All day | CA136-ED-X | Digital education exhibit
This digital education exhibit will provide background on cinematic rendering, distinguishing it from traditional 3D volume rendering, and detail its potential role in cardiovascular radiology.Drawn from the computer animation techniques made famous by Hollywood, cinematic rendering is a new approach to the 3D visualization of medical images from CT scans.
Cinematic rendering may be the next best way to improve the 3D visualization of CT scans because it takes into account complex light paths that 3D volume-rendering algorithms ignore, Dr. Harold Litt, PhD, from the University of Pennsylvania told AuntMinnie.com.
"[Cinematic rendering] simulates light transport along thousands of photon paths per pixel, which gives a more realistic representation of human anatomy," he said.
In this exhibit, Litt and colleagues will explain how to customize parameters to use cinematic rendering for the 3D visualization of CT scans and also present several cardiology case examples using the image reconstruction technique to identify features of interest.
Physicians can use cinematic rendering to improve the identification of pathologies, enhance surgical planning, help patients understand their condition, and train new medical practitioners, Litt said.
"In addition, it is believed that cinematic rendering will increase and improve interdisciplinary communication among caregivers," he said.


















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)

