This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.Accuracy of quantitative MR imaging-based neuroanatomical study is inherently limited by the acquired image quality such as resolution, distortion, and contrast-to-noise-ratio (CNR). In earlier work, we have developed a high-bandwidth multi-echo FLASH (MEF) pulse sequence that allows significant reduction in image distortion while maintaining high image SNR. In the current phase, we further investigate the benefits provided by this new pulse sequence over other commonly used sequences and develop algorithms to aid in the processing of the new type of image data.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR014075-09
Application #
7602557
Study Section
Special Emphasis Panel (ZRG1-SSS-X (40))
Project Start
2007-09-01
Project End
2008-08-31
Budget Start
2007-09-01
Budget End
2008-08-31
Support Year
9
Fiscal Year
2007
Total Cost
$105,910
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Lee, Jeungchan; Mawla, Ishtiaq; Kim, Jieun et al. (2018) Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain :
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