用跨模态生成模型修复缺失或退化的前列腺MRI，提升图像质量与诊断一致性。
A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
- 基于序列条件的跨模态生成框架，重建缺失或退化的影像
- 在10项任务中结构相似性达0.818，病变保真度优于基线模型
- 多中心验证显示模型可跨医院稳定使用，适合临床辅助诊断
缺失或退化的磁共振序列会限制前列腺多参数MRI的应用。我们开发了MSCNet，一种序列条件的跨模态生成框架，用于重建缺失对比度和恢复退化图像。在10项修复任务中，特定任务的MSCNet平均结构相似性为0.818，优于最强的同类方法（0.798）；容量匹配分析显示其在病灶保真度和边界保持上差异更显著。在盲法1000例阅片研究中，对DWI、ADC和T2W的重建图像整体质量满足预设非劣效标准，但T1W未达标。在另一次200例诊断评估中，临床显著癌症的AUC分别为：采集图像0.860，MSCNet重建0.841，基线生成0.797。一个锁定的186例三中心队列验证了模型的多中心可迁移性。这些回顾性结果支持高质量可控的跨模态重建作为前列腺MRI的辅助手段。
原文摘要 · Abstract (English)
Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions. Across ten completion tasks, task-specific MSCNet achieved mean structural similarity of 0.818 versus 0.798 for the strongest task-matched comparators; matched-capacity analyses showed larger differences in lesion fidelity and boundary preservation. In a blinded 1,000-case reader study, overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W. In a separate 200-case diagnostic assessment, AUCs for clinically significant cancer were 0.860 with acquired images, 0.841 with MSCNet and 0.797 with baseline-generated images. A locked 186-case three-hospital cohort supported multicentre transportability. These retrospective results support quality-controlled cross-modal reconstruction as an adjunct to acquired prostate MRI.
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