位置附近脂肪体素中的信号，将所得的3D肿瘤轮廓插入到经过杯状伪影校正的正常CBBCT图像上。然后重新添加杯状伪影以生成模拟异常CBBCT图像。然后将这些图像与实际异常图像在逼真程度上进行视觉比较。
基于视觉比较，模拟异常CBBCT图像在逼真程度上与实际异常CBBCT图像无显著差异。
我们成功展示了从异常CBBCT图像中提取3D肿瘤轮廓并将其插入到正常CBBCT图像上以形成用于观察者性能研究的模拟异常图像的技术。这项工作部分得到了美国国立卫生研究院 - 国立癌症研究所（NIH - NCI）的CA104759、CA13852和CA124585赠款、美国国立卫生研究院 - 国立医学图像和生物工程研究所（NIH - NIBIB）的研究赠款EB00117以及美国国家标准与技术研究院 - 先进技术计划（NIST - ATP）的分包合同的支持。
