Medical Image Registration for Posture-Adaptive Tissue Alignment
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Solution Overview
Problem
Existing image registration methods in precision medical surgery fail to accurately align and integrate soft and hard tissue images from different imaging modalities, particularly during surgeries where patient posture changes occur, leading to inaccuracies in surgical planning and execution.
Innovation Solution
An image registration method and system that utilizes artificial intelligence models to segment, align, and generate target soft and hard tissue images by obtaining scale ratios and nonlinear deformations, even when images are taken in different patient postures, using imaging devices like MRI and C-Arm equipment, and processors to perform registration and training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image registration is performed using traditional methods, then the process is simple, but the alignment accuracy between soft and hard tissue images from different modalities is insufficient
Solution Approach 1:
The patent segments the medical image into hard tissue and soft tissue components separately. Hard tissue segmentation identifies bone structures, while soft tissue segmentation isolates muscular and organ tissues. This segmentation enables independent processing and registration of different tissue types, improving alignment accuracy by accounting for their distinct deformation characteristics during patient posture changes.
Solution Approach 2:
The patent applies different transformation parameters to hard and soft tissues based on their distinct mechanical properties. Hard tissue undergoes rigid transformation with consistent scale and rotation, while soft tissue undergoes non-rigid transformation with variable local deformations. This parameter differentiation resolves the contradiction by improving alignment accuracy through tissue-specific modeling while maintaining manageable process complexity through automated parameter selection.
2Adaptability or versatility
If patient posture changes during surgery, then surgical flexibility is improved, but the accuracy of image alignment between preoperative and intraoperative images deteriorates
Solution Approach 1:
The patent implements dynamic registration that adapts to patient posture changes during surgery. The system continuously updates transformation parameters based on intraoperative hard tissue images captured in different postures. By modeling the dynamic relationship between hard and soft tissue deformations, the system maintains alignment accuracy across varying surgical positions while preserving surgical flexibility.
Solution Approach 2:
The patent performs preliminary registration of hard tissue structures before soft tissue registration. Hard tissue images are aligned first to establish a stable reference framework, then soft tissue images are registered relative to this framework with compensation for posture-induced deformations. This preliminary action ensures that subsequent soft tissue alignment remains accurate even when patient posture changes during surgery.
3Productivity
If only limited intraoperative hard tissue information is available, then surgical workflow is simplified, but the quality of generated soft and hard tissue images deteriorates
Solution Approach 1:
The patent uses preoperative hard tissue images as templates to generate intraoperative hard tissue images when direct intraoperative imaging is limited. The system copies and transforms preoperative hard tissue data according to detected posture changes and scale ratios, creating accurate surrogate images that maintain surgical workflow efficiency while preserving image generation quality through physics-based deformation modeling.
Solution Approach 2:
The patent introduces hard tissue images as an intermediary to bridge preoperative and intraoperative soft tissue images. When intraoperative hard tissue information is limited, the system uses available hard tissue data as a mediator to compute transformation fields that accurately map preoperative soft tissue images to the current surgical state, maintaining image quality without requiring complete intraoperative imaging.
Data Source
AI summary
An image registration method is read by a processing device to perform: obtaining a first medical image and a second medical image generated by different imaging devices, with the first medical image including soft and hard tissue image; segmenting the first medical image and the second medical image to obtain a first hard tissue image and a second hard tissue image, respectively; aligning a coordinate axis of the first hard tissue image and a coordinate axis of the second hard tissue image, and obtaining a registration field indicating a corresponding relationship between the first hard tissue image and the second hard tissue image; obtaining a scale ratio between the first hard tissue image and the second hard tissue image according to the registration field; and generating a target soft and hard tissue image according to the scale ratio, the soft and hard tissue image and the second hard tissue image.