Dr. Wei Zha, an imaging scientist in the Pulmonary and Metabolic Imaging Center led by Dr. Sean Fain at UW-Madison, has invented a deep learning approach to provide fast, reproducible, and robust quantification for pulmonary structure and function using Oxygen-enhanced (OE) MRI. This novel deep learning technology has great potential to create useful imaging biomarkers for assessing pulmonary diseases and was recently published in the Journal of Magnetic Resonance Imaging.
OE MRI using a 3D radial ultrashort echo time sequence supports quantitative respiratory function assessment for lung diseases. In contrast to typical lung imaging using hyperpolarized noble gas or fluorinated gas, this novel OE technique does not require specialized multinuclear hardware or expensive specialty gases while providing full chest images of regional ventilation with isotropic spatial resolution. Despite these rapid advances in pulmonary OE MRI, the development of a quantification tool for extracting potential biomarkers and regional image features remain to be developed. The analysis of pulmonary OE MR images remains challenging due to modality-specific complexities, including coil inhomogeneity, arbitrary intensity values, local magnetic susceptibility, and reduced proton density due to the large fraction of air space in the normal lung.
This newly invented deep learning method uses an efficient convolutional encoder-decoder deep learning structure and multi-plane consensus labeling to identify 3D image features and patterns, resulting in an accurate and robust classification and segmentation of pulmonary tissues on OE MR images. Subsequent analysis using the classified tissues yielded robust quantification for lung structure and function and discovered significant differences between different pulmonary disease cohorts.