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Synthesizing Complex-Valued Multicoil MRI Data from Magnitude-Only Images.

Nikhil Deveshwar1,2,3, Abhejit Rajagopal2, Sule Sahin1,2

  • 1UC Berkeley-UCSF Graduate Program in Bioengineering, Berkeley, CA 94701, USA.

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Synthesizing realistic magnetic resonance imaging (MRI) data from magnitude-only images addresses a key challenge in developing advanced MRI reconstruction techniques. This method enables the use of diverse clinical data, improving deep learning model performance.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Magnetic Resonance Imaging (MRI)

Background:

  • Deep learning for accelerated MRI and image reconstruction is advancing, but faces limitations due to the scarcity of large, diverse MRI datasets.
  • Clinical MRI archives typically store only magnitude images, lacking essential raw data (phase and multi-channel RF coil information) needed for advanced reconstruction.
  • Existing MRI raw data repositories are limited in anatomical, pathological, and acquisition type diversity.

Purpose of the Study:

  • To develop a method for synthesizing realistic MRI raw data from readily available magnitude-only images.
  • To enable the utilization of diverse clinical MRI data for training and validating advanced MRI reconstruction algorithms.
  • To improve the clinical translation of deep learning-based MRI technologies.

Main Methods:

  • A conditional Generative Adversarial Network (GAN) framework was employed to generate synthetic phase images from input magnitude images.
  • The ESPIRiT algorithm was used to derive RF coil sensitivity maps from fully sampled real data, enabling the generation of multi-coil data.
  • Variational Networks were trained using both real and synthetically generated MRI data (phase and multi-coil information) to evaluate reconstruction performance.

Main Results:

  • Variational Networks trained on the proposed synthetic MRI data demonstrated superior performance compared to those trained on synthetic data from state-of-the-art methods.
  • Reconstruction performance using synthetic k-space data generated by this method was comparable to networks trained on undersampled real k-space data.
  • The synthetic data included GAN-derived phase and multi-coil information, leading to improved reconstruction outcomes.

Conclusions:

  • The proposed method effectively synthesizes realistic MRI raw data from magnitude-only images, overcoming a significant barrier in deep learning for MRI.
  • This approach allows for leveraging vast amounts of existing clinical MRI data for advanced reconstruction development.
  • The findings support the potential of synthetic MRI data generation for enhancing the robustness and generalizability of deep learning models in clinical practice.