Hyperpolarized 13 C MRI data acquisition and analysis in prostate and brain at University of California, San Francisco

• Published in: NMR in biomedicine 2021-05, Vol.34 (5), p.e4280-e4280
• Main Authors: Crane, Jason C, Gordon, Jeremy W, Chen, Hsin-Yu, Autry, Adam W, Li, Yan, Olson, Marram P, Kurhanewicz, John, Vigneron, Daniel B, Larson, Peder E Z, Xu, Duan
• Format: Article
• Language: English
• Subjects: Brain - diagnostic imaging; Carbon Isotopes - chemistry; Echo-Planar Imaging; Humans; Magnetic Resonance Imaging; Male; Molecular Imaging; Prostate - diagnostic imaging; San Francisco; Signal-To-Noise Ratio; Universities
• ISSN: 0952-3480; 1099-1492
• DOI: 10.1002/nbm.4280

Based on the expanding set of applications for hyperpolarized carbon-13 (HP- C) MRI, this work aims to communicate standardized methodology implemented at the University of California, San Francisco, as a primer for conducting reproducible metabolic imaging studies of the prostate and brain. Current state-of-the-art HP- C acquisition, data processing/reconstruction and kinetic modeling approaches utilized in patient studies are presented together with the rationale underpinning their usage. Organized around spectroscopic and imaging-based methods, this guide provides an extensible framework for handling a variety of HP- C applications, which derives from two examples with dynamic acquisitions: 3D echo-planar spectroscopic imaging of the human prostate and frequency-specific 2D multislice echo-planar imaging of the human brain. Details of sequence-specific parameters and processing techniques contained in these examples should enable investigators to effectively tailor studies around individual-use cases. Given the importance of clinical integration in improving the utility of HP exams, practical aspects of standardizing data formats for reconstruction, analysis and visualization are also addressed alongside open-source software packages that enhance institutional interoperability and validation of methodology. To facilitate the adoption and further development of this methodology, example datasets and analysis pipelines have been made available in the supporting information.