Refine
Year of publication
- 2017 (8) (remove)
Document Type
- Article (8) (remove)
Language
- English (8)
Is part of the Bibliography
- no (8)
Keywords
Institute
We demonstrate, based on data derived from computed tomography, that integrating 2D and 3D morphological data of ammonoid shells represent an important new approach to disentangle the palaeobiology of ammonoids. Characterization of ammonite morphology has long been constrained to 2D data and only a few studies collect ontogenetic data in 180° steps. We combine this traditional approach with 3D data collected from high-resolution nano-computed tomography. For this approach, ontogenetic morphological data of the hollow shell of a juvenile ammonite Kosmoceras (Jurassic, Callovian) is collected.
2D data is collected in 10° steps and show significant changes in shell morphology. Preserved hollow spines show multiple mineralized membranes never reported before, representing temporal changes of the ammonoid mantle tissue. 3D data show that chamber volumes do not always increase exponentially, as was generally assumed, but may represent a proxy for life events, e.g. stress phases. Furthermore, chamber volume cannot be simply derived from septal spacing in forms comparable to Kosmoceras. Vogel numbers, a 3D parameter for chamber shape, of Kosmoceras are similar to other ammonoids (Arnsbergites, Amauroceras) and modern cephalopods (Nautilus, Spirula). Two methods to virtually document the suture line ontogeny, used to document phylogenetic relationships of larger taxonomic entities, were applied for the first time and seem a promising alternative to hand drawings. The curvature of the chamber surfaces increases during ontogeny due to increasing strength of ornamentation and septal complexity. As increasing curvature may allow for faster handling of cameral liquid, it could compensate for decreasing SA/V ratios through ontogeny.
In an aging society where the number of joint replacements rises, it is important to also increase the longevity of implants.
In particular hip implants have a lifetime of at most 15 years. This derives primarily from
pain due to implant migration, wear, inflammation, and dislocation, which is affected by
the positioning of the implant during the surgery. Current joint replacement practice uses
2D software tools and relies on the experience of surgeons. Especially the 2D tools fail to
take the patients’ natural range of motion as well as stress distribution in the 3D joint
induced by different daily motions into account.
Optimizing the hip joint implant position for all possible parametrized motions under the
constraint of a contact problem is prohibitively expensive as there are too many motions
and every position change demands a recalculation of the contact problem. For the
reduction of the computational effort, we use adaptive refinement on the parameter
domain coupled with the interpolation method of Kriging. A coarse initial grid is to be
locally refined using goal-oriented error estimation, reducing locally high variances. This
approach will be combined with multi-grid optimization such that numerical errors are
reduced.
Background: Currently, there is no fully sufficient way to differentiate between symptomatic and normal nasal breathing. Using the nose’s total resistance is disputed as a valid means to objectify nasal airflow, and the need for a more comprehensive diagnostic method is increasing. This work’s aim was to test a novel approach considering intranasal wall shear stress as well as static pressure maps obtained by computational fluid dynamics (CFD).
Methods: X-ray computed tomography (CT) scan data of six symptom-free subjects and seven symptomatic patients were used. Patient-specific geometries of the nasal cavity were segmented from these data sets. Inspiratory and expiratory steady airflow simulations were performed using CFD. Calculated static pressures and wall shear stresses (WSS) were mapped onto a common template of the nasal septum, allowing for comparison of these parameters between the two patient groups.
Results: Significant differences in wall shear stress distributions during the inspiratory phase could be identified between the two groups, whereas no differences were found for the expiratory phase. It is assumed that one essential feature of normal nasal breathing probably consists in distinctively different intranasal flow fields for inspiration and expiration. This is in accordance with previous investigations.
Conclusion: The proposed method seems to be a promising tool for developing a new kind of patient-specific assessment of nasal breathing. However, more studies and a greater case number of data with an expanded focus, would be ideal.
The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data.
Purpose: Morphological data pertaining to the pelvis and lower extremity muscles are increasingly being used in biomechanical modeling to compare healthy and pathological conditions. Very few data sets exist that encompass all of the muscles of the lower limb, allowing for comparisons between regions. The aims of this study were to (a) provide physiological cross-sectional area (PCSA) data for the pelvic, thigh, and leg muscles in young, healthy participants, using magnetic resonance imaging (MRI), and (b) to compare these data with summarized PCSAs obtained from the literature.
Materials and Methods: Six young and healthy volunteers participated and were scanned using 3 T MRI. PCSAs were calculated from volumetric segmentations obtained bilaterally of 28 muscles/muscle groups of the pelvis, thigh, and leg. These data were compared to published, summarized PCSA data derived from cadaveric, computed tomography, MRI and ultrasound studies.
Results: The PCSA of the pelvis, thigh, and leg muscles tended to be 20–130% larger in males than in females, except for the gemelli which were 34% smaller in males, and semitendinosus and triceps surae which did not differ (<20% different). The dominant and the non-dominant sides showed similar and minutely different PCSA with less than 18% difference between sides. Comparison to other studies revealed wide ranges within, and large differences between, the cadaveric and imaging PCSA data. Comparison of the PCSA of this study and published literature revealed major differences in the iliopsoas, gluteus minimus, tensor fasciae latae, gemelli, obturator internus, biceps femoris, quadriceps femoris, and the deep leg flexor muscles.
Conclusions: These volume-derived PCSAs of the pelvic and lower limb muscles alongside the data synthesised from the literature may serve as a basis for comparative and biomechanical studies of the living and healthy young, and enable calculation of muscle forces. Comparison of the literature revealed large variations in PCSA from each of the different investigative modalities, hampering omparability between studies. Sample size, age, post-mortem changes of muscle tone, chemical fixation of cadaveric tissues, and the underlying physics of the imaging techniques may potentially influence PCSA calculations.
Purpose: A fully automated surgical tool detection framework is proposed for endoscopic video streams. State-of-the-art surgical tool detection methods rely on supervised one-vs-all or multi-class classification techniques, completely ignoring the co-occurrence relationship of the tools and the associated class imbalance.
Methods: In this paper, we formulate tool detection as a multi-label classification task where tool co-occurrences are treated as separate classes. In addition, imbalance on tool co-occurrences is analyzed and stratification techniques are employed to address the imbalance during Convolutional Neural Network (CNN) training. Moreover, temporal smoothing is introduced as an online post-processing step to enhance run time prediction.
Results: Quantitative analysis is performed on the M2CAI16 tool detection dataset to highlight the importance of stratification, temporal smoothing and the overall framework for tool detection.
Conclusion: The analysis on tool imbalance, backed by the empirical results indicates the need and superiority of the proposed framework over state-of-the-art techniques.
Temperature based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex, but allow a higher accuracy of death time estimation as in principle all relevant cooling mechanisms can be taken into account. Here, a complete work flow for finite element based cooling simulation models is presented.
The following steps are demonstrated on CT-phantoms:
• CT-scan
• Segmentation of the CT images for thermodynamically relevant features of individual
geometries
• Conversion of the segmentation result into a Finite Element (FE) simulation model
• Computation of the model cooling curve
• Calculation of the cooling time
For the first time in FE-based cooling time estimation the steps from the CT image over segmentation to FE model generation are semi-automatically performed. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using different CTphantoms. Some of the CT phantoms thermodynamic material parameters had to be experimentally determined via independent experiments. Moreover the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis.
Purpose: Despite the success of total knee arthroplasty there continues to be a significant proportion of patients who are dissatisfied. One explanation may be a shape mismatch between pre and post-operative distal femurs. The purpose of this study was to investigate a method to match a statistical shape model (SSM) to intra-operatively acquired point cloud data from a surgical navigation system, and to validate it against the pre-operative magnetic resonance imaging (MRI) data from the same patients.
Methods: A total of 10 patients who underwent navigated total knee arthroplasty also had an MRI scan less than 2 months pre-operatively. The standard surgical protocol was followed which included partial digitization of the distal femur. Two different methods were employed to fit the SSM to the digitized point cloud data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). The available MRI data were manually segmented and the reconstructed three-dimensional surfaces used as ground truth against which the statistical shape model fit was compared.
Results: For both approaches, the difference between the statistical shape model-generated femur and the surface generated from MRI segmentation averaged less than 1.7 mm, with maximum errors occurring in less clinically important areas.
Conclusion: The results demonstrated good correspondence with the distal femoral morphology even in cases of sparse data sets. Application of this technique will allow for measurement of mismatch between pre and post-operative femurs retrospectively on any case done using the surgical navigation system and could be integrated into the surgical navigation unit to provide real-time feedback.