Denoising Low-Power CEST Imaging Using a Deep Learning Approach With a Dual-Power Feature Preparation Strategy.

ABSTRACT:

Purpose

Low-power (LP) chemical exchange saturation transfer (CEST) Z-spectra have significantly reduced confounding effects and enhanced peak resolvability, thereby improving the observation and quantification of various CEST effects. However, LP Z-spectra suffer greatly from reduced contrast-to-noise ratio (CNR). This study aims to develop a dual-power feature preparation for an autoencoder-based deep learning approach (DPDL), for denoising LP Z-spectra. This method leverages the high CNR of higher saturation power and the enhanced peak resolvability of low saturation power.

Methods

The DPDL model was trained on simulated CEST data, validated on both simulated and BSA phantoms, and then applied to measured data from rat brain and leg muscles at 4.7T. A Lorentzian differenc

SUBMITTER: Kurmi Y 

PROVIDER: S-EPMC12746379 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature