# Real-time spectroscopic photoacoustic/ultrasound (PAUS) scanner withsimultaneous fluence and motion compensation to guide and validateinterventions: system development and preclinical testing.

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2021 · $747,636

## Abstract

Abstract
The goal of this project is to develop a clinical real-time spectroscopic photoacoustic/ultrasound (PAUS) system
for molecular guidance of interventional procedures through a partnership between UW and GE Research.
Recently, we proposed a new, fast-sweep concept for PA imaging. To put this concept into practice, we first
developed a unique, compact, diode-pumped, tunable (700 -900 nm) laser operating at very high (up to 1000
Hz) repetition rates and relatively low (~ 1 mJ) pulse energies, and a fiber-optic delivery system to sequentially
couple laser pulses into the imaging probe. In addition to US B-mode, and all other US modes, the system
simultaneously produces real-time (50 Hz) spectroscopic PA images, which were combined for the first time
for real-time PAUS imaging. A unique feature is automatic on-line laser-fluence compensation and motion
correction, enabling quantitative optical absorption spectroscopy at every image pixel. Spectroscopy can
identify substances opaque to US based on their molecular constituents (drugs/contrast agents), and quantify
tissue functional changes (e.g., blood oxygenation and its concentration) within the image; in addition,
manipulation with a needle is better visualized with PA.
UW will work with GE Research to integrate spectroscopic PAUS into a high-end US scanner to create a
clinical-grade PAUS system, and test whether it can improve interventional procedure guidance in general and,
particularly, in ethanol (EA) ablation therapies of recurrent thyroid tumors.
The prognosis for most people with thyroid cancer after primary treatment is very good, but the recurrence rate
or persistence can be up to 30%. If recurrent cancer is confirmed, image-guided nonsurgical procedures such
as EA or radio frequency ablation (RFA) are commonly used alternatives to more invasive procedures.
Although US helps position EA and RFA needles, on-line imaging of the ablative area and confirmation of
ablation remain difficult for US. When the recurrent nodule (especially the capillary network in it) is not entirely
treated, the cancer will return with possible metastasis. We hypothesize here that real-time spectroscopic
PAUS will improve the efficacy of ablation procedures and dramatically reduce procedure repetitions. If
successful in this initial stage, the project will move to a clinical trial to both guide and validate ablative therapies
and explore real-time spectroscopic PAUS for other interventional procedures.
SA1 will integrate our unique laser and scanning fiber-optic delivery system with a clinical GE US scanner for
real-time spectroscopic PAUS. Then, SA2 will develop real-time signal processing tools for motion correction
and fluence compensation and imaging protocols for spectroscopic PAUS. SA3 will focus on optimizing the
PAUS system using phantom and ex vivo studies. Finally, in SA4 the developed PAUS system will be used to
test the clinical applicability of PAUS guidance with three in vivo models, including ...

## Key facts

- **NIH application ID:** 10295522
- **Project number:** 1R01EB030484-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Matthew O'Donnell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $747,636
- **Award type:** 1
- **Project period:** 2021-09-22 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10295522

## Citation

> US National Institutes of Health, RePORTER application 10295522, Real-time spectroscopic photoacoustic/ultrasound (PAUS) scanner withsimultaneous fluence and motion compensation to guide and validateinterventions: system development and preclinical testing. (1R01EB030484-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10295522. Licensed CC0.

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