Using a deep learning prior for accelerating hyperpolarized C MRSI on synthetic cancer datasets.

Journal: Magnetic resonance in medicine

PMID: 38440832

Abstract

PURPOSE: We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.

Authors

Zuojun Wang

Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China.
Guanxiong Luo

Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.
Ye Li

Environment and Plant Protection Institute, Chinese Academy of Tropical Agricultural Science, Haikou 571010, People's Republic of China; Key Laboratory of Monitoring and Control of Tropical Agricultural and Forest Invasive Alien Pests, Ministry of Agriculture, Haikou 571010, People's Republic of China.
Peng Cao

Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.

Keywords

Algorithms Animals Brain Brain Neoplasms Carbon Isotopes Deep Learning Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Mice Phantoms, Imaging Prostatic Neoplasms Signal-To-Noise Ratio

External Resources

View on PubMed Access via DOI PubMed (38440832)

Using a deep learning prior for accelerating hyperpolarized C MRSI on synthetic cancer datasets.

Abstract

Authors

Keywords

External Resources

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