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Related Experiment Video

Updated: Sep 19, 2025

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

TOGS: Gaussian Splatting With Temporal Opacity Offset for Real-Time 4D DSA Rendering.

Shuai Zhang, Huangxuan Zhao, Zhenghong Zhou

IEEE Journal of Biomedical and Health Informatics

|June 2, 2025

Summary

This summary is machine-generated.

This study introduces TOGS, a novel method for Four-dimensional Digital Subtraction Angiography (4D DSA) imaging. TOGS significantly enhances rendering quality and speed, crucial for diagnosing cerebrovascular diseases with sparse data.

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Area of Science:

Medical Imaging
Computer Vision
Radiology

Background:

Four-dimensional Digital Subtraction Angiography (4D DSA) is vital for diagnosing cerebrovascular diseases.
Current 4D DSA methods struggle with rendering quality and speed, especially with sparse data.
Improving rendering efficiency is key for lesion detection and characterization.

Purpose of the Study:

To develop a novel method for enhancing 4D DSA rendering quality and speed.
To address limitations of existing techniques in sparse view scenarios.
To improve the visualization of contrast agent dynamics in blood vessels.

Main Methods:

Proposed TOGS (Temporal Opacity Gaussian Splatting), a Gaussian splatting technique incorporating an opacity offset table per Gaussian.
Modeled temporal variations in contrast agent radiance using opacity-varying Gaussians.
Implemented a Smooth loss term to prevent overfitting in sparse views and random Gaussian pruning to reduce storage.

Main Results:

Achieved state-of-the-art rendering quality compared to previous methods under identical sparse training views.
Enabled real-time rendering capabilities for 4D DSA.
Demonstrated low storage overhead due to model optimization techniques.

Conclusions:

TOGS effectively improves rendering quality and speed for 4D DSA, particularly in sparse sampling conditions.
The method offers a significant advancement for the diagnosis and visualization of cerebrovascular diseases.
TOGS presents a promising solution for efficient and high-quality medical image rendering.