Abbey Craig K, Li Junyuan, Gang Grace J, Stayman J Webster
Department of Psychological and Brain Sciences, University of California Santa Barbara.
Department of Biomedical Engineering, Johns Hopkins University.
Proc SPIE Int Soc Opt Eng. 2022 Feb-Mar;12035. doi: 10.1117/12.2612622. Epub 2022 Apr 4.
Printed phantoms hold great potential as a tool for examining task-based image quality of x-ray imaging systems. Their ability to produce complex shapes rendered in materials with adjustable attenuation coefficients allows a new level of flexibility in the design of tasks for the evaluation of physical imaging systems. We investigate performance in a fine "boundary discrimination" task in which fine features at the margin of a clearly visible "lesion" are used to classify the lesion as malignant or benign. These tasks are appealing because of their relevance to clinical tasks, and because they typically emphasize higher spatial frequencies relative to more common lesion detection tasks. A 3D printed phantom containing cylindrical shells of varying thickness was used to generate lesions profiles that differed in their edge profiles. This was intended to approximate lesions with indistinct margins that are clinically associated with malignancy. Wall thickness in the phantom ranged from 0.4mm to 0.8mm, which allows for task difficulty to be varied by choosing different thicknesses to represent malignant and benign lesions. The phantom was immersed in a tub filled with water and potassium phosphate to approximate the attenuating background, and imaged repeatedly on a benchtop cone-beam CT scanner. After preparing the image data (reconstruction, ROI Selection, sub-pixel registration), we find that the mean frequency of the lesion profile is 0.11 cyc/mm. The mean frequency of the lesion-difference profile, representative of the discrimination task, is approximately 6 times larger. Model observers show appropriate dose performance in these tasks as well.
打印体模作为一种用于检查X射线成像系统基于任务的图像质量的工具，具有巨大的潜力。它们能够制造出由具有可调节衰减系数的材料呈现的复杂形状，这使得在设计用于评估物理成像系统的任务时具有了新的灵活性水平。我们研究了在一项精细的“边界辨别”任务中的性能，在该任务中，利用清晰可见的“病变”边缘的精细特征将病变分类为恶性或良性。这些任务很有吸引力，因为它们与临床任务相关，并且因为相对于更常见的病变检测任务，它们通常更强调更高的空间频率。使用一个包含不同厚度圆柱壳的3D打印体模来生成边缘轮廓不同的病变轮廓。这旨在模拟临床上与恶性肿瘤相关的边缘不清晰的病变。体模中的壁厚范围为0.4毫米至0.8毫米，这使得通过选择不同的厚度来代表恶性和良性病变，可以改变任务的难度。将体模浸入装满水和磷酸钾的桶中以近似衰减背景，并在台式锥束CT扫描仪上反复成像。在准备好图像数据（重建、感兴趣区域选择、亚像素配准）后，我们发现病变轮廓的平均频率为0.11周/毫米。代表辨别任务的病变差异轮廓的平均频率大约大6倍。模型观察者在这些任务中也表现出了适当的剂量性能。
