# Learning an Optimized Variational Network for Medical Image Reconstruction

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $498,014

## Abstract

Project Summary
We propose a novel way of reconstructing medical images rooted in deep learning and computer vision that
models the process how human radiologists are using years of experience from reading thousands of cases to
recognize anatomical structures, pathologies and image artifacts. Our approach is based on the novel idea of a
variational network, which embeds a generalized compressed sensing concept within a deep learning
framework. We propose to learn a complete reconstruction procedure, including filter kernels and penalty
functions to separate between true image content and artifacts, all parameters that normally have to be tuned
manually as well as the associated numerical algorithm described by this variational network. The training step
is decoupled from the time critical image reconstruction step, which can then be performed in near-real-time
without interruption of clinical workflow. Our preliminary patient data from accelerated magnetic resonance
imaging (MRI) acquisitions suggest that our learning approach outperforms the state-of-the-art of currently
existing image reconstruction methods and is robust with respect to the variations that arise in a daily clinical
imaging situation. In our first aim, we will test the hypothesis that learning can be performed such that it is
robust against changes in data acquisition. In the second aim, we will answer the question if it is possible to
learn a single reconstruction procedure for multiple MR imaging applications. Finally, we will perform a clinical
reader study for 300 patients undergoing imaging for internal derangement of the knee. We will compare our
proposed approach to a clinical standard reconstruction. Our hypothesis is that our approach will lead to the
same clinical diagnosis and patient management decisions when using a 5min exam. The immediate benefit of
the project is to bring accelerated imaging to an application with wide public-health impact, thereby improving
clinical outcomes and reducing health-care costs. Additionally, the insights gained from the developments in
this project will answer the currently most important open questions in the emerging field of machine learning
for medical image reconstruction. Finally, given the recent increase of activities in this field, there is a
significant demand for a publicly available data repository for raw k-space data that can be used for training
and validation. Since all data that will be acquired in this project will be made available to the research
community, this project will be a first step to meet this demand.

## Key facts

- **NIH application ID:** 9997914
- **Project number:** 5R01EB024532-03
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Florian Knoll
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $498,014
- **Award type:** 5
- **Project period:** 2018-09-30 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9997914

## Citation

> US National Institutes of Health, RePORTER application 9997914, Learning an Optimized Variational Network for Medical Image Reconstruction (5R01EB024532-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9997914. Licensed CC0.

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