Motion Estimation Quality Factor for Radiotherapy Image Registration
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Solution Overview
Problem
Existing machine learning systems for medical imaging lack a mechanism to assess the similarity between new data points and training data, leading to unreliable and inaccurate motion estimation during radiotherapy treatments.
Innovation Solution
A quality factor is generated by identifying a cluster of training data samples similar to a new data sample using locally linear embedding (LLE) and comparing the motion estimations, allowing for adjustment of image registration weights based on similarity criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion estimation is performed using existing machine learning systems, then motion information is obtained, but the accuracy is unreliable and inaccurate due to lack of similarity assessment
Solution Approach 1:
The system implements feedback by computing a quality factor that measures the similarity between new data samples and training data. This quality factor feeds back into the motion estimation process by adjusting weights, thereby improving the reliability and accuracy of motion estimation when similarity is high, and preventing unreliable estimates when similarity is low.
Solution Approach 2:
The system changes parameters dynamically by adjusting weights based on the computed quality factor. When the quality factor indicates high similarity between new and training data, higher weights are applied to enhance accuracy. This parameter adjustment resolves the contradiction by adapting the estimation process to the actual data quality.
2Productivity
If image registration is performed without quality assessment, then motion information is generated, but computational resources are wasted on inaccurate estimations
Solution Approach 1:
The system performs preliminary action by computing the quality factor and assessing similarity between new data samples and training data before performing full image registration and motion estimation. This preliminary quality assessment prevents wasting computational resources on samples that are unlikely to produce accurate results, thereby improving overall computational efficiency.
Solution Approach 2:
The system applies local quality by treating different data samples differently based on their individual quality factors. Instead of uniformly processing all samples, it adjusts weights locally for each sample based on its similarity to training data, optimizing computational resource allocation to focus on high-quality estimates while skipping or downweighting low-quality ones.
Data Source
AI summary
Systems and methods are disclosed for performing operations comprising: receiving a new data sample comprising estimated motion; identifying a cluster of training data samples from a plurality of training data samples that corresponds to the new data sample; obtaining motion estimations associated with the cluster of training data samples; determining a reconstruction from the cluster of training data samples to the new data sample; transforming the motion estimations for the cluster of training data samples based on the reconstruction from the cluster of training data samples to the new data sample; and computing a quality factor indicating a measure of similarity between the estimated motion of the new data sample and the motion estimations associated with the cluster of training data samples.