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Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection

Award Number: R15EB029172
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2023
PERIOD OF PERFORMANCE END DATE: 06/30/2024

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Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection - PROJECT SUMMARY Magnetic resonance imaging (MRI) is a versatile imaging modality that suffers from slow acquisition times, which is a challenge, for both time sensitive applications and for patient throughput. Accelerating MRI would benefit patients both by reducing the time they need to be in the scanner and in reducing the cost of healthcare. This project is part of a larger scientific effort to accelerate MRI while maintaining the diagnostic quality. Acceleration, even by a factor of two, would result in a major advance for public health. Two of the current approaches to accelerate MRI rely on collecting less data (under-sampling) and deep learning reconstruction. These approaches can lead to images with diagnostic quality using significant under-sampling but may suffer from artifacts which are hard to characterize and may sometimes resemble anatomy. Specifically, this project will optimize the performance of accelerated MRI, including undersampling patterns and deep learning reconstructions, on detecting and localizing subtle lesions. To carry out this optimization, we will first develop the methods required for detection of lesions by machine and human observer models. The human observer models will be validated by psychophysical studies where humans perform the detection task. In the first aim of this project, we will apply and develop detection tasks and model observers. We will consider under-sampled acquisition strategies in MRI including one and two-dimensional subsampling methods using deep learning reconstructions which enforce data consistency. We will develop detection tasks for signals in anatomical backgrounds were the signal location is known and when the observer needs to search for the signal. The human and machine performance in these tasks will be modeled. In the second aim, we will optimize data acquisition and neural network reconstruction using signal detection with observer models and psychophysical experiments. We will also introduce a detectability-based loss function to neural network reconstructions. There is recent interest in exploring the benefits of low/mid field MRI which has a trade-off with higher noise. In the third aim, we will evaluate the effect of field strength on signal detection. We will use data from high field acquisitions from a publicly available database to model images from lower magnetic fields. Using the detection of subtle lesions, we will evaluate detection performance with varying field strength. This research project will help to strengthen the research environment and broaden participation at Manhattan College by involving students in biomedical research incorporating applied mathematics, statistics and data science.


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 = -$2,911 )
2025	2023	MANHATTAN COLLEGE	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	2	12/5/2024	COMPETING CONTINUATION	-$323,106
2025	2023	MANHATTAN UNIVERSITY	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	003	2	5/20/2025	COMPETING CONTINUATION	$0
2025	2023	HOFSTRA UNIVERSITY	128 HOFSTRA UNIVERSITY	HEMPSTEAD	NY	11549	NASSAU	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	3	12/6/2024	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$320,195
2025	2020	MANHATTAN UNIVERSITY	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	002	1	1/14/2025	NEW	$0
														Subtotal = -$2,911

Issue Date FY: 2023 ( Subtotal = $434,531 )
2023	2023	MANHATTAN COLLEGE	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	002	2	8/28/2023	COMPETING CONTINUATION	$434,531
2023	2020	MANHATTAN COLLEGE	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	001	1	8/18/2023	NEW	$0
2023	2020	MANHATTAN COLLEGE	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	4/17/2023	NEW	$0
														Subtotal = $434,531

Issue Date FY: 2020 ( Subtotal = $395,210 )
2020	2020	MANHATTAN COLLEGE CORP	4513 MANHATTAN COLLEGE PKWY	BRONX	NY	10471	BRONX	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	5/1/2020	NEW	$395,210
														Subtotal = $395,210

Grand Total All Awards = $826,830

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Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection

Award Number: R15EB029172

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/01/2023

PERIOD OF PERFORMANCE END DATE: 06/30/2024

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