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Model Based Deep Learning Framework for Ultra-High Resolution Multi-Contrast MRI

Award Number: R01AG067078
ORGANIZATION: NATIONAL INSTITUTE ON AGING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/01/2021
PERIOD OF PERFORMANCE END DATE: 12/31/2025

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Model Based Deep Learning Framework for Ultra-High Resolution Multi-Contrast MRI - Sensitive imaging biomarkers are urgently needed for screening of high‐risk subjects, determine early disease progression, and assess response to therapies in neurodegenerative disorders. The atrophy of several brain regions is an established biomarker in AD, which strongly correlates with AD neuropathology. The accuracy of subfield volumes and cortical thickness estimated from current MRI methods is limited because of the vulnerability to motion, low spatial resolution, low contrast between brain sub‐structures, and dependence of current segmentation frameworks on image quality. Short motion‐compensated MRI protocols to map the human brain at high spatial resolution with multiple contrasts, along with accurate and computationally efficient segmentation algorithms, are urgently needed tor early detection and management of subjects with neurodegenerative disorders. We propose to introduce a 15‐minute motion‐robust 3‐D acquisition and reconstruction scheme to recover whole‐brain MRI data with 0.2 mm isotropic resolution with several different inversion times on 7T, along with segmentation algorithms that are robust to acceleration. The key difference of this framework from current approaches, which rely on MRI data 1 mm resolution, is the quite significant increase in spatial resolution to 0.2 mm as well as the availability of multiple conteasts. This improvement is enabled by innovations in all areas of the data‐processing pipeline, including acquisition, reconstruction, and analysis. These innovations are facilitated and integrated by the model based deep learning framework (MoDL); this framework facilitates the joint exploitation the available prior information, including motion and models for magnetization evolution, with convolutional neural network blocks that learn anatomical information from exemplar data. The successful completion of this framework will yield sensitive biomarkers, which will be considerably less expensive than PET and does not involve radiation exposure. As 7T clinical scanners become more common, this framework can emerge as a screening tool for high‐risk subjects (e.g. APOE, PSEN mutations) and assess progression in patients with short follow‐up duration.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $550,607 )
2025	2025	RECTOR & VISITORS OF THE UNIVERSITY OF VIRGINIA	1001 EMMET ST N	CHARLOTTESVILLE	VA	22903	CHARLOTTESVILLE CITY	USA	Aging Research	004	5	3/19/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$851,613
2025	2025	RECTOR & VISITORS OF THE UNIVERSITY OF VIRGINIA	1001 EMMET ST N	CHARLOTTESVILLE	VA	22903	CHARLOTTESVILLE CITY	USA	Aging Research	001	5	12/4/2024	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$550,607
2025	2024	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	003	4	3/19/2025	NON-COMPETING CONTINUATION	-$665,142
2025	2024	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	000	4	12/4/2024	NON-COMPETING CONTINUATION	$0
2025	2023	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	002	3	3/19/2025	NON-COMPETING CONTINUATION	-$186,471
														Subtotal = $550,607

Issue Date FY: 2024 ( Subtotal = $665,142 )
2024	2024	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	001	4	6/3/2024	NON-COMPETING CONTINUATION	$66,514
2024	2024	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	000	4	12/20/2023	NON-COMPETING CONTINUATION	$598,628
														Subtotal = $665,142

Issue Date FY: 2023 ( Subtotal = $699,003 )
2023	2023	THE UNIVERSITY OF IOWA	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	000	3	1/23/2023	NON-COMPETING CONTINUATION	$699,003
														Subtotal = $699,003

Issue Date FY: 2022 ( Subtotal = $738,757 )
2022	2022	UNIVERSITY OF IOWA, THE	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	000	2	12/6/2021	NON-COMPETING CONTINUATION	$664,881
2022	2022	UNIVERSITY OF IOWA, THE	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	001	2	4/28/2022	NON-COMPETING CONTINUATION	$73,876
														Subtotal = $738,757

Issue Date FY: 2021 ( Subtotal = $727,486 )
2021	2021	UNIVERSITY OF IOWA, THE	105 JESSUP HALL	IOWA CITY	IA	52242	JOHNSON	USA	Aging Research	000	1	12/23/2020	NEW	$727,486
														Subtotal = $727,486

Grand Total All Awards = $3,380,995

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Model Based Deep Learning Framework for Ultra-High Resolution Multi-Contrast MRI

Award Number: R01AG067078

ORGANIZATION: NATIONAL INSTITUTE ON AGING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 01/01/2021

PERIOD OF PERFORMANCE END DATE: 12/31/2025

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