From the RadNet Inc (S.B., L.N.T.), Los Angeles, California
Subtle Medical (L.W., E.G., A.S.), Menlo Park, California.
AJNR Am J Neuroradiol. 2021 Dec;42(12):2130-2137. doi: 10.3174/ajnr.A7358. Epub 2021 Nov 25.
In this prospective, multicenter, multireader study, we evaluated the impact on both image quality and quantitative image-analysis consistency of 60% accelerated volumetric MR imaging sequences processed with a commercially available, vendor-agnostic, DICOM-based, deep learning tool (SubtleMR) compared with that of standard of care.
Forty subjects underwent brain MR imaging examinations on 6 scanners from 5 institutions. Standard of care and accelerated datasets were acquired for each subject, and the accelerated scans were enhanced with deep learning processing. Standard of care, accelerated scans, and accelerated-deep learning were subjected to NeuroQuant quantitative analysis and classified by a neuroradiologist into clinical disease categories. Concordance of standard of care and accelerated-deep learning biomarker measurements were assessed. Randomized, side-by-side, multiplanar datasets (360 series) were presented blinded to 2 neuroradiologists and rated for apparent SNR, image sharpness, artifacts, anatomic/lesion conspicuity, image contrast, and gray-white differentiation to evaluate image quality.
Accelerated-deep learning was statistically superior to standard of care for perceived quality across imaging features despite a 60% sequence scan-time reduction. Both accelerated-deep learning and standard of care were superior to accelerated scans for all features. There was no difference in quantitative volumetric biomarkers or clinical classification for standard of care and accelerated-deep learning datasets.
Deep learning reconstruction allows 60% sequence scan-time reduction while maintaining high volumetric quantification accuracy, consistent clinical classification, and what radiologists perceive as superior image quality compared with standard of care. This trial supports the reliability, efficiency, and utility of deep learning-based enhancement for quantitative imaging. Shorter scan times may heighten the use of volumetric quantitative MR imaging in routine clinical settings.
在这项前瞻性、多中心、多读者研究中，我们评估了商业上可用的、与供应商无关的基于 DICOM 的深度学习工具（SubtleMR）处理的 60%加速容积 MR 成像序列对图像质量和定量图像分析一致性的影响，与标准护理相比。
40 名受试者在 5 家机构的 6 台扫描仪上进行了脑部 MRI 检查。为每位受试者采集标准护理和加速数据集，并使用深度学习处理增强加速扫描。对标准护理、加速扫描和加速-深度学习进行神经定量分析，并由神经放射科医生将其分类为临床疾病类别。评估标准护理和加速深度学习生物标志物测量的一致性。将随机、并排、多平面数据集（360 个系列）呈现给 2 位神经放射科医生，并对其进行盲法评估，以评估图像质量的表观信噪比、图像锐度、伪影、解剖/病变显著性、图像对比度和灰白质分化。
尽管加速-深度学习序列扫描时间减少了 60%，但在所有成像特征方面，其感知质量均优于标准护理。加速-深度学习和标准护理均优于加速扫描的所有特征。标准护理和加速-深度学习数据集的定量容积生物标志物或临床分类无差异。
深度学习重建允许序列扫描时间减少 60%，同时保持高容积定量准确性、一致的临床分类以及放射科医生认为优于标准护理的图像质量。这项试验支持基于深度学习的增强在定量成像中的可靠性、效率和实用性。较短的扫描时间可能会增加容积定量 MRI 在常规临床环境中的应用。
