发表机构
Cleveland Clinic; Case Western Reserve University（克利夫兰诊所; 凯斯西储大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出多任务条件生成对抗网络（MT-cGAN），直接从qMRI回波图像合成DESS样图像并分割软骨和半月板，实现可靠T1ρ和T2定量，免除高分辨率形态学扫描，减少扫描时间，促进qMRI临床转化。
AI 中文摘要
通过定量MRI（qMRI）早期检测骨关节炎需要精确的软骨和半月板分割，传统上需要耗时且昂贵的3D高分辨率双回波稳态（DESS）MRI扫描。本研究开发了一种多任务条件生成对抗网络（MT-cGAN），可直接从qMRI回波图像中同时合成DESS样图像并分割组织。这项回顾性研究评估了来自三个队列的361名受试者（平均年龄：40.4±12.2岁；179名女性）的508个膝关节MRI体积。使用预训练模型并辅以人工校正，从DESS图像生成真实分割掩膜，并从磁化制备角度调制分区k空间 spoiled gradient echo snapshot（MAPSS）回波图像计算T1ρ和T2图。MT-cGAN被训练为直接从回波图像联合合成DESS样图像并分割软骨和半月板。使用Dice分数评估分割准确性，使用变异系数（CV）评估T1ρ和T2定量。MT-cGAN在所有软骨和半月板分区中实现了最高的分割性能，平均Dice分数为0.84（范围：0.80–0.86），显著优于使用迁移学习的最先进条件GAN模型（平均Dice，0.82；p < 0.001，Wilcoxon符号秩检验）。对于弛豫定量，MT-cGAN与参考DESS协议的一致性最高，CV最低（T1ρ：1.84%，T2：1.81%）。所提出的MT-cGAN能够直接从回波图像准确分割软骨和半月板，并提供可靠的T1ρ和T2定量。通过消除对单独形态学DESS扫描的需求，该工作流程减少了所需扫描时间，有助于qMRI的临床转化。
英文摘要
Early osteoarthritis detection through quantitative MRI (qMRI) requires accurate cartilage and meniscus segmentation, traditionally necessitating time-consuming, costly 3D high-resolution Double Echo Steady-State (DESS) MRI scans. This study developed a multi-task conditional generative adversarial network (MT-cGAN) to simultaneously synthesize DESS-like images and segment tissues directly from qMRI echo images. This retrospective study evaluated 508 knee MRI volumes from 361 subjects (mean age: $40.4 \pm 12.2$ years; 179 female) across three cohorts. Ground truth segmentation masks were generated from DESS images using a pretrained model with manual correction, and $T_{1ρ}$ and $T_2$ maps were computed from magnetization-prepared angle-modulated partitioned $k$-space spoiled gradient echo snapshots (MAPSS) echo images. MT-cGAN was trained to jointly synthesize DESS-like images and segment cartilage and meniscus directly from echo images. Model performance was evaluated using Dice score for segmentation accuracy and coefficient of variation (CV) for $T_{1ρ}$ and $T_2$ quantification. MT-cGAN achieved the highest segmentation performance, mean Dice score 0.84 (range: 0.80--0.86) across all cartilage and meniscus compartments and significantly outperformed the state-of-the-art conditional GAN model with transfer learning (mean Dice, 0.82; $p < 0.001$, Wilcoxon signed-rank test). For relaxometry quantification, MT-cGAN demonstrated the highest consistency with the reference DESS protocol, yielding the lowest CV ($T_{1ρ}$: 1.84%, $T_2$: 1.81%). The proposed MT-cGAN accurately segmented cartilage and menisci while providing reliable $T_{1ρ}$ and $T_2$ quantification directly from echo images. By eliminating the need for separate morphological DESS scans, this workflow reduces required scan times to facilitate the clinical translation of qMRI.
