From the cluster to the clinic: Real-time treatment planning for transcranial ultrasound therapy using deep learning (Ext.)
Lead Research Organisation:
University College London
Department Name: Medical Physics and Biomedical Eng
Abstract
This is an extension of the Early Career Fellowship: Model-Based Treatment Planning for Focused Ultrasound Surgery.
Brain disorders present a huge challenge for health services across the world, with studies showing these conditions affect as many as one third of the adult population. In the UK, approximately 1 in 6 people are affected by a neurological disorder and 1 in 6 by a common psychiatric disorder. The total annual cost of these conditions is estimated to exceed £100 billion. These disorders can be devastating for patients and greatly reduce their quality of life. Today, patients are often treated by the prescription of drugs that alter the way the brain functions. For many patients, this causes a reduction in their symptoms. However, if these drugs are used for long periods of time, their effectiveness often decreases and there can be many side-effects. It can also be difficult for drugs to exit the blood-stream and enter the brain as desired because of a protective lining called the blood-brain barrier. Depending on their diagnosis, some patients may also be offered surgical procedures to remove part of the brain or implant small wires that use electricity to stimulate brain cells.
One exciting alternative to drugs and surgery is the use of ultrasound. Ultrasound imaging is well known for taking pictures of developing babies during pregnancy. However, ultrasound is now also starting to be used to treat brain disorders. This is possible because ultrasound waves cause mechanical vibrations that affect the brain in different ways. For example, they can cause the tissue to heat up or generate forces that agitate the brain cells and tissue scaffolding. Several different types of treatment are possible depending on the pattern of ultrasound pulses used. This includes precisely destroying small regions of tissue, generating or suppressing electrical signals in the brain, or temporarily opening the blood-brain barrier to allow drugs to be delivered more effectively. These treatments are all completely non-invasive and have the potential to significantly improve outcomes for patients.
A major challenge for ultrasound therapy is ensuring the ultrasound energy is delivered to the precise location identified by the doctor. This is difficult because the skull bone is very rigid and causes the ultrasound waves to be reflected and distorted. It is possible to predict and correct for these distortions using powerful computer models of how ultrasound waves travel through the body. However, these models can take many hours or days to run on large supercomputers, so cannot currently be used for patient treatments.
The aim of this fellowship extension is to develop a new type of model that can make very fast predictions of how sound waves travel in the brain. This will be based on a special type of artificial intelligence called deep learning. The deep learning models will be trained to predict the distortion caused by the skull bone. The models will learn this using a large number of training examples generated using the powerful computer models mentioned above. As part of the project, the models will be rigorously tested using patient data from previous clinical treatments. Carefully planned laboratory experiments using phantom materials designed to mimic the skull and brain will also be conducted. The new models will allow doctors to automatically correct for distortions caused by the skull and quickly predict the treatment outcomes. This would be a major breakthrough in the treatment of brain disorders and enable the wide-spread application of ground-breaking ultrasound therapies.
Brain disorders present a huge challenge for health services across the world, with studies showing these conditions affect as many as one third of the adult population. In the UK, approximately 1 in 6 people are affected by a neurological disorder and 1 in 6 by a common psychiatric disorder. The total annual cost of these conditions is estimated to exceed £100 billion. These disorders can be devastating for patients and greatly reduce their quality of life. Today, patients are often treated by the prescription of drugs that alter the way the brain functions. For many patients, this causes a reduction in their symptoms. However, if these drugs are used for long periods of time, their effectiveness often decreases and there can be many side-effects. It can also be difficult for drugs to exit the blood-stream and enter the brain as desired because of a protective lining called the blood-brain barrier. Depending on their diagnosis, some patients may also be offered surgical procedures to remove part of the brain or implant small wires that use electricity to stimulate brain cells.
One exciting alternative to drugs and surgery is the use of ultrasound. Ultrasound imaging is well known for taking pictures of developing babies during pregnancy. However, ultrasound is now also starting to be used to treat brain disorders. This is possible because ultrasound waves cause mechanical vibrations that affect the brain in different ways. For example, they can cause the tissue to heat up or generate forces that agitate the brain cells and tissue scaffolding. Several different types of treatment are possible depending on the pattern of ultrasound pulses used. This includes precisely destroying small regions of tissue, generating or suppressing electrical signals in the brain, or temporarily opening the blood-brain barrier to allow drugs to be delivered more effectively. These treatments are all completely non-invasive and have the potential to significantly improve outcomes for patients.
A major challenge for ultrasound therapy is ensuring the ultrasound energy is delivered to the precise location identified by the doctor. This is difficult because the skull bone is very rigid and causes the ultrasound waves to be reflected and distorted. It is possible to predict and correct for these distortions using powerful computer models of how ultrasound waves travel through the body. However, these models can take many hours or days to run on large supercomputers, so cannot currently be used for patient treatments.
The aim of this fellowship extension is to develop a new type of model that can make very fast predictions of how sound waves travel in the brain. This will be based on a special type of artificial intelligence called deep learning. The deep learning models will be trained to predict the distortion caused by the skull bone. The models will learn this using a large number of training examples generated using the powerful computer models mentioned above. As part of the project, the models will be rigorously tested using patient data from previous clinical treatments. Carefully planned laboratory experiments using phantom materials designed to mimic the skull and brain will also be conducted. The new models will allow doctors to automatically correct for distortions caused by the skull and quickly predict the treatment outcomes. This would be a major breakthrough in the treatment of brain disorders and enable the wide-spread application of ground-breaking ultrasound therapies.
Planned Impact
The direct beneficiaries of this project are patients suffering neurological and psychiatric brain disorders. This covers a wide spectrum of conditions, including Parkinson's disease, Alzheimer's disease, essential tremor, and depression. These disorders are extremely debilitating and have a significant impact on quality of life for patients and carers. Taken together, these conditions comprise the largest single cause of morbidity in the EU in terms of disability adjusted life years. This has clear implications for healthcare budgets and the economy more broadly.
In the last decade, new treatments for brain disorders based on therapeutic uses of ultrasound have generated huge excitement in the research and medical communities. Ultrasound offers the unique ability to non-invasively ablate brain tissue, deliver drugs, stimulate or modulate brain activity, and open the blood-brain barrier. However, one major barrier to the wider clinical adoption of this technology is the lack of accurate online treatment planning tools. The skull can significantly distort the ultrasound waves as they propagate into the brain, so planning tools are essential to correct for these distortions and predict treatment outcomes ahead of time. However, even with large supercomputers, existing treatment planning models can take hours or days to run, making them unsuitable for online use in many clinical applications.
The novel tools for treatment planning based on deep learning outlined in this proposal could provide a major breakthrough in computing performance and act as a catalyst for the widespread clinical application of therapeutic ultrasound technologies in the brain. Impact will arise from: (i) the enhanced accuracy compared to existing models used in commercial devices, (ii) the unprecedented levels of computational performance which will allow model-based treatment planning predictions to be made in real-time, (iii) the extensive validation of the models, and (iv) adherence to the regulatory framework required for the clinical application of scientific software. In the context of delivering value-based healthcare, these tools could also play a significant role in decreasing procedural costs and optimising clinical outcomes. This impact will be enhanced by open-source software releases and the establishment of a new subject repository for ultrasound metrology data.
The enhanced capabilities for ultrasound therapy offered by real-time treatment planning software will provide a significant competitive edge over other planning approaches currently used in academia and industry. This will make the software commercially attractive to manufacturers of therapeutic ultrasound equipment, two of whom are already directly engaged with this project. It is expected the generated IP will lead to licensing agreements or the development of new start-ups, with the UK becoming a base for future international investment. The developed tools will also act as a platform technology for wide-reaching investigations into the interaction of ultrasound with the human body.
In the last decade, new treatments for brain disorders based on therapeutic uses of ultrasound have generated huge excitement in the research and medical communities. Ultrasound offers the unique ability to non-invasively ablate brain tissue, deliver drugs, stimulate or modulate brain activity, and open the blood-brain barrier. However, one major barrier to the wider clinical adoption of this technology is the lack of accurate online treatment planning tools. The skull can significantly distort the ultrasound waves as they propagate into the brain, so planning tools are essential to correct for these distortions and predict treatment outcomes ahead of time. However, even with large supercomputers, existing treatment planning models can take hours or days to run, making them unsuitable for online use in many clinical applications.
The novel tools for treatment planning based on deep learning outlined in this proposal could provide a major breakthrough in computing performance and act as a catalyst for the widespread clinical application of therapeutic ultrasound technologies in the brain. Impact will arise from: (i) the enhanced accuracy compared to existing models used in commercial devices, (ii) the unprecedented levels of computational performance which will allow model-based treatment planning predictions to be made in real-time, (iii) the extensive validation of the models, and (iv) adherence to the regulatory framework required for the clinical application of scientific software. In the context of delivering value-based healthcare, these tools could also play a significant role in decreasing procedural costs and optimising clinical outcomes. This impact will be enhanced by open-source software releases and the establishment of a new subject repository for ultrasound metrology data.
The enhanced capabilities for ultrasound therapy offered by real-time treatment planning software will provide a significant competitive edge over other planning approaches currently used in academia and industry. This will make the software commercially attractive to manufacturers of therapeutic ultrasound equipment, two of whom are already directly engaged with this project. It is expected the generated IP will lead to licensing agreements or the development of new start-ups, with the UK becoming a base for future international investment. The developed tools will also act as a platform technology for wide-reaching investigations into the interaction of ultrasound with the human body.
People |
ORCID iD |
Bradley Treeby (Principal Investigator / Fellow) |
Publications
Aubry JF
(2022)
Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models.
in The Journal of the Acoustical Society of America
Aytac-Kipergil E
(2021)
Modelling and measurement of laser-generated focused ultrasound: Can interventional transducers achieve therapeutic effects?
in The Journal of the Acoustical Society of America
Bakaric M
(2021)
Measurement of the temperature-dependent output of lead zirconate titanate transducers.
in Ultrasonics
Bakaric M
(2023)
Characterisation of hydrophone sensitivity with temperature using a broadband laser-generated ultrasound source
in Metrologia
Bakaric M
(2021)
Measurement of the ultrasound attenuation and dispersion in 3D-printed photopolymer materials from 1 to 3.5 MHz.
in The Journal of the Acoustical Society of America
Brown M
(2022)
Binary volume acoustic holograms
in The Journal of the Acoustical Society of America
Brown M
(2023)
Binary Volume Acoustic Holograms
in Physical Review Applied
Brown M
(2020)
Stackable acoustic holograms
in Applied Physics Letters
Hosseini S
(2023)
A head template for computational dose modelling for transcranial focused ultrasound stimulation.
in NeuroImage
Jaros M
(2020)
k-Dispatch
Johnstone A
(2021)
A range of pulses commonly used for human transcranial ultrasound stimulation are clearly audible.
in Brain stimulation
Kleparnik P
(2022)
On-the-Fly Calculation of Time-Averaged Acoustic Intensity in Time-Domain Ultrasound Simulations Using a k-Space Pseudospectral Method.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Lucka F
(2021)
High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion
in Inverse Problems
Martin E
(2021)
Measurement and simulation of steered acoustic fields generated by a multielement array for therapeutic ultrasound.
in JASA express letters
Miscouridou M
(2022)
Classical and Learned MR to Pseudo-CT Mappings for Accurate Transcranial Ultrasound Simulation.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nabergoj Makovec U
(2023)
Pharmacist-led clinical medication review service in primary care: the perspective of general practitioners.
in BMC primary care
Nandi T
(2023)
Ramped V1 transcranial ultrasonic stimulation modulates but does not evoke visual evoked potentials.
in Brain stimulation
Ramasawmy D
(2020)
ElasticMatrix: A MATLAB toolbox for anisotropic elastic wave propagation in layered media
in SoftwareX
Roberts M
(2023)
open-UST: An Open-Source Ultrasound Tomography Transducer Array System.
in IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Singh S
(2021)
Prostatic calcifications: Quantifying occurrence, radiodensity, and spatial distribution in prostate cancer patients.
in Urologic oncology
Stanziola A
(2022)
Transcranial ultrasound simulation with uncertainty estimation
Stanziola A
(2023)
A learned Born series for highly-scattering media
in JASA Express Letters
Stanziola A
(2022)
Transcranial ultrasound simulation with uncertainty estimation
in arXiv
Stanziola A
(2022)
A Learned Born Series for Highly-Scattering Media
Stanziola A
(2021)
A Helmholtz equation solver using unsupervised learning: Application to transcranial ultrasound
in Journal of Computational Physics
Stanziola A
(2022)
j-Wave: An open-source differentiable wave simulator
Stanziola A
(2023)
j-Wave: An open-source differentiable wave simulator
in SoftwareX
Stanziola A
(2023)
Transcranial ultrasound simulation with uncertainty estimation.
in JASA express letters
Stanziola, A
(2022)
A Learned Born Series for Highly-Scattering Media
in arXiv
Treeby BE
(2020)
Nonlinear ultrasound simulation in an axisymmetric coordinate system using a k-space pseudospectral method.
in The Journal of the Acoustical Society of America
Wise E
(2020)
Pseudospectral Time-Domain (PSTD) Methods for the Wave Equation: Realizing Boundary Conditions with Discrete Sine and Cosine Transforms
in Journal of Theoretical and Computational Acoustics
Xu R
(2024)
Safety Review of Therapeutic Ultrasound for Spinal Cord Neuromodulation and Blood-Spinal Cord Barrier Opening.
in Ultrasound in medicine & biology
Xu R
(2023)
Experiments and simulations demonstrating the rapid ultrasonic rewarming of frozen tissue cryovials.
in The Journal of the Acoustical Society of America
Description | The aim of this proposal was to develop new types of computer modelling tools based on deep-learning that allow us to predict the path of ultrasound waves through the human brain. We have developed a new type of fully-learned solver that allows the distortion due to the skull to be rapidly predicted (https://doi.org/10.1016/j.jcp.2021.110430). We have developed a open-source coding framework for writing differentiable numerical simulators with arbitrary discretizations (https://arxiv.org/abs/2111.05218), which was used as the basis for a new open-source wave simulation toolbox, j-Wave (https://doi.org/10.1016/j.softx.2023.101338). j-Wave is a library of simulators for acoustic applications that can be used as a collection of modular blocks that can be easily included into any machine learning pipeline. j-Wave was validated as part of a major international benchmarking effort led by UCL (https://doi.org/10.1121/10.0013426), and used to study the uncertainty in simulations through the skull (https://doi.org/10.48550/arXiv.2212.04405). We developed a new approach for rapidly solving the Helmholtz equation based on a learned Born series (https://arxiv.org/abs/2212.04948), and learned methods for mapping from MRI image to pseudo-CT images for treatment planning (https://doi.org/10.1109/TUFFC.2022.3198522). These breakthroughs formed the basis of extensions to k-Plan (https://www.k-plan.io), a cloud-based ultrasound simulation platform that was brought to market in collaboration with Brainbox (a UK SME). This software is now being widely used to support human brain neuromodulation. This software package epitomises the goal of the project, which was to bring the "cluster to the clinic". |
Exploitation Route | The models we are developing are likely to be of interest to other research groups and industry. |
Sectors | Digital/Communication/Information Technologies (including Software) Education Healthcare Manufacturing including Industrial Biotechology |
Description | The breakthroughs made in this proposal have been incorporated into k-Plan. k-Plan is an advanced modelling tool for planning transcranial ultrasound procedures. It uses a streamlined and intuitive workflow that allows users to select an ultrasound device, position the device using a template or medical image, and specify the sonication parameters. High-resolution calculations of the ultrasound field and temperature inside the skull and brain are then automatically calculated in the cloud with a single click. This software was developed and extended as part of this proposal, and then commercialised by UCLB in partnership with Brainbox (a UK SME). This software is now commercially available and is already being used across several continents to run planning simulations for human studies, embodying the primary aim of the proposal which was to bring "the cluster to the clinic". |
First Year Of Impact | 2023 |
Sector | Healthcare,Pharmaceuticals and Medical Biotechnology |
Impact Types | Societal Economic Policy & public services |
Description | Capital Award for Core Equipment at UCL |
Amount | £650,000 (GBP) |
Funding ID | EP/T023651/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 11/2019 |
End | 05/2021 |
Description | Spectral element methods for fractional differential equations, with applications in applied analysis and medical imaging |
Amount | £103,887 (GBP) |
Funding ID | EP/T022280/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 06/2021 |
End | 06/2024 |
Description | UCL EPSRC IAA 2022-25 FUNDING |
Amount | £87,417 (GBP) |
Funding ID | EPSRC IAA 2022-25 KEI2022-02-03 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 02/2023 |
End | 01/2024 |
Description | UCL Knowledge Exchange and Innovation Fund |
Amount | £86,087 (GBP) |
Funding ID | EPSRC IAA 2017-20 Discovery-To-Use |
Organisation | University College London |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2021 |
End | 12/2021 |
Title | Experimental Assessment of Skull Aberration and Transmission Loss at 270 kHz for Focused Ultrasound Stimulation of the Primary Visual Cortex |
Description | This data was collected in order to assess acoustic field aberrations and transmission loss induced by human skulls in the context of focused ultrasound stimulation of the primary visual cortex (V1) region of the brain. A 2 element spherically focusing annular array ultrasound transducer (H115, driven at 270 kHz, Sonic Concepts) was used to generate an acoustic field. Measurements were performed with a 0.2 mm PVDF needle hydrophone (Precision Acoustics) with right angle connector to reduce its length so it could be accommodated within the skull cavity. The transducer was driven under quasi continuous wave conditions at low drive level to produce a linear field. The transducer was held in a fixed position, the skull was positioned to obtain the correct focal alignment and the hydrophone was held in a 3D printed mount with manual alignment in the axial direction and automated scanning in the lateral directions. Measurements were performed inside 3 human skulls which had previously had the superior section of the parietal and frontal bones removed. Measurements were made with the transducer positioned at two locations for each skull corresponding to the focal region intersecting with the positions of the left and right V1 regions of the brain, with a 1 cm separation between source and skull. For each position, the hydrophone was aligned with the focus inside the skull, then a planar scan was performed covering the largest possible area while avoiding collision of the hydrophone with the skull bone. The skull was then removed and a 2nd scan was performed in water as a reference, the axial position was determined from time of flight in free field during these reference water scans. The study consists of 6 datasets, each of which contains a planar scan made within the skull cavity, and a reference planar scan in water after the skull was removed, preserving the coordinates. File 1: skull 2120, left V1 File 2: skull 2120, right V1 File 3: skull 2150, left V1 File 4: skull 2150, right V1 File 5: skull 2125, left V1 File 6: skull 2125, right V1 |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
URL | https://rdr.ucl.ac.uk/articles/dataset/Experimental_Assessment_of_Skull_Aberration_and_Transmission_... |
Title | Ramped V1 transcranial ultrasonic stimulation modulates but does not evoke visual evoked potentials |
Description | Raw EEG data for the study "Ramped V1 transcranial ultrasonic stimulation modulates but does not evoke visual evoked potentials" |
Type Of Material | Database/Collection of data |
Year Produced | 2022 |
Provided To Others? | Yes |
Impact | Raw data |
URL | https://osf.io/rbcfy/ |
Title | Repeatability and reproducibility of hydrophone measurements of medical ultrasound fields |
Description | This data was collected in order to study the repeatability and reproducibility of hydrophone measurements of ultrasound fields. Sets of independent measurements were made with two probe (0.2 mm, 40 µm) and two membrane hydrophones (0.4 mm, 0.2 mm differential) (all from Precision Acoustics) to examine the repeatability of measurements. The pressures measured by these hydrophones in three different ultrasound fields, with both linear and nonlinear, pulsed and steady state driving conditions, were acquired to assess the reproducibility of measurements between hydrophones. Repeatability measurements: Sets of five independent measurements were made with each hydrophone of the field generated by a single element focusing bowl transducer (Sonic Concepts H151) driven at a frequency of 1.1 MHz, with both a 4 cycle burst and under quasi steady state conditions. Axial and lateral line scans passing through the focus were acquired at a drive level which generated a weakly nonlinear field. Reproducibility measurements: Two single element focusing bowl transducers (H151 at 1.1 MHz, and H101 at 3.3 MHz, Sonic Concepts) and one diagnostic linear array (L14-5 at 5 MHz, Ultrasonix) sources were used. For the single element transducers, axial and lateral line scans passing through the focus were acquired with each hydrophone at two drive levels to generate both a linear and a weakly nonlinear field, with both a 4 cycle burst and under quasi steady state conditions. For the diagnostic linear array, lateral line scans were acquired passing through the beam axis at an axial distance of 40 mm. The transducer was driven with a 4 cycle burst at a power level that generated harmonics up to 30 MHz.All measurements were acquired using an automated scanning tank filled with degassed, deionised water. The transducers mounted in a fixed xyz position with automated tilt, rotate adjustment. Hydrophones were mounted on an automated xyz stage, with manual tilt, rotate adjustment. In total this study contains 12 datasets, the corresponding figure or table in the paper is given in brackets: 1-4: Repeatability and reproducibility - H151 x 4 hydrophones (Figs 1-4, Table 3) Each dataset contains axial and lateral line scans at 2 drive levels, with a 4 cycle and a 40 cycle burst, with 5 sets of scans at the high drive level and one set of scans at the low drive level 5-8: Reproducibility - H101 x 4 hydrophones (Figs 4-5, Table 3) Each dataset contains a single set of axial and lateral line scan at each of 2 drive levels, with a 4 cycle and a 120 cycle burst. 9-12: Reproducibility - L14-5 x 4 hydrophones (Fig 6, Table 3) Each dataset contains lateral scans at 1 power level. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Experimental measurements are critical for the development of medical ultrasound software and devices, including for validation of modelling tools and for comparison of measurement equipment and protocols. Data sharing encourages reproducibility and consistency across labs, and provides access to other researchers who may not have the equipment or expertise to conduct their own measurements. |
URL | https://rdr.ucl.ac.uk/articles/dataset/Repeatability_and_reproducibility_of_hydrophone_measurements_... |
Title | Simulating Focused Ultrasound Transducers using Discrete Sources on Regular Cartesian Grids |
Description | This data was collected in order to validate models of curved sources on cartesian grids. A single element spherically focusing ultrasound transducer (H101 at 1.1 MHz, Sonic Concepts) was used to generate an acoustic field. Measurements were performed with a 0.2 mm PVDF needle hydrophone (Precision Acoustics) to characterise the source under quasi continuous wave and short burst conditions. These measurements include planar scans in the prefocal region for the two driving regimes, and axial scans at the same drive level for both drive regimes. There are additional axial scans at one further higher drive level (very weakly nonlinear) for each of the driving regimes which were acquired for comparison with the model with scaled input source amplitude. All measurements were acquired using an automated scanning tank filled with degassed, deionised water. The transducers mounted in a fixed xyz position with automated tilt, rotate adjustment. Hydrophones were mounted on an automated xyz stage, with manual tilt, rotate adjustment. In total this study contains 6 datasets contained in one file, the corresponding figure or table in the paper is given in brackets: 1: Planar scan with 45 cycle burst (qCW) at z = 42.5 mm, linear field 2: Axial scan 45 cycle burst (qCW), linear field (conditions as in 1), Fig 8, 9. 3: Axial scan 45 cycle burst (qCW), weakly nonlinear field, Fig 8, 9. 4: Planar scan with 4 cycle burst at z = 42.5 mm, linear field 5: Axial scan 4 cycle burst, linear field (conditions as in 4), Fig 10, 11. 6. Axial scan 4 cycle burst, weakly nonlinear field, Fig 10, 11. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Experimental measurements are critical for the development of medical ultrasound software and devices, including for validation of modelling tools and for comparison of measurement equipment and protocols. Data sharing encourages reproducibility and consistency across labs, and provides access to other researchers who may not have the equipment or expertise to conduct their own measurements. |
URL | https://rdr.ucl.ac.uk/articles/dataset/Simulating_Focused_Ultrasound_Transducers_using_Discrete_Sour... |
Title | PETRA-TO-CT: MATLAB toolbox for converting a Siemens PETRA image to a pseudo-CT. |
Description | MATLAB toolbox for converting a Siemens PETRA image to a pseudo-CT. |
Type Of Technology | Software |
Year Produced | 2023 |
Open Source License? | Yes |
Impact | Being used for CITRUS European project. |
URL | https://github.com/ucl-bug/petra-to-ct |
Title | TPO Control Toolbox |
Description | Third-party toolbox for controlling the Sonic Concepts Transducer Power Output (TPO) systems using a USB connection and MATLAB. The toolbox functions are based on MATLAB codes provided by Sonic Concepts, Inc (distributed under an MIT license), extended to give a uniform interface, add documentation and error checking, and to specify all input parameters in base SI units (Hz, s, etc). The toolbox contains a series of functions for connecting to the TPO (using serial commands over USB), setting the TPO parameters, and triggering the TPO output. |
Type Of Technology | Software |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | This toolbox was used for two human transcranial ultrasound stimulation studies. |
URL | https://github.com/ucl-bug/tpo-control-toolbox |
Title | j-Wave: Differentiable acoustic simulations in JAX |
Description | j-Wave is a library of simulators for acoustic applications. Is heavily inspired by k-Wave (a big portion of j-Wave is a port of k-Wave in JAX), and its intended to be used as a collection of modular blocks that can be easily included into any machine learning pipeline. Following the philosophy of JAX, j-Wave is developed with the following principles in mind: to be differentiable, to be fast via jit compilation, easy to run on GPUs, easy to customize. |
Type Of Technology | Software |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | j-Wave was used in a recent modelling intercomparison, and to study uncertainty in transcranial ultrasound simulation. |
URL | https://github.com/ucl-bug/jwave |
Title | jaxdf - JAX-based Discretization Framework |
Description | jaxdf is a JAX-based package defining a coding framework for writing differentiable numerical simulators with arbitrary discretizations. The intended use is to build numerical models of physical systems, such as wave propagation, or the numerical solution of partial differential equations, that are easy to customize to the user's research needs. Such models are pure functions that can be included into arbitrary differentiable programs written in JAX: for example, they can be used as layers of neural networks, or to build a physics loss function. |
Type Of Technology | Software |
Year Produced | 2022 |
Open Source License? | Yes |
Impact | jaxdf is being used by researchers interested in differentiable models, and forms the basis for the j-Wave acoustic simulation software. |
URL | https://github.com/ucl-bug/jaxdf |
Title | k-Plan: Ultrasound Therapy Planning |
Description | k-Plan is an advanced modelling tool for precision planning of transcranial ultrasound procedures. It uses a streamlined and intuitive workflow that allows users to select an ultrasound device, position the device using a template or medical image, and specify the sonication parameters. High-resolution calculations of the ultrasound field and temperature inside the skull and brain are then automatically calculated in the cloud with a single click. |
Type Of Technology | Software |
Year Produced | 2022 |
Impact | k-Plan is developed by researchers at University College London and the Brno University of Technology based on more than a decade of cutting-edge research into ultrasound modelling and planning for transcranial ultrasound therapy. It is the first software tool for model-based treatment planning for ultrasound therapy, and is being brought to market in collaboration with Brainbox, Ltd. |
URL | https://k-plan.io/ |