Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction

Chuyu Liu; Zhongsen Li; Zhensen Chen; Benqi Zhao; Zhuozhao Zheng; Xiaolei Song

doi:10.1002/mrm.30089

Highly-accelerated CEST MRI using frequency-offset-dependent k-space sampling and deep-learning reconstruction

Magn Reson Med. 2024 Apr 16. doi: 10.1002/mrm.30089. Online ahead of print.

Authors

Chuyu Liu¹, Zhongsen Li¹, Zhensen Chen^{2

3}, Benqi Zhao⁴, Zhuozhao Zheng⁴, Xiaolei Song¹

Affiliations

¹ Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
² Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
³ Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
⁴ Department of Radiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

PMID: 38623899
DOI: 10.1002/mrm.30089

Abstract

Purpose: To develop a highly accelerated CEST Z-spectral acquisition method using a specifically-designed k-space sampling pattern and corresponding deep-learning-based reconstruction.

Methods: For k-space down-sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency-offset-dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self-acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k-space resampling. The prospective performance was also assessed on six healthy subjects.

Results: In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTR_asym, MTR_rex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones.

Conclusion: The combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.

Keywords: chemical exchange saturation transfer (CEST); deep learning; fast imaging; magnetic resonance imaging (MRI); partially separable function.

Abstract

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