深度学习超分辨率在骨关节炎 MRI 生物标志物中的应用。
Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.
机构信息
Department of Radiology, Stanford University, Stanford, California, USA.
Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA.
出版信息
J Magn Reson Imaging. 2020 Mar;51(3):768-779. doi: 10.1002/jmri.26872. Epub 2019 Jul 16.
BACKGROUND
Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.
PURPOSE
To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring.
STUDY TYPE
Retrospective.
POPULATION
In all, 176 MRI studies of subjects at varying stages of osteoarthritis.
FIELD STRENGTH/SEQUENCE: Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T.
ASSESSMENT
A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans.
STATISTICAL TESTS
Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference.
RESULTS
DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22).
DATA CONCLUSION
Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers.
LEVEL OF EVIDENCE
2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.
背景
超分辨率是一种新兴的提高 MRI 分辨率的方法，但它对图像质量的影响尚不清楚。
目的
使用软骨形态计量学、骨赘检测和整体图像模糊的定量和定性指标来评估 MRI 超分辨率。
研究类型
回顾性。
人群
共有 176 名处于不同骨关节炎阶段的患者的 MRI 研究。
磁场强度/序列：在 3T 上，使用超分辨率和三次立方插值（TCI）对原始分辨率的 3D 双回波稳态（DESS）和 DESS 进行回顾性增强，厚度增加 3 倍。
评估
对 17 名受试者的原始分辨率 DESS、超分辨率和 TCI 扫描进行了股骨软骨形态计量学的定量比较。三位肌肉骨骼放射科医生进行了读者研究，评估了所有三组扫描中的软骨图像质量、整体图像锐度和骨赘发生率。一种无参考模糊度指标评估了所有三组扫描中三个图像维度的模糊度。
统计检验
Mann-Whitney U 检验比较了 DESS、超分辨率和 TCI 图像的分割准确性的 Dice 系数（DC），以及图像质量读数和模糊度指标。与原始分辨率相比，超分辨率和 TCI 图像的骨赘检测的敏感性、特异性和诊断优势比（DOR）及其 95%置信区间。
结果
原始分辨率（90.2±1.7%）和超分辨率（89.6±2.0%）的 DC 显著更高（P<0.001）比 TCI（86.3±5.6%）。超分辨率与原始分辨率的分割重叠（DC=97.6±0.7%）显著更高（P<0.0001）比 TCI 重叠（DC=95.0±1.1%）。超分辨率图像的软骨图像质量的锐利度和对比度水平，以及平面内定量模糊因子，显著（P<0.001）优于 TCI。超分辨率骨赘检测的敏感性为 80%（76-82%），特异性为 93%（92-94%），DOR 为 32（22-46），显著高于 TCI 的敏感性 73%（69-76%），特异性为 90%（89-91%），DOR 为 17（13-22）。
数据结论
超分辨率似乎始终优于天真插值，并且可以在不影响定量生物标志物的情况下提高图像质量。
成像效果分级
2 级
磁共振成像 2020 年；51：768-779
Zotero 插件