Brain Atrophy Rate Estimation Using Reference Data Models
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Solution Overview
Problem
Current techniques for measuring brain atrophy rates are prone to high measurement noise and error, especially when using MRI, due to variations in imaging hardware and protocols, making it difficult to detect subtle changes in brain volume over short periods.
Innovation Solution
A method and system that use a reference data model based on long-term measurements from healthy subjects to reduce measurement noise, allowing for reliable estimation of brain volume change rates from a small number of scans taken over a short period, typically 12 months.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If measurements are taken over a short period to enable timely detection, then productivity is improved, but measurement precision deteriorates due to high noise levels
Solution Approach 1:
The method performs preliminary actions by collecting longitudinal measurements from healthy control subjects to establish a reference model before measuring the patient. This reference model, built from multiple time points, captures the natural variability and noise patterns, enabling subsequent short-term patient measurements to be interpreted more accurately by comparing against the established baseline of normal variation.
Solution Approach 2:
The reference model derived from healthy control data acts as an intermediary between the noisy short-term patient measurements and the true atrophy rate. This intermediary model provides context by characterizing normal measurement variability, allowing the system to distinguish between noise and actual pathological change even when measurement intervals are short.
2Measurement precision
If multiple measurements are taken over a long period to improve measurement precision, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The reference model is built in advance from longitudinal healthy control data, storing the characteristics of normal variation. When a patient is measured, this pre-established model allows accurate interpretation of short-term changes without requiring lengthy measurement periods, thus reducing time loss while maintaining precision.
Solution Approach 2:
The reference model creates a copy or representation of normal healthy brain atrophy patterns from longitudinal healthy control data. This copied model can be repeatedly applied to patient data without requiring repeated long-term studies, enabling precise measurements in short timeframes by comparing patient deviations from the established normal pattern.
3Ease of operation
If a simple additive correction is applied to MRI protocols to improve ease of operation, then ease of operation is improved, but measurement precision deteriorates due to inability to capture complex protocol variations
Solution Approach 1:
The method transforms the approach from correcting individual protocol parameters to changing the fundamental parameter being measured - instead of attempting to correct each acquisition parameter, the system measures the composite effect of all protocol variations through the reference model, which captures the net impact on atrophy rate estimation.
Solution Approach 2:
The reference model serves as an intermediary that absorbs and accounts for complex protocol variations without requiring explicit correction of each parameter. By modeling normal variation including protocol effects in healthy controls, the system indirectly compensates for protocol differences when comparing to patient data, maintaining precision without simple additive corrections.
Data Source
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AI summary
The present invention concerns a method (100) and a system (200) for determining a regularized estimate of a change rate of a quantitative parameter measured for a biological object of a new subject, the method comprising: - measuring (105) said quantitative parameter at a first time and at a second time within a short period of time; - determining (108) an approximative change rate r of the measured quantitative parameter; the method being characterized in that it automatically determines (109) the regularized estimate rreg from rreg=1−λr+λm wherein m is a mean value for said change rate of the quantitative parameter obtained by applying a reference data model to values of said quantitative parameters obtained by measurements of the latter for said biological object of healthy subjects, wherein for each healthy subject, values of the quantitative parameter have been obtained by making a series of said measurements over a long period of time with respect to said short period of time; and λ is computed analytically using an estimate of the variance of the values of the quantitative parameters measured for said healthy subjects.