This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The desire to study the structure of brain function at a finer and finer scale has been a major impetus for the development of high field strength MR scanners (3 Tesla and above). A major goal of this effort is to bring the instrumental resolution of the fMRI experiment to the level where we can explore the biological spatial limits of fMRI. As currently practiced with 3mm isotropic resolution, functional imaging cannot even claim to resolve the cortical ribbon. An order of magnitude improvement is needed to readily resolve the next lower level of the cerebral cortex: that of laminar and columnar structures. While considerable progress has been made toward this goal, a major problem plaguing the study of these complexly shaped 3D structures has been the 2D nature of the fMRI acquisition. The overall goal of this study is therefore to develop 3D isotropic fMRI encoding methods to reduce the spatial resolution of fMRI while avoiding the """"""""french fry"""""""" shaped voxels which have plagued studies. With large aspect ratio voxels (commonly up to 10 fold longer in the slice direction), desired laminar or columnar structures are diluted by partial volume effects except in small regions of the cortex where a fortuitous alignment occurs. Because spatial resolution is limited by both intrinsic sensitivity and limited spatial encoding capability, we will develop highly parallel detection strategies to address these issues and provide true 3D single-shot volumetric fMRI with high isotropic resolution. We address the sensitivity and encoding issues by developing a high sensitivity 96 channel receive- only array system for functional brain imaging at 7 Tesla. Additionally we will develop analog mode-mixing strategies to form orthogonalized linear combinations of detection elements which maximize the sensitivity and encoding ability suitable for systems with a small number of RF channels (such as the 32 channels found on clinical systems). We develop single-shot methods to reduce the temporal instabilities in multi-shot methods using highly accelerated echo- volumnar and stack-of-spirals trajectories. For the highest resolution studies we will develop zoomed EVI utilizing parallel transmission acceleration of spatially tailored 2D and 3D excitations.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Biotechnology Resource Grants (P41)
Project #
5P41RR014075-12
Application #
8171482
Study Section
Special Emphasis Panel (ZRG1-SBIB-L (40))
Project Start
2010-06-01
Project End
2011-05-31
Budget Start
2010-06-01
Budget End
2011-05-31
Support Year
12
Fiscal Year
2010
Total Cost
$302,586
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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