Deep Learning for Ultrasound Image Formation:CUBDL Evaluation Framework and Open Datasets

Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-and-sum beamforming, compared with a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm when formed with networks submitted by the challenge winners. We also describe contributed CUBDL data that may be used for training of future networks. The compiled database includes a total of 576 image acquisition sequences. We additionally introduce a neural-network-based global sound speed estimator implementation that was necessary to fairly evaluate the results obtained with this international database. The integration of CUBDL evaluation methods, evaluation code, network weights from the challenge winners, and all datasets described herein are publicly available (visit https://cubdl.jhu.edu for details). </p

A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, and develop new techniques for diagnosis and treatment. Although lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT, multiple studies have demonstrated its promise to diagnose COVID-19 patients. At the same time, many deep learning models have been built to improve the diagnostic efficiency of medical imaging. The integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients, most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of COVID-19. This invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance

Improved Endocardial Border Definition with Short-Lag Spatial Coherence (SLSC) Imaging

<p>Clutter is a problematic noise artifact in a variety of ultrasound applications. Clinical tasks complicated by the presence of clutter include detecting cancerous lesions in abdominal organs (e.g. livers, bladders) and visualizing endocardial borders to assess cardiovascular health. In this dissertation, an analytical expression for contrast loss due to clutter is derived, clutter is quantified in abdominal images, and sources of abdominal clutter are identified. Novel clutter reduction methods are also presented and tested in abdominal and cardiac images. </p><p>One of the novel clutter reduction methods is Short-Lag Spatial Coherence (SLSC) imaging. Instead of applying a conventional delay-and-sum beamformer to measure the amplitude of received echoes and form B-mode images, the spatial coherence of received echoes are measured to form SLSC images. The world's first SLSC images of simulated, phantom, and <italic>in vivo</italic> data are presented herein. They demonstrate reduced clutter and improved contrast, contrast-to-noise, and signal-to-noise ratios compared to conventional B-mode images. In addition, the resolution characteristics of SLSC images are quantified and compared to resolution in B-mode images. </p><p>A clinical study with 14 volunteers was conducted to demonstrate that SLSC imaging offers 19-33% improvement in the visualization of endocardial borders when the quality of B-mode images formed from the same echo data was poor. There were no statistically significant improvements in endocardial border visualization with SLSC imaging when the quality of matched B-mode images was medium to good.</p>Dissertatio

Improving the detectability of highly coherent targets in short-lag spatial coherence images with multi-line transmission

We hypothesize that the combination of ultrasound Short-Lag Spatial Coherence (SLSC) imaging and Multi-Line Transmission (MLT) can provide enhanced visibility of highly-coherent targets within soft tissues, improving the frame-rate at the same time. Images of a biopsy needle inserted within a lab-made agar/gelatin phantom and an ex vivo bovine meat sample were acquired after implementing single-line transmission (SLT) and MLT with an increasing number of simultaneously transmitted beams. The received signals were then beamformed with standard Delay and Sum (DAS) and SLSC algorithms. Results show that MLT SLSC images have darker background than SLT SLSC and DAS B-mode images, which is due to the rapid signal decorrelation at short lags caused by inter-beam interferences. This effect in turn provides increased needle contrast with MLT SLSC imaging, particularly as the number of multiple beams increase, which is promising for a more accurate biopsy needle detection

Deep Learning in Medical Ultrasound - From Image Formation to Image Analysis

Over the past years, deep learning has established itself as a powerful tool across a broad spectrum of domains. While deep neural networks initially found nurture in the computer vision community, they have quickly spread over medical imaging applications, ranging from image analysis and interpretation to-more recently-image formation and reconstruction. Deep learning is now rapidly gaining attention in the ultrasound community, with many groups around the world exploring a wealth of opportunities to improve ultrasound imaging in several key aspects, ranging from beamforming and compressive sampling to speckle suppression, segmentation, and super-resolution imaging

In vivo reproducibility of robotic probe placement for a novel ultrasound-guided radiation therapy system In vivo reproducibility of robotic probe placement for a novel ultrasound-guided radiation therapy system

Abstract. Ultrasound can provide real-time image guidance of radiation therapy, but the probe-induced tissue deformations cause local deviations from the treatment plan. If placed during treatment planning, the probe causes streak artifacts in required computed tomography (CT) images. To overcome these challenges, we propose robot-assisted placement of an ultrasound probe, followed by replacement with a geometrically identical, CT-compatible model probe. In vivo reproducibility was investigated by implanting a canine prostate, liver, and pancreas with three 2.38-mm spherical markers in each organ. The real probe was placed to visualize the markers and subsequently replaced with the model probe. Each probe was automatically removed and returned to the same position or force. Under position control, the median three-dimensional reproducibility of marker positions was 0.6 to 0.7 mm, 0.3 to 0.6 mm, and 1.1 to 1.6 mm in the prostate, liver, and pancreas, respectively. Reproducibility was worse under force control. Probe substitution errors were smallest for the prostate (0.2 to 0.6 mm) and larger for the liver and pancreas (4.1 to 6.3 mm), where force control generally produced larger errors than position control. Results indicate that position control is better than force control for this application, and the robotic approach has potential, particularly for relatively constrained organs and reproducibility errors that are smaller than established treatment margins