@article{MelznerPfeifferSuessetal., author = {Melzner, Maximilian and Pfeiffer, Christian and Suess, Franz and Dendorfer, Sebastian}, title = {Musculoskeletal simulation of elbow stability for common injury patterns}, series = {Journal of Orthopaedic Research}, volume = {41}, journal = {Journal of Orthopaedic Research}, number = {6}, publisher = {Wiley}, issn = {1554-527X}, doi = {10.1002/jor.25460}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-54819}, pages = {1356 -- 1364}, abstract = {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.}, language = {en} } @article{WieczorekFrikelVogeletal., author = {Wieczorek, Matthias and Frikel, J{\"u}rgen and Vogel, Jakob and Eggl, Elena and Kopp, Felix and No{\"e}l, Peter B. and Pfeiffer, Franz and Demaret, Laurent and Lasser, Tobias}, title = {X-ray computed tomography using curvelet sparse regularization}, series = {Medical physics}, volume = {42}, journal = {Medical physics}, number = {4}, publisher = {American Association of Physicists in Medicine}, issn = {0094-2405}, doi = {10.1118/1.4914368}, pages = {1555 -- 1565}, abstract = {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.}, language = {en} } @inproceedings{WieczorekFrikelVogeletal., author = {Wieczorek, Matthias and Frikel, J{\"u}rgen and Vogel, Jakob and Pfeiffer, Franz and Demaret, Laurent and Lasser, Tobias}, title = {Curvelet sparse regularization for differential phase-contrast X-ray imaging}, series = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine : Proceedings ; June 16-21, 2013, Granlibakken Resort, Lake Tahoe, California}, booktitle = {Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine : Proceedings ; June 16-21, 2013, Granlibakken Resort, Lake Tahoe, California}, pages = {489 -- 492}, abstract = {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}, language = {en} }