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Related Experiment Video

Updated: May 9, 2025

Universal Hand-held Three-dimensional Optoacoustic Imaging Probe for Deep Tissue Human Angiography and Functional Preclinical Studies in Real Time

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Towards real-time diffuse optical tomography with a handheld scanning probe.

Robin Dale¹, Nicholas Ross², Scott Howard²

¹University of Birmingham, Medical Imaging Lab, School of Computer Science, University Rd W, Birmingham, B15 2TT, UK.

Biomedical Optics Express

|May 5, 2025

Summary

This summary is machine-generated.

A new deep learning model uses a transformer architecture for fast diffuse optical tomography (DOT) imaging. This approach allows a single model to reconstruct optical properties from various scanning pathways, improving breast imaging potential.

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Area of Science:

Biomedical Optics
Medical Imaging
Machine Learning

Background:

Diffuse optical tomography (DOT) enables high-speed reconstruction of tissue optical properties for applications like image-guided breast imaging.
Existing DOT models are geometry-specific, necessitating extensive data generation and training for each new use case.
This limitation restricts scanning protocols and adaptability in clinical settings.

Purpose of the Study:

To develop a versatile deep learning model for diffuse optical tomography (DOT) that overcomes geometry-specific limitations.
To enable a single trained model to handle arbitrary scanning pathways and measurement densities for DOT reconstruction.
To enhance the speed and applicability of DOT for clinical applications, particularly breast imaging.

Main Methods:

A transformer-based deep learning architecture was proposed to encode spatially unstructured DOT measurements.
The model was trained and validated using simulated data and phantom data emulating breast tissue.
Performance was evaluated based on Root Mean Square Error (RMSE), Sørensen-Dice coefficients, and anomaly contrast.

Main Results:

The model achieved average RMSEs of 0.0095±0.0023 cm⁻¹ for absorption (μa) and 1.95±0.78 cm⁻¹ for reduced scattering (μs').
Sørensen-Dice coefficients were 0.55±0.12 for μa and 0.67±0.1 for μs', with anomaly contrasts of 79±10% and 93.3±4.6%, respectively.
An effective imaging speed of 14 Hz was achieved, with average absolute μa and μs' values within 10% of ground truth for homogeneous examples.

Conclusions:

The proposed transformer-based DOT model demonstrates high-speed reconstruction capabilities adaptable to various scanning configurations.
This approach overcomes the limitations of geometry-specific models, offering a more flexible solution for DOT imaging.
The findings support the potential of this deep learning model for enhanced clinical breast imaging and other DOT applications.