Task-Specific Model Maps Raw Data to Clinical Outcomes
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Solution Overview
Problem
Current medical imaging techniques suffer from information loss during the reconstruction process, which adversely impacts clinical outcomes due to the traditional need for reconstructing images before analysis.
Innovation Solution
A processor-implemented method that directly maps raw data onto an application-specific manifold using a task-specific model to generate clinical outputs, bypassing the reconstruction step and enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional reconstruction techniques are used to process raw data, then reconstructed images can be generated for medical analytics, but information loss occurs which adversely impacts clinical outcomes
Solution Approach 1:
The patent extracts and eliminates the harmful reconstruction step from the workflow by directly processing raw data in the k-space domain. Instead of reconstructing images and then analyzing them (which causes information loss), the system performs clinical analytics directly on the raw data, removing the intermediate reconstruction stage that introduces errors and information loss.
Solution Approach 2:
The patent introduces a task-specific model as an intermediary that directly maps raw data to clinical outcomes. This model acts as a mediator that bypasses the need for image reconstruction, establishing a direct transformation from raw data space to clinical analytics space, thereby preventing information loss while maintaining analytical accuracy.
2Measurement precision
If raw data is directly mapped to clinical outcomes using task-specific models, then prediction accuracy is enhanced, but the system complexity increases due to model training requirements
Solution Approach 1:
The patent performs preliminary action by pre-training task-specific models during the development phase using labeled data. These models are trained in advance to map raw data to specific clinical outcomes (e.g., tumor segmentation, survival rate prediction). Once trained, the models can be deployed for inference without requiring complex real-time training, thus managing system complexity while maintaining high prediction accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of the processing approach by operating in the raw data space (k-space) rather than the image space. This parameter change allows direct manipulation of data at its most fundamental level, enabling more accurate predictions while the model architecture handles the complexity of transformation through learned parameters rather than explicit complex algorithms.
Data Source
AI summary
The subject matter discussed herein relates to systems and methods for generating a clinical outcome based on creating a task-specific model associated with processing raw image(s). In one such example, input raw data is acquired using an imaging system, a selection input corresponding to a clinical task is received, and a task-specific model corresponding to the clinical task is retrieved. Using the task-specific model, the raw data is mapped onto an application specific manifold. Based on the mapping of the raw data onto the application specific manifold the clinical outcome is generated, and subsequently providing the clinical outcome for review.