Projects

With a background in experimental physics, quantitative imaging, and pulmonary pathology and physiology, I have long had a focus on research that incorporates quantitative methods in translational imaging, in both adults and pediatrics. Over the last decade, my focus ranges from the development of hyperpolarized-gas and self-gated UTE MRI in pediatrics to the application and validation of extract structure-function relationships using CT and MRI in patients with a range of pulmonary abnormalities. Recently, research has been focused on cystic fibrosis, neonatal lung diseases and novel imaging methods.

Neonatal MRI

The goal of this research is to translate new and innovative MRI techniques to sick and challenging patient populations, like those that are born prematurely. Our studies include both proton MRI and hyperpolarized gas MRI in infants. This has begun to revolutionize our understanding of both parenchymal and airway components of disease.

Neonatal MRI.
Expand image to learn more.
Neonatal MRI.
Expand image to learn more.
Neonatal MRI.
Expand image to learn more.
Neonatal MRI.
Expand image to learn more.
Neonatal MRI.
Expand image to learn more.

Multi-Site Clinical Trials

HyPOINT (Hyperpolarized Imaging for New Therapies (Hypoint) in Pediatric CF)

Primary Objectives:

  • Phase 1: To define the intra-subject variability of hyperpolarized Xenon (129Xe) MRI outcomes within a multi-center setting.
  • Phase 2: To measure change in 129Xe MRI ventilation defect percentage (VDP; i.e., the volume percentage of lung which falls below a given 129Xe ventilation signal threshold; calculation described below in detail) and ventilation histogram values in CF patients after initiating triple combination CFTR modulator therapy compared with baseline (no previous modulator therapy for F508del (+/-) and previous CFTR modulator therapy for F508del (+/+) patients).
  • BEGINNING (BEGIN Novel ImagiNG Biomarkers (BEGINNING)): To determine the treatment effect of triple-combination therapy in 6-8 year olds after presumed FDA approval, using rapid structural and functional pulmonary and abdominal MRI (UTE and 129Xe).

Artificial Intelligence (AI)

We have demonstrated that an AI-driven semantic quantification of lung structural alterations is feasible in CF by building an automated scoring system. Clinical validation expects that a biomarker reflects the clinical severity, correlates to a known outcome and may improve with an effective therapy. Our objective was to develop an algorithm enabling recognition of five structural alteration hallmarks on CT slices. We then aimed to assess the clinical validity of the quantitative scoring method by correlating to the patient's disease severity, as assessed by the CT Brody score. Additional objectives were to support the clinical validity to correlate to PFTs, assess variations in patients with and without lumacaftor/ivacaftor, and evaluate the reproducibility.

Our studies have demonstrated that AI-driven quantitative measurement of lung structural abnormalities on CT scanning in CF is, indeed, feasible, and can provide clinically important information in a broad range of patients using a wide range of CT scanners and CT techniques. The system showed good similarity and very good agreement with ground-truth identification of expert observers’ abnormalities, but dramatically quicker, with high reproducibility. Volumetric measurements showed a strong correlation to PFTs and a well-validated visual CT score at several time-points. The automated quantifications were found to sensitively detect longitudinal changes, either a reduction in CF patients with lumacaftor/ivacaftor treatment or an increase during the natural course of the disease. As a fully automated outcome measurement, the reproducibility was almost perfect.

Funding & Acknowledgements

  • Cystic Fibrosis Foundation
    WOODS19A0
    08/2019–07/2022
  • NIH, NHLBI
    R01HL146689
    04/2019–03/2024
  • NIH, NHLBI
    R01HL131012
    04/2016-03/2022
  • Cystic Fibrosis Foundation
    WOODS21K0
    08/2021-07/2024
Artificial Intelligence.
Expand image to learn more.