What Can We Quantify with Multicontrast MRI: Analysis Approaches for Neurological Applications
Akifumi Hagiwara1
1Department of Radiology, Juntendo University School of Medicine, Japan

Synopsis

With the advancement of MRI and processing methods, multiple quantitative values have been developed to investigate the brains. Some acquisition models simultaneously acquire multi-contrast images to reduce scan times and avoid potential issues associated with registration of different images. Machine learning models, including radiomic analysis, have been gaining popularity in research settings. Some other techniques combine multiple images to extract single images associated with the meaningful aspects of biology in the brain. The current talk will cover techniques to acquire multiple quantitative values and analysis methods to deal with the acquired quantitative values.

Abstract

With the advancement of MRI and processing methods, multiple quantitative values have been developed to investigate normal and pathological brains. Some acquisition models, including synthetic MRI1 and MR fingerprinting,2 were developed to simultaneously acquire multi-contrast images to reduce scan times and avoid potential issues associated with the registration of different images. Various techniques for analysis of the multi-contrast images are also available. Machine learning models, including radiomic analysis, have been gaining popularity in research settings. Some other techniques, such as g-ratio3 and aerobic glycolytic index,4 combine multiple quantitative values to extract single values associated with the meaningful aspects of biology in the brain.

Acknowledgements

No acknowledgement found.

References

1. Hagiwara A, Warntjes M, Hori M, et al. SyMRI of the Brain: Rapid Quantification of Relaxation Rates and Proton Density, With Synthetic MRI, Automatic Brain Segmentation, and Myelin Measurement. Invest Radiol. 2017;52(10):647-657.

2. Ma D, Gulani V, Seiberlich N, et al. Magnetic resonance fingerprinting. Nature. 2013;495(7440):187-192.

3. Stikov N, Campbell JS, Stroh T, et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage. 2015;118:397-405.

4. Hagiwara A, Yao J, Raymond C, et al. "Aerobic glycolytic imaging" of human gliomas using combined pH-, oxygen-, and perfusion-weighted magnetic resonance imaging. Neuroimage Clin. 2021;32:102882.

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)