A Louisiana State University (LSU) radiologist joined with an evolutionary anatomist to create revolutionary 3D models of COVID-19 patients’ lungs by implementing the same techniques used for reptiles and birds. Emma R. Schachner, Ph.D., Associate Professor of Cell Biology & Anatomy, and Bradley Spieler, MD, Vice Chairman of Radiology Research and Associate Professor of Radiology, Internal Medicine, Urology, & Cell Biology and Anatomy at LSU Health New Orleans School of Medicine, created the models from CT scans of COVID-19 patients.
Three patients underwent contrast-enhanced thoracic CT when their symptoms worsened. Two tested positive for COVID-19; another was presumed a false-negative based on the symptoms they experienced combined with compelling imaging. Since false-negative tests are a known diagnostic challenge, CT can be beneficial for establishing a COVID-19 diagnosis. The lungs’ form and structure appear to correlate to disease progression, which allows for the 3D segmentations to model airflow patterns or quantify lung tissue volumetrically.
Spieler said, “The full effect of COVID-19 on the respiratory system remains unknown, but the 3D digital segmented models provide clinicians a new tool to evaluate the extent and distribution of the disease in one encapsulated view. This is especially useful in the case where RT-PCR for SARS-CoV-2 [current testing system] is negative but there is a strong clinical suspicion for COVID-19.”
This combined technology is a novel discovery as there has never before been accurate models of the COVID-19 disease progression in the lungs. Previous models published include volume-rendered models and straight 2D screenshots of CT scans and radiographs (X-Rays). The 3D models are vastly more detailed but do require some more effort.
Schachner explained, “Previously published 3D models of lungs with COVID-19 have been crated using automated volume rendering techniques. Our method is more challenging and time-consuming, but results in a highly accurate and detailed anatomical model where the layers can be pulled apart, volumes quantified, and it can be 3D printed.”
The in-depth view rendered by this model makes it easier for the broader medical audience to understand the severity and extent of this disease.
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