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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Updated: Jan 12, 2026

Real-time Monitoring of High Intensity Focused Ultrasound HIFU Ablation of In Vitro Canine Livers Using Harmonic Motion Imaging for Focused Ultrasound HMIFU
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    This study introduces a novel deep learning algorithm on an FPGA to reconstruct ultrasound (US) channels, enabling wearable bladder imaging. This approach halves hardware needs and power consumption while maintaining image quality for early disease detection.

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    Area of Science:

    • Biomedical Engineering
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Wearable ultrasound (US) imaging for real-time bladder monitoring faces challenges in power, cost, and resolution.
    • High-channel-count US systems generate large data streams, complicating computational efficiency and wireless transmission.
    • Existing wearable US sensors often lack the clinical accuracy and efficiency required for effective healthcare applications.

    Purpose of the Study:

    • To develop an algorithm-centric approach for reconstructing missing ultrasound channels using field-programmable gate array (FPGA)-accelerated deep learning.
    • To reduce analog front-end requirements and power consumption in wearable ultrasound systems.
    • To enable high-quality, real-time ultrasound imaging for targeted organs like the urinary bladder.

    Main Methods:

    • Developed a lightweight U-Net convolutional neural network (L-UNET) optimized for sparse-array radiofrequency (RF) data reconstruction.
    • Deployed the L-UNET on a deep learning processing unit (DPU) using mixed quantization-aware training (Mixed-QAT) with 8-bit integer and 16-bit floating-point precision.
    • Implemented a single-core FPGA accelerator for real-time inference, processing only odd-indexed physical channels and inferring even-indexed channels.

    Main Results:

    • Achieved a mean-squared error (MSE) of 1.48 × 10⁻², significantly lower than 32-bit floating-point precision.
    • Maintained B-mode image quality with peak signal-to-noise ratio (PSNR) >18 dB and structural similarity index (SSIM) > 0.5.
    • Demonstrated an average power consumption of 0.918 W in a 32-channel configuration with deterministic latency of 221 ms/frame.

    Conclusions:

    • Algorithm-centric reconstruction of missing US channels on embedded FPGAs is a viable strategy for wearable imaging.
    • This approach effectively doubles the imaging aperture while halving analog front-end requirements and power consumption.
    • The developed system offers a pathway toward fully integrated, low-power ultrasound monitoring systems for preventive healthcare and early disease diagnosis.