CT Trabecular Bone Density Calibration Without Phantoms
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Solution Overview
Problem
Existing bone density measurement techniques using computed tomography (CT) lack accuracy and convenience due to the need for phantoms, and fail to account for variations in patient-specific tissue densities, large blood vessels, Schmorl's nodes, and vertebral fractures, leading to inaccurate bone density assessments.
Innovation Solution
A phantomless CT-based system that uses patient-specific calibration with known tissue densities (e.g., fat, heart, muscle) and automated detection of vertebral fractures to exclude large blood vessels and Schmorl's nodes, focusing on trabecular bone density measurements across multiple vertebrae slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a phantom is used for calibration in CT-based bone density measurement, then measurement accuracy is improved, but device complexity and convenience are worsened due to the need for additional calibration equipment and procedures
Solution Approach 1:
The system uses the patient's own body tissues (fat, heart, muscle) with known densities as internal calibration references, eliminating the need for external phantoms. The calibration is performed automatically using the patient's anatomical structures visible in the CT scan, making the system self-calibrating and removing the need for separate calibration equipment.
Solution Approach 2:
The patent introduces software-based calibration algorithms that use the known densities of body tissues as intermediaries to calibrate the bone density measurements. These algorithms act as mediators between the raw CT data and the final bone density values, replacing the need for physical phantom intermediaries.
2Measurement precision
If all CT slices are used for bone density determination, then measurement precision is improved, but the impact of harmful factors like large blood vessels, Schmorl's nodes, and vertebral fractures worsens accuracy
Solution Approach 1:
The system automatically detects and extracts regions containing harmful factors (large blood vessels, Schmorl's nodes, vertebral fractures) from the CT slices. These problematic regions are identified and excluded from the bone density calculation, allowing the system to use all available slices while maintaining accuracy by removing only the harmful portions.
Solution Approach 2:
The patent segments the CT slices into different regions: valid bone tissue regions and harmful factor regions. By segmenting the image data and selectively processing only the valid regions, the system maximizes the use of available data while excluding areas that would degrade measurement accuracy.
3Measurement precision
If patient-specific calibration using body tissues is implemented, then measurement accuracy is improved, but processing complexity increases
Solution Approach 1:
The system automatically identifies and uses the patient's own body tissues (fat, heart, muscle) as calibration references. The calibration process is performed autonomously by the software, which automatically locates these tissues, retrieves their known density values, and applies the calibration factors without requiring manual intervention or complex external calibration procedures.
Solution Approach 2:
The patent replaces manual calibration procedures with automated software-based calibration. The system uses computational algorithms to perform the calibration that would otherwise require manual measurement and calculation, substituting mechanical/manual processes with automated digital processing.
4Measurement precision
If automated detection and exclusion of harmful regions is performed, then bone density measurement accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system replaces manual detection and exclusion of harmful regions with automated image processing algorithms. The software automatically analyzes the CT slices, identifies regions containing blood vessels, Schmorl's nodes, and fractures, and excludes them from calculations, replacing time-consuming manual processes with rapid automated computation.
Solution Approach 2:
The calibration and detection processes are performed automatically by the system using the patient's own anatomical structures as references. The software self-calibrates using body tissues and self-detects harmful regions without requiring external equipment or manual intervention, streamlining the overall process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy of bone density determinations by reducing precision error from 2.5% to 1.2-1.5%, providing more precise T and Z scores and FRAX-CT scores, and enabling automated detection of vertebral fractures.
Implementation Method 1
obtaining one or more images of a spine region comprising thoracic and/or lumbar vertebrae, and/or a hip, by computed tomography (CT)
Implementation Method 2
obtaining one or more images of a spine region comprising thoracic and/or lumbar vertebrae, and/or a hip, by computed tomography (CT)
Data Source
AI summary
CT images from the neck, lung, cardiac, abdominal, pelvis, hip, spine or lower extremity areas are obtained and analyzed by a computer to measure trabecular bone density on each level it is available, in the spine and hip. Any bone tissue associated with vertebrae that is fractured is excluded, along with Schmorl's nodes, large blood vessels, and cortical bone, to accurately average the trabecular bone density on a scan. The density of fat, heart, and muscle tissue of a subject is used to calibrate the results. That data may be input to an absolute fracture risk model (Fracture Risk Algorithm calculator). Information such as bone density, T score, Z score, fracture risk, and identification of vertebral fractures (if present) may be determined and reported.