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Hand-held Clinical Photoacoustic Imaging System for Real-time Non-invasive Small Animal Imaging
Published on: October 16, 2017
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A Handheld Real-Time Photoacoustic Imaging System for Animal Neurological Disease Models: From Simulation to
Yu-Hang Liu1, Yu Xu2,3, Lun-De Liao4,5
1Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore 117456, Singapore. lsiliuy@nus.edu.sg.
Sensors (Basel, Switzerland)
|November 25, 2018
Summary
This study details a handheld photoacoustic (PA) imaging system for real-time monitoring of vascular functions in animal models. The system effectively visualizes cerebral blood volume changes and tumor characteristics, aiding disease progression studies.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Preclinical Research
Background:
- Vascular functional impairments are critical in neurological diseases and tumors.
- Real-time, non-invasive imaging is needed for monitoring disease progression.
- Existing imaging modalities may have limitations in assessing specific vascular parameters.
Purpose of the Study:
- To design and build a handheld, real-time photoacoustic (PA) imaging system.
- To evaluate the system's performance in animal models of neurological disease and tumors.
- To demonstrate the system's capability in assessing vascular functions and disease characteristics.
Main Methods:
- Development of a PA imaging system using a pulsed laser and ultrasound array.
- Utilization of finite element (FE) simulations for optimizing light delivery and ultrasound resolution.
- Testing the system in rat models of focal ischemia and subcutaneous tumors.
Main Results:
- Successful reconstruction of PA C-scan images with depth information.
- Real-time monitoring of cerebral blood volume (CBV) changes in an ischemia model.
- Characterization of anomalous hemoglobin distribution (HbT, SO₂) in subcutaneous tumors with 3D and MIP PA imaging.
Conclusions:
- The developed handheld PA system is effective for real-time imaging of vascular functions.
- The system shows promise for monitoring disease progression in preclinical models.
- This technology offers a valuable tool for studying neurological diseases and tumors.
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