Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement.
Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement.
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DOI：
10.2214/ajr.20.24172
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Stevens KJ
中科院分区：
文献类型：
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作者：
Chaudhari AS;Grissom MJ;Fang Z;Sveinsson B;Lee JH;Gold GE;Hargreaves BA;Stevens KJ
膝关节简化MRI的潜在方法，包括深度学习的前瞻性加速，已经实现了有限的临床实施。本研究的目的是评价传统膝关节MRI与5分钟3D定量双回波稳态（qDESS）序列（自动T2标测和深度学习超分辨率增强）之间的阅片者间一致性，并比较两种方法对关节镜手术结果的诊断性能。51例膝关节疼痛患者接受了膝关节MRI，其中包括额外的3D qDESS序列和自动T2标测。傅立叶插值之后是前瞻性深度学习超分辨率，以将qDESS切片分辨率提高一倍。一名肌肉骨骼放射科医师和一名放射科住院医师使用常规MRI对关节软骨、韧带、韧带、骨骼、伸肌机制和滑膜进行了独立的回顾性评价。在2个月的洗脱期后，阅片者仅审查了qDESS图像，随后审查了qDESS和自动T2标测图。使用百分比一致性和Cohen kappa计算常规MRI和qDESS之间的阅片员一致性。采用精确McNemar检验，将常规MRI、单独qDESS和qDESS + T2标测的敏感性和特异性与关节镜检查结果进行比较。常规MRI和qDESS在评价所有组织时显示92%的一致性。所有影像学结果的Kappa值为0.79（95% CI，0.76-0.81）。在43名接受关节镜检查的患者中，对于软骨、韧带、韧带和滑膜，常规MRI（敏感性，58-93%;特异性，27-87%）和单独qDESS（敏感性，54-90%;特异性，23-91%）的敏感性和特异性没有显著差异（p = 0.23至> 0.99）。对于1级软骨病变，常规MRI的敏感性和特异性分别为33%和56%; qDESS为23%和53%（p = 0.81）; qDESS + T2标测为46%和39%（p = 0.80）。对于2A级病变，常规MRI的数值分别为27%和53%，qDESS的数值分别为26%和52%（p = 0.02），qDESS + T2标测的数值分别为58%和40%（p <0.001）。通过深度学习进行前瞻性增强的qDESS方法显示出与传统膝关节MRI的良好的阅片者间一致性，以及与关节镜检查几乎等同的诊断性能。qDESS自动生成T2图的能力增加了软骨异常的灵敏度。使用前瞻性人工智能增强qDESS图像质量可能有助于简化膝关节MRI方案，同时生成定量T2图。
Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76–0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58–93%; specificity, 27–87%) and qDESS alone (sensitivity, 54–90%; specificity, 23–91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001). The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.