Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Radiation Epidemiology Branch/DCEG/NCI/NIH, 9609 Medical Center Drive, Rockville, MD, 20850, USA.
J Digit Imaging. 2019 Feb;32(1):175-182. doi: 10.1007/s10278-018-0119-2.
To develop an algorithm to automatically map CT scan locations of patients onto computational human phantoms to provide with patient-specific organ doses. We developed an algorithm that compares a two-dimensional skeletal mask generated from patient CTs with that of a whole body computational human phantom. The algorithm selected the scan locations showing the highest Dice Similarity Coefficient (DSC) calculated between the skeletal masks of a patient and a phantom. To test the performance of the algorithm, we randomly selected five sets of neck, chest, and abdominal CT images from the National Institutes of Health Clinical Center. We first automatically mapped scan locations of the CT images on a computational human phantom using our algorithm. We had several radiologists to manually map the same CT images on the phantom and compared the results with the automated mapping. Finally, organ doses for automated and manual mapping locations were calculated by an in-house CT dose calculator and compared to each other. The visual comparison showed excellent agreement between manual and automatic mapping locations for neck, chest, and abdomen-pelvis CTs. The difference in mapping locations averaged over the start and end in the five patients was less than 1 cm for all neck, chest, and AP scans: 0.9, 0.7, and 0.9 cm for neck, chest, and AP scans, respectively. Five cases out of ten in the neck scans show zero difference between the average manual and automatic mappings. Average of absolute dose differences between manual and automatic mappings was 2.3, 2.7, and 4.0% for neck, chest, and AP scans, respectively. The automatic mapping algorithm provided accurate scan locations and organ doses compared to manual mapping. The algorithm will be useful in cases requiring patient-specific organ dose for a large number of patients such as patient dose monitoring, clinical trials, and epidemiologic studies.
为了开发一种算法，以便自动将患者的 CT 扫描位置映射到计算人体模型上，从而提供患者特定的器官剂量。我们开发了一种算法，该算法比较从患者 CT 生成的二维骨骼掩模与全身计算人体模型的骨骼掩模。该算法选择显示出患者骨骼与幻影之间计算的 Dice 相似系数（DSC）最高的扫描位置。为了测试该算法的性能，我们从美国国立卫生研究院临床中心随机选择了五组颈部，胸部和腹部 CT 图像。我们首先使用我们的算法自动将 CT 图像的扫描位置映射到计算人体模型上。我们让几位放射科医生手动将相同的 CT 图像映射到幻影上，并将结果与自动映射进行比较。最后，使用内部 CT 剂量计算器计算自动和手动映射位置的器官剂量，并将其彼此进行比较。视觉比较显示，颈部，胸部和腹部-骨盆 CT 的手动和自动映射位置之间具有极好的一致性。在五名患者中，所有颈部，胸部和 AP 扫描的起始和结束位置的映射位置差异均小于 1cm：颈部，胸部和 AP 扫描的分别为 0.9、0.7 和 0.9cm。在颈部扫描中，有五个病例的平均手动和自动映射之间没有差异。手动和自动映射之间的平均绝对剂量差异分别为颈部，胸部和 AP 扫描的 2.3％，2.7％和 4.0％。与手动映射相比，自动映射算法提供了准确的扫描位置和器官剂量。该算法将在需要大量患者的特定患者器官剂量的情况下（例如患者剂量监测，临床试验和流行病学研究）非常有用。
