Purpose

Clinical whole-body (WB) PET images can be compensated for respiratory motion using data-driven gating (DDG). However, PET DDG images may still exhibit motion artefacts at the diaphragm if the CT is acquired in a different respiratory phase than the PET image. This study evaluates the combined use of PET DDG and a deep-learning model (AIR-PETCT) for elastic registration of CT (WarpCT) to the non attenuation- and non scatter-corrected PET image (PET NAC), enabling improved PET reconstruction.

Methods

The validation cohort included 20 patients referred for clinical FDG PET/CT, undergoing two CT scans: a free respiration CT_free and an end-expiration breath-hold CT_ex. AIR-PETCT registered each CT to the PET NAC and PET DDG NAC images. The image quality of PET and PET DDG images reconstructed using CTs and WarpCTs was evaluated by three blinded readers. Additionally, a clinical impact cohort of 20 patients with significant “banana” artefacts from FDG, PSMA, and DOTATOC scans was assessed for image quality and tumor-to-background ratios.

Results

AIR-PETCT was robust and generated consistent WarpCTs when registering different CTs to the same PET NAC. The use of WarpCT instead of CT consistently led to equivalent or improved PET image quality. The algorithm significantly reduced “banana” artefacts and improved lesion-to-background ratios around the diaphragm. The blinded clinicians clearly preferred PET DDG images reconstructed using WarpCT.

Conclusion

AIR-PETCT effectively reduces respiratory motion artefacts from PET images, while improving lesion contrast. The combination of PET DDG and WarpCT holds promise for clinical application, improving PET image evaluation and diagnostic confidence.

中文翻译：

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使用数据驱动的呼吸门控和弹性 PET-CT 配准进行运动自由 PET 成像的验证和临床影响

 目的

临床全身 （WB） PET 图像可以使用数据驱动门控 （DDG） 补偿呼吸运动。然而，如果 CT 是在与 PET 图像不同的呼吸期采集的，则 PET DDG 图像可能仍会在隔膜处显示运动伪影。本研究评估了 PET DDG 和深度学习模型 （AIR-PETCT） 的联合使用，用于 CT （WarpCT） 对非衰减和非散射校正 PET 图像 （PET NAC） 的弹性配准，从而改进 PET 重建。

 方法

验证队列包括 20 名转诊接受临床 FDG PET/CT 的患者，接受了两次 CT 扫描：游离呼吸 CT_免费和呼气末屏气 CT_ex。AIR-PETCT 将每个 CT 配准到 PET NAC 和 PET DDG NAC 图像中。使用 CT 和 WarpCT 重建的 PET 和 PET DDG 图像的图像质量由 3 个盲法阅读器评估。此外，对 20 名来自 FDG、PSMA 和 DOTATOC 扫描的明显“香蕉”伪影患者的临床影响队列进行了图像质量和肿瘤背景比评估。

 结果

AIR-PETCT 很稳健，并且在将不同的 CT 配准到同一 PET NAC 时产生了一致的 WarpCT。使用 WarpCT 而不是 CT 始终导致等效或改进的 PET 图像质量。该算法显著减少了 “banana” 伪影，并改善了横膈膜周围的病变与背景比率。盲法临床医生显然更喜欢使用 WarpCT 重建的 PET DDG 图像。

 结论

AIR-PETCT 可有效减少 PET 图像中的呼吸运动伪影，同时提高病变对比度。PET DDG 和 WarpCT 的结合有望在临床应用中提高 PET 图像评估和诊断信心。