VMLS: Visualization in Medicine and Life Scienceshttps://diglib.eg.org:443/handle/10.2312/10112024-03-29T01:55:12Z2024-03-29T01:55:12ZAmyloid Analyzer - A software assistant for qualitative and quantitative analysis of Florbetaben PET scansWeiler, F.Dicken, V.Strehlow, J.Geisler, B.Scarpa, M.Pessel, M.Stephens, A.Hahn, H. K.https://diglib.eg.org:443/handle/10.2312/PE.VMLS.VMLS2013.061-0642022-03-28T07:06:34Z2013-01-01T00:00:00ZAmyloid Analyzer - A software assistant for qualitative and quantitative analysis of Florbetaben PET scans
Weiler, F.; Dicken, V.; Strehlow, J.; Geisler, B.; Scarpa, M.; Pessel, M.; Stephens, A.; Hahn, H. K.
L. Linsen and H. -C. Hege and B. Hamann
Amyloid imaging is currently on the verge of becoming a vital imaging biomarker for the diagnosis and progressmonitoring of Alzheimer's disease. It is a positron emission tomography (PET) imaging technique based on tracers binding to b-amyloid plaques in the brain. These plaques are known to accumulate over time in the gray matter of the brains of AD patients. Images acquired with an amyloid binding tracer can be difficult to interpret, especially for cases showing an early stage of the disease. Also, precise quantification is challenging, because the cortical gray matter can not be well delineated from the images. In this work, we present a software assistant targeted at both qualitative and quantitative analysis of amyloid PET scans. It has been designed with the aim to be easy to use and integrate well into clinical workflows, while at the same time providing solid quantitative results for use e.g. in pharmaceutical trials.
2013-01-01T00:00:00ZInteractive Visualization of Neuroanatomical Data for a Hands-On Multimedia ExhibitRieder, C.Brachmann, C.Hofmann, B.Klein, J.Köhn, A.Ojdanic, D.Schumann, C.Weiler, F.Hahn, H. K.https://diglib.eg.org:443/handle/10.2312/PE.VMLS.VMLS2013.037-0412022-03-28T07:06:35Z2013-01-01T00:00:00ZInteractive Visualization of Neuroanatomical Data for a Hands-On Multimedia Exhibit
Rieder, C.; Brachmann, C.; Hofmann, B.; Klein, J.; Köhn, A.; Ojdanic, D.; Schumann, C.; Weiler, F.; Hahn, H. K.
L. Linsen and H. -C. Hege and B. Hamann
Magnetic resonance imaging is a technique which is routinely used by neuroradiologists. Within the last decade, several techniques have been developed to visualize those MR images so that medical experts, and thus the patients, can benefit from it. However, very little work has been done to use neuroanatomical MR data for educational purposes and to bring the general public into closer contact with the scientific knowledge. In this paper, an interactive visualization of neuroanatomical data, which is controlled by a dedicated user input device, is presented for a novel neuroscience exhibit. State-of-the-art visualization methods are combined to facilitate easy perception of the complexity of the medical data. For that, fiber tubes and diffusion-weighted image overlays are integrated into a volume rendering of the brain. Ambient occlusion algorithms are utilized to calculate self-shadowing of the brain anatomy and the fiber tubes. Further, a physical model of the brain and a touch display are used as user input devices. The visibility of fiber bundles can be intuitively controlled by activating touch sensors, which have been inserted into the physical brain model at the corresponding functional areas.
2013-01-01T00:00:00ZVisualization for Understanding Uncertainty in the Simulation of Myocardial IschemiaRosen, PaulBurton, BrettPotter, KristinJohnson, Chris R.https://diglib.eg.org:443/handle/10.2312/PE.VMLS.VMLS2013.043-0472022-03-28T07:06:33Z2013-01-01T00:00:00ZVisualization for Understanding Uncertainty in the Simulation of Myocardial Ischemia
Rosen, Paul; Burton, Brett; Potter, Kristin; Johnson, Chris R.
L. Linsen and H. -C. Hege and B. Hamann
We have created the Myocardial Uncertainty Viewer (muView or µView) tool for exploring data stemming from the forward simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multivalued and volumetric and thus, for every data point, we have a collection of samples describing cardiac electrical properties. µView combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables changes the propagation of their effects.
2013-01-01T00:00:00ZSupervised Kernel Principal Component Analysis for Visual Sample-based Analysis of Gene Expression DataLong, Tran VanLinsen, Larshttps://diglib.eg.org:443/handle/10.2312/PE.VMLS.VMLS2013.055-0592022-03-28T07:06:37Z2013-01-01T00:00:00ZSupervised Kernel Principal Component Analysis for Visual Sample-based Analysis of Gene Expression Data
Long, Tran Van; Linsen, Lars
L. Linsen and H. -C. Hege and B. Hamann
DNA microarray technology has enabled researchers to simultaneously investigate thousands of genes over hundreds of samples. Automatic classification of such data faces the challenge of dealing with smaller number of samples compared to a larger dimensionality. Dimension reduction techniques are often applied to overcome this. Recently, a number of supervised dimension reduction techniques have been developed. We present a novel supervised dimension reduction technique called supervised kernel principal component analysis and demonstrate its effectiveness for visual representation and visual analysis of gene expression data.
2013-01-01T00:00:00Z