Publications by authors named "C L Schlett"
Purpose: To evaluate the feasibility of aortoiliac CT-Angiography (CTA) using dual-source photon-counting detector (PCD)-CT with minimal iodine dose.
Methods: This IRB-approved, single-center prospective study enrolled patients with indications for aortoiliac CTA from December 2022 to March 2023. All scans were performed using a first-generation dual-source PCD-CT.
: To validate the automated quantification of cardiac chamber volumes and myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference. : We retrospectively included 53 consecutive patients who received non-contrast chest CT and CMR within three weeks. A deep learning model created cardiac segmentations on axial soft-tissue reconstructions from CT, covering all four cardiac chambers and the left ventricular myocardium.
View Article and Find Full Text PDFBackground: For characterizing health states, fat distribution is more informative than overall body size. We used population-based whole-body magnetic resonance imaging (MRI) to identify distinct body composition subphenotypes and characterize associations with cardiovascular disease (CVD) risk.
Methods: Bone marrow, visceral, subcutaneous, cardiac, renal, hepatic, skeletal muscle and pancreatic adipose tissue were measured by MRI in n = 299 individuals from the population-based KORA cohort.
Article Synopsis
- This study explores the use of advanced deep learning methods to automatically measure body composition from whole-body MRI scans, aiming to assess their ability to predict mortality in the general population.
- The investigation was based on data from two large Western European cohort studies, focusing on key body composition metrics such as subcutaneous and visceral adipose tissue, skeletal muscle, and intramuscular fat.
- Results indicate significant associations between several volumetric body composition measures and mortality risk, highlighting the potential of automated techniques to improve clinical outcomes related to cardiometabolic diseases and cancer.
Article Synopsis
- MRI can identify key biomarkers like bone marrow fat-fraction (BMFF), skeletal muscle fat-fraction (SMFF), and total adipose tissue (TAT) to assess conditions related to osteosarcopenic adiposity (OSA) in individuals.
- In a study with 363 participants, 81 (22.3%) were classified with OSA, characterized by older age, higher SMFF levels, and greater body mass index (BMI).
- The OSA subgroup also showed the highest prevalence of health issues such as impaired glucose tolerance, high blood pressure, higher cholesterol levels, and liver fat accumulation, suggesting the importance of MRI in monitoring these health risks.