Anatomy-Based Medical Image Co-Registration Using Learned Modality Mapping
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Solution Overview
Problem
The challenge lies in effectively co-registering medical images from different modalities, such as CT and MRI, which have distinct visualization strengths and weaknesses, making it difficult to combine and display them effectively during surgical procedures, especially when anatomical structures have different contrasts.
Innovation Solution
A system and method that identify anatomical features in each modality's images, utilizing known spatial relationships between these features to determine registration parameters, allowing for accurate alignment and co-registration of images from different imaging modalities, including bony structures and fibrous connective tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If images from different modalities (CT and MRI) are combined to leverage complementary strengths, then the completeness and quality of anatomical visualization is improved, but the difficulty of registration increases due to different contrast characteristics of anatomical structures
Solution Approach 1:
The patent introduces an intermediary transformation process that converts MRI images into a CT-like appearance through learned mapping functions. This intermediary representation allows registration algorithms to operate on images with similar contrast characteristics, effectively mediating between the different modality representations and enabling accurate alignment despite the original contrast differences.
Solution Approach 2:
The patent transforms the registration problem by changing the parameter space - instead of directly registering images with different contrast characteristics, it applies parameter transformations that map one modality's appearance to another's. This involves learning and applying transformation parameters that adjust intensity distributions, contrast patterns, and structural emphases to make images from different modalities comparable for registration purposes.
2Ease of manufacture
If traditional landmark-based registration methods are used, then the process is relatively simple, but registration accuracy deteriorates when anatomical structures have significantly different contrasts between modalities
Solution Approach 1:
The patent replaces the mechanical/ geometric landmark-based registration system with a learned image transformation system. Instead of relying on manual or automated identification of corresponding landmarks across modalities, it uses machine learning models to automatically learn the transformation parameters that map one modality's appearance to another's, substituting the traditional geometric approach with a data-driven transformation approach.
Solution Approach 2:
The patent performs preliminary transformation of images to a common appearance space before registration is attempted. By pre-processing the images through learned mapping functions that adjust contrast and appearance characteristics, it prepares the data in advance to make subsequent registration more accurate and reliable, rather than attempting direct registration of raw multi-modal images.
Data Source
AI summary
Systems and methods for co-registering medical images obtained with different imaging modalities are provided. For instance, images obtained with x-ray imaging, such as x-ray computed tomography (“CT”), can be co-registered with images obtained with magnetic resonance imaging (“MRI”). The different imaging modalities generate images that have different visualization characteristics for tissues; thus, in general, co-registration is accomplished by identifying different anatomical features in the different images and then utilizing a known spatial relationship between those anatomical features to co-register the different images.