Magnetic Resonance Assessment of Bone Quality in Metabolic Bone Diseases.

Metabolic bone diseases (MBDs) are a diverse group of diseases, affecting the mass or structure of bones and leading to reduced bone quality. Parameters representing different aspects of bone health can be obtained from various magnetic resonance imaging (MRI) methods such as proton MR spectroscopy, as well as chemical shift encoding-based water-fat imaging, that have been frequently applied to study bone marrow in particular. Furthermore, T2* mapping and high-resolution trabecular bone imaging have been implemented to study bone microstructure.

Impact of patient habitus and acquisition protocol on iodine quantification in dual-source photon-counting computed tomography.

Purpose: Evaluation of iodine quantification accuracy with varying iterative reconstruction level, patient habitus, and acquisition mode on a first-generation dual-source photon-counting computed tomography (PCCT) system.

Approach: A multi-energy CT phantom with and without its extension ring equipped with various iodine inserts (0.2 to 15.

Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework.

Background: To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment.

Methods: Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis.

Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans.

Objectives: To investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models.

Methods: This single-center retrospective study included patients who underwent two thoraco-abdominal CT scans during clinical routine with an average inter-scan interval of 21.7 ± 13.

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.

Background: Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare.

Methods: A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging.