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Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes

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Published on: November 12, 2016

Deep-learning-based super-resolution for accelerating chemical exchange saturation transfer MRI.

Rohith Saai Pemmasani Prabakaran^1,2, Se Weon Park^1,2, Joseph H C Lai¹

¹Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China.

NMR in Biomedicine

|March 16, 2024

Summary

Deep-learning-based super-resolution (DLSR) reconstructs high-resolution Chemical Exchange Saturation Transfer (CEST) MRI images faster. This DLSR-CEST method improves spatial resolution and preserves crucial Z-spectrum information for clinical applications.

Keywords:

acquisition time amide CEST (amideCEST)brain chemical exchange saturation transfer (CEST)deep‐learning‐based super‐resolution (DLSR)relayed nuclear Overhauser effect (rNOE)

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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Published on: December 18, 2016

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Chemical Exchange Saturation Transfer (CEST) MRI is vital for molecular imaging in disease diagnosis and treatment.
High-resolution CEST MRI is crucial for detecting subtle in vivo tissue changes but is time-consuming.
Acquisition time limits clinical application, necessitating faster imaging techniques.

Purpose of the Study:

To develop a Deep Learning-based Super-Resolution (DLSR) method, named DLSR-CEST, to accelerate CEST MRI acquisition.
To reconstruct high-resolution CEST MRI images from fast, low-resolution acquisitions.
To address the challenge of limited large CEST datasets for network development.

Main Methods:

Pretraining the DLSR-CEST network on human brain T1w and T2w images to initialize weights.
Fine-tuning the network on small human and mouse brain CEST datasets.
Evaluating reconstructed images using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

Main Results:

DLSR-CEST significantly improved spatial resolution (2-8x downsampling factors) in reconstructed CEST source images.
Amide CEST and relayed nuclear Overhauser effect (rNOE) maps showed high spatial resolution and low normalized root mean square error (NRMSE).
The method demonstrated negligible loss of Z-spectrum information, preserving quantitative accuracy.

Conclusions:

DLSR-CEST effectively reconstructs high-resolution CEST MRI images from accelerated low-resolution scans.
The developed method enhances spatial resolution while maintaining spectral information integrity.
DLSR-CEST offers a promising solution for faster clinical implementation of CEST MRI.