Surgeons Prefer Cinematic Rendering of Ankle Injuries
By MedImaging International staff writers Posted on 06 Sep 2018 |
Image: Volume-rendered image (L), compared to a cinematically rendered image (R) (Photo courtesy of AJR).
A new study suggests that photorealistic, cinematically rendered ankle scans depict relevant findings better than conventional volume-rendered three-dimensional (3D) reconstructions.
Researchers at the University of Zurich (UZH; Switzerland) first acquired computerized tomography (CT) imaging data from 10 patients who underwent scanning for different types of ankle injuries. They then reconstructed the CT datasets, once as 3D images using a conventional volume-rendering technique, and then again via cinematic-rendering, using the Siemens Healthineers syngo.via Frontier prototype software. All 3D reconstructions had similar field-of-view, perspective, and opacity settings.
After randomizing both sets of images, the researchers presented them to 12 radiologists and 10 orthopedic surgeons for subjective evaluation. The physicians compared the volume-rendered and cinematically rendered images, and chose which one offered the best visualization for each type of injury. Overall, the radiologists and surgeons preferred the cinematically rendered images for the majority of the 10 ankle injuries. Conventional volume-rendered images were favored mainly in injuries not involving fractures, and in some cases, both imaging techniques were considered as equally good.
The physicians' preference for cinematically rendered images stemmed from various advantages of the technique over conventional volume rendering, including realistic shadowing, which provides a more natural depth and more clearly depicts the individual bony structures; depth-of-field effects, which help to minimize distractions; and enhanced perception of soft tissue, which could help in the representation of ligament or muscle ruptures in radiology reports. The study was published on August 14, 2018, in the American Journal of Roentgenology (AJR).
“Cinematic rendering is an advanced visualization method involving complex lighting models that simulate light and shadow to photorealistically display the anatomical structures of CT scans,” said senior author Florian Berger, MD. “Various groups have demonstrated the technique's potential to improve the clinical management of intricate cases such as acute aortic injury and highly vascularized kidney aneurysms. Cinematic-rendering technique may play an integral part in preoperative 3D examination of fractures and dislocations in clinical routine.”
Related Links:
University of Zurich
Researchers at the University of Zurich (UZH; Switzerland) first acquired computerized tomography (CT) imaging data from 10 patients who underwent scanning for different types of ankle injuries. They then reconstructed the CT datasets, once as 3D images using a conventional volume-rendering technique, and then again via cinematic-rendering, using the Siemens Healthineers syngo.via Frontier prototype software. All 3D reconstructions had similar field-of-view, perspective, and opacity settings.
After randomizing both sets of images, the researchers presented them to 12 radiologists and 10 orthopedic surgeons for subjective evaluation. The physicians compared the volume-rendered and cinematically rendered images, and chose which one offered the best visualization for each type of injury. Overall, the radiologists and surgeons preferred the cinematically rendered images for the majority of the 10 ankle injuries. Conventional volume-rendered images were favored mainly in injuries not involving fractures, and in some cases, both imaging techniques were considered as equally good.
The physicians' preference for cinematically rendered images stemmed from various advantages of the technique over conventional volume rendering, including realistic shadowing, which provides a more natural depth and more clearly depicts the individual bony structures; depth-of-field effects, which help to minimize distractions; and enhanced perception of soft tissue, which could help in the representation of ligament or muscle ruptures in radiology reports. The study was published on August 14, 2018, in the American Journal of Roentgenology (AJR).
“Cinematic rendering is an advanced visualization method involving complex lighting models that simulate light and shadow to photorealistically display the anatomical structures of CT scans,” said senior author Florian Berger, MD. “Various groups have demonstrated the technique's potential to improve the clinical management of intricate cases such as acute aortic injury and highly vascularized kidney aneurysms. Cinematic-rendering technique may play an integral part in preoperative 3D examination of fractures and dislocations in clinical routine.”
Related Links:
University of Zurich
Latest General/Advanced Imaging News
- PET Scans Reveal Hidden Inflammation in Multiple Sclerosis Patients
- Artificial Intelligence Evaluates Cardiovascular Risk from CT Scans
- New AI Method Captures Uncertainty in Medical Images
- CT Coronary Angiography Reduces Need for Invasive Tests to Diagnose Coronary Artery Disease
- Novel Blood Test Could Reduce Need for PET Imaging of Patients with Alzheimer’s
- CT-Based Deep Learning Algorithm Accurately Differentiates Benign From Malignant Vertebral Fractures
- Minimally Invasive Procedure Could Help Patients Avoid Thyroid Surgery
- Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery
- AR Application Turns Medical Scans Into Holograms for Assistance in Surgical Planning
- Imaging Technology Provides Ground-Breaking New Approach for Diagnosing and Treating Bowel Cancer
- CT Coronary Calcium Scoring Predicts Heart Attacks and Strokes
- AI Model Detects 90% of Lymphatic Cancer Cases from PET and CT Images
- Breakthrough Technology Revolutionizes Breast Imaging
- State-Of-The-Art System Enhances Accuracy of Image-Guided Diagnostic and Interventional Procedures
- Catheter-Based Device with New Cardiovascular Imaging Approach Offers Unprecedented View of Dangerous Plaques
- AI Model Draws Maps to Accurately Identify Tumors and Diseases in Medical Images