Dr. Stephan G. Erberich Profile

Dr. Stephan G. Erberich

SPIE Involvement:

Author

Proceedings Article | 26 January 2017 Paper

Extending PACS functionality: towards facilitating the conversion of clinical necessities into research-derived applications

Fernando Yepes-Calderon, Frisca Wihardja, Edward Melamed, Min Song, Giustino Paladini, Natasha Lepore, Marvin Nelson, Stephan Erberich, Stefan Bluml, J. Gordon McComb

Proceedings Volume 10160, 1016015 (2017) https://doi.org/10.1117/12.2264350

KEYWORDS: Picture Archiving and Communication System, Clinical research, Image processing, Telecommunications, Image analysis, Medical imaging, Internet, Data processing, Visualization, Human-machine interfaces

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Proceedings Article | 16 March 2006 Paper

Grid based medical image workflow and archiving for research and enterprise PACS applications

Stephan Erberich, Manasee Dixit, Vincent Chen, Ann Chervenak, Marvin Nelson, Carl Kesselmann

Proceedings Volume 6145, 61450V (2006) https://doi.org/10.1117/12.654514

KEYWORDS: Picture Archiving and Communication System, Medical imaging, Standards development, Medical research, Medicine, Surgery, Health informatics, Interfaces, Cancer, Web services

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Proceedings Article | 14 April 2005 Paper

Relevant information retrieval and fusion of anatomic, physiologic, and metabolic neuroimaging

Stephan Erberich, Jon Nielsen, Ashok Panigrahy, Stefan Bluml, Istvan Seri, Marvin Nelson

Proceedings Volume 5746, (2005) https://doi.org/10.1117/12.595127

KEYWORDS: Magnetic resonance imaging, Functional magnetic resonance imaging, Neuroimaging, Diffusion tensor imaging, Brain, Information fusion, Data storage, Visualization, Picture Archiving and Communication System, Diffusion

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Proceedings Article | 30 April 2004 Paper

Neurodevelopment assessment of newborns with combined fMRI and DTI

Stephan Erberich, Jon Nielsen, Stefan Bluml, Philippe Friedlich, Istvan Seri, Marvin Nelson

Proceedings Volume 5369, (2004) https://doi.org/10.1117/12.536236

KEYWORDS: Functional magnetic resonance imaging, Diffusion tensor imaging, Brain, Neuroimaging, Magnetic resonance imaging, Brain mapping, Visualization, Sensors, Functional imaging, Cortical activation

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Functional Magnetic Resonance Imaging (fMRI) provides the location and regional extent of a task correlated activation in the brain. Recently we have demonstrated, that fMRI of passive sensory tasks (visual, auditory, motor) can be successfully used to map cortical activation in the newborn brain. However the interpretation of the functional response in the immature brain is difficult, as the blood oxygen level dependent (BOLD) physiological signal and location of the activation is quite different compared to adult fMRI responses of similar tasks. We expect, that the major reason for these differences are primarily caused by the immature myelination of the white matter tracts at this age. Diffusion tensor imaging (DTI) can be used to measure the white matter tract development in the newborn brain. The purpose of this paper is to report how to obtain and to combine fMRI and DTI data processing to enhance functional brain mapping in newborns. We obtained simultaneous fMRI and DTI data of 18 newborns, post-conceptional age (gestational age at study) between 34-week and 52-week, which were referred for clinical indicated MRI. 16 out of 18 subjects have been successfully investigated with combined fMRI and DTI and functional activation could be obtained. Fiber tracking was successfully in the visual and auditory cortex, but proofed difficult in the motor-cortex. The additional tract information supported the functional findings and the interpretation in the immature brain. The novel functional imaging in newborn is challenging because of the yet unknown physiological response and location of activation in the newborn brain. Therefore one need additional evidence that the functional findings are valid in the context of structural development. The maturation of myelination is an essential information to compare and to interpret fMRI in newborns. We conclude that the proposed method of combined fMRI and DTI, derived from adult neuroimaging, will be most relevant to understand the physiological response and thus the neurodevelopment of the newborn brain.

Proceedings Article | 30 April 2004 Paper

Diffusion tensor imaging in newborns

Jon Nielsen, Ashok Panigrahy, Stephan Erberich

Proceedings Volume 5369, (2004) https://doi.org/10.1117/12.536240

KEYWORDS: Diffusion tensor imaging, Brain, Diffusion, Magnetic resonance imaging, Neodymium, 3D metrology, Anisotropy, Image registration, Neuroimaging, 3D image processing

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Proceedings Article | 19 May 2003 Paper

Wireless-PDA-controlled image workflow from PACS: the next trend in the health care enterprise?

Stephan Erberich, Jorge Documet, Michael Zhou, Fei Cao, Brent Liu, Greg Mogel, H. Huang

Proceedings Volume 5033, (2003) https://doi.org/10.1117/12.481934

KEYWORDS: Picture Archiving and Communication System, Personal digital assistants, Medicine, Medical imaging, Radiology, Control systems, Databases, Internet, Image processing, Wireless communications

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Proceedings Article | 2 May 2003 Paper

Analysis of brain fMRI time-series using HRF knowledge-based correlation classifier on unsupervised self-organizing neural network map

Stephan Erberich, Stefan Bluml, Marvin Nelson

Proceedings Volume 5031, (2003) https://doi.org/10.1117/12.480667

KEYWORDS: Brain, Functional magnetic resonance imaging, Neural networks, Neurons, Neuroimaging, Statistical analysis, Brain mapping, Magnetic resonance imaging, Data modeling, Statistical modeling

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Proceedings Article | 24 April 2002 Paper

Knowledge-based approach for functional MRI analysis by SOM neural network using prior labels from Talairach stereotaxic space

Stephan Erberich, Klaus Willmes, Armin Thron, Walter Oberschelp, H. Huang

Proceedings Volume 4683, (2002) https://doi.org/10.1117/12.463603

KEYWORDS: Brain, Neural networks, Visualization, Neurons, Magnetic resonance imaging, Brain mapping, Functional magnetic resonance imaging, Neuroimaging, Data modeling, Hemodynamics

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Proceedings Article | 7 August 2001 Paper

Hospital integrated parallel cluster for fast and cost-efficient image analysis: clinical experience and research evaluation

Stephan Erberich, Martin Hoppe, Christian Jansen, Thomas Schmidt, Armin Thron, Walter Oberschelp

Proceedings Volume 4323, (2001) https://doi.org/10.1117/12.435471

KEYWORDS: Image processing, Functional magnetic resonance imaging, Picture Archiving and Communication System, Clinical research, Magnetic resonance imaging, Diagnostics, Databases, Image segmentation, 3D image processing, Image analysis

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Proceedings Article | 21 May 2001 Paper

Analysis of event-related fMRI using nonlinear regression self-organizing map neural network

Stephan Erberich, Manou Liebert, Klaus Willmes, Armin Thron, Walter Oberschelp

Proceedings Volume 4321, (2001) https://doi.org/10.1117/12.428153

KEYWORDS: Functional magnetic resonance imaging, Neurons, Brain mapping, Neural networks, 3D modeling, Statistical analysis, Feature extraction, Magnetic resonance imaging, Data modeling, Visualization

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Proceedings Article | 21 May 2001 Paper

Optimized knowledge-based motion correction of fMRI time series using parallel algorithms

Thomas Schmidt, Stephan Erberich, Martin Hoppe, Christian Jansen, Armin Thron, Walter Oberschelp

Proceedings Volume 4321, (2001) https://doi.org/10.1117/12.428155

KEYWORDS: Functional magnetic resonance imaging, Bismuth, Image quality, Image processing, Image registration, Quality measurement, Laser sintering, Brain, Motion estimation, Motion measurement

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Proceedings Article | 20 April 2000 Paper

Analysis of short single rest/activation epoch fMRI by self-organizing map neural network

Stephan Erberich, Thomas Dietrich, Stefan Kemeny, Timo Krings, Klaus Willmes, Armin Thron, Walter Oberschelp

Proceedings Volume 3978, (2000) https://doi.org/10.1117/12.383405

KEYWORDS: Functional magnetic resonance imaging, Neural networks, Neurons, Magnetic resonance imaging, Visualization, Brain mapping, Brain, Tissues, Magnetism, Statistical analysis

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Functional magnet resonance imaging (fMRI) has become a standard non invasive brain imaging technique delivering high spatial resolution. Brain activation is determined by magnetic susceptibility of the blood oxygen level (BOLD effect) during an activation task, e.g. motor, auditory and visual tasks. Usually box-car paradigms have 2 - 4 rest/activation epochs with at least an overall of 50 volumes per scan in the time domain. Statistical test based analysis methods need a large amount of repetitively acquired brain volumes to gain statistical power, like Student's t-test. The introduced technique based on a self-organizing neural network (SOM) makes use of the intrinsic features of the condition change between rest and activation epoch and demonstrated to differentiate between the conditions with less time points having only one rest and one activation epoch. The method reduces scan and analysis time and the probability of possible motion artifacts from the relaxation of the patients head. Functional magnet resonance imaging (fMRI) of patients for pre-surgical evaluation and volunteers were acquired with motor (hand clenching and finger tapping), sensory (ice application), auditory (phonological and semantic word recognition task) and visual paradigms (mental rotation). For imaging we used different BOLD contrast sensitive Gradient Echo Planar Imaging (GE-EPI) single-shot pulse sequences (TR 2000 and 4000, 64 X 64 and 128 X 128, 15 - 40 slices) on a Philips Gyroscan NT 1.5 Tesla MR imager. All paradigms were RARARA (R equals rest, A equals activation) with an epoch width of 11 time points each. We used the self-organizing neural network implementation described by T. Kohonen with a 4 X 2 2D neuron map. The presented time course vectors were clustered by similar features in the 2D neuron map. Three neural networks were trained and used for labeling with the time course vectors of one, two and all three on/off epochs. The results were also compared by using a Kolmogorov-Smirnov statistical test of all 66 time points. To remove non- periodical time courses from training an auto-correlation function and bandwidth limiting Fourier filtering in combination with Gauss temporal smoothing was used. None of the trained maps, with one, two and three epochs, were significantly different which indicates that the feature space of only one on/off epoch is sufficient to differentiate between the rest and task condition. We found, that without pre-processing of the data no meaningful results can be achieved because of the huge amount of the non-activated and background voxels represents the majority of the features and is therefore learned by the SOM. Thus it is crucial to remove unnecessary capacity load of the neural network by selection of the training input, using auto-correlation function and/or Fourier spectrum analysis. However by reducing the time points to one rest and one activation epoch either strong auto- correlation or a precise periodical frequency is vanishing. Self-organizing maps can be used to separate rest and activation epochs of with only a 1/3 of the usually acquired time points. Because of the nature of the SOM technique, the pattern or feature separation, only the presence of a state change between the conditions is necessary for differentiation. Also the variance of the individual hemodynamic response function (HRF) and the variance of the spatial different regional cerebral blood flow (rCBF) is learned from the subject and not compared with a fixed model done by statistical evaluation. We found that reducing the information to only a few time points around the BOLD effect was not successful due to delays of rCBF and the insufficient extension of the BOLD feature in the time space. Especially for patient routine observation and pre-surgical planing a reduced scan time is of interest.

Proceedings Article | 20 May 1999 Paper

Unsupervised time course analysis of functional magnetic resonance imaging (fMRI) using self-organizing maps (SOMs)

Stephan Erberich, Matthias Fellenberg, Timo Krings, Stefan Kemeny, Wolfgang Reith, Klaus Willmes, Walter Oberschelp

Proceedings Volume 3660, (1999) https://doi.org/10.1117/12.349596

KEYWORDS: Neurons, Functional magnetic resonance imaging, Brain, Visualization, Brain mapping, Magnetic resonance imaging, Neuroimaging, Statistical analysis, Tissues, Blood

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Showing 5 of 13 publications

Conference Committee Involvement (1)

Physiology and Function: Methods, Systems, and Applications

16 February 2003 | San Diego, California, United States

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