Jeon Young Hun, Park Chanrim, Lee Kyung Hoon, Choi Kyu Sung, Lee Ji Ye, Hwang Inpyeong, Yoo Roh-Eul, Yun Tae Jin, Choi Seung Hong, Kim Ji-Hoon, Sohn Chul-Ho, Kang Koung Mi
Seoul National University Hospital, Seoul, Republic of Korea.
Kangbuk Samsung Hospital, Seoul, Republic of Korea.
Neuroradiology. 2025 Mar 17. doi: 10.1007/s00234-025-03564-7.
Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learning-based reconstruction have shown the potential to improve image quality and reduce scan times. This study aimed to evaluate the effectiveness of accelerated intracranial TOF-MRA using deep learning-based image enhancement (TOF-DL) compared to conventional TOF-MRA (TOF-Con) at both 3-T and 1.5-T.
In this retrospective study, patients who underwent both conventional and 40% accelerated TOF-MRA protocols on 1.5-T or 3-T scanners from July 2022 to March 2023 were included. A commercially available DL-based image enhancement algorithm was applied to the accelerated MRA. Quantitative image quality assessments included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), contrast ratio (CR), and vessel sharpness (VS), while qualitative assessments were conducted using a five-point Likert scale. Cohen's d was used to compare the quantitative image metrics, and a cumulative link mixed regression model analyzed the readers' scores.
A total of 129 patients (mean age, 64 years ± 12 [SD], 99 at 3-T and 30 at 1.5-T) were included. TOF-DL showed significantly higher SNR, CNR, CR, and VS compared to TOF-Con (CNR = 183.89 vs. 45.58; CR = 0.63 vs. 0.59; VS = 0.73 vs. 0.61; all p < 0.001). The improvement in VS was more pronounced at 1.5-T (Cohen's d = 2.39) compared to 3-T HR and routine (Cohen's d = 0.83 and 0.75, respectively). TOF-DL also outperformed TOF-Con in qualitative image parameters, enhancing the visibility of small- and medium-sized vessels, regardless of the degree of resolution and field strength. TOF-DL showed comparable diagnostic accuracy (AUC: 0.77-0.85) to TOF-Con (AUC: 0.79-0.87) but had higher specificity for steno-occlusive lesions.
Accelerated intracranial MRA with deep learning-based reconstruction reduces scan times by 40% and significantly enhances image quality over conventional TOF-MRA at both 3-T and 1.5-T.
三维时间飞跃磁共振血管造影（TOF-MRA）对脑血管疾病评估有效，但临床应用受扫描时间长和空间分辨率低的限制。基于深度学习的重建技术的最新进展显示出改善图像质量和缩短扫描时间的潜力。本研究旨在评估在3-T和1.5-T场强下，与传统TOF-MRA（TOF-Con）相比，使用基于深度学习的图像增强技术（TOF-DL）的加速颅内TOF-MRA的有效性。
在这项回顾性研究中，纳入了2022年7月至2023年3月期间在1.5-T或3-T扫描仪上接受传统和加速40%的TOF-MRA检查的患者。将一种商用的基于深度学习的图像增强算法应用于加速后的MRA。定量图像质量评估包括信噪比（SNR）、对比噪声比（CNR）、对比度比（CR）和血管锐度（VS），而定性评估采用五点李克特量表。使用Cohen's d比较定量图像指标，并采用累积链接混合回归模型分析读者评分。
共纳入129例患者（平均年龄64岁±12[标准差]，3-T场强下99例，1.5-T场强下30例）。与TOF-Con相比，TOF-DL的SNR、CNR、CR和VS显著更高（CNR = 183.89对45.58；CR = 0.63对0.59；VS = 0.73对0.61；所有p < 0.001）。与3-T高分辨率和常规检查相比，1.5-T场强下VS的改善更为明显（Cohen's d = 2.39）（3-T高分辨率和常规检查的Cohen's d分别为0.83和0.75）。在定性图像参数方面，TOF-DL也优于TOF-Con，增强了中小血管的可视性，无论分辨率和场强程度如何。TOF-DL显示出与TOF-Con相当的诊断准确性（AUC：0.77 - 0.85）（TOF-Con的AUC：0.79 - 0.87），但对狭窄闭塞性病变具有更高的特异性。
基于深度学习重建的加速颅内MRA可将扫描时间缩短40%，并在3-T和1.5-T场强下比传统TOF-MRA显著提高图像质量。
