评估[68Ga]Ga-PSMA PET/CT 图像中的前列腺癌及其转移灶:深度学习方法与传统 PET/CT 处理方法。

IF 1.3 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Nuclear Medicine Communications Pub Date : 2024-11-01 Epub Date: 2024-09-03 DOI:10.1097/MNM.0000000000001891
Masoumeh Dorri Giv, Hossein Arabi, Shahrokh Naseri, Leila Alipour Firouzabad, Atena Aghaei, Emran Askari, Nasrin Raeisi, Amin Saber Tanha, Zahra Bakhshi Golestani, Amir Hossein Dabbagh Kakhki, Vahid Reza Dabbagh Kakhki
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引用次数: 0

摘要

目的:本研究证明了使用基于深度学习的方法对[68Ga]Ga-PSMA PET扫描进行衰减校正的可行性和益处:收集了 700 名接受[68Ga]Ga-PSMA PET/计算机断层扫描的前列腺癌患者(平均年龄:67.6 ± 5.9 岁,范围:45-85 岁)的数据集。对深度学习模型进行了训练,以便对这些图像进行衰减校正。利用 92 名患者的临床数据评估了定量准确性,使用平均误差、平均绝对误差和基于标准摄取值的均方根误差比较了基于深度学习的衰减校正(DLAC)和基于计算机断层扫描的 PET 衰减校正(PET-CTAC)。临床评估由三位专家进行,他们对 50 名受试者的病变可探测性和整体图像质量进行了盲法评估,并对 DLAC 和 PET-CTAC 图像进行了比较:DLAC模型的平均误差、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。在病灶检测和图像质量方面,DLAC 在 50 个病例中的 16 个病例中表现出更优越的性能,而在 56% 的病例中,DLAC 和 PET-CTAC 生成的图像在质量和病灶可检测性方面非常接近:本研究强调了通过在[68Ga]Ga-PSMA PET 成像中整合 DLAC,图像质量和病灶检测能力得到了显著提高。这一创新方法不仅解决了膀胱放射性等难题,还代表了通过整合低剂量计算机断层扫描和 DLAC 来最大限度减少患者辐射暴露的一种可行方法,最终提高了诊断准确性和患者预后。
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Evaluation of the prostate cancer and its metastases in the [ 68 Ga]Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing.

Purpose: This study demonstrates the feasibility and benefits of using a deep learning-based approach for attenuation correction in [ 68 Ga]Ga-PSMA PET scans.

Methods: 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.

Results: 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.

Conclusion: 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.

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来源期刊
CiteScore
2.20
自引率
6.70%
发文量
212
审稿时长
3-8 weeks
期刊介绍: Nuclear Medicine Communications, the official journal of the British Nuclear Medicine Society, is a rapid communications journal covering nuclear medicine and molecular imaging with radionuclides, and the basic supporting sciences. As well as clinical research and commentary, manuscripts describing research on preclinical and basic sciences (radiochemistry, radiopharmacy, radiobiology, radiopharmacology, medical physics, computing and engineering, and technical and nursing professions involved in delivering nuclear medicine services) are welcomed, as the journal is intended to be of interest internationally to all members of the many medical and non-medical disciplines involved in nuclear medicine. In addition to papers reporting original studies, frankly written editorials and topical reviews are a regular feature of the journal.
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