Hemosuccus pancreaticus - Multidisciplinary therapy for a splenic artery aneurysm, ruptured into the pancreatic duct.

Background: Numerous conditions may lead to gastrointestinal bleeding (GIB). Compared with common causes, hemosuccus pancreaticus (HP) is a scarce and potentially life-threatening condition.

Case Presentation: We report the case of a 45-year-old female patient who suffered from hematemesis and subsequent hemorrhagic shock.

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.

Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.

Non-contrast computed tomography (CT) is commonly used for the evaluation of various pathologies including pulmonary infections or urolithiasis but, especially in low-dose protocols, image quality is reduced. To improve this, deep learning-based post-processing approaches are being developed. Therefore, we aimed to compare the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast chest and low-dose abdominal CTs.

Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study.

Background: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC).

Patients And Methods: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers.

Determining body height and weight from thoracic and abdominal CT localizers in pediatric and young adult patients using deep learning.

In this retrospective study, we aimed to predict the body height and weight of pediatric patients using CT localizers, which are overview scans performed before the acquisition of the CT. We trained three commonly used networks (EfficientNetV2-S, ResNet-18, and ResNet-34) on a cohort of 1009 and 1111 CT localizers of pediatric patients with recorded body height and weight (between January 2013 and December 2019) and validated them in an additional cohort of 116 and 127 localizers (acquired in 2020). The best-performing model was then tested in an independent cohort of 203 and 225 CT localizers (acquired between January 2021 and March 2023).

Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

Objectives: Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose.

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning.

Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age assessment can be performed using CT scout views from thoracic and abdominal CT scans using a deep neural network. Hence, we retrospectively collected 1949 CT scout views from pediatric patients (acquired between January 2013 and December 2018) to train a deep neural network to predict the chronological age from CT scout views.

Diagnostic reference levels for chest computed tomography in children as a function of patient size.

Background: Radiation exposures from computed tomography (CT) in children are inadequately studied. Diagnostic reference levels (DRLs) can help optimise radiation doses.

Objective: To determine local DRLs for paediatric chest CT performed mainly on modern dual-source, multi-slice CT scanners as a function of patient size.

Determining the anatomical site in knee radiographs using deep learning.

An important quality criterion for radiographs is the correct anatomical side marking. A deep neural network is evaluated to predict the correct anatomical side in radiographs of the knee acquired in anterior-posterior direction. In this retrospective study, a ResNet-34 network was trained on 2892 radiographs from 2540 patients to predict the anatomical side of knees in radiographs.

Pediatric age estimation from radiographs of the knee using deep learning.

Objectives: Age estimation, especially in pediatric patients, is regularly used in different contexts ranging from forensic over medicolegal to clinical applications. A deep neural network has been developed to automatically estimate chronological age from knee radiographs in pediatric patients.

Methods: In this retrospective study, 3816 radiographs of the knee from pediatric patients from a German population (acquired between January 2008 and December 2018) were collected to train a neural network.

Patterns of cardiac involvement in different muscular dystrophies assessed by magnetic resonance imaging.

Background: In muscular dystrophies, it is not only skeletal muscles that can be affected, but also the myocardium. This cardiac involvement can represent a major cause of morbidity and mortality.

Purpose: To investigate cardiac involvement in Duchenne (DMD), Becker (BMD), and limb girdle muscular dystrophy (LGMD) patients, and carriers of DMD/BMD by cardiac magnetic resonance (CMR) imaging and to search for differences in the pattern of cardiac involvement.

A spear to the heart-the accidental discovery of a giant cement embolism in the right heart: a case report.

Background: The incidence of recognized cardiopulmonary cement embolism in the context of percutaneous vertebroplasty varies between 0% and 23%. In most cases, only small fragments embolize in the pulmonary arteries or the right heart cavities. The latter can cause potential harm by right ventricular perforation.

IMAGE QUALITY STUDY OF RADIATION-REDUCED COMBINED CHEST AND ABDOMEN/PELVIS CT COMPARED WITH A STANDARD PROTOCOL.

The purpose of this study is to examine radiation doses and image quality of a low-dose (LD) protocol for chest and abdomen/pelvis (CAP) CT compared with a standard (STD) protocol. A total of 361 patients were included between October 2019 and April 2020; 104 patients with LD-protocol (100 kV, ref mAs 80 (chest)/145 (abdomen/pelvis)) and 257 patients with STD-protocol (100 kV, ref mAs 100 (chest)/180 (abdomen/pelvis)) at second-generation dual-source CT. Radiation doses for CTDIvol and DLP, and objective and subjective image qualities of 50 examinations from each group were evaluated.

Generating Virtual Short Tau Inversion Recovery (STIR) Images from T1- and T2-Weighted Images Using a Conditional Generative Adversarial Network in Spine Imaging.

Short tau inversion recovery (STIR) sequences are frequently used in magnetic resonance imaging (MRI) of the spine. However, STIR sequences require a significant amount of scanning time. The purpose of the present study was to generate virtual STIR (vSTIR) images from non-contrast, non-fat-suppressed T1- and T2-weighted images using a conditional generative adversarial network (cGAN).

Association of QRS-T angle and Late Gadolinium Enhancement in patients with a Clinical Suspicion of Myocarditis.

To investigate the association of a wide QRS-T angle on the surface ECG and late gadolinium enhancement on contrast-enhanced cardiovascular magnetic (CMR) imaging in patients with clinically suspected myocarditis. Diagnosis and risk stratification in patients with suspected myocarditis is particularly challenging due to a great spectrum of clinical presentations. Late gadolinium enhancement (LGE) visualizes myocardial necrosis and fibrosis in patients with biopsy-proven myocarditis.

Automatic Scan Range Delimitation in Chest CT Using Deep Learning.

Purpose: To develop and evaluate fully automatic scan range delimitation for chest CT by using deep learning.

Materials And Methods: For this retrospective study, scan ranges were annotated by two expert radiologists in consensus in 1149 (mean age, 65 years ± 16 [standard deviation]; 595 male patients) chest CT topograms acquired between March 2002 and February 2019 (350 with pleural effusion, 376 with atelectasis, 409 with neither, 14 with both). A conditional generative adversarial neural network was trained on 1000 randomly selected topograms to generate virtual scan range delimitations.

Detecting the pulmonary trunk in CT scout views using deep learning.

For CT pulmonary angiograms, a scout view obtained in anterior-posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods.

Assessing the Role of Pericardial Fat as a Biomarker Connected to Coronary Calcification-A Deep Learning Based Approach Using Fully Automated Body Composition Analysis.

(1) Background: Epi- and Paracardial Adipose Tissue (EAT, PAT) have been spotlighted as important biomarkers in cardiological assessment in recent years. Since biomarker quantification is an increasingly important method for clinical use, we wanted to examine fully automated EAT and PAT quantification for possible use in cardiovascular risk stratification. (2) Methods: 966 patients with intermediate Framingham risk scores for Coronary Artery Disease referred for coronary calcium scans were included in clinical routine retrospectively.

QRS-T angle in patients with Hypertrophic Cardiomyopathy - A comparison with Cardiac Magnetic Resonance Imaging.

We sought to investigate the possible association of a wide QRS-T angle on the surface EKG and myocardial fibrosis on contrast-enhanced cardiovascular magnetic (CMR) imaging in patients with hypertrophic cardiomyopathy (HCM). Risk stratification in HCM patients is challenging. Late gadolinium enhancement (LGE) visualizes myocardial fibrosis with unique spatial resolution and is a strong and independent prognosticator in these patients.

QRS-T-angle in Patients with ST-Segment Elevation Myocardial Infarction (STEMI) - a Comparison with Cardiac Magnetic Resonance Imaging.

: The QRS-T angle from the surface EKG is a promising prognostic marker in patients with coronary artery disease. Cardiovascular magnetic resonance (CMR) imaging with late gadolinium enhancement (LGE) offers high resolution imaging of myocardial damage. We investigated the association of the QRS-T angle and the extent of myocardial damage as assessed by LGE in patients with acute ST-segment myocardial infarction (STEMI) : 169 patients with STEMI obtained a standardized digital 12-lead EKG on admission for the calculation of the QRS-T angle and underwent CMR imaging for analysis of infarct size by LGE within the first week.

Bacterial contamination of automated MRI contrast injectors in clinical routine.

To quantify the frequency of bacterial contamination of the injected contrast agent/saline solution by an automated contrast injection system, and to evaluate whether usage of a novel tube system can reduce it. For bacterial contamination quantification two identical automated piston pump MRI contrast injectors were used in combination with a standard tube system. 3-5 ml of the contrast agent/saline solution was collected from the system prior to its connection to the patients' venous cannula in 104 consecutive patients.

Feasibility of aortic valve planimetry at 7 T ultrahigh field MRI: Comparison to aortic valve MRI at 3 T and 1.5 T.

Introduction: This study examined the feasibility of aortic valve planimetry at 7 T ultrahigh field MRI in intraindividual comparison to 3 T and 1.5 T MRI.

Material And Methods: Aortic valves of eleven healthy volunteers (mean age, 26.