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使用深度学习重建的超分辨率合成磁共振成像用于膝关节骨关节炎的准确诊断。

Super-resolution synthetic MRI using deep learning reconstruction for accurate diagnosis of knee osteoarthritis.

作者信息

Wang Kejun, Liu Weiyin Vivian, Yang Renjie, Li Liang, Lu Xuefang, Lei Haoran, Jiang Jiawei, Zha Yunfei

机构信息

Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.

MR Research, GE Healthcare, Beijing, China.

出版信息

Insights Imaging. 2025 Feb 17;16(1):44. doi: 10.1186/s13244-025-01911-z.

Abstract

OBJECTIVE

To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRI) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.

MATERIALS AND METHODS

This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRI, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2). The inter-reader agreement on qualitative evaluation of SyMRI and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.

RESULTS

DLR significantly narrowed the quantitative differences between T2-SyMRI and T2 for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRI and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRI MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRI measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.

CONCLUSION

DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.

CRITICAL RELEVANCE STATEMENT

One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.

KEY POINTS

Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.

摘要

目的

以传统磁共振成像(MRI)作为标准参考,评估深度学习重建(DLR)技术在合成MRI(SyMRI)上的准确性,包括T2测量以及DLR合成MRI(SyMRI)在膝骨关节炎(KOA)患者中的诊断性能。

材料与方法

这项前瞻性研究于2023年5月至10月招募了36名志愿者和70名疑似KOA患者。将用于体模和体内膝关节软骨的DLR和非DLR合成T2测量值(T2-SyMRI、T2-SyMRI)与通过多回波快速自旋回波(MESE)序列获取的标准T2值(T2)进行比较。以常规图像作为标准诊断,分析阅片者之间对SyMRI定性评估的一致性以及阳性百分比一致性(PPA)和阴性百分比一致性(NPA)。

结果

DLR显著缩小了T2-SyMRI与T2之间的定量差异,差值为0.8毫秒,95%一致性界限(LOA)为[-5.5, 7.1]。DLR合成MR图像与传统MRI之间的主观评估具有可比性(所有p>0.05);SyMRI与传统MRI之间的阅片者间一致性为实质性到几乎完美,值在0.62至0.88之间。以传统MRI作为标准参考,SyMRI的膝关节骨关节炎评分系统(MOAKS)具有实质性的阅片者间一致性以及较高的PPA/NPA值(95%/99%)。此外,T2-SyMRI测量而非非DLR测量能够显著区分外观正常的软骨与可见损伤的软骨。

结论

DLR合成膝关节MRI既提供了用于临床诊断的加权图像,又提供了准确的T2测量值,从而能够更有把握地从外观正常的软骨中识别出早期软骨退变。

关键相关性声明

基于深度学习重建的单次采集合成MRI在相对较短的扫描时间内提供了准确的定量T2图和形态学图像,与传统的膝关节形态学序列相比,能够更有把握地从外观正常的软骨中识别出早期软骨退变。

要点

深度学习重建(DLR)合成膝关节软骨T2值与传统值无差异。使用MRI膝关节骨关节炎评分特征,DLR合成的T1加权、质子密度加权、短TI反转恢复(STIR)加权图像具有较高的阳性百分比一致性和阴性百分比一致性。DLR合成T2测量能够从外观正常的软骨中识别出早期软骨退变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e52f/11832993/f1c804150a00/13244_2025_1911_Fig2_HTML.jpg

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