@inproceedings{bc49f98ccc7943d8986d6d792e24f277,
title = "Quantitative Phosphocreatine Mapping with a Transformer-Based Regression Network",
abstract = "Phosphocreatine (PCr) is the key element of buffering adenosine triphosphate (ATP) concentration for sustaining the high energy demand of cardiomyocytes, thus maintaining normal mechanical function. CEST MRI has been employed to capture potential changes in myocardial PCr. A quantitative PCr network (QPCrNet) has been developed to provide accurate and fast PCr maps for the assessment of cardiac energy metabolism. In the simulation study, QPCrNet showed superior accuracy (lowest mean absolute error) in predicting PCr concentration and exchange rate from PCr pool to water pool. The transfer learning of QPCrNet on a canine stenosis model further proved the network capability of predicting regional difference between the diseased and remote areas and accurately predicted PCr reductions from the resting state to Dobutamine-induced stress.",
keywords = "CEST, MRI, PCr, QPCrNet",
author = "Qi Huang and Haoteng Tang and Han Tang and Caleb Berberet and Liya Dai and Thomas Schindler and Linda Peterson and Yang Yang and Yan Yan and Pamela Woodard and Jie Zheng",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025 ; Conference date: 12-11-2025 Through 15-11-2025",
year = "2025",
doi = "10.1109/ICDMW69685.2025.00244",
language = "English",
series = "IEEE International Conference on Data Mining Workshops, ICDMW",
publisher = "IEEE Computer Society",
pages = "2015--2019",
booktitle = "Proceedings - 25th IEEE International Conference on Data Mining Workshops, ICDMW 2025",
}