# CEST MR fingerprinting (CEST-MRF) for Brain Tumor Quantification Using EPI Readout and Deep Learning Reconstruction

arXiv: https://arxiv.org/abs/2108.08333 · HTML: https://mlsanity.com/paper/2108.08333/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [Multi-Parameter Molecular MRI Quantification using Physics-Informed Self-Supervised Learning](https://mlsanity.com/paper/2411.06447/) (2024)
- [CEST-KAN: Kolmogorov-Arnold Networks for CEST MRI Data Analysis](https://mlsanity.com/paper/2406.16026/) (2024)
- [Decoding the human brain tissue response to radiofrequency excitation using a biophysical-model-free deep MRI on a chip framework](https://mlsanity.com/paper/2408.08376/) (2024)
- [Quantitative multi-metabolite imaging of Parkinson's disease using AI boosted molecular MRI](https://mlsanity.com/paper/2507.11329/) (2025)

## Abstract (arXiv)

Purpose: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. Methods: A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time ≤ 2 minutes. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the 6 parameter DRONE reconstruction was tested in simulations in a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient (CCC) calculated for white matter (WM) and grey matter (GM). The clinical utility of CEST-MRF was demonstrated in 4 patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. Results: The DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤ 7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean CCC for all 6 parameters was 0.98±0.01 in WM and 0.98±0.02 in GM. The CEST-MRF values in nearly all tumor regions were significantly different (P=0.05) from each other and the contra-lateral side. Conclusion: Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.