Rapid whole-brain T2* and susceptibility mapping using 3D multiple overlapping-echo detachment acquisition and missing modality synthesis embedded simulation.

IF 3 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Qinqin Yang, Longkun Chen, Nuowei Ge, Jie Chen, Jingying Yang, Zejun Wu, Chenyang Dai, Shuhui Cai, Zhong Chen, Lijun Bao, Liuhong Zhu, Jianfeng Bao, Congbo Cai
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引用次数: 0

Abstract

Purpose: To develop a 3D multiple overlapping-echo detachment (3D-MOLED) imaging technique, along with data generation and reconstruction strategies, for rapid whole-brain T2* and QSM.

Methods: MOLED encoding was extended to a 3D multi-shot acquisition and combined with dual-echo blip-reversed EPI trains to simultaneously acquire T2* and QSM signals while reducing image distortion. To enable Bloch simulation for training data generation, a deep learning-based missing modality synthesis approach was employed to produce co-registered multi-parametric templates. In addition, a pseudo-3D Bloch simulation was proposed to accelerate synthetic data generation for network training. A cohort of healthy volunteers and clinical participants were recruited to evaluate the motion robustness of the proposed method in comparison with conventional 3D-GRE.

Results: Compared to 3D-GRE, 3D-MOLED achieved significant improvements in both scan speed and motion robustness, with over 70% of scans rated as good image quality in both healthy and clinical cohorts. The missing modality synthesis approach generated high-quality 3D multi-parametric maps. Combined with the pseudo-3D Bloch simulation framework, it enabled efficient generation of paired training data with acceptable computational cost, thereby facilitating accurate quantitative mapping.

Conclusion: 3D-MOLED enables simultaneous whole-brain T2* and QSM mapping at 1 mm isotropic resolution in 50 s, offering superior motion robustness compared to conventional 3D-GRE.

利用三维多重重叠回声分离采集和缺失模态合成嵌入式模拟快速全脑T2*和敏感性制图。
目的:发展3D多重重叠回声脱离(3D- moled)成像技术,以及数据生成和重建策略,用于快速全脑T2*和QSM。方法:将MOLED编码扩展到三维多镜头采集,结合双回波反转EPI序列,同时采集T2*和QSM信号,同时降低图像失真。为了实现Bloch模拟训练数据生成,采用基于深度学习的缺失模态综合方法生成共注册多参数模板。此外,提出了一种伪三维Bloch仿真方法,以加速网络训练合成数据的生成。招募了一组健康志愿者和临床参与者来评估所提出方法与传统3D-GRE方法的运动稳健性。结果:与3D-GRE相比,3D-MOLED在扫描速度和运动稳健性方面都有显著改善,在健康和临床队列中,超过70%的扫描被评为良好的图像质量。缺失模态综合方法生成高质量的三维多参数地图。结合伪三维Bloch仿真框架,以可接受的计算成本高效生成成对训练数据,从而促进准确的定量映射。结论:3D-MOLED能够在50秒内以1mm各向同性分辨率同时绘制全脑T2*和QSM,与传统3D-GRE相比,具有更好的运动稳健性。
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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来源期刊
CiteScore
6.70
自引率
24.20%
发文量
376
审稿时长
2-4 weeks
期刊介绍: Magnetic Resonance in Medicine (Magn Reson Med) is an international journal devoted to the publication of original investigations concerned with all aspects of the development and use of nuclear magnetic resonance and electron paramagnetic resonance techniques for medical applications. Reports of original investigations in the areas of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.
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