在 uMI Panorama PET/CT 系统上验证和评估供应商提供的头部运动校正算法。

Fei Kang, Zhaojuan Xie, Wenhui Ma, Zhiyong Quan, Guiyu Li, Kun Guo, Xiang Li, Taoqi Ma, Weidong Yang, Yizhang Zhao, Hongyuan Yi, Yumo Zhao, Yihuan Lu, Jing Wang
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引用次数: 0

摘要

脑 PET 成像经常面临头部运动(HM)带来的挑战,HM 会产生伪影并降低图像分辨率,而这在临床环境中对于准确的治疗计划、诊断和监测至关重要。United Imaging Healthcare 采用数据驱动、基于统计的方法,为 uMI Panorama PET/CT 系统开发了一种 HM 校正(HMC)算法 NeuroFocus。HMC 算法使用分布中心技术自动检测 HM,无需调整参数。本研究旨在验证 NeuroFocus 并评估 HM 在临床短时 18F-FDG 扫描中的流行程度。研究方法研究涉及接受脑 PET 扫描的 317 名患者,分为两组:15 组用于 HMC 验证,302 组用于评估。验证组患者接受 2 次连续 3 分钟的单床位脑 18F-FDG 扫描,一次指示保持静止,另一次指示大幅度移动。评估检查了 302 次临床单床位置脑部扫描,扫描对象为患有各种神经系统诊断的患者。根据 HMC 后额叶 SUV 5%的变化,将移动分为小移动和大移动。报告了 11 个脑区 SUV 平均值的百分比差异。结果显示验证组显示出较大的负差异(-10.1%),无 HM 扫描和 HM 扫描之间的差异为 5.2%。使用 HMC 后,这一差异显著缩小(-0.8%),变异更小(3.2%),表明 HMC 的应用效果显著。在评估组中,302 位患者中有 38 位出现了大 HM,HMC 后 SUV 增加了 10.9% ± 8.9%,而大多数患者的摄取量变化很小(0.1% ± 1.3%)。HMC 算法不仅提高了图像分辨率和对比度,还有助于疾病识别,减少了重复扫描的需要,有望优化临床工作流程。结论该研究证实了 NeuroFocus 在 uMI Panorama PET/CT 系统的 18F-FDG 短期临床研究中管理 HM 的有效性。研究发现,约有 12% 的扫描需要 HMC,从而确立了 HMC 作为临床脑 18F-FDG 研究可靠工具的地位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Validation and Evaluation of a Vendor-Provided Head Motion Correction Algorithm on the uMI Panorama PET/CT System.

Brain PET imaging often faces challenges from head motion (HM), which can introduce artifacts and reduce image resolution, crucial in clinical settings for accurate treatment planning, diagnosis, and monitoring. United Imaging Healthcare has developed NeuroFocus, an HM correction (HMC) algorithm for the uMI Panorama PET/CT system, using a data-driven, statistics-based approach. The HMC algorithm automatically detects HM using a centroid-of-distribution technique, requiring no parameter adjustments. This study aimed to validate NeuroFocus and assess the prevalence of HM in clinical short-duration 18F-FDG scans. Methods: The study involved 317 patients undergoing brain PET scans, divided into 2 groups: 15 for HMC validation and 302 for evaluation. Validation involved patients undergoing 2 consecutive 3-min single-bed-position brain 18F-FDG scans-one with instructions to remain still and another with instructions to move substantially. The evaluation examined 302 clinical single-bed-position brain scans for patients with various neurologic diagnoses. Motion was categorized as small or large on the basis of a 5% SUV change in the frontal lobe after HMC. Percentage differences in SUVmean were reported across 11 brain regions. Results: The validation group displayed a large negative difference (-10.1%), with variation of 5.2% between no-HM and HM scans. After HMC, this difference decreased dramatically (-0.8%), with less variation (3.2%), indicating effective HMC application. In the evaluation group, 38 of 302 patients experienced large HM, showing a 10.9% ± 8.9% SUV increase after HMC, whereas most exhibited minimal uptake changes (0.1% ± 1.3%). The HMC algorithm not only enhanced the image resolution and contrast but also aided in disease identification and reduced the need for repeat scans, potentially optimizing clinical workflows. Conclusion: The study confirmed the effectiveness of NeuroFocus in managing HM in short clinical 18F-FDG studies on the uMI Panorama PET/CT system. It found that approximately 12% of scans required HMC, establishing HMC as a reliable tool for clinical brain 18F-FDG studies.

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