Deep Learning-Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial.

IF 3.1 3区 医学 Q2 CLINICAL NEUROLOGY
American Journal of Neuroradiology Pub Date : 2023-08-01 Epub Date: 2023-07-06 DOI:10.3174/ajnr.A7920
L N Tanenbaum, S C Bash, G Zaharchuk, A Shankaranarayanan, R Chamberlain, M Wintermark, C Beaulieu, M Novick, L Wang
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Abstract

Background and purpose: Deep learning image reconstruction allows faster MR imaging acquisitions while matching or exceeding the standard of care and can create synthetic images from existing data sets. This multicenter, multireader spine study evaluated the performance of synthetically created STIR compared with acquired STIR.

Materials and methods: From a multicenter, multiscanner data base of 328 clinical cases, a nonreader neuroradiologist randomly selected 110 spine MR imaging studies in 93 patients (sagittal T1, T2, and STIR) and classified them into 5 categories of disease and healthy. A DICOM-based deep learning application generated a synthetically created STIR series from the sagittal T1 and T2 images. Five radiologists (3 neuroradiologists, 1 musculoskeletal radiologist, and 1 general radiologist) rated the STIR quality and classified disease pathology (study 1, n = 80). They then assessed the presence or absence of findings typically evaluated with STIR in patients with trauma (study 2, n = 30). The readers evaluated studies with either acquired STIR or synthetically created STIR in a blinded and randomized fashion with a 1-month washout period. The interchangeability of acquired STIR and synthetically created STIR was assessed using a noninferiority threshold of 10%.

Results: For classification, there was a decrease in interreader agreement expected by randomly introducing synthetically created STIR of 3.23%. For trauma, there was an overall increase in interreader agreement by +1.9%. The lower bound of confidence for both exceeded the noninferiority threshold, indicating interchangeability of synthetically created STIR with acquired STIR. Both the Wilcoxon signed-rank and t tests showed higher image-quality scores for synthetically created STIR over acquired STIR (P < .0001).

Conclusions: Synthetically created STIR spine MR images were diagnostically interchangeable with acquired STIR, while providing significantly higher image quality, suggesting routine clinical practice potential.

深度学习生成的合成 MR 成像 STIR 脊柱图像在图像质量上优于传统 STIR,在诊断上等同于传统 STIR:一项多中心、多载体试验。
背景和目的:深度学习图像重建可加快磁共振成像采集速度,同时达到或超过医疗标准,并能从现有数据集创建合成图像。这项多中心、多装载机脊柱研究评估了合成创建的 STIR 与获取的 STIR 相比的性能:从一个包含 328 个临床病例的多中心、多扫描仪数据库中,一位非阅读神经放射科医生随机选取了 93 位患者的 110 个脊柱 MR 成像研究(矢状 T1、T2 和 STIR),并将其分为 5 类疾病和健康。基于 DICOM 的深度学习应用从矢状 T1 和 T2 图像中生成了合成的 STIR 序列。五位放射科医生(3 位神经放射科医生、1 位肌肉骨骼放射科医生和 1 位普通放射科医生)对 STIR 质量进行评分,并对疾病病理进行分类(研究 1,n = 80)。然后,他们评估创伤患者是否存在通常用 STIR 评估的结果(研究 2,n = 30)。读者们以盲法和随机的方式对获得的 STIR 或合成的 STIR 进行了评估,并有 1 个月的冲洗期。以 10%的非劣效性阈值评估获得的 STIR 和合成的 STIR 的互换性:在分类方面,通过随机引入合成 STIR,读片者之间的一致性降低了 3.23%。在创伤方面,读数间一致性总体提高了+1.9%。两者的置信度下限都超过了非劣效性阈值,表明合成 STIR 与获得 STIR 具有互换性。Wilcoxon 符号秩检验和 t 检验均显示,合成 STIR 的图像质量得分高于获得的 STIR(P 结论:合成 STIR 的图像质量得分高于获得的 STIR):合成的 STIR 脊柱 MR 图像在诊断上可与获取的 STIR 互换,同时图像质量明显更高,这表明合成的 STIR 具有临床实践的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.10
自引率
5.70%
发文量
506
审稿时长
2 months
期刊介绍: The mission of AJNR is to further knowledge in all aspects of neuroimaging, head and neck imaging, and spine imaging for neuroradiologists, radiologists, trainees, scientists, and associated professionals through print and/or electronic publication of quality peer-reviewed articles that lead to the highest standards in patient care, research, and education and to promote discussion of these and other issues through its electronic activities.
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