Medical Image Processor for 3D Blood Vessel Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The interpretation of medical images from percutaneous coronary intervention procedures, such as IVUS, OCT, and OFDI, is challenging and requires automation to accurately diagnose and treat vascular lesions, necessitating improved image processing techniques for generating accurate blood vessel anatomical features.
Innovation Solution
A medical image processing apparatus that uses a catheter with an ultrasonic probe to acquire cross-sectional images, which are then processed by a processor connected to a machine learning model to classify pixels, identify boundaries, and generate a 3-D image of the blood vessel, allowing for accurate anatomical feature representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation of medical images is performed, then diagnostic accuracy may be maintained, but interpretation difficulty and time consumption increase
Solution Approach 1:
The patent replaces manual mechanical interpretation with an automated machine learning system. The processor automatically processes medical images, generates 3-D blood vessel images, and identifies anatomical features without requiring manual intervention, thereby reducing interpretation time while maintaining diagnostic accuracy through algorithmic analysis.
Solution Approach 2:
The system enables self-service automated interpretation where the machine learning model independently processes images, generates 3-D reconstructions, and provides diagnostic information without human intervention. The apparatus autonomously performs the full workflow from image acquisition to 3-D visualization, eliminating the need for manual interpretation steps.
2Manufacturing precision
If complex image processing algorithms are used, then anatomical feature accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent segments the complex image processing task into distinct functional modules: a first machine learning model for pixel classification, a second machine learning model for boundary identification, and a 3-D image generation module. This segmentation allows each component to specialize in a specific aspect of anatomical feature extraction, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system transforms 2-D cross-sectional medical images into 3-D blood vessel images, adding a spatial dimension to the data. This dimensional transformation enables more accurate anatomical feature representation by providing spatial context and relationships that are not visible in 2-D slices, while the automated processing handles the computational complexity.
3Measurement precision
If 3-D image generation is implemented, then anatomical visualization is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary processing by generating 3-D blood vessel images before detailed anatomical feature analysis. The first machine learning model pre-classifies pixels and the second model pre-identifies boundaries in the 3-D space, preparing the data structure in advance. This preliminary action reduces the computational energy required for subsequent detailed analysis by organizing data efficiently.
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
The apparatus effectively generates a 3-D image of the blood vessel, enhancing the accuracy of anatomical feature representation and aiding in the diagnosis and treatment of vascular lesions by providing a clear and intuitive visualization of the vascular structure.
Implementation Method 1
an ultrasonic probe that emits an ultrasonic wave to a vascular tissue and receives a reflected wave
Data Source
AI summary
An image processing apparatus for processing images of a luminal organ includes a first circuit connectable to a catheter having an ultrasonic probe and insertable into the organ, a second circuit connectable to a display, and a processor configured to: control the catheter to acquire cross-sectional images of the organ when the catheter is inserted thereinto and moved along a longitudinal direction thereof, input the images into a learning model and for each image, obtain position data indicating a boundary between regions of the organ based on segmentation data output from the model, select two consecutive images and identify a group of points corresponding to the boundary in each image based on the position data, associate points in one selected image with points in the other image, and display a 3-D image in which the points in one selected image are connected to the points in the other image.