3D Imaging and Treatment Planning for Medical Tissue
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing tissue treatment and planning methods face challenges such as inaccurate energy delivery, complex user interfaces, and sensitivity to probe alignment, leading to potential over or under treatment and increased energy exposure to unintended tissues.
Innovation Solution
A user interface is developed to facilitate treatment planning by providing rotated images and 3D views, allowing users to adjust and verify treatment profiles, with AI algorithms to identify tissue structures and optimize energy delivery angles, ensuring precise targeting and minimizing interaction with adjacent tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior image-guided treatment approaches are used, then treatment planning can be performed with image overlay, but the user interface becomes complex and gaps appear in treatment region review
Solution Approach 1:
The patent transitions from 2D image overlays to a 3D volumetric representation of treatment regions. The system renders three-dimensional models that allow users to visually inspect treatment gaps and overlaps from multiple angles, eliminating the complexity of coordinating multiple 2D images while providing comprehensive treatment region review.
2Reliability
If energy delivery is increased to ensure complete treatment, then treatment effectiveness improves, but energy exposure to unintended adjacent tissue increases
Solution Approach 1:
The system applies different energy delivery parameters to different regions within the treatment zone. By segmenting the treatment volume into sub-regions with varying tissue characteristics, the system optimizes energy delivery locally for each region, ensuring complete treatment of target tissue while minimizing energy exposure to adjacent unintended tissue through region-specific parameter adjustment.
3Measurement precision
If probe alignment sensitivity is increased to improve imaging accuracy, then image quality improves, but the system becomes more sensitive to misalignment between treatment and imaging probes
Solution Approach 1:
The system introduces a coordinate transformation framework that acts as an intermediary between the imaging probe coordinate system and the treatment probe coordinate system. This framework includes registration algorithms and transformation matrices that automatically compensate for misalignment, allowing high imaging accuracy to be maintained while increasing tolerance to probe positioning variations through computational correction.
Data Source
AI summary
A user interface presents a 3D view of the tissue and treatment plan. The 3D view comprises a plurality of transverse images arranged along a one or more longitudinal images. The user adjusts the treatment profile with input to the user interface, and an updated treatment profile is shown on the other views. The user to one or more of zoom, pan, or rotate the 3D view with the treatment profile overlaid on the 3D view, and the treatment profile moves with the 3D view to maintain registration with the 3D view. In some embodiments, a 3D treatment plan is generated in accordance with a plurality of angles between a treatment probe and the one or more tissue structures. An AI algorithm can be used to identify tissue structures and plan the treatment angles and energy delivery in accordance with the tissue structures, which can provide a more customized treatment.