arXiv subjects

Mahesh Raveendranatha Panicker

Publications and source records attributed to Mahesh Raveendranatha Panicker.

At least 19 recordsLinked to original sources

arXiv2026

Differentiable Co-Design of Ultrasound Probe Geometry and Acquisition for Plane-Wave Imaging

Ultrasound image quality depends on both the probe geometry and the transmit and receive processing, but engineers usually design the two separately. Physics-based simulators (Field II, k-Wave, MUST/SIMUS) compute the radio-frequency channel data but give no gradients, so design studies must use grid or black-box searches that become slow as the number of parameters increases. We present a fully differentiable end-to-end pipeline for plane-wave ultrasound imaging, with synthetic speckle phantoms, an analytical frequency-domain RF simulator, delay-and-sum beamforming and an extensible image-quality objective. The element pitch, the transmit and receive apodization and the global speed of sound are differentiable parameters of one computation graph, so one reverse-mode pass gives their exact joint gradients. Studies of each parameter rank the design parameters: the optimized pitch decreases the grating lobes, and the speed-of-sound autofocus gives clear contrast and resolution gains. Over 15 test phantoms, the optimized pitch increases the cyst gCNR from 0.55 to 0.66 (about 20%), and the speed-of-sound correction adds a further 0.05. The lateral point-spread function agrees with Field II within about 10%, and on 49 real CUBDL plane-wave acquisitions from five institutions, the framework finds the global speed of sound with a mean absolute difference of 16.6 m/s from the published corrections and phantom speed of sound.

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arXiv2026

Physics-Grounded Objective-Conditioned Deep Beamforming for Adaptive Resolution-Contrast Control in Plane-Wave Ultrasound Imaging

Plane-wave ultrasound enables ultrafast imaging but remains constrained by a receive-aperture-dependent resolution- contrast trade-off. Fine structures benefit from high spatial resolution, whereas diffuse tissue and lesions often require improved contrast and clutter suppression. These objectives favor different receive-aperture configurations, yet conventional beamformers operate at a fixed imaging point. We propose a physics-grounded, objective-conditioned deep beamformer that enables controllable image formation within a single network. From single-plane-wave channel data, the network predicts spatially varying receive-apodization weights, conditioned on the desired imaging objective through feature-wise linear modulation. The conditioning variable is explicitly tied to a physically defined receive-aperture configuration and supervised using the correspondingcoherent-plane-wave-compounding target. Differentiable, region-of-interest-aware, physics-guided quality losses further promote objective-specific resolution and contrast during end-to-end training. The model is trained exclusively on simulated channel data spanning diverse target morphologies and echogenicities, and evaluated zero-shot on the PICMUS and CUBDL datasets, including unseen probe geometries. The proposed framework generally improves upon single-plane-wave delay-and-sum reconstruction while providing objective-dependent control. On PICMUS, the proposed method improves gCNR by up to 16.8% and axial resolution by 22% compared with the reference beamforming method. Moreover, switching between the contrast- and resolution-oriented objectives enables controllable changes of up to 4.9% in lesion contrast and 11.4% in lateral resolution. Compared with the best-performing learning-based baselines, the proposed method reduces lateral full-width-at-half-maximum by 5.9% and improves generalized contrast-to-noise ratio by 6.6%.

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arXiv2025

CapsBeam: Accelerating Capsule Network based Beamformer for Ultrasound Non-Steered Plane Wave Imaging on Field Programmable Gate Array

In recent years, there has been a growing trend in accelerating computationally complex non-real-time beamforming algorithms in ultrasound imaging using deep learning models. However, due to the large size and complexity these state-of-the-art deep learning techniques poses significant challenges when deploying on resource-constrained edge devices. In this work, we propose a novel capsule network based beamformer called CapsBeam, designed to operate on raw radio-frequency data and provide an envelope of beamformed data through non-steered plane wave insonification. Experiments on in-vivo data, CapsBeam reduced artifacts compared to the standard Delay-and-Sum (DAS) beamforming. For in-vitro data, CapsBeam demonstrated a 32.31% increase in contrast, along with gains of 16.54% and 6.7% in axial and lateral resolution compared to the DAS. Similarly, in-silico data showed a 26% enhancement in contrast, along with improvements of 13.6% and 21.5% in axial and lateral resolution, respectively, compared to the DAS. To reduce the parameter redundancy and enhance the computational efficiency, we pruned the model using our multi-layer LookAhead Kernel Pruning (LAKP-ML) methodology, achieving a compression ratio of 85% without affecting the image quality. Additionally, the hardware complexity of the proposed model is reduced by applying quantization, simplification of non-linear operations, and parallelizing operations. Finally, we proposed a specialized accelerator architecture for the pruned and optimized CapsBeam model, implemented on a Xilinx ZU7EV FPGA. The proposed accelerator achieved a throughput of 30 GOPS for the convolution operation and 17.4 GOPS for the dynamic routing operation.

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arXiv2025

BEAM-Net: A Deep Learning Framework with Bone Enhancement Attention Mechanism for High Resolution High Frame Rate Ultrasound Beamforming

Pocket-sized, low-cost point-of-care ultrasound (POCUS) devices are increasingly used in musculoskeletal (MSK) applications for structural examination of bone tissue. However, the image quality in MSK ultrasound is often limited by speckle noise, low resolution, poor contrast, and anisotropic reflections, making bone images difficult to interpret without additional post-processing. Typically, medical ultrasound systems use delay and sum beamforming (DASB) for image reconstruction, which is not specifically optimized for bone structures. To address these limitations, we propose BEAM-Net, a novel end-to-end deep neural network (DNN) that performs high-frame-rate ultrasound beamforming with integrated bone enhancement, using single-plane-wave (SPW) radio frequency (RF) data as input. Our approach embeds a Bone Probability Map (BPM), which acts as an attention mechanism to enforce higher structural similarity around bony regions in the image. The proposed approach is the first of its kind to incorporate bone enhancement directly into ultrasound beamforming using deep learning. BEAM-Net was trained and evaluated on in-vivo MSK and synthetic RF ultrasound datasets. This paper introduces the Edge Preservation Index (EPI) as a new region-focused metric for evaluating structural fidelity in bone-enhanced ultrasound images. The performance of BEAM-Net was compared with conventional DASB and existing deep learning architectures using the EPI, Contrast Ratio (CR), Signal-to-Noise ratio (SNR), Speckle Similarity Index (SSI), and Structural Similarity Index (SSIM). BEAM-Net showed substantial gains over SPW-DASB, achieving 51.4-51% higher CR and 94.2-73.3% higher SNR on in-vivo MSK and synthetic RF datasets. It outperformed multiple steered plane wave DASB (MPW-DASB), with 19.8-24.0% improvements in CR and SNR on in-vivo MSK and 2.5-12.8% improvements on synthetic data.

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arXiv2024

Orthogonal Linear Array based Product Beamforming for Real Time Underwater 3D Acoustical Imaging

Ocean exploration using acoustical 3D imaging is gaining popularity as it provides information about the 3D geometry of the targets even under mild turbid conditions. A major challenge in underwater 3D imaging is the high cost of the planar arrays and the computational complexity of the image reconstruction algorithms. In this work, we introduce two novel aspects, an L shaped array and a quadrant based time domain receive beamforming with a focus on achieving relatively low computational complexity for real-time 3D imaging in underwater environments. An orthogonal combination of two linear arrays to form an L shape is used to perform two independent and parallel 2D delay and sum beamforming and the 3D image of the target is reconstructed using the product of the resulting beams. In the proposed quadrant-based beamforming, each quadrant in the imaging slice is reconstructed in parallel using the orthogonal L-shaped linear arrays which reside at the edges of the planar array for the quadrant to be reconstructed. The proposed L-array solves the multiple-target ambiguity issue of the cross-array and an L-array placed at the center of the uniform planar array. The proposed method has reduced the main lobe width by 1 degree with 11.8 dB increase in the peak side lobe level when compared to the conventional delay and sum beamforming using a uniform planar array. Although there is an increase in the side lobe level, the asymmetric beam pattern of the proposed L array and the quadrant-based beamforming restrict the side lobes within a quadrant. The proposed method achieves a reduction in computation time by a factor of 97 for 3D imaging compared to the conventional method, while maintaining acceptable image quality. For qualitative analysis, the 3D images of different underwater targets have been reconstructed and compared in simulation and experiment.

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arXiv2024

AI Enabled High Frame Rate Portable Ultrasound Imaging Pipeline: Prototype Implementation with GPU Acceleration

In this paper, we present a GPU-accelerated prototype implementation of a portable ultrasound imaging pipeline on an Nvidia CLARA AGX development kit. The raw data is acquired with nonsteered plane wave transmit using a programmable handheld open platform that supports 128-channel transmit and 64-channel receive. The received signals are transferred to the Nvidia CLARA AGX developer platform through a host system for accelerated imaging. GPU-accelerated implementation of the conventional delay and sum (DAS) beamformer along with two adaptive nonlinear beamformers and two Fourier-based techniques was performed. The feasibility of the complete pipeline and its imaging performance was evaluated with in-vitro phantom imaging experiments and the efficacy is demonstrated with preliminary in-vivo scans. The image quality quantified by the standard contrast and resolution metrics was comparable with that of the CPU implementation. The execution speed of the implemented beamformers was also investigated for different sizes of imaging grids and a significant speedup as high as 180 times that of the CPU implementation was observed. Since the proposed pipeline involves Nvidia CLARA AGX, there is always the potential for easy incorporation of online/active learning approaches.

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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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arXiv2024

On the Echogenicity of Natural Starch-Based Blood Mimicking Fluids for Contrast Enhanced Ultrasound Imaging: Preliminary In-vitro Experiments

Natural starch-based blood-mimicking fluid (BMF) has been used as an alternative to commercially available BMFs for in-vitro Doppler investigations in low-resource settings. Most reported works in the literature have used corn starch-based BMF. Evaluation of other natural starches for potential BMF and their characterization have relatively been unexplored in the literature. To this end, this work investigates the echogenicity of corn-, potato-, tapioca-, and wheat starch-based BMFs prepared using a liquid base of pure water-glycerol mixture with three different starch concentrations (1%, 3%, and 5%). The experiments were performed by manually pumping the BMFs to a PolyVinyl alcohol (PVA) based flow phantom using a syringe and raw datasets were acquired using a Verasonics Vantage 128 Research Ultrasound System. Echogenicity was measured as the mean pixel intensity in a selected region of interest (ROI) in the beamformed image. Among the four natural starch-based BMFs, potato starch-based BMF showed the highest echogenicity and contrast with almost 13%, 14%, and 10% higher pixel intensities (dB) than that of the least echoic BMF with 1%, 3%, and 5% starch concentrations respectively. Moreover, the echogenicity of corn, tapioca, and wheat starch-based BMF was observed to be similar, and the results suggest that these BMFs with higher starch concentrations shall be employed for in-vitro contrast-enhanced ultrasound imaging.

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arXiv2024

A Performance Evaluation of Filtered Delay Multiply and Sum Beamforming for Ultrasound Localization Microscopy: Preliminary Results

Ultrafast ultrasound localization microscopy (ULM), which has shown promising results in microvascular imaging, overcomes the typical trade-off between resolution and penetration depth. Combining ultrasound contrast agents and high frame rate imaging enables ULM to visualize microvasculature and quantify flow. However, the quality of the microvascular maps obtained depends on the signal-to-noise ratio of the received signals, image reconstruction techniques, and the microbubble (MB) localization and tracking algorithms used. Most reported research in ULM employs the conventional delay and sum (DAS) beamforming technique for image reconstruction despite its limited contrast and resolution. In this work, a filtered delay multiply and sum (F-DMAS) beamforming approach with non-steered plane wave transmit was employed for ULM, and its performance was compared with the conventional DAS-based approach for the different localization algorithms available in the Localization and Tracking Toolbox for Ultrasound Localization Microscopy. We also introduce two novel image quality measures that can overcome the limitations of conventional quality metrics that require suitable targets for evaluation. We also report the preliminary in-vitro investigation of F-DMAS with B-mode and power Doppler maps for microvascular imaging. The results are promising with enhanced contrast and lateral resolution, and suggest that further experimental studies are warranted.

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arXiv2024

Tiny-VBF: Resource-Efficient Vision Transformer based Lightweight Beamformer for Ultrasound Single-Angle Plane Wave Imaging

Accelerating compute intensive non-real-time beam-forming algorithms in ultrasound imaging using deep learning architectures has been gaining momentum in the recent past. Nonetheless, the complexity of the state-of-the-art deep learning techniques poses challenges for deployment on resource-constrained edge devices. In this work, we propose a novel vision transformer based tiny beamformer (Tiny-VBF), which works on the raw radio-frequency channel data acquired through single-angle plane wave insonification. The output of our Tiny-VBF provides fast envelope detection requiring very low frame rate, i.e. 0.34 GOPs/Frame for a frame size of 368 x 128 in comparison to the state-of-the-art deep learning models. It also exhibited an 8% increase in contrast and gains of 5% and 33% in axial and lateral resolution respectively when compared to Tiny-CNN on in-vitro dataset. Additionally, our model showed a 4.2% increase in contrast and gains of 4% and 20% in axial and lateral resolution respectively when compared against conventional Delay-and-Sum (DAS) beamformer. We further propose an accelerator architecture and implement our Tiny-VBF model on a Zynq UltraScale+ MPSoC ZCU104 FPGA using a hybrid quantization scheme with 50% less resource consumption compared to the floating-point implementation, while preserving the image quality.

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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

Ultrasound based Gas Detection: Analyzing Acoustic Impedance for High-Performance and Low-Cost Solutions

Gas leakage is a serious hazard in a variety of sectors, including industrial, domestic, and gas-powered vehicles. The installation of gas leak detection systems has emerged as a critical preventative measure to solve this problem. Traditional gas sensors, such as electrochemical, infrared point, and Metal Oxide Semiconductor sensors, have been widely used for gas leak detection. However, these sensors have limitations in terms of their adaptation to various gases, as well as their high cost and difficulties in scaling. In this paper, a novel non-contact gas detection technique based on a 40 kHz ultrasonic signal is described. The proposed approach employs the reflections of the emitted ultrasonic wave to detect the gas leaks and also able to identify the exact gas in real-time. To confirm the method's effectiveness, trials were carried out using Hydrogen, Helium, Argon, and Butane gas. The system identified gas flow breaches in 0.01 seconds, whereas the gas identification procedure took 0.8 seconds. An interesting extension of the proposed approach is real-time visualisation of gas flow employing an array of transducers.

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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 Prototype System for High Frame Rate Ultrasound Imaging based Prosthetic Arm Control

The creation of unique control methods for a hand prosthesis is still a problem that has to be addressed. The best choice of a human-machine interface (HMI) that should be used to enable natural control is still a challenge. Surface electromyography (sEMG), the most popular option, has a variety of difficult-to-fix issues (electrode displacement, sweat, fatigue). The ultrasound imaging-based methodology offers a means of recognising complex muscle activity and configuration with a greater SNR and less hardware requirements as compared to sEMG. In this study, a prototype system for high frame rate ultrasound imaging for prosthetic arm control is proposed. Using the proposed framework, a virtual robotic hand simulation is developed that can mimic a human hand as illustrated in the link [10]. The proposed classification model simulating four hand gestures has a classification accuracy of more than 90%.

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arXiv2023

On The Application Of Log Compression and Enhanced Denoising In Contrast Enhancement Of Digital Radiography Images

Digital radiography (DR) is becoming popular for the point of care imaging in the recent past. To reduce the radiation exposure, controlled radiation based on as low as reasonably achievable (ALARA) principle is employed and this results in low contrast images. To address this issue, post-processing algorithms such as the Multiscale Image Contrast Amplification (MUSICA) algorithm can be used to enhance the contrast of DR images even with a low radiation dose. In this study, a modification of the MUSICA algorithm is investigated to determine the potential for further contrast improvement specifically for DR images. The conclusion is that combining log compression and its inverse at the appropriate stage with a multi-stage MUSICA and denoising is very promising. The proposed method resulted in an average of 66.5 % increase in the mean contrast-to-noise ratio (CNR) for the test images considered.

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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

Software Package for Automated Analysis of Lung Ultrasound Videos

In the recent past with the rapid surge of COVID-19 infections, lung ultrasound has emerged as a fast and powerful diagnostic tool particularly for continuous and periodic monitoring of the lung. There have been many attempts towards severity classification, segmentation and detection of key landmarks in the lung. Leveraging the progress, an automated lung ultrasound video analysis package is presented in this work, which can provide summary of key frames in the video, flagging of the key frames with lung infection and options to automatically detect and segment the lung landmarks. The integrated package is implemented as an open-source web application and available in the link https://github.com/anitoanto/alus-package.

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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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