Deep-Learning Reconstruction for 7T MP2RAGE and SPACE MRI: Improving Image Quality at High Acceleration Factors.

Zeyu Liu, Vishal Patel, Xiangzhi Zhou, Shengzhen Tao, Thomas Yu, Jun Ma, Dominik Nickel, Patrick Liebig, Erin M Westerhold, Hamed Mojahed, Vivek Gupta, Erik H Middlebrooks
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Abstract

Background and purpose: Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving image quality in conventional MRI field strengths; however, there has been limited application to ultra-high field MRI.The objective of this study was to evaluate the performance of a prototype DL-based image reconstruction technique in 7T MRI of the brain utilizing MP2RAGE and SPACE acquisitions, in comparison to reconstructions in conventional compressed sensing (CS) and controlled aliasing in parallel imaging (CAIPIRINHA) techniques.

Materials and methods: This retrospective study involved 60 patients who underwent 7T brain MRI between June 2024 and October 2024, comprised of 30 patients with MP2RAGE data and 30 patients with SPACE FLAIR data. Each set of raw data was reconstructed with DL-based reconstruction and conventional reconstruction. Image quality was independently assessed by two neuroradiologists using a 5-point Likert scale, which included overall image quality, artifacts, sharpness, structural conspicuity, and noise level. Inter-observer agreement was determined using top-box analysis. Contrast-to-noise ratio (CNR) and noise levels were quantitatively evaluated and compared using the Wilcoxon signed-rank test.

Results: DL-based reconstruction resulted in a significant increase in overall image quality and a reduction in subjective noise level for both MP2RAGE and SPACE FLAIR data (all P<0.001), with no significant differences in image artifacts (all P>0.05). When compared to standard reconstruction, the implementation of DL-based reconstruction yielded an increase in CNR of 49.5% [95% CI 33.0-59.0%] for MP2RAGE data and 90.6% [95% CI 73.2-117.7%] for SPACE FLAIR data, along with a decrease in noise of 33.5% [95% CI 23.0-38.0%] for MP2RAGE data and 47.5% [95% CI 41.9-52.6%] for SPACE FLAIR data.

Conclusions: DL-based reconstruction of 7T MRI significantly enhanced image quality compared to conventional reconstruction without introducing image artifacts. The achievable high acceleration factors have the potential to substantially improve image quality and resolution in 7T MRI.

Abbreviations: CAIPIRINHA = Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration; CNR = contrast-to-noise ratio; CS = compressed sensing; DL = deep learning; MNI = Montreal Neurological Institute; MP2RAGE = Magnetization-Prepared 2 Rapid Acquisition Gradient Echoes; SPACE = Sampling Perfection with Application-Optimized Contrasts using Different Flip Angle Evolutions.

7T MP2RAGE和SPACE MRI的深度学习重建:提高高加速因子下的图像质量。
背景与目的:深度学习(DL)重建已经成功地实现了传统MRI场强中不可实现的加速因子和提高图像质量;然而,在超高场核磁共振成像中的应用有限。本研究的目的是评估基于dl的原型图像重建技术在利用MP2RAGE和SPACE采集的7T脑MRI中的性能,并与传统压缩感知(CS)和并行成像控制混叠(CAIPIRINHA)技术的重建进行比较。材料与方法:本回顾性研究纳入60例于2024年6月至2024年10月行7T脑MRI的患者,其中MP2RAGE数据30例,SPACE FLAIR数据30例。对每组原始数据分别进行基于dl的重构和常规重构。图像质量由两名神经放射学家使用5点李克特量表独立评估,包括整体图像质量、伪影、清晰度、结构显著性和噪声水平。采用顶盒分析确定观察员间的一致意见。采用Wilcoxon符号秩检验定量评价和比较噪声比(CNR)和噪声水平。结果:基于dl的重建导致MP2RAGE和SPACE FLAIR数据的整体图像质量显著提高,主观噪声水平降低(均P0.05)。与标准重建相比,基于dl的重建使MP2RAGE数据的CNR提高了49.5% [95% CI 33.0-59.0%], SPACE FLAIR数据的CNR提高了90.6% [95% CI 73.2-117.7%], MP2RAGE数据的噪声降低了33.5% [95% CI 23.0-38.0%], SPACE FLAIR数据的噪声降低了47.5% [95% CI 41.9-52.6%]。结论:与不引入图像伪影的常规重建相比,基于dl的7T MRI重建可显著提高图像质量。可实现的高加速因子有可能大幅提高7T MRI的图像质量和分辨率。缩写:CAIPIRINHA =并行成像中的可控混叠导致更高的加速度;CNR =噪声对比比;压缩感知;DL =深度学习;蒙特利尔神经学研究所;MP2RAGE =磁化制备的2快速采集梯度回波;空间=使用不同翻转角度演化的应用优化对比的采样完美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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