Further development in anatomically constrained MR image reconstruction: application to multimodal imaging of mouse stroke.
MR imaging can leverage a wide variety of intrinsic contrast mechanisms to provide detailed information regarding the anatomy, function, physiology, and metabolism of biological tissues. However, because of low sensitivity, many experiments that reveal higher-order structure and function have been limited due to inherent trade-offs between data acquisition time, signal-to-noise ratio, and resolution. This paper describes the further development of a statistical framework for MR image reconstruction which helps to mitigate these effects. Specifically, we advocate the collection of high-resolution multi-modal MR imaging data, and utilize the correlation between features in these different images to reduce noise while maintaining resolution. The proposed approach is illustrated with joint reconstruction of relaxometry and spectroscopic imaging data in a mouse model of stroke.
Haldar
Justin P
JP
Department of Electrical and Computer Engineering, the Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. haldar@uiuc.edu
Wu
Tzy-Haw
TH
Wang
Qing
Q
Chen
Chin-I
CI
Song
Sheng-Kwei
SK
Liang
Zhi-Pei
ZP
D000465:Algorithms; D000818:Animals; D015415:Biomarkers / Q000032:analysis*; D001921:Brain / Q000378:metabolism* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D051379:Mice; D020521:Stroke / Q000175:diagnosis* / Q000378:metabolism*
D015415:Biomarkers