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Accelerating Prostate Diffusion-weighted MRI Using a Guided Denoising Convolutional Neural Network: Retrospective

Elena A Kaye1, Emily A Aherne1, Cihan Duzgol1

  • 1Departments of Medical Physics (E.A.K., I.H., Y.M., R.O.), Radiology (E.A.A., C.D., R.O., H.A.V., O.A.), and Epidemiology and Biostatistics (Z.Z.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Room S1212B, New York, NY 10065; Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria (E.K.); and MR Applications & Workflow Team, GE Healthcare, Chicago, Ill (M.M.F.).

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Accelerating prostate diffusion-weighted imaging (DWI) is feasible by reducing scan averages and using a guided deep learning model for denoising. This method enhances image quality and maintains accuracy for apparent diffusion coefficient (ADC) mapping.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Prostate Cancer Diagnostics

Background:

  • Diffusion-weighted imaging (DWI) is crucial for prostate cancer assessment.
  • Acquisition time for prostate DWI can be lengthy due to the need for multiple signal averages.
  • Image denoising techniques are essential for improving the quality of accelerated MRI sequences.

Purpose of the Study:

  • To evaluate the feasibility of accelerating prostate DWI by reducing acquired averages.
  • To develop and assess a guided deep learning model (guided DnCNN) for denoising accelerated DWI data.
  • To compare the performance of the guided DnCNN against conventional denoising methods.

Main Methods:

  • Retrospective analysis of 155 prostate DWI datasets (103 training, 15 validation, 37 testing).
  • Reconstruction of high b-value (hb DW) images using 2 averages and reference images using 16 averages.
  • Development of a guided DnCNN incorporating low b-value images for denoising.

Main Results:

  • Guided DnCNN significantly improved image quality metrics (PSNR, SSIM) and reduced noise (NMSE) compared to original DnCNN (P < .001).
  • Denoised images received higher qualitative scores from readers (P < .0001).
  • Apparent diffusion coefficient (ADC) maps derived from denoised images showed good agreement with reference ADC maps.

Conclusions:

  • The proposed guided DnCNN effectively denoises accelerated prostate DWI data acquired with fewer averages.
  • This approach is technically feasible for accelerating prostate DWI acquisition while maintaining diagnostic quality.
  • The method shows promise for reducing scan times in prostate MRI.