Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark.
Phys Med Biol. 2021 Feb 25;66(5):054003. doi: 10.1088/1361-6560/abe225.
Cardiac [F]FDG-PET is widely used for viability testing in patients with chronic ischemic heart disease. Guidelines recommend injection of 200-350 MBq [F]FDG, however, a reduction of radiation exposure has become increasingly important, but might come at the cost of reduced diagnostic accuracy due to the increased noise in the images. We aimed to explore the use of a common deep learning (DL) network for noise reduction in low-dose PET images, and to validate its accuracy using the clinical quantitative metrics used to determine cardiac viability in patients with ischemic heart disease.
We included 168 patients imaged with cardiac [F]FDG-PET/CT. We simulated a reduced dose by keeping counts at thresholds 1% and 10%. 3D U-net with five blocks was trained to de-noise full PET volumes (128 × 128 × 111). The low-dose and de-noised images were compared in Corridor4DM to the full-dose PET images. We used the default segmentation of the left ventricle to extract the quantitative metrics end-diastolic volume (EDV), end-systolic volume (ESV), and left ventricular ejection fraction (LVEF) from the gated images, and FDG defect extent from the static images.
Our de-noising models were able to recover the PET signal for both the static and gated images in either dose-reduction. For the 1% low-dose images, the error is most pronounced for EDV and ESV, where the average underestimation is 25%. No bias was observed using the proposed DL de-noising method. De-noising minimized the outliers found for the 1% and 10% low-dose measurements of LVEF and extent. Accuracy of differential diagnosis based on LVEF threshold was highly improved after de-noising.
A significant dose reduction can be achieved for cardiac [F]FDG images used for viability testing in patients with ischemic heart disease without significant loss of diagnostic accuracy when using our DL model for noise reduction. Both 1% and 10% dose reductions are possible with clinically quantitative metrics comparable to that obtained with a full dose.
心脏 [F]FDG-PET 在慢性缺血性心脏病患者的存活能力检测中得到广泛应用。指南建议注射 200-350MBq [F]FDG，但减少辐射暴露变得越来越重要，但由于图像中的噪声增加，可能会以降低诊断准确性为代价。我们旨在探索使用常见的深度学习（DL）网络来降低低剂量 PET 图像的噪声，并使用用于确定缺血性心脏病患者心脏存活能力的临床定量指标来验证其准确性。
我们纳入了 168 例接受心脏 [F]FDG-PET/CT 检查的患者。我们通过将计数保持在 1%和 10%的阈值来模拟减少剂量。带有五个块的 3D U-net 用于对全剂量 PET 体积（128×128×111）进行去噪。将低剂量和去噪后的图像与 Corridor4DM 中的全剂量 PET 图像进行比较。我们使用门控图像默认的左心室分割来提取定量指标舒张末期容积（EDV）、收缩末期容积（ESV）和左心室射血分数（LVEF），以及静态图像中的 FDG 缺陷程度。
我们的去噪模型能够恢复两种剂量减少情况下的静态和门控图像的 PET 信号。对于 1%低剂量图像，EDV 和 ESV 的误差最为明显，平均低估了 25%。使用提出的 DL 去噪方法没有观察到偏差。去噪最小化了在 1%和 10%低剂量测量的 LVEF 和程度中发现的异常值。在去噪后，基于 LVEF 阈值的鉴别诊断准确性得到了显著提高。
当使用我们的 DL 模型进行降噪时，对于用于缺血性心脏病患者存活能力检测的心脏 [F]FDG 图像，可以实现显著的剂量减少，而不会显著降低诊断准确性。使用临床定量指标可以实现 1%和 10%的剂量减少，其结果与全剂量相当。
