Machine Learning Rendering Settings for Medical Image Consistency
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Solution Overview
Problem
Existing medical imaging techniques, particularly physically-based volume rendering, face challenges in producing consistent and reproducible images due to variability in scan settings and patient data, leading to inconsistent visualization and increased sensitivity to rendering parameter changes, which hinders diagnostic reliability.
Innovation Solution
The implementation of a machine learning-based approach using deep learning to determine optimal rendering settings for physically-based rendering parameters such as lighting, viewing, and material properties, allowing for consistent photorealistic image generation across different datasets and diagnostic contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physically-based volume rendering with path tracing is used to produce photorealistic images, then image quality and spatial understanding are improved, but sensitivity to rendering parameter changes increases and consistency across datasets deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the input medical data and the physically-based rendering pipeline. This model learns optimal rendering parameter settings from training data, acting as a mediator that translates varying medical datasets into consistent rendering parameters, thereby resolving the contradiction between achieving high-quality photorealistic images and maintaining consistency across different datasets.
Solution Approach 2:
The patent dynamically adjusts rendering parameters based on learned patterns from training data. Instead of using fixed rendering parameters that cause inconsistency, the system changes parameters adaptively according to the specific characteristics of each dataset, while the machine learning model ensures these changes maintain consistency in the final rendered output.
2Reliability
If manual adjustment of rendering parameters is performed to achieve consistent results, then diagnostic reliability is improved, but workflow complexity and time consumption increase
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically determines optimal rendering parameters without requiring manual intervention. The model serves itself by learning from training data and autonomously selecting appropriate parameters for new datasets, thereby achieving diagnostic reliability while eliminating workflow complexity associated with manual parameter adjustment.
Solution Approach 2:
The patent performs preliminary training of the machine learning model on representative datasets before actual rendering tasks. This preliminary action enables the model to pre-learn optimal parameter settings and relationships, so that during actual use, consistent diagnostic-quality renders are produced automatically without requiring manual adjustment or complex workflow intervention.
3Ease of operation
If static visualization presets are used to reduce variability, then ease of operation is improved, but adaptability to different diagnostic contexts and data characteristics deteriorates
Solution Approach 1:
The patent transforms static visualization presets into a dynamic system. Instead of using fixed presets that cannot adapt, the machine learning model continuously adapts rendering parameters based on the specific characteristics of each dataset and diagnostic context. This dynamic approach maintains ease of operation through automation while achieving high adaptability to varying conditions.
Solution Approach 2:
The patent enables automatic parameter changes based on learned patterns from training data. The system changes rendering parameters dynamically according to the specific diagnostic context and data characteristics, eliminating the need for static presets while maintaining ease of operation through automated adaptation.
Data Source
AI summary
An artificial intelligence agent is machine trained and used to provide physically-based rendering settings. By using deep learning and/or other machine training, settings of multiple rendering parameters may be provided for consistent imaging even in physically-based rendering.