We will develop technology that improves MRI's acquisition efficiency by 20 to create a comprehensive 9- minute brain exam that provides more detailed structural and functional information than current 40-minute clinical exams. Such exam should improve MRI's diagnostic power while greatly increase patient throughput and compliance. We first apply this technology to pediatric low-grade gliomas (LGG), where frequent tumor monitoring and anesthesia services are required. Anesthesia is detrimental to the developing brain and results in a nine-fold increase in cost. We will couple our short exam with a commercially available optical real-time motion tracking system to robustly remove the need for anesthesia. The proposed methods allow acquisition of more detailed quantitative physiology that could potentially improve diagnosis and prognosis of LGG. The slow image encoding in MRI has been its critical limiting factor. To provide whole-brain imaging quickly, clinical protocos use two-dimensional (2D) slice-by-slice imaging with high in-plane resolution but 4-5 thicker slices with a 20-40% gap. The gaps can result in missed information, while thick slices limit the ability to perform multi-planar reformats, which necessitates re-imaging if viewing in a different image plane is desired. On the other hand, quantitative measures of perfusion and vascularization require high temporal resolution single-shot EPI. Here, the slow encoding in the phase encode direction results in detrimental image distortion, compromised resolution/coverage, and limited physiological information. Moreover, with a wide variety of tissue contrast mechanisms-each sensitive to different aspects of pathology-patients are imaged with 5-8 scans with overlapping information. The result is exams of up to 40 minutes with suboptimal resolution/information. To overcome these issues and achieve rapid, high-quality and detailed imaging, we will develop Wave-CAIPI technology, a data acquisition/reconstruction scheme designed to optimally exploit available information in modern multi-channel receivers and in multi-contrast/time-series data for improved image encoding, to achieve high-quality 20 acceleration. With Wave-CAIPI, we will also replace standard 2D imaging with far more SNR-efficient Simultaneous Multi-Slice (SMS) and 3D imaging to achieve rapid imaging with high SNR. Initially, to achieve 10-15 acceleration, we will develop Wave-CAIPI, which applies efficient data sub- sampling concepts fully to all three spatial directions of an imaging volume. To achieve 20 acceleration, Wave-CAIPI will be augmented with CS-Wave-CAIPI, which extends efficient sub-sampling across contrasts and time-series data. Joint Bayesian and temporal Compressed Sensing reconstructions will be developed, and a highly efficient HSS solver will be created to facilitate near/real-time reconstruction. Finally, we wil test the hypothesis that Wave-CAIPI can reduce MRI exam time for pediatric low-grade gliomas from 40 min to 9 min and eliminate anesthesia while providing more comprehensive diagnostic information.

Public Health Relevance

We will develop a highly-efficient MRI acquisition technology to create a comprehensive 9-minute brain exam that provides more detailed anatomical and functional information than current 40-minute exam. Such exam will improve MRI's diagnostic power while greatly increase patient throughput and compliance. We first apply this technology to pediatric low-grade gliomas (LGG), where frequent tumor monitoring and anesthesia services are required. Anesthesia is detrimental to the developing brain and results in a nine-fold increase in cost. We will couple our short exam with a commercially available optical real-time motion tracking system to robustly remove the need for anesthesia in this population. The proposed methods allow acquisition of more detailed quantitative physiological measures of tumor diffusivity, perfusion, vessel permeability and microvasculature to improve assessment of LGG.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB020613-01A1
Application #
9102450
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Liu, Guoying
Project Start
2016-04-01
Project End
2019-12-31
Budget Start
2016-04-01
Budget End
2016-12-31
Support Year
1
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
Cauley, Stephen F; Setsompop, Kawin; Bilgic, Berkin et al. (2016) Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction. Magn Reson Med :
Chatnuntawech, Itthi; McDaniel, Patrick; Cauley, Stephen F et al. (2016) Single-step quantitative susceptibility mapping with variational penalties. NMR Biomed :