Patient Structure Estimation Using Bracketed Exposure Depth Imaging
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Solution Overview
Problem
Current medical imaging systems face challenges in accurately estimating patient structure and orientation due to issues like variable illumination and reflection, leading to incomplete or inaccurate 3D depth images, which can result in repeat scans and increased radiation exposure.
Innovation Solution
The method involves capturing depth images of a patient and applying bracketed exposure depth imaging (BEDI) and coefficient of illumination variation (CoIV)-based corrections to eliminate the effects of variable illumination and reflection, ensuring accurate 3D patient structure estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional depth imaging is used without correction, then the imaging process is simple and fast, but the depth information is lost due to uneven illumination and reflection
Solution Approach 1:
The system performs preliminary actions by capturing multiple depth images at different exposure settings before the final 3D reconstruction. This preliminary multi-exposure capture allows the system to later select and combine the best-exposed regions, preventing information loss before it occurs rather than trying to recover it afterward.
Solution Approach 2:
The system changes the exposure parameter by capturing depth images at multiple different exposure settings. This parameter variation allows different regions of the patient's body to be captured at optimal exposure levels, compensating for uneven illumination and reflection across the imaging field.
2Loss of information
If multiple exposure settings are used to capture complete depth information, then depth information completeness is improved, but the imaging time increases
Solution Approach 1:
The system performs preliminary multi-exposure capture quickly using automated exposure settings, then immediately selects and combines the best regions from each exposure. This preliminary action approach captures complete depth information faster than sequential imaging would allow.
Solution Approach 2:
The system captures more depth information than initially needed by taking multiple exposures, but this excessive action is performed efficiently through automated selection and combination algorithms that quickly identify and merge the useful portions from each exposure, minimizing the time penalty.
3Measurement precision
If manual patient positioning is used, then the system is simple to operate, but the patient structure estimation accuracy is insufficient leading to repeat scans
Solution Approach 1:
The system provides visual feedback by overlaying the estimated 3D patient structure on the depth images, allowing operators to verify positioning accuracy before the actual scan. This feedback loop enables immediate correction of positioning errors, preventing repeat scans and improving overall scanning efficiency.
Solution Approach 2:
The system performs automatic patient structure estimation and positioning verification without requiring manual intervention. The automated algorithms analyze the depth images, estimate the 3D patient structure, and provide positioning guidance independently, reducing operator workload while improving accuracy and preventing repeat scans.
Data Source
AI summary
Methods and systems are provided for estimating patient structure prior to a scan by a medical imaging system. As one example, a method may include acquiring depth images of a patient positioned on a table of the medical imaging system, correcting the depth images based on histogram data from the depth images, and extracting a three-dimensional structure of the patient based on the corrected depth images.