Marsh Jeffrey, Rajendran Kishore, Tao Shengzhen, Vercnocke Andrew, Anderson Jill, Leng Shuai, Ritman Erik, McCollough Cynthia
Department of Radiology, Mayo Clinic, 200 First Street SW, Rochester, MN, USA 55905.
Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First Street SW, Rochester, MN, USA 55905.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11312. doi: 10.1117/12.2549348. Epub 2020 Mar 16.
Proliferation of vasa vasorum, the microvasculature within artery walls, is an early marker of atherosclerosis. Detection of subtle changes in the spatial density of vasa vasorum using contrast-enhanced CT is challenging due to the limited spatial resolution and blooming effects. We report a forward model-based blooming correction technique to improve vasa vasorum detection in a porcine model imaged using an ultra-high resolution photon-counting detector CT. Six weeks preceding the CT study the animal received autologous blood injections in its left carotid artery to stimulate vasa vasorum proliferation within the arterial wall (right carotid served as control). The forward model predicted radial extent and magnitude of the luminal blooming affecting the wall signal by using prior data acquired with a vessel phantom of known dimensions. The predicted contamination from blooming was then subtracted from the original wall signal measurement to recover the obscured vasa vasorum signal. Attenuation measurements made on a testing vessel phantom before and after blooming corrections revealed a reduction in mean squared error by ~99.9% when compared to the ground truth. Applying corrections to contrast-enhanced carotid arteries from scan data demonstrated consistent reductions of blooming contamination within the vessel walls. An unpaired student t-test applied to measurements from the uncorrected porcine scan data revealed no significant difference between the vessel walls (p=0.26). However, after employing blooming correction, the mean enhancement was significantly greater in the injured vessel wall (p=0.0006).
血管滋养管(动脉壁内的微血管系统)的增殖是动脉粥样硬化的早期标志物。由于空间分辨率有限和伪影效应,使用对比增强CT检测血管滋养管空间密度的细微变化具有挑战性。我们报告了一种基于前向模型的伪影校正技术,以改善在使用超高分辨率光子计数探测器CT成像的猪模型中对血管滋养管的检测。在CT研究前六周,该动物在其左颈动脉接受自体血液注射,以刺激动脉壁内血管滋养管的增殖(右颈动脉作为对照)。前向模型通过使用从已知尺寸的血管模型获取的先验数据,预测影响壁信号的管腔伪影的径向范围和大小。然后从原始壁信号测量值中减去预测的伪影污染,以恢复被掩盖的血管滋养管信号。在对测试血管模型进行伪影校正前后进行的衰减测量显示,与真实值相比,均方误差降低了约99.9%。将校正应用于对比增强的颈动脉扫描数据,结果表明血管壁内的伪影污染持续减少。对未校正的猪扫描数据测量值进行的不成对学生t检验显示,血管壁之间无显著差异(p = 0.26)。然而,采用伪影校正后,受损血管壁的平均增强显著更大(p = 0.0006)。