# 3D real-time super-resolution cavitation mapping in laser lithotripsy of urinary stone disease

> **NIH NIH R01** · DUKE UNIVERSITY · 2024 · $37,462

## Abstract

Laser lithotripsy (LL) is commonly used for kidney stone treatment, with cavitation playing a crucial role in stone
fragmentation. However, the relationship between cavitation activities and stone damage remains unclear. This supplement
study by Anthony DiSpirito will utilize deep learning to predict stone damage based on passive cavitation mapping (PCM)
signals. A three-dimensional PCM system will be employed, along with B-mode Ultrasound (US) for data acquisition. Deep
learning enables the optimization of LL procedures by automating feature selection and identifying key factors driving stone
damage. Our approach will offer valuable insights into medical practice, advancing the efficacy of LL treatments. We will
adopt deep learning module combined with cavitation activities information, and we can better predict the potential damaged
induced by bubble collapsing. Our result will also demonstrate the strong correlation between bubble collapsing information
with stone crater damage. This approach can also be further explored with more sophisticated scenarios and clinical
applications.

## Key facts

- **NIH application ID:** 11100982
- **Project number:** 3R01DK139109-01S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Michael E Lipkin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $37,462
- **Award type:** 3
- **Project period:** 2024-09-01 → 2025-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11100982

## Citation

> US National Institutes of Health, RePORTER application 11100982, 3D real-time super-resolution cavitation mapping in laser lithotripsy of urinary stone disease (3R01DK139109-01S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/11100982. Licensed CC0.

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