高分辨率头部 CTA:比较光子计数探测器 CT 和能量输入探测器 CT 图像质量的前瞻性患者研究。

Felix E Diehn, Zhongxing Zhou, Jamison E Thorne, Norbert G Campeau, Alex A Nagelschneider, Laurence J Eckel, John C Benson, Ajay A Madhavan, Girish Bathla, Vance T Lehman, Nathan R Huber, Francis Baffour, Joel G Fletcher, Cynthia H McCollough, Lifeng Yu
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引用次数: 0

摘要

背景和目的:光子计数探测器 CT(PCD-CT)现已应用于临床,可提供超高分辨率(UHR)成像。我们的目的是对使用 PCD-CT 和能量积分探测器 CT(EID-CT)获得的头部 CTA 图像的相对图像质量和对诊断信心的影响进行前瞻性评估:使用 EID-CT 进行头部 CTA 检查的成人患者也接受了 PCD-CT 研究检查。在这两项 CT 检查中,均使用匹配的标准分辨率 (SR) 内核以 0.6 mm 重建图像。此外,PCD-CT 图像以最薄的切片厚度 0.2 毫米(UHR)用最锐利的内核重建,并用深度卷积神经网络(CNN)算法(PCD-UHR-CNN)去噪。两名读者(R1、R2)在随机、盲法的基础上,分 PCD-SR 与 EID-SR 和 PCD-UHR-CNN 与 EID-SR 两个阶段独立评估图像质量。在并排比较多个图像质量特征(包括特定动脉节段的可视化)时,读者对两个系列的图像质量进行总体评分(1 分[最差]至 5 分[最佳]),并提供 Likert 比较得分(-2 分[明显较差]至 2 分[明显较好])。如果存在特定的动脉检查结果,则对 PCD 和 EID 的诊断可信度(0-100)进行评分:结果:28 名成年患者接受了检查。容积 CT 剂量指数相似(EID:37.1 ± 4.7 mGy;PCD:36.1 ± 4.0 mGy)。PCD-SR 和 PCD-UHR-CNN 的总体图像质量高于 EID-SR(例如,PCD-UHR-CNN 与 EID-SR:4.0 ± 0.0 对 3.0 ± 0.0(R1),4.9 ± 0.3 对 3.0 ± 0.0(R2);所有 P 值 P 值 结论:与 EID-CT 相比,PCD-CT 可改善头部 CTA 图像的质量,在 PCD 和 EID 重建相匹配的情况下都是如此,而在 PCD-UHR 重建与 CNN 去噪算法相结合的情况下,PCD-CT 可在更大程度上改善头部 CTA 图像的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-Resolution Head CTA: A Prospective Patient Study Comparing Image Quality of Photon-Counting Detector CT and Energy-Integrating Detector CT.

Background and purpose: Photon-counting detector CT (PCD-CT) is now clinically available and offers ultra-high-resolution (UHR) imaging. Our purpose was to prospectively evaluate the relative image quality and impact on diagnostic confidence of head CTA images acquired by using a PCD-CT compared with an energy-integrating detector CT (EID-CT).

Materials and methods: Adult patients undergoing head CTA on EID-CT also underwent a PCD-CT research examination. For both CT examinations, images were reconstructed at 0.6 mm by using a matched standard resolution (SR) kernel. Additionally, PCD-CT images were reconstructed at the thinnest section thickness of 0.2 mm (UHR) with the sharpest kernel, and denoised with a deep convolutional neural network (CNN) algorithm (PCD-UHR-CNN). Two readers (R1, R2) independently evaluated image quality in randomized, blinded fashion in 2 sessions, PCD-SR versus EID-SR and PCD-UHR-CNN versus EID-SR. The readers rated overall image quality (1 [worst] to 5 [best]) and provided a Likert comparison score (-2 [significantly inferior] to 2 [significantly superior]) for the 2 series when compared side-by-side for several image quality features, including visualization of specific arterial segments. Diagnostic confidence (0-100) was rated for PCD versus EID for specific arterial findings, if present.

Results: Twenty-eight adult patients were enrolled. The volume CT dose index was similar (EID: 37.1 ± 4.7 mGy; PCD: 36.1 ± 4.0 mGy). Overall image quality for PCD-SR and PCD-UHR-CNN was higher than EID-SR (eg, PCD-UHR-CNN versus EID-SR: 4.0 ± 0.0 versus 3.0 ± 0.0 (R1), 4.9 ± 0.3 versus 3.0 ± 0.0 (R2); all P values < .001). For depiction of arterial segments, PCD-SR was preferred over EID-SR (R1: 1.0-1.3; R2: 1.0-1.8), and PCD-UHR-CNN over EID-SR (R1: 0.9-1.4; R2: 1.9-2.0). Diagnostic confidence of arterial findings for PCD-SR and PCD-UHR-CNN was significantly higher than EID-SR: eg, PCD-UHR-CNN versus EID-SR: 93.0 ± 5.8 versus 78.2 ± 9.3 (R1), 88.6 ± 5.9 versus 70.4 ± 5.0 (R2); all P values < .001.

Conclusions: PCD-CT provides improved image quality for head CTA images compared with EID-CT, both when PCD and EID reconstructions are matched, and to an even greater extent when PCD-UHR reconstruction is combined with a CNN denoising algorithm.

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