Recursive Aperture Decoded Ultrasound Imaging (READI) With Estimated Motion-Compensated Compounding (EMC2)

Fast Orthogonal Row-Column Electronic Scanning (FORCES) is a Hadamard-encoded Synthetic Transmit Aperture (STA) imaging sequence using bias-sensitive Top-Orthogonal to Bottom Electrode (TOBE) arrays. It produces images with a higher Signal-to-Noise Ratio (SNR) and improved penetration depth compared to traditional STA techniques, but suffers from motion sensitivity due to ensemble size and aperture encoding. This work presents Recursive Aperture Decoded Ultrasound Imaging (READI), a novel decoding and beamforming technique for FORCES that produces multiple low-resolution images out of subsets of the FORCES sequence that are less susceptible to motion, but sum to form the complete FORCES image. Estimated Motion-Compensated Compounding (EMC2) describes the process of comparing these low-resolution images to estimate the underlying motion, then warping them to align before coherent compounding. READI with EMC2 is shown to fully recover images corrupted by probe motion, and restore tissue speckle and sharpness to an image of a beating heart. READI low-resolution images by themselves are demonstrated to be a marked improvement over sparse STA schemes with the same transmit count, and are shown to recover blood speckle at a flow rate of 42 cm/s.

Improving the Elevational Focusing of Fast Orthogonal Row-Column Electronic Scanning (FORCES) Ultrasound Imaging using Retrospective Transmit Beamforming (RTB)

Recent developments in Row Column Arrays (RCAs) have presented promising options for volumetric imaging without the need for the excessive channel counts of fully wired 2D-arrays. Bias programmable RCAs, also known as Top Orthogonal to Bottom Electrode (TOBE) Arrays, show further promise in that imaging schemes, such as Fast Orthogonal Row-Column Electronic Scanning (FORCES) allow for full transmit and receive focusing everywhere in the image plane. However, due to its fixed elevational focus and large transmit aperture, FORCES experiences poor elevational focusing away from the focal point. In this study we present a modification to the FORCES imaging scheme by applying Retrospective Transmit Beamforming (RTB) in the elevational direction to allow for elevational transmit focusing everywhere in the imaging plane. We evaluate FORCES and uFORCES methods, with and without RTB applied, when imaging both a cyst and wire phantom. With experiment we show improved elevational focusing capabilities away from the focal point when RTB is applied to both FORCES and uFORCES. At the focal point, performance with RTB remains comparable or improved relative to standard FORCES. This is quantified by the measurement of Full Width Half Max when imaging the wire phantom, and by the generalized Contrast to Noise Ratio when imaging the tubular cyst phantom. We also demonstrate the volumetric imaging capabilities of FORCES RTB with the wire phantom.

Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) with Bias-Switchable Row-Column Arrays

Top-Orthogonal-to-Bottom-Electrode (TOBE) arrays, also known as bias-switchable row-column arrays (RCAs), allow for imaging techniques otherwise impossible for non-bias-switachable RCAs. Hadamard Encoded Row Column Ultrasonic Expansive Scanning (HERCULES) is a novel imaging technique that allows for expansive 3D scanning by transmitting plane or cylindrical wavefronts and receiving using Hadamard-Encoded-Read-Out (HERO) to perform beamforming on what is effectively a full 2D synthetic receive aperture. This allows imaging beyond the shadow of the aperture of the RCA array, potentially allows for whole organ imaging and 3D visualization of tissue morphology. It additionally enables view large volumes through limited windows. In this work we demonstrated with simulation that we are able to image at comparable resolution to existing RCA imaging methods at hundreds of frames per second. We validated these simulations by demonstrating an experimental implementation of HERCULES using a custom fabricated TOBE array, custom biasing electronics, and a research ultrasound system. Furthermore, we assess our imaging capabilities by imaging a commercial phantom, and comparing our results to those taken with traditional RCA imaging methods. Finally, we verified our ability to image real tissue by imaging a xenograft mouse model.

Bias-Switchable Row-Column Array Imaging using Fast Orthogonal Row-Column Electronic Scanning (FORCES) Compared with Conventional Row-Column Array Imaging

Row-Column Arrays (RCAs) offer an attractive alternative to fully wired 2D-arrays for 3D-ultrasound, due to their greatly simplified wiring. However, conventional RCAs face challenges related to their long elements. These include an inability to image beyond the shadow of the aperture and an inability to focus in both transmit and receive for desired scan planes. To address these limitations, we recently developed bias-switchable RCAs, also known as Top Orthogonal to Bottom Electrode (TOBE) arrays. These arrays provide novel opportunities to read out from every element of the array and achieve high-quality images. While TOBE arrays and their associated imaging schemes have shown promise, they have not yet been directly compared experimentally to conventional RCA imaging techniques. This study aims to provide such a comparison, demonstrating superior B-scan and volumetric images from two electrostrictive relaxor TOBE arrays, using a method called Fast Orthogonal Row-Column Electronic scanning (FORCES), compared to conventional RCA imaging schemes, including Tilted Plane Wave (TPW) compounding and Virtual Line Source (VLS) imaging. The study quantifies resolution and Generalized Contrast to Noise Ratio (gCNR) in phantoms, and also demonstrates volumetric acquisitions in phantom and animal models.

MyriadAL: Active Few Shot Learning for Histopathology

Active Learning (AL) and Few Shot Learning (FSL) are two label-efficient methods which have achieved excellent results recently. However, most prior arts in both learning paradigms fail to explore the wealth of the vast unlabelled data. In this study, we address this issue in the scenario where the annotation budget is very limited, yet a large amount of unlabelled data for the target task is available. We frame this work in the context of histopathology where labelling is prohibitively expensive. To this end, we introduce an active few shot learning framework, Myriad Active Learning (MAL), including a contrastive-learning encoder, pseudo-label generation, and novel query sample selection in the loop. Specifically, we propose to massage unlabelled data in a self-supervised manner, where the obtained data representations and clustering knowledge form the basis to activate the AL loop. With feedback from the oracle in each AL cycle, the pseudo-labels of the unlabelled data are refined by optimizing a shallow task-specific net on top of the encoder. These updated pseudo-labels serve to inform and improve the active learning query selection process. Furthermore, we introduce a novel recipe to combine existing uncertainty measures and utilize the entire uncertainty list to reduce sample redundancy in AL. Extensive experiments on two public histopathology datasets show that MAL has superior test accuracy, macro F1-score, and label efficiency compared to prior works, and can achieve a comparable test accuracy to a fully supervised algorithm while labelling only 5% of the dataset.