Near Real-Time Quantitative Ultrasound Imaging Using an Ultra-Portable Handheld Ultrasound System.
TL;DR
This protocol presents a QUS imaging pipeline implemented on the DKPOC probe (portable linear ultrasound probe), which interfaces directly with a laptop through a USB-C connection, providing a practical framework for quantitative point-of-care ultrasound imaging and clinician-led QUS studies in human subjects.
Abstract
Clinical ultrasound is widely used because of its low cost, portability, safety, and real-time imaging capability, making it well suited for point-of-care applications. However, conventional B-mode imaging is inherently qualitative and primarily sensitive to tissue morphology. Quantitative ultrasound (QUS) methods exploit information contained in backscattered radiofrequency (RF) signals to extract fundamental acoustic tissue properties, such as the backscatter coefficient (BSC) and attenuation, which more directly quantify tissue microstructure and provide complementary information to B-mode imaging. Although QUS methods have been implemented on conventional cart-based ultrasound scanners, their integration into portable handheld systems remains limited. Motivated by the growing availability and clinical potential of ultra-portable ultrasound devices, this protocol presents a QUS imaging pipeline implemented on the DKPOC probe (portable linear ultrasound probe; Vermon SA, Tours, France), which interfaces directly with a laptop through a USB-C connection. The protocol integrates multiple QUS processing methods based on spectral analysis and envelope statistics of RF data acquired using focused-wave or plane-wave transmit modes. QUS processing begins with a user-defined region of interest (ROI). The reference phantom method is used to estimate the BSC and attenuation within sliding sub-ROIs. Model-based fitting of the estimated BSC is then performed to extract tissue microstructure-related parameters, including effective scatterer diameter and effective acoustic concentration. In addition, envelope statistics parameters are estimated using three probability density functions. Computed QUS parameter maps are displayed as translucent color overlays on corresponding B-mode images. The workflow is implemented in a numerical computing software-based graphical user interface, enabling near real-time QUS processing, parameter map visualization, interactive parameter adjustment, and rapid evaluation of different processing configurations. The pipeline is adaptable to different imaging sequences and ultrasound probes, providing a practical framework for quantitative point-of-care ultrasound imaging and clinician-led QUS studies in human subjects.