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Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosisof late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos ona frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to ourprevious work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained expertsand improves over the video-based method of our previous work on pleural effusions.

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype. With the aim of localizing angiogenic activity in a non-invasive manner, Dynamic Contrast Enhanced Ultrasound (DCE-US) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsy-proven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiver-operating-characteristic curve area of 0.85) for the detection of prostate cancer.

B-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, ten lung-phantoms were designed. A layer of microbubbles was trapped in tissue-mimicking gel. To simulate the alveolar size reduction typical of various pathologies, 166 and 80-micrometer bubbles were used for phantom-type 1 and phantom-type 2, respectively. A normal alveolar diameter is around 280 micrometer. A LA332 linear-array connected to the ULA-OP platform was used for imaging. Standard ultrasound imaging at 4.5 MHz was performed. Next, a multi-frequency approach was used: images were sequentially generated using orthogonal sub-bands centered at different frequencies (3, 4, 5, and 6MHz). Results show that B-lines appear predominantly with the phantom-type 2, suggesting a link between increased artifact formation and the reduction of the alveolar size. Moreover, the multifrequency approach revealed that the B-lines have a native frequency: B-lines appeared with significantly stronger amplitude in one of the 4 images, and spectral-analysis confirmed B-lines to be centered at specific frequencies. These results can find relevant clinical application since, if confirmed by in-vivo studies, the native frequency of B-lines could serve as a quantitative-measure of the state of the lung.