Trabecular Bone MRI Texture Analysis for Opportunistic Osteoporosis Screening
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Solution Overview
Problem
Current methods for screening osteoporosis, such as dual energy x-ray absorptiometry (DXA), are underutilized, and opportunistic imaging methods are needed to identify individuals at risk for osteoporosis, as traditional techniques are limited to prospective applications and fail to account for structural and material properties beyond bone mineral density (BMD).
Innovation Solution
A method utilizing magnetic resonance imaging (MRI) to analyze trabecular bone texture through metrics like inverse difference moment, angular second moment, entropy, and contrast to determine bone risk or health, enabling opportunistic screening for osteoporosis and fragility fractures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DXA-based TBS is used to assess bone quality, then trabecular microarchitecture can be evaluated, but the method is underutilized and many high-risk patients are not screened
Solution Approach 1:
The patent applies opportunistic screening by utilizing MRI scans obtained for other clinical purposes (non-bone-specific indications) to perform bone texture analysis. This multi-functional use of existing imaging data enables TBS assessment without requiring dedicated bone imaging procedures, thereby expanding screening coverage to patients who would not otherwise be evaluated for osteoporosis
Solution Approach 2:
The method leverages clinical MRI scans that are already performed for other diagnostic purposes, allowing bone quality assessment to occur as a secondary analysis of existing data. This self-service approach eliminates the need for separate screening procedures and enables automatic identification of at-risk patients from routine clinical imaging
2Measurement precision
If prospective bone quality measurement techniques (QCT, bone biopsy, high-resolution MRI) are used, then detailed bone properties can be measured, but these methods are not suitable for widespread screening
Solution Approach 1:
The patent extracts bone texture information from MRI scans that were obtained for non-bone purposes. By isolating and analyzing only the relevant textural features from the existing imaging data, the method achieves detailed bone quality assessment without requiring dedicated bone imaging protocols or complex specialized equipment
Solution Approach 2:
The method uses standard clinical MRI imaging protocols to obtain images that can serve dual purposes - the primary clinical indication and secondary bone texture analysis. This copying approach allows the same imaging data to provide both clinical diagnostic information and bone quality assessment, eliminating the need for separate specialized imaging procedures
3Productivity
If DXA is used for osteoporosis screening, then BMD can be measured, but structural and material properties beyond BMD are not captured
Solution Approach 1:
The patent combines BMD measurement with texture analysis by utilizing the same DXA or MRI imaging data to derive both bone mineral density and trabecular microarchitecture information. This merging approach captures comprehensive bone quality attributes including structural properties that go beyond BMD alone, providing a more complete assessment of fracture risk
Solution Approach 2:
The method creates a composite assessment of bone quality by integrating multiple parameters - BMD values and texture metrics (such as TBS) - into a unified evaluation framework. This composite approach combines information from different bone properties to provide a more comprehensive risk assessment than any single parameter could achieve alone
Data Source
AI summary
In an example method, a computer system receives one or more images of one or more bones of a patient. The one or more images are generated by a magnetic resonance imaging (MRI). The computer system determines one or more metrics indicative of an image texture of the one or more images; and determines at least one of a bone risk or a bone health of the patient based on the one or more metrics.