DeepCEST: 9.4 T Chemical Exchange Saturation Transfer MRI contrast predicted from 3 T data - a proof of concept study

TL;DR

This study demonstrates that a deep learning model can predict high-field 9.4 T CEST MRI contrast from 3 T data, potentially enhancing clinical imaging capabilities at lower field strengths.

Contribution

The paper introduces a deep neural network approach that accurately predicts 9.4 T CEST MRI contrast from 3 T data, enabling broader clinical application of ultra-high field imaging.

Findings

Predicted 9.4 T CEST contrast with small deviations from actual measurements.

Reduced noise in predicted CEST maps compared to direct 9.4 T measurements.

Successful application to tumor data showing potential for clinical diagnostics.

Abstract

Purpose: Separation of different CEST signals in the Z-spectrum is a challenge especially at low field strengths where amide, amine, and NOE peaks coalesce with each other or with the water peak. The purpose of this work is to investigate if the information in 3T spectra can be extracted by a deep learning approach trained by 9.4T human brain target data. Methods: Highly-spectrally-resolved Z-spectra from the same volunteer were acquired by 3D-snapshot CEST MRI at 3 T and 9.4 T with similar saturation schemes. The volume-registered 3 T Z-spectra-stack was then used as input data for a 3-layer deep neural network with the volume-registered 9.4 T fitted parameter stack as target data. The neural network was optimized and applied to training data, to unseen data from a different volunteer, and as well to a tumor patient data set. Results: A useful neural net architecture could be found and verified in healthy volunteers. The principle gray-/white matter contrast of the different CEST effects was predicted with only small deviations. The 9.4 T prediction was less noisy compared to the directly measured CEST maps, however at the cost of slightly lower tissue contrast. Application to a tumor patient measured at 3 T and 9.4 T revealed that tumorous tissue Z-spectra and corresponding hyper/hypo-intensities of different CEST effects can also be predicted. Conclusion: Deep learning might be a powerful tool for CEST data processing and deepCEST could bring the benefits and insights of the few ultra-high field sites to a broader clinical use. Vice versa deepCEST might help for determining which subjects are good candidates to measure additionally at UHF.