Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine.

IF 8.6 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Journal of Nuclear Medicine and Molecular Imaging Pub Date : 2022-07-01 Epub Date: 2022-03-12 DOI:10.1007/s00259-022-05748-2
Takuya Toyonaga, Dan Shao, Luyao Shi, Jiazhen Zhang, Enette Mae Revilla, David Menard, Joseph Ankrah, Kenji Hirata, Ming-Kai Chen, John A Onofrey, Yihuan Lu
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引用次数: 4

Abstract

A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT).

Methods: Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics.

Results: µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: - 3.6 ± 4.4% vs. - 1.7 ± 4.5% for 18F-FDG (N = 152), - 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and - 7.3 ± 2.9% vs. - 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., - 8.4 ± 14.5% (OSEMMLAA) vs. - 3.0 ± 15.0% for 18F-FDG, - 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and - 15.9 ± 9.1% vs. - 6.4 ± 6.4% for 18F-Fluciclovine.

Conclusions: The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers.

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基于深度学习的全身PET衰减校正——使用18F-FDG、68 Ga-DOTATATE和18f -氟氯蓝的多示踪剂研究
一种基于深度学习(DL)的新型衰减校正(AC)框架被应用于使用18F-FDG、68Ga-DOTATATE和18F-呋喃妥因进行的临床全身肿瘤学研究。该框架使用活性(λ-MLAA)和衰减(µ-MLAA)图,这些图是由活性和衰减的最大似然重建(MLAA)算法估算的,并将其作为改进的 U-net 神经网络的输入,该网络具有新颖的基于成像物理学的损失函数,可学习 CT 衍生的衰减图(µ-CT):临床全身 PET/CT 数据集包括 18F-FDG(113 例)、68Ga-DOTATATE(76 例)和 18F-Fluciclovine(90 例),用于训练和测试特定示踪剂的神经网络。每种示踪剂都有 40 个受试者用于训练神经网络,以预测衰减图 (µ-DL)。µ-DL和µ-MLAA与黄金标准µ-CT进行了比较。使用 OSEM 算法与 µ-DL (OSEMDL) 和 µ-MLAA (OSEMMLAA) 重建的 PET 图像与基于 CT 的重建 (OSEMCT) 进行了比较。两名放射科医生对感兴趣的肿瘤区域进行了分割,报告了肿瘤 SUV 和体积测量值,并使用传统的图像分析指标进行了评估:结果:µ-DL可获得高分辨率和精细的衰减图恢复,与µ-MLAA相比,µ-DL在所有示踪剂的所有指标上都更胜一筹。将 OSEMCT 作为黄金标准,对于所有三种示踪剂,OSEMDL 都比 OSEMMLAA 提供了更准确的肿瘤定量,例如,OSEMDL 的 SUVmax 误差比 OSEMMLAA 小、OSEMMLAA与OSEMDL的SUVmax误差:18F-FDG为- 3.6 ± 4.4% vs. - 1.7 ± 4.5%(N = 152),68 Ga-DOTATATE为- 4.3 ± 5.1% vs. 0.4 ± 2.8%(N = 70),18F-Fluciclovine为- 7.3 ± 2.9% vs. - 2.8 ± 2.3%(N = 44)。OSEMDL得出的肿瘤体积测量结果也比OSEMMLAA更准确,即- 8.4 ± 14.5%(OSEMMLAA)比18F-FDG的- 3.0 ± 15.0%,- 14.1 ± 19.7%比68 Ga-DOTATATE的1.8 ± 11.6%,以及- 15.9 ± 9.1%比18F-Fluciclovine的- 6.4 ± 6.4%:所提出的框架为全身18F-FDG、68 Ga-DOTATATE和18F-Fluciclovine的肿瘤SUV测量以及肿瘤体积估算提供了准确、稳健的衰减校正。在三种示踪剂的衰减校正方面,所提出的方法与 CT 相比具有同等的临床质量。
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来源期刊
CiteScore
15.60
自引率
9.90%
发文量
392
审稿时长
3 months
期刊介绍: The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.
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