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An ensemble approach for accelerated and noise-resilient parallel MRI reconstruction utilizing CycleGANs

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Abstract

Magnetic Resonance Imaging (MRI) is often constrained by long acquisition times. Accelerated acquisition techniques can reduce scan time but may introduce artifacts and decrease image resolution. Additionally, MRI data invariably contains noise (often Gaussian or Rician), further impacting image quality and diagnostic value. Conventional approaches often address noise and undersampling artifacts separately, leading to suboptimal image reconstruction. This work proposes a novel ensemble of CycleGAN models integrated within the Joint Sensitivity Encoding (JSENSE) framework to address these challenges in a comprehensive manner. Each CycleGAN within the ensemble is specifically trained to target distinct aspects of image degradation. This approach leverages the complementary strengths of the models to achieve a superior image reconstruction. Experiments demonstrate the effectiveness of the proposed ensemble model, outperforming conventional methods and our prior work in terms of visual quality and quantitative metrics. Significant gains were observed at higher acceleration factors and greater noise levels. The integration of CycleGANs with JSENSE yielded superior structural similarity and image clarity compared to traditional parallel MRI (pMRI) and single-model methods.

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Funding

This work was partially supported by the National Science Foundation under Grant No. 2050972.

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, Univeristy of Massachusetts Dartmouth, North Dartmouth, MA, 02747, USA

    Gulfam Ahmed Saju & Yuchou Chang

  2. Department of Electrical & Computer Engineering, Univeristy of Massachusetts Dartmouth, North Dartmouth, MA, 02747, USA

    Marjan Akhi

  3. Physics Department, Ursinus College, Collegeville, PA, 19426, USA

    Alan Okinaka

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  1. Gulfam Ahmed Saju
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  2. Alan Okinaka
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  4. Yuchou Chang
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Contributions

G.A.S. worked on the data acquisition, coding, writing, and data analysis. A.O. worked on coding, data analysis, and writing. M.A. worked on data analysis and interpretation. Y.C. worked on writing and revision.

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Correspondence to Yuchou Chang.

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Saju, G.A., Okinaka, A., Akhi, M. et al. An ensemble approach for accelerated and noise-resilient parallel MRI reconstruction utilizing CycleGANs. Machine Vision and Applications 35, 136 (2024). https://doi.org/10.1007/s00138-024-01617-0

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