Ultrasound Dose Estimation with Tissue Segmentation and Bubble Correction
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Solution Overview
Problem
Current ultrasound dosimetry methods are inadequate for accurately determining the ultrasound dose delivered to a target tissue volume during therapeutic applications, particularly when using microbubble contrast agents, as they rely on conservative and homogeneous tissue path assumptions, failing to account for varying tissue types and ultrasound coupling bubble elements.
Innovation Solution
A method and system for calculating a point estimate of ultrasound dose by segmenting and categorizing tissue types, estimating the presence of ultrasound coupling bubble elements, and adjusting medium property values to derive a unique propagation correction factor, which is used to refine mechanical and thermal indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conservative and homogeneous tissue path assumptions are used in ultrasound dosimetry, then the calculation method is simple, but the accuracy of ultrasound dose determination is insufficient
Solution Approach 1:
The tissue path is segmented into multiple discrete segments, each with its own attenuation coefficient. The system identifies different tissue types (e.g., fat, muscle, organ tissue) along the ultrasound propagation path and assigns specific attenuation coefficients to each segment, replacing the previous homogeneous assumption with a segmented, heterogeneous model that accurately reflects actual tissue composition.
Solution Approach 2:
Different attenuation coefficients are assigned to different local regions along the ultrasound path based on tissue type. Instead of using a single conservative attenuation value for the entire path, the system applies location-specific attenuation coefficients that match the actual tissue properties at each segment, thereby improving dosimetry accuracy without requiring overly complex measurement equipment.
2Measurement precision
If microbubble contrast agents are used in ultrasound therapy, then therapeutic outcomes can be optimized, but the accuracy of ultrasound dose calculation deteriorates due to unaccounted bubble elements
Solution Approach 1:
The system performs preliminary identification and characterization of microbubble contrast agents before ultrasound therapy delivery. By detecting the presence and concentration of microbubbles in advance and incorporating their attenuation characteristics into the dosimetry calculation, the system prepares accurate dose estimates that account for the additional attenuation caused by bubbles, enabling safe and effective microbubble-based therapeutic applications.
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 a more accurate estimation of ultrasound dose, enabling precise control of ultrasound delivery to avoid bio-effects and optimize therapeutic outcomes by adjusting ultrasound sources in real-time.
Implementation Method 1
As an ultrasound wave propagates through tissue, the energy of the wave is attenuated through several mechanisms, including scattering and absorption. The absorption mechanism transfers energy from the ultrasound wave to the tissue, wherein the energy is dissipated as heat.
Implementation Method 2
As an ultrasound wave propagates through tissue, the energy of the wave is attenuated through several mechanisms, including scattering and absorption.
Implementation Method 3
The combination of high rarefactional pressure and low frequency in an ultrasound wave may lead to a mechanical effect known as cavitation. In cavitation, gas bubbles can form, oscillate, and collapse with varying degrees of violence, and produce unwanted biological effects.
Data Source
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AI summary
A method for creating a medium property map of a region of interest of a subject, wherein the medium property map provides a plurality of different medium property values in different segments of the region of interest dependent on the medium within each of said segments, comprising: obtaining an image of the region of interest, wherein the region of interest comprises a target treatment area and a surrounding region of the target treatment area; processing the image to identify different components of the region of interest; segmenting and categorizing the different components into predetermined media categories; retrieving a medium property value associated with each media category and attributing said medium property value to each respective component of the segmented region of interest.