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 the authors' knowledge, this is the first work considering the effect of SoS on image quality for deeper imaging.Clinical relevance- The proposed approach when employed with an approximate SoS estimation technique can aid in overcoming the fat-induced signal aberrations and thereby in the accurate imaging of various pathologies of liver and abdomen.

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: https://youtu.be/LBcwQ0xzQK0. The proposed classification model simulating four hand gestures has a classification accuracy of more than 90%.Clinical relevance-The proposed system enables an ultrasound imaging based human machine interface that can be a research and development platform for novel control strategies of a hand prosthesis.

Single Image based Super Resolution Ultrasound Imaging Using Residual Learning of Wavelet Features.

The generation of super resolution ultrasound images from the low-resolution (LR) brightness mode (B-mode) images acquired by the portable point of care ultrasound systems has been of sufficient interest in the recent past. With the advancements in deep learning, there have been numerous attempts in this direction. However, all the approaches have been concentrated on employing the direct image as the input to the neural network. In this work, a stationary wavelet (SWT) decomposition is employed to extract the features from the input LR image which is passed through a modified residual network and the learned features are combined using the inverse SWT to reconstruct the high resolution (HR) image at a 4× scale factor. The proposed approach when compared to the state-of-the art approaches, results in an improved high resolution reconstruction.Clinical relevance- The proposed approach will enable the generation of high-resolution images from portable ultrasound systems, allowing for easier interpretation and faster diagnostics in primary care settings.

Enhanced Needle Visualization With Reflection Tuned Apodization Based on the Radon Transform for Ultrasound Imaging.

In ultrasound (US)-guided interventions, accurately tracking and visualizing needles during in-plane insertions are significant challenges due to strong directional specular reflections. These reflections violate the geometrical delay and apodization estimations in the conventional delay and sum beamforming (DASB) degrading the visualization of needles. This study proposes a novel reflection tuned apodization (RTA) to address this issue and facilitate needle enhancement through DASB. The method leverages both temporal and angular information derived from the Radon transforms of the radio frequency (RF) data from plane-wave imaging to filter the specular reflections from the needle and their directivity. The directivity information is translated into apodization center maps through time-to-space mapping in the Radon domain, which is subsequently integrated into DASB. We assess the influence of needle angulations, projection angles in the Radon transform, needle gauge sizes, and the presence of multiple specular interfaces on the approach. The analysis shows that the method surpasses conventional DASB in enhancing the image quality of needle interfaces while preserving the diffuse scattering from the surrounding tissues without significant computational overhead. The work offers promising prospects for improved outcomes in US-guided interventions and better insights into characterizing US reflections with Radon transforms.

On the physics of ultrasound transmission for in-plane needle tracking in guided interventions.

Objective.In ultrasound (US) guided interventions, the accurate visualization and tracking of needles is a critical challenge, particularly during in-plane insertions. An inaccurate identification and localization of needles lead to severe inadvertent complications and increased procedure times. This is due to the inherent specular reflections from the needle with directivity depending on the angle of incidence of the US beam, and the needle inclination.Approach.Though several methods have been proposed for improved needle visualization, a detailed study emphasizing the physics of specular reflections resulting from the interaction of transmitted US beam with the needle remains to be explored. In this work, we discuss the properties of specular reflections from planar and spherical wave US transmissions respectively through multi-angle plane wave (PW) and synthetic transmit aperture (STA) techniques for in-plane needle insertion angles between 15°-50°.Main Results.The qualitative and quantitative results from simulations and experiments reveal that the spherical waves enable better visualization and characterization of needles than planar wavefronts. The needle visibility in PW transmissions is severely degraded by the receive aperture weighting during image reconstruction than STA due to greater deviation in reflection directivity. It is also observed that the spherical wave characteristics starts to alter to planar characteristics due to wave divergence at large needle insertion depths.Significance.The study highlights that synergistic transmit-receive imaging schemes addressing the physical properties of reflections from the transmit wavefronts are imperative for the precise imaging of needle interfaces and hence have strong potential in elevating the quality of outcomes from US guided interventional practices.

Unsupervised landmark detection and classification of lung infection using transporter neural networks.

Supervised deep learning techniques have been very popular in medical imaging for various tasks of classification, segmentation, and object detection. However, they require a large number of labelled data which is expensive and requires many hours of careful annotation by experts. In this paper, an unsupervised transporter neural network framework with an attention mechanism is proposed to automatically identify relevant landmarks with applications in lung ultrasound (LUS) imaging. The proposed framework identifies key points that provide a concise geometric representation highlighting regions with high structural variation in the LUS videos. In order for the landmarks to be clinically relevant, we have employed acoustic propagation physics driven feature maps and angle-controlled Radon Transformed frames at the input instead of directly employing the gray scale LUS frames. Once the landmarks are identified, the presence of these landmarks can be employed for classification of the given frame into various classes of severity of infection in lung. The proposed framework has been trained on 130 LUS videos and validated on 100 LUS videos acquired from multiple centres at Spain and India. Frames were independently assessed by experts to identify clinically relevant features such as A-lines, B-lines, and pleura in LUS videos. The key points detected showed high sensitivity of 99% in detecting the image landmarks identified by experts. Also, on employing for classification of the given lung image into normal and abnormal classes, the proposed approach, even with no prior training, achieved an average accuracy of 97% and an average F1-score of 95% respectively on the task of co-classification with 3-fold cross-validation.

Unsupervised Multi-Latent Space RL Framework for Video Summarization in Ultrasound Imaging.

The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. To this end, we propose a new unsupervised reinforcement learning (RL) framework with novel rewards to facilitate unsupervised learning by avoiding tedious and impractical manual labelling for summarizing ultrasound videos. The proposed framework is capable of delivering video summaries with classification labels and segmentations of key landmarks which enhances its utility as a triage tool in the emergency department (ED) and for use in telemedicine. Using an attention ensemble of encoders, the high dimensional image is projected into a low dimensional latent space in terms of: a) reduced distance with a normal or abnormal class (classifier encoder), b) following a topology of landmarks (segmentation encoder), and c) the distance or topology agnostic latent representation (autoencoders). The summarization network is implemented using a bi-directional long short term memory (Bi-LSTM) which utilizes the latent space representation from the encoder. Validation is performed on lung ultrasound (LUS), that typically represent potential use cases in telemedicine and ED triage acquired from different medical centers across geographies (India and Spain). The proposed approach trained and tested on 126 LUS videos showed high agreement with the ground truth with an average precision of over 80% and average F1 score of well over 44 ±1.7 %. The approach resulted in an average reduction in storage space of 77% which can ease bandwidth and storage requirements in telemedicine.

Domain-aware contrastive learning for ultrasound hip image analysis.

Early diagnosis of Developmental Dysplasia of Hip (DDH) using ultrasound can result in simpler and more effective treatment options. Handheld ultrasound probes are ideally suited for such screening due to their low cost and portability. However, images from the pocket-sized probes are of lower quality than conventional probes. Image quality can be enhanced by image translation techniques that generate a pseudo-image mimicking the image quality of conventional probes. This can also help in generalizing the performance of AI-based automatic interpretation techniques to multiple probes. We develop a new domain-aware contrastive unpaired translation (D-CUT) technique for translating between images acquired from different ultrasound probes. Our approach embeds a Bone Probability Map (BPM) as part of the loss function which enforces higher structural similarity around bony regions in the image. Using the D-CUT model we translated 575 images acquired from a Philips Lumify handheld probe to generate pseudo-3D ultrasound (3DUS) images similar (Fréchet Inception Distance = 92) to those acquired from a conventional ultrasound probe (Philips iU22). The pseudo-3DUS images showed high structural similarity (SSIM = 0.68, Cosine Similarity = 0.65) with the original images and improved the contrast around the bony regions. This study establishes the feasibility of using D-CUT to improve the quality of data acquired from handheld ultrasound probes. Among other potential applications, clinical use of this tool could result in wider use of ultrasound for DDH screening programs.

A nonlinear beamforming for enhanced spatiotemporal sensitivity in high frame rate ultrasound flow imaging.

Typically, an ultrasound flow imaging system employs the conventional delay and sum (DAS) beamformer due to its inherent low complexity. But the conventional DAS technique offers poor contrast, low imaging resolution, and limited spatiotemporal sensitivity. This article attempts to improve the spatiotemporal sensitivity of the conventional flow imaging with a novel multiply and sum based nonlinear high-resolution (NLHR) beamforming approach. The major advantages of the proposed beamformer are the harmonic generation and the enhanced coherence in beamformed signals that improve the spatiotemporal sensitivity towards flow transients. We demonstrate the proposed beamformer for a directional cross-correlation as well as an autocorrelation based velocity estimator with simulated parabolic flow profiles of different velocities and flow directions, an in-vitro rotating disk dataset, and pulsatile flow experiments. The sensitivity of NLHR beamforming towards the flow transients is validated in-vitro with a sudden reversal of flow direction and air bubble tracking experiments. The comparison between the time-frequency plots of DAS and NLHR beamforming indicates that the impulsive spatiotemporal changes induced by the flow of air bubbles are clearly characterized by nonlinear beamforming than that of DAS beamforming. Furthermore, better spatiotemporal velocity tracking of a single air bubble and a clear distinguishability between the tracking of two proximal air bubbles are observed in-vitro. Preliminary studies on the in-vivo carotid data also show comparable, if not better, results than that of the DAS algorithm. Detailed results for each test case in simulation, phantom, and in-vivo studies are available as movies with the supplementary material and online [Online Link].

vid-SAMGRAH: A PyTorch framework for multi-latent space reinforcement learning driven video summarization in ultrasound imaging.

The COVID-19 pandemic has accelerated the need for automatic triaging and summarization of ultrasound videos for fast access to pathologically relevant information in the Emergency Department and lowering resource requirements for telemedicine. In this work, a PyTorch based unsupervised reinforcement learning methodology which incorporates multi feature fusion to output classification labels, segmentation maps and summary videos for lung ultrasound is presented. The use of unsupervised training eliminates tedious manual labeling of key-frames by clinicians opening new frontiers in scalability in training using unlabeled or weakly labeled data. Our approach was benchmarked against expert clinicians from different geographies displaying superior Precision and F1 scores (over 80% and 44%).

Pixel Intensity Vector Field: An Inside Out Approach of Looking at Ultrasound Reflections from the Lung at High Frame Rates.

Ultrasound (US) imaging is becoming the routine modality for the diagnosis and prognosis of lung pathologies. Lung US imaging relies on artifacts from acoustic impedance (Z) mismatches to distinguish and interpret the normal and pathological lung conditions. The air-pleura interface of the normal lung displays specularity due to the huge Z mismatches. However, in the presence of pathologies, the interface alters exhibiting a diffuse behavior due to increased density and reduced spatial distribution of air in the sub-pleural space. The specular or the diffuse behavior influences the reflected acoustic intensity distribution. This study aims to understand the reflection pattern in a normal and pathological lung through a novel approach of determining pixel-level acoustic intensity vector field (IVF) at high frame rates. Detailed lung modeling procedures using k-Wave US toolbox under normal, edematous, and consolidated conditions are illustrated. The analysis of the IVF maps on the three lung models clearly shows the drifting of the air-pleura interface from specular to diffuse with the severity of the pathology.Clinical Relevance- The presented acoustic simulation lung models are an aid to teaching and research by providing a quick visual and intuitive understanding of lung ultrasound physics. The proposed intensity vector field maps are supplementary information to aid diagnostics and characterization of any tissue composed of specular and diffuse components.

Towards A Pixel-Level Reconfigurable Digital Beamforming Core for Ultrasound Imaging.

Ultrasound (US) imaging systems typically employ a single beamforming scheme which is the delay and sum (DAS) beamforming due to its reduced complexity. However, DAS results in images with limited resolution and contrast. The limitations of DAS have been overcome by, delay multiply and sum (DMAS) beamforming, making it especially preferable in cases where finer image details are required in larger depth of scans for an accurate diagnosis. But, DMAS is confined to transducer frequencies where the generated harmonics also fall in the processable frequency range of the US system. However, if US systems could provide the flexibility to reconfigure beamforming considering the restrictions of each beamforming scheme, it is possible to select the best beamforming according to the clinical requirement and system constraints. This work is a fundamental step towards enabling reconfigurable beamforming for on-the-fly selection among the DAS and DMAS beamforming schemes, with low reconfiguration overhead, specifically for each imaging scenario to aid better diagnosis. Two novel architectures are proposed, that reconfigures between DAS and DMAS beamforming as a function of transducer's center frequency with minimum additional computational overhead. The implementation results of the proposed architectures on xc7z010clg400-1 FPGA are reported. The possibilities of pixel-level beamforming reconfigurability, where the different tissue regions are beamformed with either DAS or DMAS are also shown through simulations.