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

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

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

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

Three-dimensional histopathological reconstruction as a reliable ground truth for prostate cancer studies

To validate new imaging modalities for prostate cancer, images must be three-dimensionally correlated with the histological ground truth. In this work, an interpolation algorithm is described to construct a reliable three-dimensional reference from two-dimensional (2D) histological slices. Eight clinically relevant in silico phantoms were designed to represent difficult-to-reconstruct tumour structures. These phantoms were subjected to different slicing procedures. Additionally, controlled errors were added to investigate the impact of varying slicing distance, front-face orientation, and inter-slice misalignment on the reconstruction performance. Using a radial-basis-function interpolation algorithm, the 2D data were reconstructed in three dimensions. Our results demonstrate that slice thicknesses up to 4 mm can be used to reliably reconstruct tumours of clinically significant size; the surfaces lay within a 1.5 mm 90%-error margin from each other and the volume difference between the original and reconstructed tumour structures does not exceed 10%. With these settings, Dice coefficients above 0.85 are obtained. The presented interpolation algorithm is able to reconstruct clinically significant tumour structures from 2D histology slices. Errors occurring are in the order of magnitude of common registration artefacts. The method’s applicability to real histopathological data is also shown in two resected prostates. An inter-slice spacing of 4 mm or less is recommended during histopathology; the use of a 1.5 mm error margin along the tumour contours can then ensure reliable mapping of the ground truth

Implementation of parallel transmit beamforming using orthogonal frequency division multiplexing : achievable resolution and interbeam interference

textabstractThe speed of sound in the human body limits the achievable data acquisition rate of pulsed ultrasound scanners. To overcome this limitation, parallel beamforming techniques are used in ultrasound 2-D and 3-D imaging systems. Different parallel beamforming approaches have been proposed. They may be grouped into two major categories: parallel beamforming in reception and parallel beamforming in transmission. The first category is not optimal for harmonic imaging; the second category may be more easily applied to harmonic imaging. However, inter-beam interference represents an issue. To overcome these shortcomings and exploit the benefit of combining harmonic imaging and high data acquisition rate, a new approach has been recently presented which relies on orthogonal frequency division multiplexing (OFDM) to perform parallel beamforming in transmission. In this paper, parallel transmit beamforming using OFDM is implemented for the first time on an ultrasound scanner. An advanced open platform for ultrasound research is used to investigate the axial resolution and interbeam interference achievable with parallel transmit beamforming using OFDM. Both fundamental and second-harmonic imaging modalities have been considered. Results show that, for fundamental imaging, axial resolution in the order of 2 mm can be achieved in combination with interbeam interference in the order of-30 dB. For second-harmonic imaging, axial resolution in the order of 1 mm can be achieved in combination with interbeam interference in the order of-35 dB