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

> **NIH NIH R15** · MANHATTAN COLLEGE · 2020 · $395,210

## Abstract

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 constrained or deep learning reconstruction. These approaches
can lead to images with diagnostic quality with significant under-sampling but may suffer from
artifacts which are hard to characterize. Specifically, this project will optimize the performance
of constrained reconstruction and deep learning on detecting subtle lesions in acquiring and
reconstructing under-sampled MRI. To carry out this optimization, we will first develop the
methods required for detection of lesions by machine and human observer models. Then the
models will be validated by psychophysical studies where humans perform the detection task. In
the first aim of this project, we will optimize constrained reconstruction based on the ideal linear
observer. We will consider under-sampled acquisition strategies in 2D MRI including one and two
dimensional subsampling methods with constrained reconstruction using both wavelet and total
variation constraints. We will perform simulations using anatomical backgrounds both for lesions
which match the prior information of the constraints and those which do not to better understand
how choices in acquisition and reconstruction affect ideal detection. While the ideal linear observer
approximates the best possible detection, typically the signal detection is carried out by a human.
In the second aim, we will optimize constrained reconstruction using human observer models and
validate the models using human observer studies. A recent approach to reconstruction of under-
sampled images is based on deep learning. In the third aim, this work will optimize deep learning
reconstruction based on ideal and human observers. Due to the complexity of the deep learning
approach, having this task-based approach to optimization is particularly relevant. This project will
optimize a network using signal detection to better understand how training and architecture choices
in the neural network affect detection of lesions which are not included in training images. This
research project will help to strengthen the research environment at Manhattan College by involving
students in biomedical research incorporating applied mathematics, statistics and data science.

## Key facts

- **NIH application ID:** 9880534
- **Project number:** 1R15EB029172-01
- **Recipient organization:** MANHATTAN COLLEGE
- **Principal Investigator:** Angel Ramon Pineda
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $395,210
- **Award type:** 1
- **Project period:** 2020-05-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9880534

## Citation

> US National Institutes of Health, RePORTER application 9880534, Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection (1R15EB029172-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9880534. Licensed CC0.

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