Deep learning-based reconstruction for three-dimensional volumetric brain MRI: a qualitative and quantitative assessment.

IF 2.9 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yeseul Kang, Sang-Young Kim, Jun Hwee Kim, Nak-Hoon Son, Chae Jung Park
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Abstract

Background: To evaluate the performance of a deep learning reconstruction (DLR) based on Adaptive-Compressed sensing (CS)-Network for brain MRI and validate it in a clinical setting.

Methods: Ten healthy volunteers and 22 consecutive patients were prospectively enrolled. Volunteers underwent 3D brain MRI including T1 without CS factor (9:16 min, reference standard); with CS factor of 2 without DLR (CS2, 4:6 min); with CS factor of 2 with DLR (DLR-CS2); with CS factor of 4 without DLR (CS4, 2:6 min); and with CS factor of 4 with DLR (DLR-CS4). The patients' MRI included the CS2 and DLR-CS4. The volumes of lateral ventricles, hippocampus, choroid plexus, and white matter hypointensity were calculated and compared among the sequences. Three radiologists independently assessed anatomical conspicuity, overall image quality, artifacts, signal-to-noise ratio (SNR), and sharpness using a 5-point scale for each sequence.

Results: Applying acceleration factors of 2 and 4 reduced the scan time to 65.4% and 33.5%, respectively, of that of the reference standard. Volumes of all the measured subregions showed no significant differences among different sequences in all participants. In qualitative analysis, the interrater agreement was excellent (κ = 0.844-0.926). In volunteers, quality of DLR-CS4 were comparable to those of CS2 for all metrics except for the overall image quality and SNR despite a 51.2% scan time reduction. In patients, DLR-CS4 showed quality comparable to that of CS2 for all metrics.

Conclusions: DLR allowed the scan time reduction by at least half without sacrificing image quality and volumetric quantification accuracy, supporting its reliability and efficiency.

基于深度学习的三维脑体积MRI重建:定性和定量评估。
背景:评估基于自适应压缩感知(CS)网络的脑MRI深度学习重建(DLR)的性能,并在临床环境中进行验证。方法:前瞻性纳入10名健康志愿者和22名连续患者。志愿者行三维脑MRI,包括T1,无CS因素(9:16 min,参考标准);无DLR的CS因子为2 (CS2, 4:6 min);与DLR (DLR- cs2)的CS因子为2;无DLR的CS因子为4 (CS4, 2:6 min);DLR的CS因子为4 (DLR- cs4)。患者MRI包括CS2和DLR-CS4。计算各序列侧脑室、海马、脉络膜丛和白质低密度的体积并进行比较。三位放射科医生独立评估解剖显著性、整体图像质量、伪影、信噪比(SNR)和清晰度,采用5分制对每个序列进行评估。结果:采用2和4的加速因子,扫描时间分别缩短至参比标准的65.4%和33.5%。在所有参与者中,所有测量的子区域的体积在不同序列之间没有显着差异。在定性分析中,判据一致性极好(κ = 0.844 ~ 0.926)。在志愿者中,DLR-CS4的质量与CS2的质量相当,除了整体图像质量和信噪比,尽管扫描时间减少了51.2%。在患者中,DLR-CS4在所有指标上显示出与CS2相当的质量。结论:DLR在不牺牲图像质量和体积定量精度的情况下,使扫描时间减少至少一半,支持其可靠性和效率。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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