HIFU Treatment Device Parameterization With Real-Time Field Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing HIFU treatment simulation methods are slow and complex when modeling heterogeneous tissue areas, particularly when high spatial resolution is required, and often lead to suboptimal treatment protocols due to the need for extensive offline simulations and empirical adjustments.
Innovation Solution
A method and system for parameterizing HIFU treatment devices using a meta-model database to estimate ultrasound fields and thermal doses, allowing for real-time simulation and optimization of treatment parameters to achieve quasi-optimal tissue necrosis and preservation, involving tissue region modeling, ultrasound field interpolation, and cost function minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional simulation methods are used to model heterogeneous tissue areas with high spatial resolution, then measurement precision is improved, but productivity deteriorates due to simulation times taking several hours
Solution Approach 1:
The patent pre-calculates ultrasound field distributions for multiple tissue parameter configurations before the actual treatment planning. These pre-computed fields are stored and then interpolated during treatment planning to achieve both high precision and fast computation. This preliminary action eliminates the need for time-consuming simulations during the actual treatment workflow.
Solution Approach 2:
The patent creates a simplified computational model that copies the essential physics of ultrasound propagation through heterogeneous tissues. By using pre-computed field distributions and interpolation rather than full-wave simulations, the system replicates accurate tissue heating patterns at a fraction of the computational cost, enabling real-time treatment planning.
2Manufacturing precision
If offline simulations are performed for all possible configurations, then manufacturing precision is improved, but loss of time worsens due to the extensive computation required
Solution Approach 1:
The patent changes the approach from computing all possible configurations to computing a limited set of representative configurations and then interpolating for intermediate cases. By parameterizing tissue properties and using interpolation, the system achieves high accuracy for any tissue configuration without the exponential computation time that would result from enumerating all possibilities.
Solution Approach 2:
The system performs preliminary computations for a discrete set of tissue parameter configurations, storing these results for rapid retrieval and interpolation during treatment planning. This preliminary computation phase separates the heavy computational burden from the actual treatment workflow, enabling both accuracy and speed.
3Ease of operation
If empirical adjustments are made to treatment protocols, then ease of operation is improved, but productivity deteriorates due to slow convergence and suboptimal solutions
Solution Approach 1:
The patent implements an automated feedback loop that computes the predicted thermal dose distribution, compares it with the desired treatment outcome, and automatically adjusts treatment parameters to minimize the cost function. This closed-loop optimization eliminates the need for manual empirical adjustments while ensuring convergence to optimal or near-optimal solutions, improving both ease of operation and productivity.
Solution Approach 2:
The system performs self-optimization by automatically adjusting treatment parameters based on the computed thermal dose and desired outcome. The optimization algorithm autonomously refines the treatment protocol without requiring practitioner intervention, thereby improving convergence speed and solution quality while maintaining operational simplicity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables rapid, quasi-optimal HIFU treatment planning by minimizing the difference between intended and estimated necrotic regions while preserving healthy tissues, facilitating outpatient treatment through near-real-time simulation and iterative parameter adjustment.
Implementation Method 1
High-intensity focused ultrasound (HIFU) therapy uses the energy of a focused ultrasound beam to modify or destroy biological tissues. Tissue destruction occurs through protein coagulation, inducing irreversible cell damage and apoptosis.
Implementation Method 2
Tissue destruction occurs through protein coagulation, inducing irreversible cell damage and apoptosis.
Implementation Method 3
HIFU treatment typically uses a multi-transducer phase-array probe (phased array). With each ultrasonic pulse, a delay law and an amplitude law are applied to the different elements of the transducer, so as to form a beam focused at a predetermined point.
Implementation Method 4
The temperature rise at each point is then deduced from solving the heat transfer equation in the tissues.
Implementation Method 5
a step of estimating the thermal dose deposited at each point of a grid within the area to be treated from the ultrasound field in that area
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The present invention relates to a parameterising method and system (100) integrated within a high-intensity focused ultrasound (HIFU) treatment device. The parameterising system comprises a real-time simulation unit (140) that makes it possible to predict, on the basis of geometric and physiological parameters of tissue regions in the area to be treated, on the one hand, and treatment parameters, on the other hand, the distribution of the ultrasound field within said area. The calculation is performed in real time by means of a metamodel: the ultrasound field is estimated from an interpolation of maps of the ultrasound field which are pre-calculated and stored in a database, the maps being associated with different values of the geometric and physiological parameters of the tissue regions in question. The thermal dose applied at each point during treatment is subsequently calculated and the tissue response is estimated. It is possible for the practitioner to check at any time that the simulated treatment is being used in accordance with tissue regions to be necrotised and tissue regions to be spared. The treatment parameters can be iteratively adjusted in order to conform to said objective.