Publications by authors named "Dieckmeyer M"

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.

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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.

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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.

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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.

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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.

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Purpose: To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).

Methods: A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference.

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In 1971, the first computed tomography (CT) scan was performed on a patient's brain. Clinical CT systems were introduced in 1974 and dedicated to head imaging only. New technological developments, broader availability, and the clinical success of CT led to a steady growth in examination numbers.

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The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technological advancement, increasing availability and clinical success of CT led to a rapidly growing number of CT examinations, also of the spine. After initially being primarily used for trauma evaluation, new indications continued to emerge, such as assessment of vertebral fractures or degenerative spine disease, preoperative and postoperative evaluation, or CT-guided interventions at the spine; however, improvements in patient management and clinical outcomes come along with higher radiation exposure, which increases the risk for secondary malignancies.

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Article Synopsis
  • This study explored how chemical shift encoding-based MRI (CSE-MRI) can provide insights into body fat distribution and texture analysis for understanding vertebral bone marrow and paraspinal muscle composition in healthy subjects.* -
  • A cohort of 44 healthy individuals underwent 3T MRI scans, and their body compartments were analyzed to compare differences in fat density and texture features, particularly between males and females.* -
  • Significant differences were found in texture features and proton density fat fraction (PDFF) measurements between genders, highlighting the potential of texture analysis in MRI for better understanding of body composition.*
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Purpose: Osteoporosis is prevalent and entails alterations of vertebral bone and marrow. Yet, the spine is also a common site of metastatic spread. Parameters that can be non-invasively measured and could capture these alterations are the volumetric bone mineral density (vBMD), proton density fat fraction (PDFF) as an estimate of relative fat content, and failure displacement and load from finite element analysis (FEA) for assessment of bone strength.

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(1) Background: To study the feasibility of developing finite element (FE) models of the whole lumbar spine using clinical routine multi-detector computed tomography (MDCT) scans to predict failure load (FL) and range of motion (ROM) parameters. (2) Methods: MDCT scans of 12 subjects (6 healthy controls (HC), mean age ± standard deviation (SD): 62.16 ± 10.

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Purpose: To establish and evaluate the diagnostic accuracy of volumetric bone mineral density (vBMD) threshold values at different spinal levels, derived from opportunistic quantitative computed tomography (QCT), for the prediction of incident vertebral fractures (VF).

Materials And Methods: In this case-control study, 35 incident VF cases (23 women, 12 men; mean age: 67 years) and 70 sex- and age-matched controls were included, based on routine multi detector CT (MDCT) scans of the thoracolumbar spine. Trabecular vBMD was measured from routine baseline CT scans of the thoracolumbar spine using an automated pipeline including vertebral segmentation, asynchronous calibration for HU-to-vBMD conversion, and correction of intravenous contrast medium (https://anduin.

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Opportunistic osteoporosis screening in nondedicated routine computed tomography (CT) is of increasing importance. The purpose of this study was to compare lumbar volumetric bone mineral density (vBMD) assessed by a convolutional neural network (CNN)-based framework in routine CT to vBMD from dedicated quantitative CT (QCT), and to evaluate the ability of vBMD and surrogate measurements of Hounsfield units (HU) to distinguish between patients with and without osteoporotic vertebral fractures (VFs). A total of 144 patients (median age: 70.

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Article Synopsis
  • Osteoporosis is a widespread disease marked by low bone mass and deterioration that leads to an increased risk of fragility fractures, particularly in the vertebrae.
  • Dual-energy X-ray absorptiometry (DXA) has been the standard method for diagnosing osteoporosis but comes with limitations, prompting interest in newer imaging techniques like CT and MRI.
  • Advanced analysis methods such as texture analysis and finite element analysis can enhance fracture prediction when combined with CT and MRI, but standardized diagnostic criteria for these methods are still lacking, preventing easier adoption in clinical settings.
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Purpose: To identify long-term reproducible texture features (TFs) of spinal computed tomography (CT), and characterize variations with regard to gender, age and vertebral level using our automated quantification framework.

Methods: We performed texture analysis (TA) on baseline and follow-up CT (follow-up duration: 30-90 days) of 21 subjects (8 females, 13 males, age at baseline 61.2 ± 9.

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Article Synopsis
  • Osteoporosis is a common disease leading to fractures, particularly in the spine, and while areal bone mineral density (BMD) from DXA is the standard measurement, it has limitations that texture analysis from imaging techniques like CT and MRI might address.
  • A study of 26 patients, mostly older females, explored whether MRI texture analysis could predict volumetric BMD and differentiate between those with and without osteoporotic fractures, using advanced imaging data and machine learning methods.
  • Results showed that patients with fractures had significantly lower BMD values, and a model incorporating MRI data explained 40% of the variance in integral BMD linked to fracture status, revealing potential for improved risk estimation.
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Whole genome sequencing of Epstein-Barr virus (EBV) isolates from around the world has uncovered pervasive strain heterogeneity, but the forces driving strain diversification and the impact on immune recognition remained largely unknown. Using a data mining approach, we analyzed more than 300 T-cell epitopes in 168 published EBV strains. Polymorphisms were detected in approximately 65% of all CD8+ and 80% of all CD4+ T-cell epitopes and these numbers further increased when epitope flanking regions were included.

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Article Synopsis
  • - This study analyzed the cervical and lumbar paraspinal muscles of 79 volunteers (25 men, 54 women) using proton density fat fraction (PDFF) maps to identify structural patterns based on gender and muscle location.
  • - Significant differences were found in the fat composition of the erector spinae muscle between men and women, but not in the cervical and psoas muscles; men showed higher variability in texture features across all muscles.
  • - Overall, the results suggest that the composition of paraspinal musculature is influenced by gender and varies with anatomical location, with notable differences in specific texture analysis metrics.
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Purpose: To (a) develop a preconditioned water-fat total field inversion (wfTFI) algorithm that directly estimates the susceptibility map from complex multi-echo gradient echo data for water-fat regions and to (b) evaluate the performance of the proposed wfTFI quantitative susceptibility mapping (QSM) method in comparison with a local field inversion (LFI) method and a linear total field inversion (TFI) method in the spine.

Methods: Numerical simulations and in vivo spine multi-echo gradient echo measurements were performed to compare wfTFI to an algorithm based on disjoint background field removal (BFR) and LFI and to a formerly proposed TFI algorithm. The data from 1 healthy volunteer and 10 patients with metastatic bone disease were included in the analysis.

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Article Synopsis
  • Wasting diseases like cachexia and sarcopenia lead to muscle loss, necessitating reliable methods to assess muscle composition, with multi-detector computed tomography (MDCT) being a common imaging choice despite its radiation risks.
  • This study examined how reducing radiation exposure during MDCT scans affected the assessment of paravertebral muscles by simulating lower X-ray tube currents and using statistical iterative reconstruction algorithms.
  • Results indicated that mean muscle density remained consistent at lower radiation levels down to 5% of original projections for certain muscle groups, suggesting that this technique could effectively minimize radiation exposure while maintaining diagnostic accuracy.
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Background: Osteoporosis is a systemic skeletal disease that is characterized by low bone mass and microarchitectural deterioration, predisposing affected individuals to fragility fractures. Yet, standard measurement of areal bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) as the current reference standard has limitations for correctly detecting osteoporosis and fracture risk, with opportunistic osteoporosis screening using computed tomography (CT) showing increasing importance. This study's objective is to compare finite element analysis (FEA)-based vertebral failure load with parameters of texture analysis (TA) derived from multi-detector CT (MDCT).

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Purpose: In this case-control study, we evaluated different quantitative parameters derived from routine multi-detector computed tomography (MDCT) scans with respect to their ability to predict incident osteoporotic vertebral fractures of the thoracolumbar spine.

Methods: 16 patients who received baseline and follow-up contrast-enhanced MDCT and were diagnosed with an incident osteoporotic vertebral fracture at follow-up, and 16 age-, sex-, and follow-up-time-matched controls were included in the study. Vertebrae were labelled and segmented using a fully automated pipeline.

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Background: Chemical shift encoding-based water-fat magnetic resonance imaging (CSE-MRI) measures a quantitative biomarker: the proton density fat fraction (PDFF). The aim was to assess regional and proximo-distal PDFF variations at the thigh in patients with myotonic dystrophy type 2 (DM2), limb-girdle muscular dystrophy type 2A (LGMD2A), and late-onset Pompe disease (LOPD) as compared to healthy controls.

Methods: Seven patients (n=2 DM2, n=2 LGMD2A, n=3 LOPD) and 20 controls were recruited.

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Assessment of osteoporosis-associated fracture risk during clinical routine is based on the evaluation of clinical risk factors and T-scores, as derived from measurements of areal bone mineral density (aBMD). However, these parameters are limited in their ability to identify patients at high fracture risk. Finite element models (FEMs) have shown to improve bone strength prediction beyond aBMD.

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Purpose: Based on conventional and quantitative magnetic resonance imaging (MRI), texture analysis (TA) has shown encouraging results as a biomarker for tissue structure. Chemical shift encoding-based water-fat MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of thigh muscles has been associated with musculoskeletal, metabolic, and neuromuscular disorders and was demonstrated to predict muscle strength. The purpose of this study was to investigate PDFF-based TA of thigh muscles as a predictor of thigh muscle strength in comparison to mean PDFF.

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