Functional Lung Imaging Using a Single kV CT Acquisition

ABSTRACT
Venous thromboembolism is a major global health and economic burden with about 10 million cases occurring
every year, and a high lifetime risk of 8% after age 45 years. Pulmonary embolism (PE) is a venous
thromboembolic event associated with high morbidity and mortality, with about 20% incidence of death before
diagnosis or shortly thereafter. Most recently, the COVID-19 pandemic has contributed to a marked increase in
patients presenting with acute pulmonary thromboembolic disease, most likely created when the infectious
vasculitis involving the endothelium creates local arterial thrombosis and subsequent lung infarction, with a
superimpose hypercoagulable state that promotes clot formation. In these patients, it is increasingly being
recognized that pulmonary perfusion abnormalities associated with the lung consolidations and ground-glass
opacities are important predictors of poor prognosis. Currently, pulmonary CT angiography (CTA) has become
the preferred method for diagnosing PE and planar lung ventilation/perfusion (V/Q) scintigraphy is used in cases
when pulmonary CTA is contraindicated. A compelling unmet clinical need is to develop a method for
simultaneous pulmonary CTA and parenchymal perfusion assessment without the use of two modalities like CTA
and SPECT perfusion in the same patient. In this project, an imaging physics-based deep learning method will
be developed to extract the previously overlooked spectral information inherently encoded in the acquired
contrast enhanced CT projection data. As a result of this breakthrough, this new spectral CT imaging method,
referred to as Deep-En-Chroma, will be developed and validated for perfusion defect quantification in lung
parenchyma from the currently available pulmonary CTA. This will be accomplished without the need for any
expensive dual energy CT (DECT) hardware upgrades that have been commercialized by major CT
manufacturers. In summary, upon the completion of this project, a new functional CT imaging method will have
been developed, that in addition to providing the currently available pulmonary CTA images, will also detect
perfusion defects in lung parenchyma without the requirement of high-end DECT hardware.

Grant Number: 5R01HL153594-04
NIH Institute/Center: NIH
Principal Investigator: Guang-Hong Chen