GPU-Based Parallel Beamforming for Real-Time HIFU Displacement Maps
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Solution Overview
Problem
Current treatment monitoring systems face limitations in achieving high frame rates, spatial resolution, and real-time feedback over extended monitoring periods, particularly in clinical settings for High-Intensity Focused Ultrasound (HIFU) applications.
Innovation Solution
The implementation of a sparse-matrix technique for parallel beamforming and scan conversion, combined with GPU-based algorithms, enables real-time treatment monitoring by reconstructing and displaying displacement maps from channel data, allowing for improved frame rates and spatial resolution, and facilitating continuous monitoring during HIFU treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional beamforming and reconstruction methods are used for treatment monitoring, then processing can be performed with standard computational resources, but frame rates are limited and real-time feedback is not achieved
Solution Approach 1:
The patent replaces conventional CPU-based sequential processing with GPU-based parallel processing architecture. The reconstruction matrix application and displacement estimation operations are parallelized across multiple GPU cores, enabling simultaneous processing of multiple channels and frames. This substitution of computational mechanics achieves real-time frame rates (e.g., 30 fps or higher) while maintaining processing accuracy, directly resolving the contradiction between productivity and time loss.
2Productivity
If high frame rate imaging is implemented using parallel beamforming and GPU techniques, then real-time feedback is achieved, but device complexity increases
Solution Approach 1:
The patent implements a unified GPU-based processing platform that handles multiple functions: parallel beamforming, reconstruction matrix application, displacement estimation, and real-time display rendering. This multi-functional integration consolidates what would otherwise require separate hardware units into a single versatile system, achieving high frame rates while managing complexity through functional consolidation rather than proliferation of dedicated components.
Solution Approach 2:
The patent introduces a reconstruction matrix as an intermediary computational structure that pre-calculates and stores beamforming weights and geometric transformations. This matrix serves as a mediator between raw channel data and final displacement images, enabling efficient GPU parallelization by transforming complex sequential beamforming operations into streamlined matrix-vector multiplications that can be executed simultaneously across multiple data channels.
3Productivity
If localized elasticity imaging is used focusing only on the focal spot, then computational cost is reduced, but spatial resolution and field of view are limited
Solution Approach 1:
The patent extends localized focal spot imaging into the temporal dimension by implementing continuous real-time streaming of displacement maps. While each individual frame maintains focused computational attention on the focal region for speed, the temporal sequence of frames provides continuous monitoring capability throughout the treatment window. This dimensional transition from static single-point imaging to dynamic continuous monitoring effectively expands the functional field of view without sacrificing processing speed.
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
This approach provides quantitative real-time feedback during HIFU treatments, enabling effective monitoring and preventing overtreatment by continuously tracking tissue stiffness changes, with significant improvements in processing speed and frame rates compared to conventional methods.
Implementation Method 1
acquired channel data from each of a plurality of channels of a signal array over a plurality of frames
Implementation Method 2
estimation of motion generated by external compression or acoustic radiation force such as Transient Elastography, Shear Wave Imaging (SSI), Elastography, ARFI imaging
Implementation Method 3
determining a reconstruction matrix based on a reconstruction operation to be performed on the channel data, applying the reconstruction matrix to the channel data to obtain reconstructed channel data
Implementation Method 4
the estimating the displacement data can be performed using a cross correlation technique
Implementation Method 5
estimating displacement data representing displacement of an object over the frames from the reconstructed channel data
Implementation Method 6
determining a conversion matrix based on a conversion operation to be performed on the reconstructed channel data, applying the conversion matrix to the reconstructed channel data to obtain a displacement map
Data Source
AI summary
Systems and techniques of treatment monitoring include acquiring channel data from each of a plurality of channels of a signal array over a plurality of frames, determining a reconstruction matrix based on a reconstruction operation to be performed on the channel data, applying the reconstruction matrix to the channel data to obtain reconstructed channel data, estimating displacement data representing displacement of an object over the frames from the reconstructed channel data; determining a conversion matrix based on a conversion operation to be performed on the reconstructed channel data, and applying the conversion matrix to the reconstructed channel data to obtain a displacement map.