Brain Image Fusion with Deformation Compensation
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Solution Overview
Problem
Current image fusion technologies struggle to accurately align and fuse images of the brain pre- and post-procedure, especially when anatomical deformations occur due to surgical procedures or mass occupying lesions, leading to inaccurate registration and targeting in medical procedures.
Innovation Solution
A system and method that employs a fusion engine to perform coarse and non-rigid alignment, using deformation models and fiducial markers to compensate for spatial deformations, allowing for accurate registration of preoperative, intraoperative, and postoperative images, and atlases, thereby improving the accuracy of surgical procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard image fusion is used to align pre- and post-procedure brain images, then the fusion process is simple and fast, but registration accuracy deteriorates due to brain deformation from surgery or lesions
Solution Approach 1:
The brain is divided into multiple regions of interest (ROIs) with different deformation characteristics. Each ROI is registered independently using appropriate transformation models (rigid, affine, or non-linear), allowing accurate handling of localized deformations while maintaining overall registration efficiency
Solution Approach 2:
Different transformation models and registration strategies are applied to different brain regions based on their specific deformation characteristics. Highly deformed regions receive more complex non-linear transformations, while stable regions use simpler rigid or affine transformations, optimizing both accuracy and computational efficiency
2Measurement precision
If complex deformation models are applied to compensate for brain deformation, then registration accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The brain is divided into multiple regions of interest (ROIs) with different deformation characteristics. Each ROI is registered independently using appropriate transformation models (rigid, affine, or non-linear), allowing accurate handling of localized deformations while maintaining overall registration efficiency
Solution Approach 2:
Full non-linear deformation compensation is applied only to regions exhibiting significant deformation, while other regions use simpler rigid or affine transformations. This selective application reduces computational burden while maintaining accuracy where needed
3Measurement precision
If manual registration methods are used to account for anatomical deformations, then registration accuracy can be improved, but ease of operation deteriorates due to increased manual intervention required
Solution Approach 1:
The system automatically identifies regions of interest, selects appropriate transformation models, and performs deformation-compensated registration without requiring manual intervention. The automated workflow maintains high registration accuracy while preserving ease of operation
Solution Approach 2:
The system dynamically adjusts registration parameters such as transformation model selection, ROI boundaries, and optimization criteria based on image characteristics and deformation patterns, enabling accurate automated registration without manual parameter tuning
Data Source
AI summary
A system can include a model to represent a volumetric deformation of a brain corresponding to brain tissue that has been displaced by at least one of disease, surgery or anatomical changes. A fusion engine can perform a coarse and/or fine fusion to align a first image of the brain with respect to a second image of the brain after a region of the brain has been displaced and to employ the deformation model to adjust one or more points on a displacement vector extending through a displaced region of the brain to compensate for spatial deformations that occur between the first and second image of the brain.