Nuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Mashhad University of Medical Science, Mashhad, Iran, .
Division of Nuclear Medicine and Molecular Imaging, Department of Radiology and Medical Informatics, Geneva University Hospital, Geneva, Switzerland, .
Nucl Med Commun. 2024 Nov 1;45(11):974-983. doi: 10.1097/MNM.0000000000001891. Epub 2024 Sep 3.
This study demonstrates the feasibility and benefits of using a deep learning-based approach for attenuation correction in [ 68 Ga]Ga-PSMA PET scans.
A dataset of 700 prostate cancer patients (mean age: 67.6 ± 5.9 years, range: 45-85 years) who underwent [ 68 Ga]Ga-PSMA PET/computed tomography was collected. A deep learning model was trained to perform attenuation correction on these images. Quantitative accuracy was assessed using clinical data from 92 patients, comparing the deep learning-based attenuation correction (DLAC) to computed tomography-based PET attenuation correction (PET-CTAC) using mean error, mean absolute error, and root mean square error based on standard uptake value. Clinical evaluation was conducted by three specialists who performed a blinded assessment of lesion detectability and overall image quality in a subset of 50 subjects, comparing DLAC and PET-CTAC images.
The DLAC model yielded mean error, mean absolute error, and root mean square error values of -0.007 ± 0.032, 0.08 ± 0.033, and 0.252 ± 125 standard uptake value, respectively. Regarding lesion detection and image quality, DLAC showed superior performance in 16 of the 50 cases, while in 56% of the cases, the images generated by DLAC and PET-CTAC were found to have closely comparable quality and lesion detectability.
This study highlights significant improvements in image quality and lesion detection capabilities through the integration of DLAC in [ 68 Ga]Ga-PSMA PET imaging. This innovative approach not only addresses challenges such as bladder radioactivity but also represents a promising method to minimize patient radiation exposure by integrating low-dose computed tomography and DLAC, ultimately improving diagnostic accuracy and patient outcomes.
本研究旨在展示使用基于深度学习的方法进行 [68Ga]Ga-PSMA PET 扫描衰减校正的可行性和益处。
收集了 700 例前列腺癌患者（平均年龄：67.6±5.9 岁，范围：45-85 岁）的 [68Ga]Ga-PSMA PET/CT 数据集。训练了一个深度学习模型，以对这些图像进行衰减校正。使用 92 例患者的临床数据评估定量准确性，通过标准摄取值比较基于深度学习的衰减校正（DLAC）和基于 CT 的 PET 衰减校正（PET-CTAC）的平均误差、平均绝对误差和均方根误差。通过三位专家对 50 例患者中的一部分进行了盲法评估，比较 DLAC 和 PET-CTAC 图像，进行了临床评估。
DLAC 模型产生的平均误差、平均绝对误差和均方根误差值分别为-0.007±0.032、0.08±0.033 和 0.252±125 标准摄取值。在 50 例患者中，16 例患者的 DLAC 检测和图像质量均优于 PET-CTAC，而在 56%的病例中，DLAC 和 PET-CTAC 生成的图像质量和病变检出率相当。
本研究通过在 [68Ga]Ga-PSMA PET 成像中整合 DLAC，强调了图像质量和病变检测能力的显著改善。这种创新方法不仅解决了膀胱放射性等挑战，还通过整合低剂量 CT 和 DLAC，为最小化患者辐射暴露提供了一种有前途的方法，最终提高了诊断准确性和患者结局。
