Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Background And Objectives: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs.

Dataset of prostate MRI annotated for anatomical zones and cancer.

In the present work, we present a publicly available, expert-segmented representative dataset of 158 3.0 Tesla biparametric MRIs [1]. There is an increasing number of studies investigating prostate and prostate carcinoma segmentation using deep learning (DL) with 3D architectures [2], [3], [4], [5], [6], [7].

Changing the Patient's Position: Pitfalls and Benefits for Radiation Dose and Image Quality of Computed Tomography in Polytrauma.

For computed tomography (CT), representing the diagnostic standard for trauma patients, image quality is essential. The positioning of the patient’s arms next to the abdomen causes artifacts and is also considered to increase radiation exposure. The aim of this study was to evaluate the effect of various positionings during different CT examination steps on the extent of artifacts as well as radiation dose using iterative reconstruction (IR).

Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints.

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection.

Valve embolization during transcatheter aortic valve implantation: Incidence, risk factors and follow-up by computed tomography.

Background: In most cases of transcatheter valve embolization and migration (TVEM), the embolized valve remains in the aorta after implantation of a second valve into the aortic root. There is little data on potential late complications such as valve thrombosis or aortic wall alterations by embolized valves.

Aims: The aim of this study was to analyze the incidence of TVEM in a large cohort of patients undergoing transcatheter aortic valve implantation (TAVI) and to examine embolized valves by computed tomography (CT) late after TAVI.

Bail-Out Implantation of an Embolized Balloon-Expandable Transcatheter Heart Valve in the Aortic Arch Using Self-Expandable Stents.

Embolization of a balloon expandable valve during transcatheter aortic valve implantation (TAVR) is a rare complication which generally can be managed by implantation of the embolized valve into the aorta. We present a TAVR case where the combination of an ascending aortic aneurysm and a narrow aortic arch precluded implantation of an embolized balloon-expandable valve into either the ascending and descending aorta. As a bailout strategy, the embolized valve was secured in the aortic arch using two self-expandable stents.

Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection.

Background: The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability.

Methods: Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps.

Do submillisievert-chest CT protocols impact diagnostic quality in suspected COVID-19 patients?

Background: During the ongoing global SARS-CoV-2 pandemic, there is a high demand for quick and reliable methods for early identification of infected patients. Due to its widespread availability, chest-CT is commonly used to detect early pulmonary manifestations and for follow-ups.

Purpose: This study aims to analyze image quality and reproducibility of readings of scans using low-dose chest CT protocols in patients suspected of SARS-CoV-2 infection.

Are Congenital Cervical Block Vertebrae a Risk Factor for Adjacent Segment Disease? A Retrospective Cross-Sectional CT and MR Imaging Study.

Unlabelled: Adjacent segment disease (ASDI) is a well-described complication of spinal fusion surgery that may ultimately lead to spinal stenosis and repeated surgical intervention. Although congenital block vertebrae also present with degenerative changes in the adjacent segments, this has not yet been systematically investigated. The aim of this study was to assess the presence and degree of ASDI in congenital cervical block vertebrae.

Detectability of Head and Neck Cancer via New Computed Tomography Reconstruction Tools including Iterative Reconstruction and Metal Artifact Reduction.

State-of-the-art technology in Computed Tomography (CT) includes iterative reconstruction algorithms (IR) and metal artefact reduction (MAR) techniques. The objective of the study is to show the benefits of this technology for the detection of primary and recurrent head and neck cancer. A total of 131 patients underwent contrast-enhanced CT for diagnosis of primary and recurrent Head and Neck cancer; 110 patients were included.

CT diagnostics of pulmonary embolism: Does iodine delivery rate still affect image quality in iterative reconstruction?

Background: Computed tomographic (CT) imaging in suspected pulmonary artery embolism represents the standard procedure. Studies without iterative reconstruction proved beneficial using increased iodine delivery rate (IDR). This study compares image quality in pulmonary arteries on iteratively reconstructed CT images of patients with suspected pulmonary embolism using different IDR.

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study.

Background: Minimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time.

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition.

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest.