arXiv subjects

Gayathri Malamal

Publications and source records attributed to Gayathri Malamal.

5 recordsLinked to original sources

arXiv2024

A Performance Investigation of Receive Beamforming Schemes in Specular Tissue Characterization

This work presents a comparison of the delay and sum (DAS), filtered delay multiply and sum (DMAS), minimum variance (MV) and specular receive beamforming schemes in the context of ultrasound imaging of specular reflectors. The main contributions of the study are, 1) a performance comparison of the four receive beamforming schemes through experimental studies for varying angulations of planar reflectors and for reflectors located at varying depths in the medium and, 2)an investigation on the influence of the sub-array length in MV beamforming on the imaging of specular structures. The qualitative conclusions are quantitatively validated in terms of contrast and generalized contrast-to-noise ratios. The study examines the benefits and drawbacks of each receive beamforming technique and highlights the significance of application-tailored beamforming schemes for imaging specular structures.

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arXiv2023

Towards Non-contact 3D Ultrasound for Wrist Imaging

Objective: The objective of this work is an attempt towards non-contact freehand 3D ultrasound imaging with minimal complexity added to the existing point of care ultrasound (POCUS) systems. Methods: This study proposes a novel approach of using a mechanical track for non-contact ultrasound (US) scanning. The approach thus restricts the probe motion to a linear plane, to simplify the acquisition and 3D reconstruction process. A pipeline for US 3D volume reconstruction employing an US research platform and a GPU-based edge device is developed. Results: The efficacy of the proposed approach is demonstrated through ex-vivo and in-vivo experiments. Conclusion: The proposed approach with the adjustable field of view capability, non-contact design, and low cost of deployment without significantly altering the existing setup would open doors for up gradation of traditional systems to a wide range of 3D US imaging applications. Significance: Ultrasound (US) imaging is a popular clinical imaging modality for the point-of-care bedside imaging, particularly of the wrist/knee in the pediatric population due to its non-invasive and radiation free nature. However, the limited views of tissue structures obtained with 2D US in such scenarios make the diagnosis challenging. To overcome this, 3D US imaging which uses 2D US images and their orientation/position to reconstruct 3D volumes was developed. The accurate position estimation of the US probe at low cost has always stood as a challenging task in 3D reconstruction. Additionally, US imaging involves contact, which causes difficulty to pediatric subjects while monitoring live fractures or open wounds. Towards overcoming these challenges, a novel framework is attempted in this work.

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arXiv2023

Fast Marching based Tissue Adaptive Delay Estimation for Aberration Corrected Delay and Sum Beamforming in Ultrasound Imaging

Conventional ultrasound (US) imaging employs the delay and sum (DAS) receive beamforming with dynamic receive focus for image reconstruction due to its simplicity and robustness. However, the DAS beamforming follows a geometrical method of delay estimation with a spatially constant speed-of-sound (SoS) of 1540 m/s throughout the medium irrespective of the tissue in-homogeneity. This approximation leads to errors in delay estimations that accumulate with depth and degrades the resolution, contrast and overall accuracy of the US image. In this work, we propose a fast marching based DAS for focused transmissions which leverages the approximate SoS map to estimate the refraction corrected propagation delays for each pixel in the medium. The proposed approach is validated qualitatively and quantitatively for imaging depths of upto ~ 11 cm through simulations, where fat layer induced aberration is employed to alter the SoS in the medium. To the best of authors' knowledge, this is the first work considering the effect of SoS on image quality for deeper imaging.

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arXiv2023

A Simplified 3D Ultrasound Freehand Imaging Framework Using 1D Linear Probe and Low-Cost Mechanical Track

Ultrasound imaging is the most popular medical imaging modality for point-of-care bedside imaging. However, 2D ultrasound imaging provides only limited views of the organ of interest, making diagnosis challenging. To overcome this, 3D ultrasound imaging was developed, which uses 2D ultrasound images and their orientation/position to reconstruct 3D volumes. The accurate position estimation of the ultrasound probe at low cost has always stood as a challenging task in 3D reconstruction. In this study, we propose a novel approach of using a mechanical track for ultrasound scanning, which restricts the probe motion to a linear plane, simplifying the acquisition and hence the reconstruction process. We also present an end-to-end pipeline for 3D ultrasound volume reconstruction and demonstrate its efficacy with an in-vitro tube phantom study and an ex-vivo bone experiment. The comparison between a sensorless freehand and the proposed mechanical track based acquisition is available online (shorturl.at/jqvX0).

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arXiv2022

Learning while Acquisition: Towards Active Learning Framework for Beamforming in Ultrasound Imaging

In the recent past, there have been many efforts to accelerate adaptive beamforming for ultrasound (US) imaging using neural networks (NNs). However, most of these efforts are based on static models, i.e., they are trained to learn a single adaptive beamforming approach (e.g., minimum variance distortionless response (MVDR)) assuming that they result in the best image quality. Moreover, the training of such NNs is initiated only after acquiring a large set of data that consumes several gigabytes (GBs) of storage space. In this study, an active learning framework for beamforming is described for the first time in the context of NNs. The best quality image chosen by the user serves as the ground truth for the proposed technique, which trains the NN concurrently with data acqusition. On average, the active learning approach takes 0.5 seconds to complete a single iteration of training.

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