Ludwig Boltzmann Institute for Clinical Forensic Imaging

Graz, Austria

Ludwig Boltzmann Institute for Clinical Forensic Imaging

Graz, Austria
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Langkammer C.,Medical University of Graz | Langkammer C.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Krebs N.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Goessler W.,University of Graz | And 4 more authors.
NeuroImage | Year: 2012

MR phase images have shown significantly improved contrast between cortical gray and white matter regions compared to magnitude images obtained with gradient echo sequences. A variety of underlying biophysical mechanisms (including iron, blood, myelin content, macromolecular chemical exchange, and fiber orientation) have been suggested to account for this observation but assessing the individual contribution of these factors is limited in vivo. For a closer investigation of iron and myelin induced susceptibility changes, postmortem MRI of six human corpses (age range at death: 56-80. years) was acquired in situ. Following autopsy, the iron concentrations in the frontal and occipital cortex as well as in white matter regions were chemically determined. The magnetization transfer ratio (MTR) was used as an indirect measure for myelin content. Susceptibility effects were assessed separately by determining R2* relaxation rates and quantitative phase shifts. Contributions of myelin and iron to local variations of the susceptibility were assessed by univariate and multivariate linear regression analysis. Mean iron concentration was lower in the frontal cortex than in frontal white matter (26 ± 6 vs. 45 ± 6. mg/kg wet tissue) while an inverse relation was found in the occipital lobe (cortical gray matter: 41 ± 10 vs. white matter: 34 ± 10. mg/kg wet tissue). Multiple regression analysis revealed iron and MTR as independent predictors of the effective transverse relaxation rate R2* but solely MTR was identified as source of MR phase contrast. R2*was correlated with iron concentrations in cortical gray matter only (r = 0.42, p < 0.05).In conclusion, MR phase contrast between cortical gray and white matter can be mainly attributed to variations in myelin content, but not to iron concentration. Both, myelin and iron impact the effective transverse relaxation rate R2* significantly. Magnitude contrast is limited because it only reflects the extent but not the direction of the susceptibility shift. © 2011 Elsevier Inc.


Langkammer C.,Medical University of Graz | Langkammer C.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Schweser F.,Friedrich - Schiller University of Jena | Krebs N.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | And 11 more authors.
NeuroImage | Year: 2012

Quantitative susceptibility mapping (QSM) is a novel technique which allows determining the bulk magnetic susceptibility distribution of tissue in vivo from gradient echo magnetic resonance phase images. It is commonly assumed that paramagnetic iron is the predominant source of susceptibility variations in gray matter as many studies have reported a reasonable correlation of magnetic susceptibility with brain iron concentrations in vivo. Instead of performing direct comparisons, however, all these studies used the putative iron concentrations reported in the hallmark study by Hallgren and Sourander (1958) for their analysis. Consequently, the extent to which QSM can serve to reliably assess brain iron levels is not yet fully clear. To provide such information we investigated the relation between bulk tissue magnetic susceptibility and brain iron concentration in unfixed (in situ) post mortem brains of 13 subjects using MRI and inductively coupled plasma mass spectrometry. A strong linear correlation between chemically determined iron concentration and bulk magnetic susceptibility was found in gray matter structures (r = 0.84, p < 0.001), whereas the correlation coefficient was much lower in white matter (r = 0.27, p < 0.001). The slope of the overall linear correlation was consistent with theoretical considerations of the magnetism of ferritin supporting that most of the iron in the brain is bound to ferritin proteins. In conclusion, iron is the dominant source of magnetic susceptibility in deep gray matter and can be assessed with QSM. In white matter regions the estimation of iron concentrations by QSM is less accurate and more complex because the counteracting contribution from diamagnetic myelinated neuronal fibers confounds the interpretation. © 2012 Elsevier Inc.


PubMed | University of Western Ontario, University of California at Irvine, University of Ljubljana, University of Sheffield and 5 more.
Type: | Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society | Year: 2016

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention.


PubMed | University of Western Ontario, is Center for Virtual Reality and Visualization, University of Queensland, Ludwig Boltzmann Institute for Clinical Forensic Imaging and 11 more.
Type: | Journal: Medical image analysis | Year: 2016

The evaluation of changes in Intervertebral Discs (IVDs) with 3D Magnetic Resonance (MR) Imaging (MRI) can be of interest for many clinical applications. This paper presents the evaluation of both IVD localization and IVD segmentation methods submitted to the Automatic 3D MRI IVD Localization and Segmentation challenge, held at the 2015 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2015) with an on-site competition. With the construction of a manually annotated reference data set composed of 25 3D T2-weighted MR images acquired from two different studies and the establishment of a standard validation framework, quantitative evaluation was performed to compare the results of methods submitted to the challenge. Experimental results show that overall the best localization method achieves a mean localization distance of 0.8mm and the best segmentation method achieves a mean Dice of 91.8%, a mean average absolute distance of 1.1mm and a mean Hausdorff distance of 4.3mm, respectively. The strengths and drawbacks of each method are discussed, which provides insights into the performance of different IVD localization and segmentation methods.


Langkammer C.,Medical University of Graz | Langkammer C.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Krebs N.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Goessler W.,University of Graz | And 5 more authors.
Radiology | Year: 2010

Purpose: To investigate the relationship between transverse relaxation rates R2 and R2*, the most frequently used surrogate markers for iron in brain tissue, and chemically determined iron concentrations. Materials and Methods: This study was approved by the local ethics committee, and informed consent was obtained from each individual's next of kin. Quantitative magnetic resonance (MR) imaging was performed at 3.0 T in seven human postmortem brains in situ (age range at death, 38-81 years). Following brain extraction, iron concentrations were determined with inductively coupled plasma mass spectrometry in prespecified gray and white matter regions and correlated with R2 and R2* by using linear regression analysis. Hemispheric differences were tested with paired t tests. Results: The highest iron concentrations were found in the globus pallidus (mean ± standard deviation, 205 mg/kg wet mass ± 32), followed by the putamen (mean, 153 mg/kg wet mass ± 29), caudate nucleus (mean, 92 mg/kg wet mass ± 15), thalamus (mean, 49 mg/kg wet mass ± 11), and white matter regions. When all tissue samples were considered, transverse relaxation rates showed a strong linear correlation with iron concentration throughout the brain(r2 = 0.67 for R2, r 2 = 0.90 for R2*; P < .001). In white matter structures, only R2* showed a linear correlation with iron concentration. Chemical analysis revealed significantly higher iron concentrations in the left hemisphere than in the right hemisphere, a finding that was not reflected in the relaxation rates. Conclusion: Because of their strong linear correlation with iron concentration, both R2 and R2* can be used to measure iron deposition in the brain. Because R2* is more sensitive than R2 to variations in brain iron concentration and can detect differences in white matter, it is the preferred parameter for the assessment of iron concentration in vivo. © RSNA, 2010.


Petrovic A.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Petrovic A.,University of Graz | Scheurer E.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Scheurer E.,Medical University of Graz | Stollberger R.,University of Graz
Magnetic Resonance in Medicine | Year: 2015

Purpose: T2 quantification with multiecho sequences is typically impaired by the contribution of stimulated echoes to the echo decay due to B1+ inhomogeneity and slice profile effects. In this work, a compact signal model based on the generating functions approach, which accounts for both sources of error, is presented. Methods: The generating functions (GF) approach is used to obtain a closed solution to the evolution of the transverse magnetization in an echo train, however, not in the time domain, but in the transformed z-domain. The approach is generalized by the incorporation of flip angle distribution across the refocusing slice profiles. The approach is tested by fitting the model to simulated data as well as to phantom and in vivo measurements, followed by a comparison with the common monoexponential fitting approach. Results: The fitting simulations indicate that T2 errors of up to 30% can be commonplace in a clinical setting using the monoexponential method. Conversely, the GF approach produced accurate results. Phantom and in vivo experiments showed a good agreement of the GF values with spectroscopic measurements and single-echo spin-echo sequences. Conclusion: A correction for stimulated echoes is necessary to compute comparable T2 values. The presented approach provides a solution to this issue. © 2014 Wiley Periodicals, Inc.


Riegler G.,Graz University of Technology | Urschler M.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Ruther M.,Graz University of Technology | Bischof H.,Graz University of Technology | Stern D.,Graz University of Technology
Proceedings of the IEEE International Conference on Computer Vision | Year: 2016

An important initial step in many medical image analysis applications is the accurate detection of anatomical landmarks. Most successful methods for this task rely on data-driven machine learning algorithms. However, modern machine learning techniques, e.g. convolutional neural networks, need a large corpus of training data, which is often an unrealistic setting for medical datasets. In this work, we investigate how to adapt synthetic image datasets from other computer vision tasks to overcome the under-representation of the anatomical pose and shape variations in medical image datasets. We transform both data domains to a common one in such a way that a convolutional neural network can be trained on the larger synthetic image dataset and fine-tuned on the smaller medical image dataset. Our evaluations on data of MR hand and whole body CT images demonstrate that this approach improves the detection results compared to training a convolutional neural network only on the medical data. The proposed approach may also be usable in other medical applications, where training data is scarce. © 2015 IEEE.


Urschler M.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Bornik A.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Donoser M.,Graz University of Technology
Proceedings of the IEEE International Conference on Computer Vision | Year: 2013

Integral image data structures are very useful in computer vision applications that involve machine learning approaches based on ensembles of weak learners. The weak learners often are simply several regional sums of intensities subtracted from each other. In this work we present a memory efficient integral volume data structure, that allows reduction of required RAM storage size in such a supervised learning framework using 3D training data. We evaluate our proposed data structure in terms of the tradeoff between computational effort and storage, and show an application for 3D object detection of liver CT data. © 2013 IEEE.


Stern D.,University of Graz | Stern D.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Ebner T.,University of Graz | Urschler M.,Ludwig Boltzmann Institute for Clinical Forensic Imaging
Proceedings - International Symposium on Biomedical Imaging | Year: 2016

Selection of set of training pixels and feature range show to be critical scale-related parameters with high impact on results in localization methods based on random regression forests (RRF). Trained on pixels randomly selected from images with long range features, RRF captures the variation in landmark location but often without reaching satisfying accuracy. Conversely, training an RRF with short range features in a landmark's close surroundings enables accurate localization, but at the cost of ambiguous localization results in the presence of locally similar structures. We present a scale-widening RRF method that effectively handles such ambiguities. On a challenging hand radiography image data set, we achieve median and 90th percentile localization errors of 0.81 and 2.64mm, respectively, outperforming related state-of-the-art methods. © 2016 IEEE.


Stern D.,University of Graz | Stern D.,Ludwig Boltzmann Institute for Clinical Forensic Imaging | Ursekter M.,University of Graz | Ursekter M.,Ludwig Boltzmann Institute for Clinical Forensic Imaging
Proceedings - International Symposium on Biomedical Imaging | Year: 2016

Increasingly important for both clinical and forensic medicine, radiological age estimation is performed by fusing independent bone age estimates from hand images. In this work, we show that the artificial separation into bone independent age estimates as used in established fusion techniques can be overcome. Thus, we treat aging as a global developmental process, by implicitly fusing developmental information from different bones in a dedicated regression algorithm. With 0.82 ± 0.56 years absolute deviation from chronological age on a database of 132 3D MR hand images, the results of this novel automatic algorithm are inline with radiologists performing visual examinations. © 2016 IEEE.

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