TY - JOUR A1 - Melzner, Maximilian A1 - Pfeiffer, Christian A1 - Suess, Franz A1 - Dendorfer, Sebastian T1 - Musculoskeletal simulation of elbow stability for common injury patterns JF - Journal of Orthopaedic Research N2 - Elbow stability is derived from a combination of muscular, ligamentous, and bony structures. After an elbow trauma the stability of the joint is an important decision criterion for the subsequent treatment. The decision regarding non-operative/operative care depends mostly on subjective assessments of medical experts. Therefore, the aim of this study is to use musculoskeletal simulations as an objective assessment tool to investigate the extent to which failure of different stabilizers affects the elbow stability and how these observations correspond to the assessment from clinical practice. A musculoskeletal elbow simulation model was developed for this aim. To investigate the stability of the elbow, varus/valgus moments were applied under 0°, 45°and 90° flexion while the respective cubital angle was analyzed. This was performed for nine different injury scenarios, which were also evaluated for stability by clinical experts. With the results, it can be determined by which injury pattern and under which flexion angle the elbow stability is impaired regarding varus/valgus moments. The scenario with a complete failure of the medial and lateral ligaments and a fracture of the radial head was identified as having the greatest instability. The study presented a numerical determination of elbow stability against varus/valgus moments regarding clinical injury patterns, as well as a comparison of the numerical outcome with experience gained in clinical practice. The numerical predictions agree well with the assessments of the clinical specialists. Thus, the results from musculoskeletal simulation can make an important contribution to a more objective assessment of the elbow stability. KW - AnyBody KW - musculoskeletal simulation KW - elbow stability Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-54819 SN - 1554-527X N1 - Corresponding author: Maximilian Melzner VL - 41 IS - 6 SP - 1356 EP - 1364 PB - Wiley ER - TY - JOUR A1 - Wieczorek, Matthias A1 - Frikel, Jürgen A1 - Vogel, Jakob A1 - Eggl, Elena A1 - Kopp, Felix A1 - Noël, Peter B. A1 - Pfeiffer, Franz A1 - Demaret, Laurent A1 - Lasser, Tobias T1 - X-ray computed tomography using curvelet sparse regularization JF - Medical physics N2 - PURPOSE Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. METHODS In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. RESULTS Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. CONCLUSIONS The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features. Y1 - 2015 U6 - https://doi.org/10.1118/1.4914368 SN - 0094-2405 SN - 2473-4209 VL - 42 IS - 4 SP - 1555 EP - 1565 PB - American Association of Physicists in Medicine ER - TY - CHAP A1 - Wieczorek, Matthias A1 - Frikel, Jürgen A1 - Vogel, Jakob A1 - Pfeiffer, Franz A1 - Demaret, Laurent A1 - Lasser, Tobias T1 - Curvelet sparse regularization for differential phase-contrast X-ray imaging T2 - Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine : Proceedings ; June 16-21, 2013, Granlibakken Resort, Lake Tahoe, California N2 - Differential phase contrast imaging (DPCI) enables the visualization of soft tissue contrast using X-rays. In this work we introduce a reconstruction framework based on curvelet expansion and sparse regularization for DPCI. We will show that curvelets provide a suitable data representation for DPCI reconstruction that allows preservation of edges as well as an exact analytic representation of the system matrix. As a first evaluation, we show results using simulated phantom data Y1 - 2013 UR - https://www.researchgate.net/publication/255913330_Curvelet_sparse_regularization_for_differential_phase-contrast_X-ray_imaging SP - 489 EP - 492 ER -