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Convolutional network denoising for acceleration of multi-shot diffusion MRI.

Or Alus1, Maria El Homsi2, Jennifer S Golia Pernicka2

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

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This summary is machine-generated.

This study introduces a convolutional neural network to denoise diffusion-weighted MRI (DWI) images, accelerating multi-shot DWI acquisition. This method significantly reduces scan times for rectal cancer imaging while maintaining image quality.

Keywords:
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Area of Science:

  • Medical Imaging
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Multi-shot echo planar imaging enhances spatial resolution and reduces geometric distortions in diffusion-weighted MRI (DWI).
  • However, multi-shot DWI requires longer scan times due to the need for multiple repetitions to achieve adequate signal-to-noise ratio, especially in body imaging.
  • Accelerating DWI acquisition is crucial for clinical applications.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN) for denoising high b-value images in multi-shot DWI.
  • To accelerate the acquisition of multi-shot DWI by reducing the number of repetitions needed.
  • To evaluate the impact of CNN denoising on image quality and geometric artifact reduction in rectal cancer patients.

Main Methods:

  • A CNN model was trained on single-shot DWI data for denoising.
  • The trained CNN was applied to accelerate 2-shot DWI acquisition by reducing repetitions.
  • Image quality was assessed by expert radiologists using qualitative scores, and statistical analysis (Wilcoxon signed-rank test) was performed to evaluate the effect of CNN denoising.

Main Results:

  • The proposed CNN denoising method successfully accelerated the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard.
  • Qualitative evaluation by expert radiologists indicated comparable or improved image quality between accelerated and non-accelerated acquisitions.
  • CNN denoising demonstrated effectiveness in reducing geometric artifacts and enhancing spatial resolution.

Conclusions:

  • CNN-based denoising enables significant acceleration of multi-shot DWI acquisition without compromising image quality.
  • This technique allows for increased numbers of shots, leading to substantial geometric artifact reduction and improved spatial resolution.
  • The findings suggest that CNN denoising can enhance the clinical utility of multi-shot DWI for body imaging, including rectal cancer diagnosis.