Medical Imaging Algorithm Sensitivity Analysis Using Differentiable Models
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Solution Overview
Problem
Existing medical imaging algorithms, particularly those using predictive machine learning, face challenges in efficiently assessing their quality and sensitivity to changes in image acquisition and processing parameters without requiring extensive real data collection or computational simulations.
Innovation Solution
A method involving differentiable models of the image formation and post-processing chains to calculate gradients and curvatures of objective functions with respect to parameters, allowing quick analysis of algorithm performance under varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive real data collection or computational simulations are used to assess algorithm quality and sensitivity, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent replaces traditional mechanical approaches (extensive data collection and computational simulations) with a mathematical gradient-based approach. By computing gradients of the objective function with respect to parameters, the system efficiently assesses algorithm sensitivity and quality without requiring time-consuming simulations or large datasets.
Solution Approach 2:
The patent changes the approach from collecting extensive data to analyzing parameter sensitivities through gradient computation. By calculating how the objective function changes with respect to parameter variations, the system achieves precise quality assessment while minimizing time loss and resource consumption.
2Reliability
If extensive computational simulations are used to assess algorithm performance under varying conditions, then reliability is improved, but use of energy and productivity deteriorate
Solution Approach 1:
The patent substitutes energy-intensive computational simulations with a mathematical gradient computation approach. By using automatic differentiation and gradient-based sensitivity analysis, the system achieves reliable performance assessment while dramatically reducing computational energy requirements.
Solution Approach 2:
Instead of running multiple computational simulations to assess performance, the patent creates a mathematical model (gradient of the objective function) that copies the essential sensitivity information. This gradient model provides reliable performance predictions without requiring repeated energy-consuming simulations.
3Measurement precision
If traditional methods are used to assess algorithm sensitivity to parameter changes, then measurement precision is improved, but device complexity and loss of time deteriorate
Solution Approach 1:
The patent replaces complex traditional sensitivity analysis methods with gradient computation. By using automatic differentiation to calculate gradients of the objective function with respect to parameters, the system achieves precise sensitivity measurements while simplifying the overall assessment framework.
Data Source
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AI summary
The invention relates to an apparatus comprising at least one processor and at least one memory storing instructions that when executed by the at least one processor cause the apparatus to perform obtaining raw medical image data and calculating a first derivative of an objective function for an algorithm configured to perform an objective. The objective function is dependent on the raw medical image data and a differentiable model for processing the raw medical image data to obtain an image for inputting into the algorithm. The differentiable model is dependent on one or more parameters and the first derivative is calculated with respect to a parameter of the one or more parameters. The apparatus is further caused to perform determining a gradient using the first derivative of the objective function based on the raw medical image data.