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Updated: Jan 9, 2026

Diffusion Imaging in the Rat Cervical Spinal Cord
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Band-limited implicit neural representations for diffusion-weighted imaging denoising.

Yunxiang Li1, Yan Dai1, Yen-Peng Liao1

  • 1Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.

Physics in Medicine and Biology
|December 9, 2025
PubMed
Summary
This summary is machine-generated.

A new band-limited implicit neural representation (BL-INR) method effectively denoises diffusion-weighted imaging (DWI) without blurring important details. This technique improves image quality and quantitative accuracy for better disease diagnosis and treatment monitoring.

Keywords:
denoisingdiffusion-weighted imagingimplicit neural representationself-supervised learning

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Processing

Background:

  • Diffusion-weighted imaging (DWI) is crucial for disease diagnosis and treatment monitoring.
  • Low signal-to-noise ratio (SNR) in DWI degrades image quality and quantification accuracy.
  • Current denoising methods often compromise essential tissue boundary information.

Purpose of the Study:

  • To develop a novel framework for denoising diffusion-weighted imaging (DWI).
  • To enhance image quality and quantitative accuracy in DWI.
  • To overcome limitations of existing denoising techniques that blur important anatomical details.

Main Methods:

  • Proposed a band-limited implicit neural representation (BL-INR) framework for DWI denoising.
  • Introduced band-limited positional encoding to filter high-frequency noise while preserving signal.
  • Integrated multi-b-value DWI and structural MRI as anatomical priors for enhanced denoising.

Main Results:

  • BL-INR demonstrated superior visualization results across brain, head and neck, abdomen, and pelvis clinical DWI data.
  • Achieved a peak SNR of 35.44 and SSIM of 0.933 under extremely low SNR (1) in simulations.
  • Showcased minimal average ADC error (4.57×10-5 mm²s⁻¹) in phantom denoising experiments.

Conclusions:

  • BL-INR offers a novel, frequency-limited approach for DWI denoising using implicit neural representations.
  • The self-supervised method requires no paired data, facilitating convenient clinical application.
  • Enables accurate diffusion parameter derivation, supporting reliable quantitative analysis in clinical settings.