Ultrasonography of the kidneys is essential in the diagnosis and management of kidney-related diseases. The kidneys are easily examined, and most pathological changes in the kidneys are distinguishable with ultrasound. In this pictorial review, the most common findings in renal ultrasound are highlighted.

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68–99.6% and a detection accuracy between 80.6–94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

Ziel: Die herkömmlichen Ultraschallverfahren zur Erfassung von Color-Flow-Bildern der Blutströmung sind durch eine relativ geringe Vollbildübertragungsrate begrenzt und beschränken sich auf die Schätzung von Geschwindigkeiten entlang eines Schallstrahls. Als Ansatz zur Umgehung dieser Einschränkungen wurde die Plane-Wave-Excitation-Methode (PWE-Methode) vorgeschlagen. Material und Methoden: Mithilfe der PWE-Methode kann die 2-D-Vektorgeschwindigkeit des Blutes mit einer hohen Vollbildübertragungsrate bestimmt werden. Die Schätzung der Vektorgeschwindigkeiten erfolgt folgender Maßen: Der Ultraschall wird während der Schallübertragung nicht gebündelt, wodurch ein Speckle-Vollbild des Blutes für jede gepulste Emission erhalten wird. Der Puls ist ein 13-bit-Barker-Code, der zeitgleich von jeder Transducer-Einheit übertragen wird. Die 2-D-Vektorgeschwindigkeit des Blutes wird durch die Aufarbeitungung des 2-D-Speckle-Nachlaufs zwischen den Segmenten aufeinander folgender Speckle-Bilder bestimmt. Dieses Prinzip wurde bei einem experimentellen RASMUS-Scanner mit einem 100-CPU-Linux-Cluster für die Nacharbeitung angewendet. Bei der PWE-Methode kann somit ein Vollbild von 100 Hz erlangt werden, bei dem eine Vektorgeschwindigkeitssequenz von ca. 3 s 10 h zur Speicherung und 48 h zur Nacharbeitung benötigt wird. In diesem Beitrag wird eine Fallstudie über die Schätzung der Vektorgeschwindigkeit in verschiedenen komplexen In-vivo-Gefäßstrukturen beschrieben. Ergebnisse: Die Flussmuster von 6 Bifurkationen und 2 Venen wurden untersucht. Nachgewiesen wurde Folgendes: 1. Ein stabiler Wirbel ist im Bulbus carotis im Gegensatz zu den anderen untersuchten Gefäßen immer anwesend. 2. Ein retrograder Fluss konnte im R. superficialis der A. femoralis während der Diastole nachgewiesen werden. 3. In der A. subclavia konnte ein retrograder Fluss, in der A. carotis communis konnte ein anterograder Fluss in der Diastole festgestellt werden. 4. Wirbel wurden in den Sinustaschen hinter den venösen Klappen bei anterogradem sowie bei retrogradem Fluss erzeugt. 5. Sekundärer Fluss konnte in den verschiedenen Gefäßen nachgewiesen werden. Schlussfolgerung: Durch die Anwendung der schnellen Vektorgeschwindigkeitsmethode können In-vivo-Aufnahmen erstellt werden, die komplexe Flussmuster mit einer größeren Genauigkeit darstellen, welche mit den herkömmlichen Color-Flow-Methoden bisher nicht möglich gewesen ist. Purpose: Conventional ultrasound methods for acquiring color flow images of the blood motion are limited by a relatively low frame rate and are restricted to only giving velocity estimates along the ultrasound beam direction. To circumvent these limitations, the Plane Wave Excitation (PWE) method has been proposed. Material and Methods: The PWE method can estimate the 2D vector velocity of the blood with a high frame rate. Vector velocity estimates are acquired by using the following approach: The ultrasound is not focused during the ultrasound transmission, and a full speckle image of the blood can be acquired for each pulse emission. The pulse is a 13 bit Barker code transmitted simultaneously from each transducer element. The 2D vector velocity of the blood is found using 2D speckle tracking between segments in consecutive speckle images. Implemented on the experimental scanner RASMUS and using a 100 CPU linux cluster for post processing, PWE can achieve a frame of 100 Hz where one vector velocity sequence of approximately 3 sec, takes 10 h to store and 48 h to process. In this paper a case study is presented of in-vivo vector velocity estimates in different complex vessel geometries. Results: The flow patterns of six bifurcations and two veins were investigated. It was shown: 1. that a stable vortex in the carotid bulb was present opposed to other examined bifurcations, 2. that retrograde flow was present in the superficial branch of the femoral artery during diastole, 3. that retrograde flow was present in the subclavian artery and antegrade in the common carotid artery during diastole, 4. that vortices were formed in the sinus pockets behind the venous valves in both antegrade and retrograde flow, and 5. that secondary flow was present in various vessels. Conclusion: Using a fast vector velocity ultrasound method, in-vivo scans have been recorded where complex flow patterns were visualized in greater detail than previously visualized by conventional color flow imaging techniques.

Microbubble (MB) tracking plays an important role in ultrasound super-resolution imaging (SRI) by enabling velocity estimation and improving image quality. This work presents a new hierarchical Kalman (HK) tracker to achieve better performance at scenarios with high concentrations of MBs and high localization uncertainty. The method attempts to follow MBs with different velocity ranges using different Kalman filters. An extended simulation framework for evaluating trackers is also presented and used for comparison of the proposed HK tracker with the nearest-neighbor (NN) and Kalman (K) trackers. The HK tracks were most similar to the ground truth with the highest Jaccard similarity coefficient in 79% of the scenarios and the lowest root-mean-square error in 72% of the scenarios. The HK tracker reconstructed vessels with a more accurate diameter. In a scenario with an uncertainty of 51.2μm in MB localization, a vessel diameter of 250μm was estimated as 257μm by HK tracker, compared with 329μm and 389μm for the K and NN trackers. In the same scenario, the HK tracker estimated MB velocities with a relative bias down to 1.7% and a relative standard deviation down to 8.3%. Finally, the different tracking techniques were applied to in vivo data from rat kidneys, and trends similar to the simulations were observed. Conclusively, the results showed an improvement in tracking performance, when the HK tracker was employed in comparison with the NN and K trackers.

In vivo monitoring of the microvasculature is relevant since diseases such as diabetes, ischemia, or cancer cause microvascular impairment. Super-resolution ultrasound imaging allows in vivo examination of the microvasculature by detecting and tracking sparsely distributed intravascular microbubbles over a minute-long period. The ability to create detailed images of the renal vasculature of Sprague-Dawley rats using a modified clinical ultrasound platform was investigated in this study. Additionally, we hypothesized that early ischemic damage to the renal microcirculation could be visualized. After a baseline scan of the exposed kidney, 10 rats underwent clamping of the renal vein (n = 5) or artery (n = 5) for 45 min. The kidneys were rescanned at the onset of clamp release and after 60 min of reperfusion. Using a processing pipeline for tissue motion compensation and microbubble tracking, super-resolution images with a very high level of detail were constructed. Image filtration allowed further characterization of the vasculature by isolating specific vessels such as the ascending vasa recta with a 15–20 μm diameter. Using the super-resolution images alone, it was only possible for six assessors to consistently distinguish the healthy renal microvasculature from the microvasculature at the onset of vein clamp release. Future studies will aim at attaining quantitative estimations of alterations in the renal microvascular blood flow using super-resolution ultrasound imaging.