Automated Medical Image Rendering with Precomputed Clinical Templates
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Solution Overview
Problem
Traditional volume rendering methods in medical imaging struggle with simulating complex light scattering and extinction for photorealism, and high-performance rendering is computationally intensive, making interactive viewing of large datasets difficult, while finding optimal rendering parameters for specific clinical workflows is challenging.
Innovation Solution
A system and method for determining rendering parameters using authored snapshots or templates, employing a differentiable renderer to derive optimized settings for interactive viewing, including AI-based organ and disease segmentations, and utilizing pre-computed assets for efficient rendering on devices with limited computational power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physical rendering algorithms with Monte Carlo light transport are used to simulate complex light scattering and extinction for photorealism, then image quality and photorealism are improved, but computational power requirements and rendering time increase significantly
Solution Approach 1:
The patent pre-computes rendering parameters, lighting conditions, and material properties during an offline authoring phase, storing them as templates that can be rapidly applied during interactive viewing. This preliminary computation separates the heavy computational workload from the interactive rendering process, enabling photorealistic quality without real-time computational burden.
Solution Approach 2:
The patent creates template copies of rendering parameters, lighting setups, and material definitions that can be reused across multiple images and views. Instead of computing photorealistic rendering parameters from scratch for each interactive view, the system copies and adapts pre-computed templates, dramatically reducing computational requirements while maintaining visual fidelity.
2Manufacturing precision
If pre-rendering is used to achieve high-quality rendering, then image quality is improved, but playback flexibility is reduced to fixed sequences only
Solution Approach 1:
The patent implements dynamic adaptation of pre-computed templates through runtime parameter adjustment. The system stores rendering parameters in a flexible format that allows modification of lighting angles, camera positions, and material properties during interactive viewing, enabling users to explore different views and conditions while maintaining photorealistic quality through the pre-computed foundation.
Solution Approach 2:
The patent enables runtime modification of rendering parameters such as lighting intensity, camera angles, and material properties by adjusting values in the pre-computed templates. This allows the system to maintain high image quality while providing flexible interactive exploration, as the underlying template structure can be dynamically adapted without re-computing the entire rendering pipeline.
3Adaptability or versatility
If manual adjustment of rendering parameters is used to find optimal settings for specific clinical workflows, then customization for clinical needs is improved, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements automated parameter optimization that adapts pre-computed templates to specific clinical workflows without requiring manual adjustment. The system automatically analyzes clinical requirements, selects appropriate template parameters, and optimizes rendering settings based on the specific anatomical structures and diagnostic needs, enabling customization while eliminating time-consuming manual tuning.
Solution Approach 2:
The patent incorporates feedback mechanisms that automatically adjust rendering parameters based on clinical workflow analysis and user interactions. The system monitors how templates are used across different clinical scenarios and automatically refines parameter selections, providing customized optimal settings for specific clinical needs without requiring manual intervention or time-consuming adjustment processes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables photorealistic and efficient rendering of medical images with realistic lighting and textures, supporting interactive viewing on AR/VR devices, maintaining quality with reduced computational resources, and facilitating clinical workflows.
Implementation Method 1
Ray casting simulates only the emission and absorption of radiant energy along the primary viewing rays through the volume data. The emitted radiant energy at each point is absorbed according to the Beer-Lambert law along the ray to the observer location
Implementation Method 2
These methods do not simulate the complex light scattering and extinction associated with photorealism (global illumination)
Data Source
AI summary
Systems and methods for determining rendering parameters based on authored snapshots or templates. In one aspect, clinically relevant snapshots of patient medical data are created by experts to support educational or clinical workflows. Alternatively, the snapshots are created by automation processes from AI-based organ and disease segmentations. In another aspect, clinically relevant templates are generated. Rendering parameters are derived from the snapshots or templates, stored, and then applied for either rendering new data or interactive viewing of existing data.