Tomographic Image Reconstruction via Fluoroscopic Mediator
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Solution Overview
Problem
Tomographic images captured using a catheter system often result in missing regions of lumen organs, particularly in large blood vessels, making it difficult to accurately determine the size and state of these organs, as the imaging range does not encompass the entire vessel, leading to complicated interpretations and inaccurate calculations.
Innovation Solution
A computer program and image processing method that acquires multiple tomographic images from different locations, extracts images with missing regions, and compensates for these missing areas using machine learning models, such as U-Net or SegNet, to classify and label pixels, thereby reconstructing the complete image of the lumen organ.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a catheter is used to capture tomographic images of a lumen organ, then the imaging process can be performed minimally invasively, but the imaging range is limited and results in missing regions of the lumen organ
Solution Approach 1:
The patent uses fluoroscopic images as an intermediary to guide the reconstruction process. The fluoroscopic image provides a complete overview of the lumen organ, while the tomographic images provide detailed cross-sectional information. By using the fluoroscopic image as a mediator, the system can identify missing regions in the tomographic images and reconstruct them by referencing the corresponding areas in the fluoroscopic image, thus overcoming the limited imaging range of the catheter-based tomographic imaging.
2Productivity
If the catheter is positioned to capture images, then the imaging procedure can be performed, but the lumen organ may be biased with respect to the imaging range resulting in missing regions
Solution Approach 1:
The patent implements a feedback mechanism where the system first captures tomographic images, identifies missing regions, then uses fluoroscopic imaging to detect those same regions, and finally reconstructs the missing areas by combining information from both imaging modalities. This feedback loop ensures that information loss is detected and corrected, allowing the procedure to remain efficient while recovering complete anatomical information.
Solution Approach 2:
The patent performs preliminary fluoroscopic imaging to obtain a complete overview of the lumen organ before or during the tomographic imaging process. This preliminary action allows the system to anticipate and prepare for missing regions in the tomographic images, enabling more effective reconstruction by having reference information available in advance.
3Loss of time
If tomographic images with missing regions are used for measurement, then the imaging process is completed, but accurate calculation of lumen size and vessel wall thickness cannot be performed
Solution Approach 1:
The patent merges information from two different imaging modalities: fluoroscopic images and tomographic images. The fluoroscopic images provide complete anatomical context, while the tomographic images provide detailed cross-sectional data. By combining these complementary data sources, the system can accurately measure lumen size and vessel wall thickness even when individual images have missing regions, maintaining both time efficiency and measurement precision.
Data Source
AI summary
A non-transitory computer-readable medium storing a computer program executed by a computer, a method, and an image processing device are disclosed that are capable of compensating a missing region in a tomographic image in a state in which a part of a lumen organ is missing. In accordance with the program, a computer acquires a plurality of tomographic images of a cross section of the lumen organ captured at a plurality of places using a catheter. In addition, the computer extracts, from the plurality of tomographic images, a tomographic image in which the part of the lumen organ is missing. Then, the computer compensates a missing region of the lumen organ for the extracted tomographic image.