Abstract #2904

Predictors of 0.55T Respiratory Navigated Lung MRI Degradation

Richard Brandon Schonour¹, Felicia Tang², Kiara Bowers², Pan Su³, Peder Larson⁴, Michael Ohliger⁴, Yoo Jin Lee⁴, Jonathan Liu⁴, Yang Yang⁴, and Jae Ho Sohn⁴

¹College of Medicine, University of South Alabama, Mobile, AL, United States, ²School of Medicine, University of California San Francisco, San Francisco, CA, United States, ³Siemens Medical Solutions USA, Inc., Malvern, PA, United States, ⁴Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Lung, Low-Field MRI

Motivation: The image quality generated using respiratory triggered sequences on low-field scanners is sometimes degraded, and it is currently not well known what causes this. This degradation can lead to image misinterpretation.

Goal(s): Our goal was to determine possible predictors of image quality degradation in low-field lung MRI.

Approach: Six radiologists graded anatomic 0.55T MRI images, and then analysis of patient clinical history, demographics, and respiratory navigation was conducted to determine any patterns.

Results: We found that clinical conditions including fibrotic interstitial lung disease and pulmonary artery pathologies, as well as descriptors such as increased body mass index can predict image quality degradation.

Impact: Our observation of degradative predictors serves as the foundation for research into altering of 0.55T MRI parameters to limit the degree of degradation, so that at-risk patients can still be scanned on these low-field machines with optimal image quality.

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