Perfusion Maps Acquired From Dynamic Angiography MRI Using Deep Learning Approaches

医学 大脑中动脉 灌注 灌注扫描 核医学 血管造影 放射科 动态增强MRI 磁共振血管造影 磁共振成像 颈内动脉 冲程(发动机) 深度学习 人工智能 缺血 计算机科学 内科学 物理 热力学
作者
Muhammad Asaduddin,Hong Gee Roh,Hyun Jeong Kim,Eung Yeop Kim,Sung‐Hong Park
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:57 (2): 456-469 被引量:3
标识
DOI:10.1002/jmri.28315
摘要

A typical stroke MRI protocol includes perfusion-weighted imaging (PWI) and MR angiography (MRA), requiring a second dose of contrast agent. A deep learning method to acquire both PWI and MRA with single dose can resolve this issue.To acquire both PWI and MRA simultaneously using deep learning approaches.Retrospective.A total of 60 patients (30-73 years old, 31 females) with ischemic symptoms due to occlusion or ≥50% stenosis (measured relative to proximal artery diameter) of the internal carotid artery, middle cerebral artery, or anterior cerebral artery. The 51/1/8 patient data were used as training/validation/test.A 3 T, time-resolved angiography with stochastic trajectory (contrast-enhanced MRA) and echo planar imaging (dynamic susceptibility contrast MRI, DSC-MRI).We investigated eight different U-Net architectures with different encoder/decoder sizes and with/without an adversarial network to generate perfusion maps from contrast-enhanced MRA. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), mean transit time (MTT), and time-to-max (Tmax ) were mapped from DSC-MRI and used as ground truth to train the networks and to generate the perfusion maps from the contrast-enhanced MRA input.Normalized root mean square error, structural similarity (SSIM), peak signal-to-noise ratio (pSNR), DICE, and FID scores were calculated between the perfusion maps from DSC-MRI and contrast-enhanced MRA. One-tailed t-test was performed to check the significance of the improvements between networks. P values < 0.05 were considered significant.The four perfusion maps were successfully extracted using the deep learning networks. U-net with multiple decoders and enhanced encoders showed the best performance (pSNR 24.7 ± 3.2 and SSIM 0.89 ± 0.08 for rCBV). DICE score in hypo-perfused area showed strong agreement between the generated perfusion maps and the ground truth (highest DICE: 0.95 ± 0.04).With the proposed approach, dynamic angiography MRI may provide vessel architecture and perfusion-relevant parameters simultaneously from a single scan.3 TECHNICAL EFFICACY: Stage 5.
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