# Toward the next generation in transcranial MR-guided focused ultrasound: Innovations in thermal and acoustic model-based planning and monitoring for improved safety, efficacy and efficiency

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $607,011

## Abstract

Transcranial MRI-guided focused ultrasound (tcMRgFUS) is a completely non-invasive neuro-interventional
technique that shows exceptional promise for treating a number of neurological disorders. The success of
focused ultrasound in neurointerventional procedures depends on its ability to deliver a finely focused beam
exactly to the desired location and accurately monitor the resulting heating. Although current systems have
achieved some success there is strong evidence that patient-specific skull attributes cause the focus to be less
than ideal and that changes to the skull during treatment cause further attenuation, broadening, and shifting of
the focus. While the Insightec Exablate Neuro tcMRgFUS system has received FDA approval to treat essential
tremor, it is not able to 1) fully monitor the insonified field, 2) predict or monitor skull heating, or 3) dynamically
optimize beam focusing and power levels needed throughout the procedure. These technical limitations
adversely affect the safety, efficacy and efficiency of currently approved tcMRgFUS procedures and limit the
number of patients that could otherwise benefit from this revolutionary technology.
 With prior funding, our research team has introduced important technical advancements for tcMRgFUS,
including volumetric real-time MR temperature imaging (MRTI) techniques, T1-based ultrashort echo time
(UTE) temperature imaging in cortical bone, rapid ultrasound beam modeling using a hybrid angular spectrum
(HAS) method, and radiofrequency (RF) coils specific for tcMRgFUS. Building on this background, our goal in
this proposal is to fully develop and disseminate critically needed capabilities that will provide next generation
treatment modeling, planning, monitoring, assessment and control. We will accomplish this through three
specific aims: 1) Develop robust volumetric MRTI monitoring methods for entire brain and skull including
a novel mono flip angle method for T1-based MRTI in the skull and system-specific RF coils to improve MRTI
accuracy; 2) Develop patient-specific, dynamic modeling of transcranial ultrasound propagation that
adapts to measured temperature changes in the skull, dynamically predicting focusing phases and power
needed for accurate treatment completion; and 3) Demonstrate the clinical value of advanced treatment
modeling and monitoring tools by incorporating them into a developing tcMRgFUS visualization tool and
evaluating the methods in increasingly complex preclinical and clinical environments.
 These new patient-specific tools combined with the visualization environment will significantly advance
tcMRgFUS treatments by providing complete, patient-specific eligibility determination, treatment planning and
comprehensive monitoring. This will positively impact beam focusing, localization and tracking, treatment
accuracy, and clinical workflow, improving existing clinical indications as well as better enabling forward-
looking applications that are still in the translationa...

## Key facts

- **NIH application ID:** 9994922
- **Project number:** 5R01EB028316-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Dennis L Parker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $607,011
- **Award type:** 5
- **Project period:** 2019-08-15 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994922

## Citation

> US National Institutes of Health, RePORTER application 9994922, Toward the next generation in transcranial MR-guided focused ultrasound: Innovations in thermal and acoustic model-based planning and monitoring for improved safety, efficacy and efficiency (5R01EB028316-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9994922. Licensed CC0.

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