Localizing B-lines in lung ultrasonography by weakly-supervised deep learning, in-vivo results

\u3cp\u3eLung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represent a healthy lung surface, B-line artifacts correlate with a wide range of pathological conditions affecting the lung parenchyma. In fact, the appearance of B-lines correlates to an increase in extravascular lung water, interstitial lung diseases, cardiogenic and non-cardiogenic lung edema, interstitial pneumonia and lung contusion. Detection and localization of B-lines in a LUS video are therefore tasks of great clinical interest, with accurate, objective and timely evaluation being critical. This is particularly true in environments such as the emergency units, where timely decision may be crucial. In this work, we present and describe a method aimed at supporting clinicians by automatically detecting and localizing B-lines in an ultrasound scan. To this end, we employ modern deep learning strategies and train a fully convolutional neural network to perform this task on B-mode images of dedicated ultrasound phantoms in-vitro, and on patients in-vivo. An accuracy, sensitivity, specificity, negative and positive predictive value equal to 0.917, 0.915, 0.918, 0.950 and 0.864 were achieved in-vitro, respectively. Using a clinical system in-vivo, these statistics were 0.892, 0.871, 0.930, 0.798 and 0.958, respectively. We moreover calculate neural attention maps that visualize which components in the image triggered the network, thereby offering simultaneous weakly-supervised localization. These promising results confirm the capability of the proposed method to identify and localize the presence of B-lines in clinical lung ultrasonography.\u3c/p\u3

Deep learning in ultrasound imaging

\u3cp\u3eIn this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.\u3c/p\u3

Super-resolution using flow estimation in contrast enhanced ultrasound imaging

\u3cp\u3eUltrasound localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Despite its high spatial resolution, low microbubble concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single super-resolved image. To address this limitation, sparsity-based approaches have recently been proposed to significantly reduce the total acquisition time, by resolving the vasculature in settings with considerable microbubble overlap. Here, we report on initial results of improving the spatial resolution and visual vascular reconstruction quality of sparsity-based super-resolution ultrasound imaging from low frame-rate acquisitions, by exploiting the inherent kinematics of microbubbles' flow. Our method relies on simultaneous tracking and sparsity-based detection of individual microbubbles.\u3c/p\u3

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

\u3cp\u3eProstate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.\u3c/p\u3

Compressed sensing for beamformed ultrasound computed tomography

Ultrasound computed tomography (UCT) allows reconstruction of quantitative tissue characteristics. Lowering the acquisition time would be beneficial; however, this is limited by the time of flight and the number of transmission events. Moreover, corruption of the measurements by noise may cause inverse scattering reconstruction methods such as the Born Iterative Method (BIM) to converge to a wrong solution. Beamforming using multiple elements to obtain a narrow beam has the potential to mitigate the effects of noise; however, spatial coverage per transmission event reduces in this case. To excite the full domain, more transmissions are required and the acquisition time increases even further. We therefore consider compressive acquisitions based on parallel randomized transmissions from a circular array. Relying on the assumption that the object is compressible, we combine the BIM with sparse reconstruction to obtain the estimated image

Determination of a potential quantitative measure of the state of the lung using lung ultrasound spectroscopy

\u3cp\u3eB-lines are ultrasound-imaging artifacts, which correlate with several lung-pathologies. However, their understanding and characterization is still largely incomplete. To further study B-lines, lung-phantoms were developed by trapping a layer of microbubbles in tissue-mimicking gel. To simulate the alveolar size reduction typical of various pathologies, 170 and 80 μm bubbles were used for phantom-Type 1 and 2, respectively. A normal alveolar diameter is approximately 280 μm. A LA332 linear-Array connected to the ULA-OP platform was used for imaging. Standard ultrasound (US) imaging at 4.5 MHz was performed. Subsequently, a multi-frequency approach was used where images were sequentially generated using orthogonal sub-bands centered at different frequencies (3, 4, 5, and 6 MHz). Results show that B-lines appear predominantly with phantom-Type 2. Moreover, the multi-frequency approach revealed that the B-lines originate from a specific portion of the US spectrum. These results can give rise to significant clinical applications since, if further confirmed by extensive in-vivo studies, the native frequency of B-lines could provide a quantitative-measure of the state of the lung.\u3c/p\u3

Cumulative Phase Delay Imaging - a new contrast enhanced ultrasound modality

Recently, a new acoustic marker for ultrasound contrast agents (UCAs) has been introduced. A cumulative phase delay (CPD) between the second harmonic and fundamental pressure wave field components is in fact observable for ultrasound propagating through UCAs. This phenomenon is absent in the case of tissue nonlinearity and is dependent on insonating pressure and frequency, UCA concentration, and propagation path length through UCAs. In this paper, ultrasound images based on this marker are presented. The ULA-OP research platform, in combination with a LA332 linear array probe (Esaote, Firenze Italy), were used to image a gelatin phantom containing a PVC plate (used as a reflector) and a cylindrical cavity measuring 7 mm in diameter (placed in between the observation point and the PVC plate). The cavity contained a 240 µL/L SonoVueO® UCA concentration. Two insonating frequencies (3 MHz and 2.5 MHz) were used to scan the gelatine phantom. A mechanical index MI = 0.07, measured in water at the cavity location with a HGL-0400 hydrophone (Onda, Sunnyvale, CA), was utilized. Processing the ultrasound signals backscattered from the plate, ultrasound images were generated in a tomographic fashion using the filtered back-projection method. As already observed in previous studies, significantly higher CPD values are measured when imaging at a frequency of 2.5 MHz, as compared to imaging at 3 MHz. In conclusion, these results confirm the applicability of the discussed CPD as a marker for contrast imaging. Comparison with standard contrast-enhanced ultrasound imaging modalities will be the focus of future work

EFSUMB Young Investigator Award 2019

The winner at EUROSON 2019 of the Young Investigator 3000 euro prize was Rogier R Wildeboer, The Netherlands for the abstract entitled : 3 D Multiparametric Ultrasound for Prostate Cancer Diagnosi