Diagnosis of acute heart failure in CT pulmonary angiography: feasibility and accuracy.

Objectives: To evaluate the feasibility and accuracy of diagnosing acute heart failure (HF) with CT pulmonary angiography (CTPA) in emergency department patients.

Methods: In this retrospective single-center study, we evaluated 150 emergency department patients (mean age 65 ± 17 years) undergoing CTPA with a fixed scan (100 kVp) and contrast media protocol (60 mL, 4 mL/s) who had no pulmonary embolism (PE). Patients were subdivided into training cohort (n = 100) and test cohort (n = 50).

Detection and localization of distal radius fractures: Deep learning system versus radiologists.

Purpose: To evaluate a deep learning based image analysis software for the detection and localization of distal radius fractures.

Method: A deep learning system (DLS) was trained on 524 wrist radiographs (166 showing fractures). Performance was tested on internal (100 radiographs, 42 showing fractures) and external test sets (200 radiographs, 100 showing fractures).

Qualitative versus quantitative lumbar spinal stenosis grading by machine learning supported texture analysis-Experience from the LSOS study cohort.

Purpose: To investigate and compare the reproducibility and accuracy of qualitative ratings and quantitative texture analysis (TA) in detection and grading of lumbar spinal stenosis (LSS) in magnetic resonance imaging (MR) scans of the lumbar spine.

Materials And Methods: From a nationwide multicenter and multidisciplinary lumbar stenosis outcome study (LSOS) register 82 patients, undergoing MR scans of the lumbar spine due to clinical indication of spinal claudication, with a single level central or lateral severe LSS were included. In total 343 transaxial T2-weighted images of the lumbar spine were included from one to five levels (L1 to S1) per patient.

Determination of mammographic breast density using a deep convolutional neural network.

Objective: High breast density is a risk factor for breast cancer. The aim of this study was to develop a deep convolutional neural network (dCNN) for the automatic classification of breast density based on the mammographic appearance of the tissue according to the American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) Atlas.

Methods: In this study, 20,578 mammography single views from 5221 different patients (58.

Vessel suppressed chest Computed Tomography for semi-automated volumetric measurements of solid pulmonary nodules.

Objective: To evaluate whether vessel-suppressed computed tomography (VSCT) can be reliably used for semi-automated volumetric measurements of solid pulmonary nodules, as compared to standard CT (SCT) MATERIAL AND METHODS: Ninety-three SCT were elaborated by dedicated software (ClearRead CT, Riverain Technologies, Miamisburg, OH, USA), that allows subtracting vessels from lung parenchyma. Semi-automated volumetric measurements of 65 solid nodules were compared between SCT and VSCT. The measurements were repeated by two readers.

Dual-Energy Computed Tomography in Stroke Imaging: Technical and Clinical Considerations of Virtual Noncontrast Images for Detection of the Hyperdense Artery Sign.

Objective: The technical feasibility of virtual noncontrast (VNC) images from dual-energy computed tomography (DECT) for the detection of the hyperdense artery sign (HAS) in ischemic stroke patients was investigated.

Methods: True noncontrast (TNC) scans of 60 patients either with or without HAS (n = 30 each) were investigated. Clot presence and characteristics were assessed on VNC images from DECT angiography and compared with TNC images.

Modified Dual-Energy Algorithm for Calcified Plaque Removal: Evaluation in Carotid Computed Tomography Angiography and Comparison With Digital Subtraction Angiography.

Objectives: Computed tomography angiography (CTA) is a valuable tool for the assessment of carotid artery stenosis. However, blooming artifacts from calcified plaques might result in an overestimation of the stenosis grade. The aim of this study was to investigate a new dual-energy computed tomography (DECT) technique with a modified 3-material decomposition algorithm for calcium removal in extracranial carotid artery stenosis.