# Imaging Feedback for Histotripsy Renal Tumor Ablation

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2024 · $588,197

## Abstract

PROJECT SUMMARY: Renal cell carcinoma (RCC) is the sixth most common cancer in the United States, with
~ 82k new diagnoses estimated for 2023. Surgery is the preferred option for initial management of RCC, but the
number of patients who qualify is reduced each year due to the aging, comorbid population. Histotripsy is a
noninvasive, focused ultrasound therapy used for tissue ablation via bubble activity, and is an attractive
alternative to surgery. Indeed, a pilot clinical trial to test histotripsy for the treatment of RCC is scheduled for
2023. Standard B-mode ultrasound imaging is used to monitor histotripsy via the detection of hyperintense
bubble pixels. In the kidney, histotripsy bubbles are obscured on B-mode due to artifacts, image degradation at
depth, and a lack of contrast specificity. Patients will be disqualified from receiving treatment with histotripsy
when bubbles cannot be located and monitored with imaging. Further, B-mode bubble imaging does not provide
the information necessary to assess the likelihood of successful oncological outcomes. Real-time feedback to
adjust the histotripsy exposure and ensure ablation is of particular importance for heterogenous tumors common
to RCC. Hence, there is a need for improved histotripsy bubble detection to enable therapy automation. To
address this gap, we have developed ultrafast, bubble-specific ultrasound imaging for monitoring histotripsy.
Using this imaging sequence, we can assess the diffusive properties of histotripsy bubbles, a key marker of
ablation outcomes, with sub-millisecond resolution. The scientific premise of this study is that ultrafast imaging
will elevate histotripsy bubble monitoring, and provide feedback to ensure effective and safe RCC ablation. We
have demonstrated strong translational potential to monitor histotripsy with ultrafast imaging in vitro, ex vivo, and
in murine renal tumors on a pre-clinical system. Our objective is to refine and integrate this sequence onto a
clinical-grade imager, develop and test feedback algorithms in RCC tissues and a relevant large animal model,
and rapidly translate this imaging protocol into use in patients. To test our scientific premise, we will investigate
the following aims: We will develop a translational histotripsy system for RCC in Specific Aim 1. We will integrate
our ultrafast sequence onto a clinical-grade imaging platform, and evaluate its sensitivity and accuracy for bubble
detection. In Specific Aim 2, methods to monitor and modulate the bubble cloud lifetime will be developed. These
methods will be used to adjust the histotripsy pulsing rate to enhance the efficacy of histotripsy ablation. Specific
Aim 3 will use information on the bubble cloud dissolution rate to provide real-time feedback of treatment
outcomes using an in vivo porcine kidney ablation model. The rate of urological sequelae will be determined in
short-term survival studies. This study will deliver validated ultrafast sequences on a commercial histotr...

## Key facts

- **NIH application ID:** 10781292
- **Project number:** 1R01EB035230-01
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Kenneth B. Bader
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $588,197
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10781292

## Citation

> US National Institutes of Health, RePORTER application 10781292, Imaging Feedback for Histotripsy Renal Tumor Ablation (1R01EB035230-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10781292. Licensed CC0.

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