Full Waveform Inversion for Ultrasound Imaging Through Skull
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Solution Overview
Problem
Current ultrasound imaging techniques face challenges in reliably imaging regions of the body containing bone or gas due to scattering, attenuation, and phase distortion, particularly when imaging the brain, as the skull acts as a strong ultrasound reflector, leading to unreliable results with existing methods.
Innovation Solution
A non-invasive method using Full Waveform Inversion (FWI) to generate image data by transmitting ultrasound energy through the skull, comparing observed and predicted data sets to update models of physical properties, and identifying tissue composition and morphology within the intra-cranial cavity or body parts with interfaces between bone, soft tissue, and gas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ultrasound reflection mode imaging is used, then real-time imaging with simple algorithms is achieved, but imaging of bone or gas-containing regions fails due to scattering, attenuation and phase distortion
Solution Approach 1:
The patent applies Full Waveform Inversion (FWI) which fundamentally changes the imaging approach from simple time-delay stacking to iterative optimization of acoustic velocity models. The method transforms the raw ultrasound waveforms into quantitative acoustic velocity maps through repeated forward modeling and misfit minimization, enabling penetration through bone and gas interfaces that previously blocked imaging
Solution Approach 2:
The patent creates a composite imaging model that separately models bone and soft tissue components with different acoustic properties. By providing initial models for both skull and soft tissue with distinct velocity ranges (bone: 2300-4000 m/s, soft tissue: 1000-2000 m/s), the method achieves reliable imaging through heterogeneous media by treating each tissue type with appropriate acoustic parameters
2Ease of operation
If ultrasound transmission through skull is attempted with conventional methods, then non-invasive brain imaging is pursued, but results are unreliable due to strong reflection and signal loss
Solution Approach 1:
The patent implements iterative feedback loops where predicted waveforms from the current acoustic velocity model are continuously compared with observed ultrasound data. The misfit between predicted and observed waveforms drives repeated updates to the velocity model until convergence is achieved, progressively improving measurement precision through feedback-driven optimization
Solution Approach 2:
The patent requires provision of initial models for both skull and soft tissue components before imaging begins. These preliminary models with predetermined velocity ranges serve as starting points for the iterative FWI process, enabling the algorithm to converge to accurate tissue characterization by building upon physically realistic initial conditions
3Adaptability or versatility
If acoustic windows or thinner skull regions are used, then brain imaging becomes possible, but the method is limited to specific patient populations and cannot image thicker adult skulls
Solution Approach 1:
The patent segments the head into distinct anatomical components (skull and soft tissue) with separate acoustic velocity models. By independently modeling each component's acoustic properties and allowing different velocity ranges for bone versus soft tissue, the method adapts to varying skull thicknesses across different patient populations while maintaining imaging reliability through component-specific parameter optimization
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 enables high-resolution imaging of tissues behind bone and gas, potentially rivaling MRI quality, and can image areas previously inaccessible with conventional ultrasound methods, providing detailed diagnostic information.
Implementation Method 1
transmitting ultrasound energy through the skull, comparing observed and predicted data sets to update models of physical properties
Implementation Method 2
such materials within the body cause scattering, attenuation and phase distortion of ultrasound signals
Implementation Method 3
such materials within the body cause scattering, attenuation and phase distortion of ultrasound signals
Implementation Method 4
such materials within the body cause scattering, attenuation and phase distortion of ultrasound signals
Implementation Method 5
A non-invasive method using Full Waveform Inversion (FWI) to generate image data by transmitting ultrasound energy through the skull, comparing observed and predicted data sets to update models of physical properties
Data Source
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AI summary
Method of, and apparatus for, non-invasive medical imaging using waveform inversion There is provided a non-invasive method of generating image data of intra-cranial tissue using ultrasound energy that is transmitted across a head of a subject through the skull of the subject. The method comprises the steps of: a) providing an ultrasound observed data set derived from a measurement of one or more ultrasound waveforms generated by at least one source of ultrasound energy, the ultrasound energy being detected by a plurality of receivers located at an opposing side of a region within the intra-cranial cavity with respect to at least one source such that the receivers detect ultrasound waveforms from the source which have been transmitted through the skull and intra-cranial cavity, the observed data set comprising a plurality of observed data values; b) providing at least one starting model for at least a portion of the head comprising a skull component and a soft tissue component, the skull component comprising a plurality of model parameters representative of the physical properties and morphology of the skull through which intra-cranial tissue is being imaged, and the soft tissue component comprising a plurality of parameters representative of the physical properties of the intra-cranial tissue being imaged; c) generating a predicted data set comprising a plurality of predicted data values from the starting model of the skull and of the intra-cranial tissue; d) comparing the observed and predicted data values in order to generate an updated model of at least one physical property within at least a region of the intra-cranial cavity; and e) using the updated model to image a region of the inter-cranial cavity to identify tissue composition and/or morphology within the intra-cranial cavity.