Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Emily K Koons, Hao Gong, Andrew Missert, Shaojie Chang, Tim Winfree, Zhongxing Zhou, Cynthia H McCollough, Shuai Leng
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引用次数: 0

Abstract

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

通过超高分辨率 PCD-CT 学习高分辨率心脏 CT 成像。
冠状动脉计算机断层扫描(cCTA)是一种广泛应用于冠状动脉疾病(CAD)患者的无创诊断检查。然而,由于使用能量积分探测器(EID),大多数临床 CT 扫描仪的空间分辨率有限。由于冠状动脉较小(直径为 3-4 毫米),内部的钙化高度衰减,会产生花斑伪影,因此对 CAD 进行放射学评估极具挑战性。因此,这是一项非常适合高空间分辨率的任务。最近,光子计数探测器(PCD)CT 开始投入商业使用,从而实现了超高分辨率(UHR)数据采集。然而,PCD-CT 价格昂贵,限制了其普及。为了解决这个问题,我们提出了一种超分辨率卷积神经网络(CNN):ILUMENATE(通过人工超分辨率图像改进 LUMEN 可视化),创建模拟 UHR PCD-CT 的高分辨率(HR)图像。该网络采用改进的 U-Net 架构,使用从 8 名患者身上提取的斑块进行训练和验证。训练输入和标签由平滑核降低分辨率(LR 输入)和锐利核(HR 标签)重建的 UHR PCD-CT 图像组成。网络学习了分辨率差异,并在 5 位未见过的 LR 患者身上进行了测试。我们通过视觉检查、评估空间分辨率改进的线剖面图、用于 CT 数量稳定性和噪声评估的 ROI、结构相似性指数 (SSIM) 和管腔狭窄直径百分比,对网络性能进行了定量和定性评估。总体而言,ILUMENATE 在定量和定性方面都改善了图像,创建了更清晰的边缘,更接近于重建的 HR 参考图像,保持了稳定的 CT 编号,差异小于 4%,噪声减少了 28%,保持了结构相似性(平均 SSIM = 0.70),并减少了与输入图像相比的直径狭窄百分比。ILUMENATE 能改善 LR CT 图像的质量,使其更接近 UHR PCD-CT 图像,对 CAD 患者的管理具有潜在的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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