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Recurrent inference machines for reconstructing heterogeneous MRI data.

Kai Lønning1, Patrick Putzky2, Jan-Jakob Sonke3

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Medical Image Analysis
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Summary
This summary is machine-generated.

Recurrent Inference Machines (RIMs) accelerate magnetic resonance image (MRI) reconstruction by learning data priors, outperforming traditional Compressed Sensing (CS) methods. This deep learning approach generalizes well to diverse anatomical data and acquisition settings.

Keywords:
Deep learningInverse problemsMRIReconstruction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Accelerated magnetic resonance image (MRI) reconstruction is crucial for reducing scan times.
  • Traditional Compressed Sensing (CS) methods rely on sparsifying transforms and predefined MRI prior distributions.
  • Deep learning models need to generalize across varying anatomy and acquisition parameters for clinical utility.

Purpose of the Study:

  • To introduce Recurrent Inference Machines (RIMs) as a novel framework for accelerated MRI reconstruction.
  • To demonstrate RIMs' ability to learn MRI prior distributions directly from data, bypassing the need for sparsifying transforms.
  • To evaluate RIMs' performance and generalizability compared to Compressed Sensing (CS).

Main Methods:

  • Proposed using Recurrent Inference Machines (RIMs) for iterative, recurrent inference in MRI reconstruction.
  • RIMs reassess and incrementally adjust reconstructions based on the MRI forward model.
  • The recurrent architecture with internal states allows RIMs to learn the inferential process, reducing reliance on signal source features.

Main Results:

  • RIMs demonstrated a low tendency to overfit and a high capacity to generalize to unseen data, including brain and knee scans.
  • The model successfully reconstructed MRI scans with varying contrast, resolution, field strength, and acceleration levels.
  • In a double-blinded experiment, RIMs outperformed CS in quality metrics and neuroradiologist ratings.
  • Qualitative results showed applicability to prospectively undersampled raw data from standard acquisition protocols.

Conclusions:

  • Recurrent Inference Machines (RIMs) offer a powerful and generalizable deep learning framework for accelerated MRI reconstruction.
  • RIMs provide superior image quality and robustness compared to conventional CS methods.
  • The proposed method holds significant potential for clinical application, enabling faster and more efficient MRI scans.