Gao Yongfeng, Liang Zhengrong, Zhang Hao, Yang Jie, Ferretti John, Bilfinger Thomas, Yaddanapudi Kavitha, Schweitzer Mark, Bhattacharji Priya, Moore William
Department of Radiology, Stony Brook University, Stony Brook, NY 11794, USA.
Departments of Radiology, Biomedical Engineering, Computer Science, and Electrical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
IEEE Trans Radiat Plasma Med Sci. 2020 Jul;4(4):441-449. doi: 10.1109/trpms.2019.2957459. Epub 2019 Dec 4.
Localizing and characterizing clinically-significant lung nodules, a potential precursor to lung cancer, at the lowest achievable radiation dose is demanded to minimize the stochastic radiation effects from x-ray computed tomography (CT). A minimal dose level is heavily dependent on the image reconstruction algorithms and clinical task, in which the tissue texture always plays an important role. This study aims to investigate the dependence through a task-based evaluation at multiple dose levels and variable textures in reconstructions with prospective patient studies. 133 patients with a suspicious pulmonary nodule scheduled for biopsy were recruited and the data was acquired at120kVp with three different dose levels of 100, 40 and 20mAs. Three reconstruction algorithms were implemented: analytical filtered back-projection (FBP) with optimal noise filtering; statistical Markov random field (MRF) model with optimal Huber weighting (MRF-H) for piecewise smooth reconstruction; and tissue-specific texture model (MRF-T) for texture preserved statistical reconstruction. Experienced thoracic radiologists reviewed and scored all images at random, blind to the CT dose and reconstruction algorithms. The radiologists identified the nodules in each image including the 133 biopsy target nodules and 66 other non-target nodules. For target nodule characterization, only MRF-T at 40mAs showed no statistically significant difference from FBP at 100mAs. For localizing both the target nodules and the non-target nodules, some as small as 3mm, MRF-T at 40 and 20mAs levels showed no statistically significant difference from FBP at 100mAs, respectively. MRF-H and FBP at 40 and 20mAs levels performed statistically differently from FBP at 100mAs. This investigation concluded that (1) the textures in the MRF-T reconstructions improves both the tasks of localizing and characterizing nodules at low dose CT and (2) the task of characterizing nodules is more challenging than the task of localizing nodules and needs more dose or enhanced textures from reconstruction.
在可实现的最低辐射剂量下对具有临床意义的肺结节（肺癌的潜在前体）进行定位和特征描述，对于将X射线计算机断层扫描（CT）产生的随机辐射效应降至最低至关重要。最低剂量水平在很大程度上取决于图像重建算法和临床任务，其中组织纹理始终起着重要作用。本研究旨在通过对前瞻性患者研究中多个剂量水平和可变纹理的重建进行基于任务的评估，来探究这种依赖性。招募了133例计划进行活检的可疑肺结节患者，并在120kVp下以100、40和20mAs三种不同剂量水平采集数据。实施了三种重建算法：具有最佳噪声滤波的解析滤波反投影（FBP）；用于分段平滑重建的具有最佳Huber加权的统计马尔可夫随机场（MRF）模型（MRF-H）；以及用于保留纹理的统计重建的组织特异性纹理模型（MRF-T）。经验丰富的胸科放射科医生对所有图像进行随机审阅和评分，对CT剂量和重建算法不知情。放射科医生在每张图像中识别出结节，包括133个活检目标结节和66个其他非目标结节。对于目标结节特征描述，仅40mAs的MRF-T与100mAs的FBP相比无统计学显著差异。对于定位目标结节和非目标结节（有些小至3mm），40mAs和20mAs水平的MRF-T分别与100mAs的FBP相比无统计学显著差异。40mAs和20mAs水平的MRF-H和FBP与100mAs的FBP相比在统计学上表现不同。本研究得出结论：（1）MRF-T重建中的纹理改善了低剂量CT下结节定位和特征描述这两项任务；（2）结节特征描述任务比结节定位任务更具挑战性，需要更多剂量或重建中增强的纹理。
