4D Model for Fiducial-Less Target Tracking
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Solution Overview
Problem
Current methods for dynamically tracking moving anatomical targets, such as tumors or lesions, during medical procedures like radiosurgery are limited by the invasive nature of fiducial markers and the inability to account for non-rigid deformations and periodic motions, leading to inaccuracies in radiation delivery.
Innovation Solution
A 4D mathematical model is constructed to describe the non-rigid deformation and motion of anatomical regions, using CT images and motion sensors, allowing for real-time tracking and radiation dose distribution adjustment during periodic motions like respiration or heartbeat, without the need for fiducial markers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fiducial markers are used to track moving anatomical targets, then tracking accuracy is improved, but patient invasiveness and discomfort increase
Solution Approach 1:
The patent uses CT images and surface markers to create a virtual copy model of the patient's anatomy and motion, replacing the need for physical fiducial markers implanted in the patient. The surface markers on the skin serve as proxies to track the motion of internal anatomical structures through image processing and registration algorithms.
Solution Approach 2:
The patent introduces CT images and surface markers as intermediary elements to bridge the gap between external observable motion and internal target position. These intermediaries enable indirect tracking of anatomical targets without direct physical contact or implantation.
2Device complexity
If rigid transformation models are used for motion compensation, then computational complexity is reduced, but accuracy in accounting for non-rigid deformations deteriorates
Solution Approach 1:
The patent transitions from static rigid transformation models to dynamic non-rigid deformation models that can adapt to changing anatomical shapes during motion cycles. The system uses time-varying deformation fields that are updated based on sequential CT images captured at different phases of the motion cycle.
Solution Approach 2:
The patent divides the anatomical region into multiple deformable segments or voxels that can independently deform during motion. This segmentation allows the system to model complex non-rigid deformations by tracking the displacement of individual segments rather than requiring a single rigid body transformation.
3Measurement precision
If real-time tracking during periodic motion is implemented, then treatment accuracy is improved, but data processing requirements and computational load increase
Solution Approach 1:
The patent exploits the periodic nature of respiratory and cardiac motion to organize data processing in sync with the motion cycles. CT images are acquired at specific phases of the periodic motion, and the system uses this temporal periodicity to predict and interpolate intermediate positions, reducing the computational burden compared to continuous tracking.
Solution Approach 2:
The system performs preliminary processing by acquiring and registering multiple CT images at different motion phases before the actual treatment. Deformation models are pre-computed and stored, allowing rapid lookup and application during real-time treatment without requiring complex real-time calculations.
Data Source
AI summary
Treatment targets such as tumors or lesions, located within an anatomical region that undergoes motion (which may be periodic with cycle P), are dynamically tracked. A 4D mathematical model is established for the non-rigid motion and deformation of the anatomical region, from a set of CT or other 3D images. The 4D mathematical model relates the 3D locations of part(s) of the anatomical region with the targets being tracked, as a function of the position in time within P. Using fiducial-less non-rigid image registration between pre-operative DRRs and intra-operative x-ray images, the absolute position of the target and/or other part(s) of the anatomical region is determined. The cycle P is determined using motion sensors such as surface markers. The radiation beams are delivered using: 1) the results of non-rigid image registration; 2) the 4D model; and 3) the position in time within P.