MRI Lung Biomarker Generation via Adaptive Histogram Thresholding
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Solution Overview
Problem
Current methods for quantifying inflammatory phenomena and airway remodeling in the pulmonary region, such as CT-scan imaging, are irradiating and unable to distinguish between inflammation and scar lesions, while MRI imaging struggles with segmenting varied tissue structures, lacking efficient biomarker generation capabilities.
Innovation Solution
A method involving MRI image acquisition, processing to generate a three-dimensional image, calculating filtering thresholds, segmenting lung volumes, and normalizing signal intensity values to produce biomarkers that quantify inflammation and remodeling without irradiation, using adaptive thresholds and multi-modal acquisitions like T2 and T1 weighting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT-scan imaging is used to quantify inflammatory phenomena and airway remodeling, then measurement precision is improved, but object-affected harmful factors worsen due to ionizing radiation exposure
Solution Approach 1:
The patent replaces the mechanical/physical CT-scan imaging system with an MRI-based system that uses magnetic fields and radio waves instead of ionizing radiation. The method processes MRI signals through histogram analysis and threshold filtering to achieve quantitative measurement of lung tissue characteristics, substituting the harmful radiation-based approach with a non-ionizing alternative while maintaining measurement capabilities.
Solution Approach 2:
The patent changes the physical parameters of the imaging modality from ionizing radiation (CT) to magnetic resonance signals (MRI). By adjusting MRI acquisition parameters and processing the signal intensity distributions through histogram analysis, the method achieves quantitative assessment of lung tissue without the harmful effects of radiation exposure.
2Measurement precision
If CT-scan imaging is used to obtain three-dimensional images of lung anomalies, then measurement precision is improved, but adaptability worsens as it cannot discriminate inflammation from remodeling
Solution Approach 1:
The patent applies local quality analysis by examining the distribution of signal intensity values within specific lung tissue regions through histogram analysis. Different tissue types (inflammation, remodeling, scar lesions) exhibit distinct local signal intensity distributions, allowing the method to differentiate between various pathologies based on their unique local characteristics rather than treating all anomalies uniformly.
Solution Approach 2:
The patent introduces dynamic adaptability through automated threshold calculation that adjusts to each patient's specific tissue characteristics. The filtering threshold is dynamically determined based on the histogram distribution of signal intensities, enabling the system to adapt to varying tissue properties and differentiate between inflammatory and remodeling processes in each individual case.
3Object-affected harmful factors
If MRI imaging is used to avoid ionizing radiation, then object-affected harmful factors are reduced, but device complexity worsens due to difficulty in segmenting varied tissue structures
Solution Approach 1:
The patent implements self-service through automated processing where the system independently performs histogram analysis, calculates filtering thresholds, and segments tissue structures without requiring manual intervention. The method uses the intrinsic statistical properties of the MRI signal distributions to automatically differentiate tissue types, reducing the complexity burden on operators while maintaining accurate segmentation of varied lung tissue structures.
Solution Approach 2:
The patent replaces complex manual segmentation procedures with automated signal processing based on histogram analysis. By substituting manual tissue classification with algorithmic threshold filtering based on signal intensity distributions, the method reduces operational complexity while achieving accurate tissue segmentation without ionizing radiation.
4Productivity
If automated biomarker generation is implemented, then productivity is improved, but manufacturing precision worsens due to potential loss of data in tissue segmentation
Solution Approach 1:
The patent incorporates feedback mechanisms where the histogram analysis of signal intensity distributions provides continuous information about tissue characteristics. The automated threshold calculation uses this feedback to adjust segmentation parameters, ensuring that productivity gains from automation do not compromise segmentation accuracy. The system continuously refines its segmentation based on the statistical feedback from the MRI signal distributions.
Solution Approach 2:
The patent performs preliminary histogram analysis and threshold determination before final tissue segmentation. By pre-calculating the distribution characteristics and establishing filtering thresholds in advance, the method ensures that automated processing maintains high precision while achieving rapid biomarker generation. This preliminary characterization of tissue signal distributions safeguards against data loss during automated segmentation.
Data Source
AI summary
A Method for generating a biomarker includes acquiring an image using an MRI system; processing the MRI image to generate a three-dimensional image of the lung; generating a first function corresponding to the distribution of the different signal intensity values; automatically calculating a filtering threshold of the first function from a second signal intensity value distribution function; segmenting a lung volume comprising: a main volume; a filtered volume of a volume of voxels quantified by the first function and filtered by at least the calculated filtering threshold, normalizing the values of the three-dimensional image of the lung volume; generating a biomarker indicating a normalized segmented volume ratio.