# MRI methods for high resolution imaging of the lung

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2020 · $666,148

## Abstract

ABSTRACT
There is an enormous economic and social burden of lung disease that demands improved tools to diagnose,
stage, and follow treatment response. To assess heterogeneous and localized pulmonary diseases, cross-
sectional imaging is often performed, most commonly with computed tomography (CT) or radioactive tracers
(SPECT/PET). While these techniques provide structural and functional information, respectively, they deliver
considerable radiation dose which limits use in radiosensitive and pediatric populations.
This proposal aims to shift the current clinical practice paradigms for pulmonary imaging by making magnetic
resonance imaging (MRI) a valuable modality for lung imaging. MRI delivers no ionizing radiation and can thus
be used for longitudinal follow-up or screening in radio-sensitive populations. Furthermore, MRI provides multi-
parametric contrast based on microstructure, ventilation, perfusion, cellular metabolism, and inflammation that
can improve the assessment of lung diseases. Unfortunately, the radiation-free and multi-parametric benefits
of MRI are not currently clinically available for lung imaging due to low signal in the lung and sensitivity to
motion with current imaging methods. Recent developments by our group and others have demonstrated that
the MRI acquisition paradigm can be modified to enable dramatic improvements in the visualization of the lung
that rival CT in ventilated and cooperative subjects with the added benefit of providing improved soft tissue
contrast. However, patients often suffer from poor lung function and/or have difficulty with compliance, which
leads to complex, irregular breathing and bulk motion that cannot be handled by current MRI techniques.
We propose a next generation of pulmonary MRI techniques that are designed to address and overcome the
limitations of motion and low lung signal while also incorporating multiple MR soft tissue contrast mechanisms.
These address all aspects of MRI scanning including patient preparation and experience, the MRI acquisition,
and the reconstruction of images from the data. Specifically, we develop an audiovisual biofeedback system to
improve the patient experience while also reducing the likelihood for complex motion, develop multi-contrast
MRI sampling strategies which maximize embedded motion information, and create a reconstruction
architecture which leverages the MRI data directly to estimate and correct for motion even in the case of
complex motion. These methods would be beneficial for characterizing numerous diseases of the lung, both in
pediatric and adult populations, including pulmonary nodules, pulmonary embolism, interstitial fibrosis, cystic
fibrosis, COPD, asthma, and pulmonary infection. They will have the most significant impact in pediatrics,
where there is an urgent need to limit ionizing radiation exposure. Anticipating applications to this population,
we have included a broad evaluation in pediatric subjects and a specific pediatric imagi...

## Key facts

- **NIH application ID:** 9898434
- **Project number:** 5R01HL136965-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Kevin Michael Johnson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $666,148
- **Award type:** 5
- **Project period:** 2018-04-15 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898434

## Citation

> US National Institutes of Health, RePORTER application 9898434, MRI methods for high resolution imaging of the lung (5R01HL136965-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9898434. Licensed CC0.

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