MRI Dataset Discontinuity Correction via Local Geometry Analysis
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Solution Overview
Problem
Magnetic resonance imaging (MRI) datasets acquired at different positions of a patient support suffer from geometric distortions and lack of geometric overlaps due to changes in acquisition conditions, leading to disruptive artifacts in reformatted slice data, especially when scanning large areas like both legs, where global corrections are challenging.
Innovation Solution
A method that determines and compares geometry information across adjacent slices to detect discontinuities, applying local corrections based on geometry information to eliminate these discontinuities, using multiplanar reformation and anatomical atlases for registration, and segmentation algorithms to enhance image quality by addressing distortions specifically in each anatomical region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If partial datasets are acquired at different positions of the patient support to cover larger areas, then the coverage area is improved, but geometric distortions and acquisition condition changes occur
Solution Approach 1:
The patent divides the large acquisition area into multiple partial datasets acquired at different patient support positions. Each partial dataset is processed and corrected independently, then combined to form the complete corrected dataset. This segmentation allows the system to maintain geometric precision across large areas by addressing distortions locally in each segment rather than attempting a single global correction.
Solution Approach 2:
The patent applies local correction methods that are specifically adapted to the distortion characteristics of each partial dataset. By analyzing the acquisition conditions and distortion patterns specific to each position, the system applies targeted correction parameters rather than using a uniform correction approach, thereby maintaining high geometric precision across varying acquisition conditions.
2Device complexity
If global correction methods are applied to correct geometric distortions, then the correction process is simplified, but the correction accuracy deteriorates due to varying distortion effects across different anatomical regions
Solution Approach 1:
The correction process is segmented into multiple independent correction steps, one for each partial dataset. Each correction step processes a specific portion of the data with its own distortion characteristics, allowing for accurate local corrections without the complexity of attempting a single global correction model that would fail to account for regional variations.
Solution Approach 2:
The patent implements correction methods that are locally optimized for each anatomical region and acquisition position. By analyzing the specific distortion patterns in each partial dataset and applying region-specific correction parameters, the system achieves high correction accuracy while managing computational complexity through localized processing rather than a monolithic global approach.
3Device complexity
If theoretical correction coefficients are used for geometric distortion correction, then the correction method is simpler, but the correction does not account for patient-specific and installation site-specific conditions
Solution Approach 1:
The patent employs automatic distortion analysis and correction methods that self-adapt to the specific patient and acquisition conditions. The system analyzes the actual distortion patterns in the acquired data and automatically determines appropriate correction parameters, eliminating the need for manual configuration or pre-programmed correction coefficients. This self-service approach ensures that each correction is reliably tailored to the specific patient anatomy and acquisition conditions.
Solution Approach 2:
The correction process incorporates feedback mechanisms where the system continuously monitors and analyzes the distortion characteristics of the acquired data, adjusts correction parameters accordingly, and iterates until optimal correction is achieved. This feedback-driven approach ensures that the correction reliably accounts for patient-specific and installation site-specific conditions, moving beyond static theoretical coefficients to dynamic, condition-adaptive correction.
Data Source
AI summary
A combined dataset can be formed from partial datasets acquired at different positions of a patient support with a magnetic resonance device. The partial datasets can be of an anatomical region of a patient delimited perpendicularly to a longitudinal direction within an acquisition region. In a method for correcting the combined dataset formed from the partial datasets, for slices of a slice stack in the longitudinal direction of the combined dataset, information describing geometry of the anatomical region and/or an anatomical feature of the anatomical region is determined. For at least one slice group including adjacent slices, the geometry information is compared to detect one or more discontinuities. For at least one discontinuity of the one or more discontinuities satisfying a correction criterion, the combined dataset is corrected as a function of the geometry information to eliminate or reduce the at least one discontinuity.