Medical Image Data Registration Using Anatomical Landmark Mapping
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Solution Overview
Problem
Current surgical procedures face challenges in associating two-dimensional (2D) medical images with additional information such as patient orientation and anatomical element identities, which are crucial for surgical navigation and planning.
Innovation Solution
A method that processes medical image data by determining spatial patterns from 2D images and 3D data, allowing for the mapping of anatomical elements' positions between the two image types, thereby enabling the transfer of anatomical information and registration between images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surgeon manually aligns 2D images with 3D medical image data to perform registration, then the registration can be achieved, but the process requires significant time and manual effort
Solution Approach 1:
The system performs automatic registration by having the 2D images and 3D image data self-align through computational algorithms. The processor automatically identifies anatomical landmarks in both datasets and computes the transformation matrix without requiring manual surgeon intervention, thus reducing time while maintaining precision
Solution Approach 2:
The manual mechanical alignment process performed by the surgeon is replaced with an automated computational system. The processor uses image processing algorithms and mathematical transformations to perform the registration task that previously required manual visual alignment and physical manipulation of images
2Measurement precision
If digitally rendered radiographs (DRRs) are used to align with 2D images, then registration can be achieved, but large processing resources are required
Solution Approach 1:
Instead of generating and processing complex DRRs that require significant computational resources, the system extracts key anatomical landmarks directly from the actual 2D images and matches them with corresponding landmarks in the 3D image data. This extraction approach achieves registration without the heavy processing burden of DRR generation
Solution Approach 2:
The system uses lightweight image feature representations (landmarks and spatial patterns) instead of heavy DRR computations. These simplified representations require minimal processing resources while still enabling accurate registration, effectively replacing expensive computational objects with cheaper alternatives
3Ease of manufacture
If 2D images are acquired without associated patient orientation information, then the imaging process is simpler, but the surgeon must manually derive anatomical directions which increases time and complexity
Solution Approach 1:
The system performs preliminary computation to automatically determine patient orientation and anatomical directions from the 2D images before the surgeon needs to use this information. The processor analyzes the images, identifies anatomical landmarks, and derives orientation data in advance, making the information readily available without requiring manual derivation during surgery
Solution Approach 2:
The system introduces an intermediate computational step that processes the 2D images to extract and associate patient orientation information. This intermediary processing layer automatically derives anatomical directions and links them to the images, bridging the gap between simple image acquisition and the need for oriented anatomical information
4Loss of information
If surgeons manually identify anatomical elements in 2D images, then anatomical information can be obtained, but the process is time-consuming and prone to human error
Solution Approach 1:
The system enables automatic identification of anatomical elements by having the processor analyze the 2D images and self-determine the identities of anatomical structures. The system uses image recognition algorithms to automatically label and identify anatomical elements without requiring the surgeon to manually identify each structure, thus completing the information extraction task autonomously and efficiently
Data Source
AI summary
A method, computer program and system for processing medical image data of a patient's body are provided. Medical image data including two 2D images is obtained, each depicting two common anatomical elements of the patient's body from a known viewing direction, the known viewing direction differing between the 2D images. A first spatial pattern indicative of first 3D positions of the common anatomical elements is determined. A second spatial pattern indicative of second 3D positions of a plurality of anatomical elements of the patient's body, said plurality of anatomical elements comprising the two common anatomical elements, is obtained. A mapping is determined between the first spatial pattern and the second spatial pattern to associate at least one of the first three-dimensional positions with at least one of the second three-dimensional positions.