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Synthetic Time of Flight Magnetic Resonance Angiography Generation Model Based on Cycle‐Consistent Generative Adversarial Network Using PETRA‐MRA in the Patients With Treated Intracranial Aneurysm

医学 图像质量 麦克内马尔试验 接收机工作特性 动脉瘤 磁共振成像 放射科 计算机科学 人工智能 核医学 算法 数学 图像(数学) 统计 内科学
作者
Sung‐Hye You,Yongwon Cho,Byungjun Kim,Kyung‐Sook Yang,Bo Kyu Kim,Sang Eun Park
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:56 (5): 1513-1528 被引量:5
标识
DOI:10.1002/jmri.28114
摘要

Background Pointwise encoding time reduction with radial acquisition (PETRA) magnetic resonance angiography (MRA) is useful for evaluating intracranial aneurysm recurrence, but the problem of severe background noise and low peripheral signal‐to‐noise ratio (SNR) remain. Deep learning could reduce noise using high‐ and low‐quality images. Purpose To develop a cycle‐consistent generative adversarial network (cycleGAN)‐based deep learning model to generate synthetic TOF (synTOF) using PETRA. Study type Retrospective. Population A total of 377 patients (mean age: 60 ± 11; 293 females) with treated intracranial aneurysms who underwent both PETRA and TOF from October 2017 to January 2021. Data were randomly divided into training (49.9%, 188/377) and validation (50.1%, 189/377) groups. Field Strength/Sequence Ultra‐short echo time and TOF‐MRA on a 3‐T MR system. Assessment For the cycleGAN model, the peak SNR (PSNR) and structural similarity (SSIM) were evaluated. Image quality was compared qualitatively (5‐point Likert scale) and quantitatively (SNR). A multireader diagnostic optimality evaluation was performed with 17 radiologists (experience of 1–18 years). Statistical Tests Generalized estimating equation analysis, Friedman's test, McNemar test, and Spearman's rank correlation. P < 0.05 indicated statistical significance. Results The PSNR and SSIM between synTOF and TOF were 17.51 [16.76; 18.31] dB and 0.71 ± 0.02. The median values of overall image quality, noise, sharpness, and vascular conspicuity were significantly higher for synTOF than for PETRA (4.00 [4.00; 5.00] vs. 4.00 [3.00; 4.00]; 5.00 [4.00; 5.00] vs. 3.00 [2.00; 4.00]; 4.00 [4.00; 4.00] vs. 4.00 [3.00; 4.00]; 3.00 [3.00; 4.00] vs. 3.00 [2.00; 3.00]). The SNRs of the middle cerebral arteries were the highest for synTOF (synTOF vs. TOF vs. PETRA; 63.67 [43.25; 105.00] vs. 52.42 [32.88; 74.67] vs. 21.05 [12.34; 37.88]). In the multireader evaluation, there was no significant difference in diagnostic optimality or preference between synTOF and TOF (19.00 [18.00; 19.00] vs. 20.00 [18.00; 20.00], P = 0.510; 8.00 [6.00; 11.00] vs. 11.00 [9.00, 14.00], P = 1.000). Data Conclusion The cycleGAN‐based deep learning model provided synTOF free from background artifact. The synTOF could be a versatile alternative to TOF in patients who have undergone PETRA for evaluating treated aneurysms. Evidence Level 4 Technical Efficacy Stage 1
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