Cinematic Rendering of 3D Early and Late Gadolinium Enhancement Cardiac MRI: A Feasibility Demonstration

Summary

This article demonstrates the feasibility of applying cinematic rendering, a photorealistic Monte Carlo path-traced visualization technique previously developed for cardiovascular computed tomography, to three-dimensional early and late gadolinium enhancement cardiac magnetic resonance datasets while outlining a reproducible post-processing workflow.

Abstract

Cinematic rendering (CR) is a physically based three-dimensional visualization technique that uses Monte Carlo path tracing with global illumination to generate photorealistic images from volumetric medical imaging data. Although CR has been extensively applied to cardiovascular computed tomography, its application to cardiac magnetic resonance imaging (CMR), particularly to gadolinium-enhanced datasets, has been only rarely described. This report demonstrates the feasibility of applying CR to three-dimensional early and late gadolinium enhancement (EGE/LGE) CMR acquired in a single adult patient at 1.5T. Using a Monte Carlo path-tracing platform with vendor-integrated artificial intelligence (AI) denoising (syngo.via Cinematic Anatomy, Siemens Healthineers), matched CR reconstructions were generated in four-chamber, two-chamber, and short-axis orientations, as well as additional oblique perspectives. The protocol details acquisition parameters, segmentation methodology, rendering workflow, and parameter documentation to support reproducibility. CR is presented as a complementary visualization adjunct suitable for multidisciplinary communication, procedural planning, and trainee education, and is not intended as a primary diagnostic tool for myocardial enhancement. The photorealistic surface texture characteristic of path tracing should not be interpreted as diffuse enhancement. Future studies in scar-positive cohorts are required to evaluate comparative reader performance against conventional grayscale reconstructions and to validate any potential clinical role.

Introduction

Late gadolinium enhancement (LGE) imaging using cardiac magnetic resonance (CMR) is the reference standard for detecting myocardial fibrosis and scar formation and for distinguishing ischemic from nonischemic cardiomyopathies^1,2. Conventional LGE images are interpreted using two-dimensional grayscale reconstructions or, increasingly, three-dimensional acquisitions reformatted in standard cardiac planes. Although these displays are diagnostically robust, they provide limited depth perception and do not natively convey the three-dimensional spatial relationships among the myocardium, blood pool, pericardium, and adjacent structures.

Three-dimensional volume rendering has been used to display volumetric medical imaging data for several decades. Conventional volume rendering uses a single-bounce, locally illuminated ray-casting model with simple Phong or Blinn-Phong shading; the resulting images demonstrate hard edges, flat lighting, and limited depth cues^3,4. Cinematic rendering (CR) is a more recent technique that replaces locally illuminated ray casting with Monte Carlo path tracing under a global illumination model⁵. CR accumulates thousands of secondary light interactions per pixel and simulates physically based phenomena including soft shadows, ambient occlusion, subsurface scattering, and high-dynamic-range environment lighting. The resulting photorealistic appearance more closely approximates cadaveric visualization than conventional volume rendering. CR has been increasingly applied in cardiovascular computed tomography (CT) for coronary anomalies, aortic pathology, congenital heart disease, valvular structural assessment, and postsurgical evaluation^5,6,7,8.

Application of CR to magnetic resonance imaging (MRI) has been less extensively explored, in part because MRI's native signal-to-noise ratio is lower than CT's and the consequent challenge of generating clean, photorealistic renderings from MRI source data. To date, the application of CR to three-dimensional gadolinium-enhanced CMR datasets has been described only rarely. The purpose of this report is to demonstrate the feasibility of this workflow, provide a reproducible protocol, and clearly delineate the role of CR as a visualization adjunct rather than a primary diagnostic tool.

Case Presentation:
A single adult patient with known nonischemic cardiomyopathy underwent CMR including standard cine steady-state free precession imaging, three-dimensional early gadolinium enhancement (EGE), and three-dimensional late gadolinium enhancement (LGE) acquisitions on a 1.5T scanner. The three-dimensional acquisitions used isotropic 1.5 mm voxels with whole-heart coverage. Standard multiplanar reformations (four-chamber, two-chamber, and short-axis) were generated from the volumetric dataset to serve as the conventional reference. The same volumetric dataset was subsequently processed using cinematic rendering. No substantial transmural late gadolinium enhancement was identified on conventional reconstructions; therefore, this case is presented as a feasibility demonstration of the CR pipeline applied to a three-dimensional EGE/LGE dataset rather than as a study of scar visualization in a scar-positive substrate.

Diagnosis, Assessment, and Plan:
Conventional three-dimensional EGE and LGE CMR images were interpreted using standard grayscale multiplanar reconstructions. To explore spatial relationships within the volumetric dataset beyond those appreciable on planar tomographic inspection, the same dataset was processed using cinematic rendering. No diagnostic decisions were made using CR. Diagnostic interpretation of myocardial enhancement was performed exclusively using conventional two-dimensional and three-dimensional grayscale reconstructions, and CR was treated throughout as a complementary visualization output intended for spatial communication and educational purposes.

Protocol

Written informed consent for publication of de-identified images was obtained from the patient. The study was conducted in accordance with the Declaration of Helsinki (2013 revision) and HIPAA privacy regulations. The protocol was reviewed by the Columbia University Irving Medical Center Institutional Review Board and determined to be exempt as a retrospective single-patient case report. The software, equipment, and reagents used in this protocol are listed in the Table of Materials.​

1. Patient preparation and CMR acquisition

1. The patient was positioned supine with appropriate cardiac coil placement and electrocardiographic (ECG) vector electrodes.
2. Reliable intravenous access was established for administration of gadolinium-based contrast material.
3. Localizer sequences were acquired, and standard cardiac imaging planes were planned.
4. Cine steady-state free precession images were acquired in short-axis stack, four-chamber, and two-chamber orientations for functional assessment.

2. Three-dimensional gadolinium enhancement imaging

1. Gadobutrol was administered intravenously at 0.1 mmol/kg body weight.
2. Three-dimensional early gadolinium enhancement (EGE) images were acquired within 1-3 min after contrast administration using a three-dimensional inversion recovery-prepared gradient echo sequence.
3. Acquisition parameters included isotropic 1.5 mm voxels, whole-heart coverage, ECG gating, and respiratory navigator gating.
4. A delay of 10-15 min after contrast administration was allowed for equilibrium distribution.
5. Three-dimensional late gadolinium enhancement (LGE) images were acquired using a free-breathing, navigator-gated three-dimensional inversion recovery sequence.
6. Inversion time was optimized using a Look-Locker scout sequence to null normal myocardium (typically 280-320 ms at 1.5T).
7. Where available, artificial intelligence (AI)-based source-image reconstruction was applied to improve the baseline signal-to-noise ratio of the source dataset, which directly affected rendered image quality.

3. Image reconstruction and export

1. The three-dimensional volumetric dataset was reconstructed with isotropic resolution.
2. Standard multiplanar reformations were generated in four-chamber, two-chamber, and short-axis orientations.
3. The volumetric dataset was exported in Digital Imaging and Communications in Medicine (DICOM) format for downstream cinematic rendering processing.

4. Cinematic rendering post-processing

1. The three-dimensional EGE and LGE DICOM datasets were imported into the cinematic rendering platform.
2. Segmentation was performed using semi-automated intensity thresholding for blood-pool isolation, followed by manual refinement of myocardial contours using the platform contour brush tool.
3. The rendering engine was configured for Monte Carlo path tracing with global illumination. A total of 1,500 samples per pixel was used for converged export rendering.
4. The illumination model was configured by selecting a high-dynamic-range environment map, positioning the key light at the desired azimuth and elevation, and setting the ambient occlusion factor.
5. The vendor-integrated AI denoiser was applied during interactive rendering, followed by a final converged high-sample-count render for figure export.
6. The opacity transfer function and color lookup tables were configured to emphasize the myocardium-blood pool interface and differentiate myocardium from subepicardial fat and pericardium.
7. Matched reconstructions were generated in four-chamber, two-chamber, and short-axis orientations at identical anatomical positions relative to the conventional reformations for direct visual comparison.
8. Additional oblique and rotational views were generated to demonstrate spatial relationships not readily appreciable on standard planes.
9. Rendering parameters, including sample count, light source position, ambient occlusion factor, opacity transfer function, and denoiser settings, were documented for each exported figure to support reproducibility.

Results

Feasibility of CR applied to three-dimensional EGE/LGE CMR
Cinematic rendering (CR) reconstructions were successfully generated from both the three-dimensional early gadolinium enhancement (EGE) and three-dimensional late gadolinium enhancement (LGE) datasets in all attempted views, demonstrating the technical feasibility of the workflow. The complete pipeline, from Digital Imaging and Communications in Medicine (DICOM) import to converged figure export, was completed in a single post-processing sess...

Discussion

This report demonstrated the technical feasibility of applying cinematic rendering (CR), a Monte Carlo path-tracing visualization technique with global illumination, to three-dimensional early- and late-gadolinium-enhancement cardiac magnetic resonance (CMR) datasets from a single patient. The workflow from Digital Imaging and Communications in Medicine (DICOM) import to converged figure export was reproducible; the resulting outputs differed qualitatively from conventional volume rendering, and the protocol provides a f...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
AI source-image reconstruction (optional)	Siemens Healthineers, Erlangen, Germany	Deep Resolve	Deep-learning source-image reconstruction to improve baseline signal-to-noise of the 3D LGE source dataset.
Cardiac MRI Scanner	Siemens Healthineers, Erlangen, Germany	MAGNETOM Sola	1.5T whole-body scanner used for acquisition of 3D EGE/LGE CMR datasets. System software: syngo MR XA60.
Cinematic rendering software	Siemens Healthineers, Erlangen, Germany	syngo.via Cinematic Anatomy (VB80)	Monte Carlo path-tracing renderer with global illumination, high-dynamic-range environment lighting, and vendor-integrated AI denoiser.
Dedicated cardiac phased-array coil	Siemens Healthineers, Erlangen, Germany	Body 18 coil	18-channel body matrix coil used for cardiac signal reception.
Gadolinium-based contrast agent	Bayer HealthCare, Berlin, Germany	Gadavist (gadobutrol)	Macrocyclic gadolinium-based contrast agent administered intravenously at 0.1 mmol/kg body weight.
Post-processing workstation	Siemens Healthineers, Erlangen, Germany	syngo.via VB80	Multimodality post-processing workstation used for volumetric reconstruction, multiplanar reformation, and DICOM export. GPU: NVIDIA RTX A6000.