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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
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Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.

Kang Wang1, Matthew J Middione, Andreas M Loening

  • 1From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).

Investigative Radiology
|January 17, 2025
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Summary

New motion-compensated diffusion-encoding gradients (MCGs) and deep learning (DL) denoising significantly improve multishot pancreatic diffusion-weighted imaging (msDWI), reducing artifacts and quantitative errors for more reliable pancreatic MRI.

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Medical Imaging
  • Radiology

Background:

  • Conventional single-shot pancreatic diffusion-weighted imaging (DWI) suffers from artifacts like distortion and blurring.
  • Motion artifacts cause quantitative errors and signal loss, limiting the clinical utility of pancreatic DWI.
  • Multishot DWI (msDWI) reduces distortion but is sensitive to motion; motion-compensated diffusion-encoding gradients (MCGs) can help but increase echo time and reduce signal.

Purpose of the Study:

  • To combine MCGs generated via convex-optimized diffusion encoding (CODE) with deep learning (DL)-based denoising to improve pancreatic msDWI.
  • To hypothesize that this combined method will qualitatively and quantitatively enhance msDWI of the pancreas.

Main Methods:

  • A prospective study included 22 patients undergoing abdominal MRI on 3.0 T scanners.
  • Two-shot spin-echo echo-planar DWI was performed with and without CODE-generated MCGs (M0 and M1).
  • DL-based denoising was applied to M1 images (M1 + DL); ADC maps were reconstructed and compared by radiologists.

Main Results:

  • M1 was significantly preferred over M0 for motion artifacts and signal homogeneity.
  • DL-based denoising (M1 + DL) significantly improved perceived noise compared to M0.
  • CODE-generated motion correction (M1 and M1 + DL) resulted in homogeneous ADC values across the pancreas, unlike M0.

Conclusions:

  • CODE-generated MCGs improve multishot pancreatic DWI, eliminating ADC variation and enhancing reader interpretation.
  • DL-based denoising mitigates signal loss from motion compensation while maintaining ADC consistency.
  • Integrating CODE and DL denoising shows potential to improve the accuracy and reliability of multishot pancreatic DWI.