
Monday, November 26 | 11:20 a.m.-11:30 a.m. | SSC04-06 | Room S504AB
In this Monday presentation, researchers from Canada will detail their initial experience with cinematically rendered images in the context of acute trauma.The group, led by Dr. Savvas Nicolaou from Vancouver General Hospital, assessed the potential value of using cinematic rendering instead of traditional volume rendering to generate 3D images of patient anatomy. Unlike the single raycasting model of volume rendering, cinematic rendering makes use of a global illumination model to enhance the photorealism of medical images.
Nicolaou and colleagues processed an imaging dataset from several patients who presented with multiple types of trauma and who underwent CT exams at their institution's level I trauma center. They used computer software (syngo.via Frontier, Siemens Healthineers) to convert the CT data into cinematically rendered images.
Upon evaluating the resulting 3D reconstructions, the researchers found that the cinematically rendered images provided exquisite anatomical details of acute injuries -- allowing for simple delineation of bony, vascular, and soft tissue.
A team of trauma surgeons also assessed the 3D reconstructions and reported that the cinematically rendered images were much more useful for helping them make a clinical decision and educating trainees than were the conventional volume-rendered images.
"Cinematic rendering is a promising novel technique to display visually receptive 3D photorealistic high-definition images ... [and] provides remarkable details relative to [volume-rendered] reconstructions in context of complex acute trauma," the researchers concluded.
This paper received a Roadie 2018 award for the most popular abstract by page views in this Road to RSNA section.


















![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)
