首页|SRM-Net: Joint Sampling and Reconstruction and Mapping Network for Accelerated 3T Brain Multi-Parametric MR Imaging

SRM-Net: Joint Sampling and Reconstruction and Mapping Network for Accelerated 3T Brain Multi-Parametric MR Imaging

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  • NETL
  • NSTL
  • IEEE
Multi-parametric magnetic resonance imaging (MRI) can provide complementary quantitative information by generating multi-parametric maps and is becoming a promising imaging technique for advanced medical diagnosis. However, multi-parametric MRI requires longer acquisition time than normal MRI scanning. The existing reconstruction methods for accelerated multi-parametric MRI suffer from suboptimal performance due to stage-wise optimization, and inefficient utilization of intra- and inter-contrast information. To address these challenges, we propose an all-in-one joint Sampling, Reconstruction, and Mapping network, dubbed as SRM-Net, for multi-parametric MRI reconstruction on multi-coil and multi-contrast MR images. Specifically, our model consists of three modules including sampling, reconstruction, and mapping. In the sampling module, we introduce a sampling scheme to generate individually-optimized sampling pattern across multi-contrast images. In the reconstruction module, we adopt a spatio-temporal attention mechanism, which is embedded in a dual-domain-based unrolling framework, to better exploit inter- and intra-contrast correlations. In the mapping module, we employ multi-layer perceptron to model complex nonlinear mapping. Integrating Sampling, Reconstruction, and Mapping, our SRM-Net enables the end-to-end learning paradigm. Experimental results show that our SRM-Net generates superior multi-parametric maps including T1, T2$^*$, and PD for brain on 3T MR scanner compared to state-of-the-art methods, and meanwhile provides promising intermediate weighted MR images.

Image reconstructionMagnetic resonance imagingEstimationOptimizationBiomedical engineeringProbabilistic logicSensitivityCoilsFingerprint recognitionSun

Yuxuan Liu、Kaicong Sun、Haikun Qi、Junwei Yang、Xiaopeng Zong、Yongsheng Pan、Yuning Gu、Sifan He、Han Zhang、Yu Zhang、Dinggang Shen

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School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, China

School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China

School of Southern Medical University, China

School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China|Shanghai United Imaging Intelligence Company Ltd., Shanghai, China|Shanghai Clinical Research and Trial Center, Shanghai, China

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2025

10.1109/TBME.2024.3523480

IEEE transactions on bio-medical engineering

IEEE transactions on bio-medical engineering

ISSN：
年,卷(期)：2025.72(6)
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