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ei Thumb sm 892810 486784654720109 542312555 o
Prateek Katiyar
Ph.D. Student
ei Thumb sm thumb ilja bezrukov
Ilja Bezrukov
Alumni
ei Thumb sm thumb alexander loktyushin
Alexander Loktyushin
Research Scientist
ei Thumb sm thumb fr  d  ric mantlik
ei Thumb sm img 0113 bs
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15 results

2015


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Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to Diffusion Imaging Data

O’Donnell, L. J., Schultz, T.

In Visualization and Processing of Higher Order Descriptors for Multi-Valued Data, pages: 299-319, (Editors: Hotz, I. and Schultz, T.), Springer, 2015 (inbook)

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2015

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Blind multirigid retrospective motion correction of MR images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine, 73(4):1457-1468, April 2015 (article)

DOI Project Page [BibTex]

DOI Project Page [BibTex]

2014


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FID-guided retrospective motion correction based on autofocusing

Babayeva, M., Loktyushin, A., Kober, T., Granziera, C., Nickisch, H., Gruetter, R., Krueger, G.

Joint Annual Meeting ISMRM-ESMRMB, Milano, Italy, 2014 (poster)

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2014

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2013


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Blind Retrospective Motion Correction of MR Images

Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine (MRM), 70(6):1608–1618, 2013 (article)

DOI Project Page [BibTex]

2013

DOI Project Page [BibTex]


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MR-based Attenuation Correction Methods for Improved PET Quantification in Lesions within Bone and Susceptibility Artifact Regions

Bezrukov, I., Schmidt, H., Mantlik, F., Schwenzer, N., Brendle, C., Schölkopf, B., Pichler, B.

Journal of Nuclear Medicine, 54(10):1768-1774, 2013 (article)

Abstract
Hybrid PET/MR systems have recently entered clinical practice. Thus, the accuracy of MR-based attenuation correction in simultaneously acquired data can now be investigated. We assessed the accuracy of 4 methods of MR-based attenuation correction in lesions within soft tissue, bone, and MR susceptibility artifacts: 2 segmentation-based methods (SEG1, provided by the manufacturer, and SEG2, a method with atlas-based susceptibility artifact correction); an atlas- and pattern recognition–based method (AT&PR), which also used artifact correction; and a new method combining AT&PR and SEG2 (SEG2wBONE). Methods: Attenuation maps were calculated for the PET/MR datasets of 10 patients acquired on a whole-body PET/MR system, allowing for simultaneous acquisition of PET and MR data. Eighty percent iso-contour volumes of interest were placed on lesions in soft tissue (n = 21), in bone (n = 20), near bone (n = 19), and within or near MR susceptibility artifacts (n = 9). Relative mean volume-of-interest differences were calculated with CT-based attenuation correction as a reference. Results: For soft-tissue lesions, none of the methods revealed a significant difference in PET standardized uptake value relative to CT-based attenuation correction (SEG1, −2.6% ± 5.8%; SEG2, −1.6% ± 4.9%; AT&PR, −4.7% ± 6.5%; SEG2wBONE, 0.2% ± 5.3%). For bone lesions, underestimation of PET standardized uptake values was found for all methods, with minimized error for the atlas-based approaches (SEG1, −16.1% ± 9.7%; SEG2, −11.0% ± 6.7%; AT&PR, −6.6% ± 5.0%; SEG2wBONE, −4.7% ± 4.4%). For lesions near bone, underestimations of lower magnitude were observed (SEG1, −12.0% ± 7.4%; SEG2, −9.2% ± 6.5%; AT&PR, −4.6% ± 7.8%; SEG2wBONE, −4.2% ± 6.2%). For lesions affected by MR susceptibility artifacts, quantification errors could be reduced using the atlas-based artifact correction (SEG1, −54.0% ± 38.4%; SEG2, −15.0% ± 12.2%; AT&PR, −4.1% ± 11.2%; SEG2wBONE, 0.6% ± 11.1%). Conclusion: For soft-tissue lesions, none of the evaluated methods showed statistically significant errors. For bone lesions, significant underestimations of −16% and −11% occurred for methods in which bone tissue was ignored (SEG1 and SEG2). In the present attenuation correction schemes, uncorrected MR susceptibility artifacts typically result in reduced attenuation values, potentially leading to highly reduced PET standardized uptake values, rendering lesions indistinguishable from background. While AT&PR and SEG2wBONE show accurate results in both soft tissue and bone, SEG2wBONE uses a two-step approach for tissue classification, which increases the robustness of prediction and can be applied retrospectively if more precision in bone areas is needed.

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2012


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glm-ie: The Generalised Linear Models Inference and Estimation Toolbox

Nickisch, H.

Journal of Machine Learning Research, 13, pages: 1699-1703, May 2012 (article)

Abstract
The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some The code is fully compatible to both Matlab 7.x and GNU Octave 3.3.x. Abstract Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework.

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2012

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MICCAI, Workshop on Computational Diffusion MRI, 2012 (electronic publication)

Panagiotaki, E., O’Donnell, L., Schultz, T., Zhang, G.

15th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Workshop on Computational Diffusion MRI , 2012 (proceedings)

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PDF Project Page [BibTex]

2011


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Finding dependencies between frequencies with the kernel cross-spectral density

Besserve, M., Janzing, D., Logothetis, N., Schölkopf, B.

In pages: 2080-2083 , IEEE, Piscataway, NJ, USA, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2011 (inproceedings)

Abstract
Cross-spectral density (CSD), is widely used to find linear dependency between two real or complex valued time series. We define a non-linear extension of this measure by mapping the time series into two Reproducing Kernel Hilbert Spaces. The dependency is quantified by the Hilbert Schmidt norm of a cross-spectral density operator between these two spaces. We prove that, by choosing a characteristic kernel for the mapping, this quantity detects any pairwise dependency between the time series. Then we provide a fast estimator for the Hilbert-Schmidt norm based on the Fast Fourier Trans form. We demonstrate the interest of this approach to quantify non-linear dependencies between frequency bands of simulated signals and intra-cortical neural recordings.

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2011

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MRI-Based Attenuation Correction for Whole-Body PET/MRI: Quantitative Evaluation of Segmentation- and Atlas-Based Methods

Hofmann, M., Bezrukov, I., Mantlik, F., Aschoff, P., Steinke, F., Beyer, T., Pichler, B., Schölkopf, B.

Journal of Nuclear Medicine, 52(9):1392-1399, September 2011 (article)

Abstract
PET/MRI is an emerging dual-modality imaging technology that requires new approaches to PET attenuation correction (AC). We assessed 2 algorithms for whole-body MRI-based AC (MRAC): a basic MR image segmentation algorithm and a method based on atlas registration and pattern recognition (AT&PR). METHODS: Eleven patients each underwent a whole-body PET/CT study and a separate multibed whole-body MRI study. The MR image segmentation algorithm uses a combination of image thresholds, Dixon fat-water segmentation, and component analysis to detect the lungs. MR images are segmented into 5 tissue classes (not including bone), and each class is assigned a default linear attenuation value. The AT&PR algorithm uses a database of previously aligned pairs of MRI/CT image volumes. For each patient, these pairs are registered to the patient MRI volume, and machine-learning techniques are used to predict attenuation values on a continuous scale. MRAC methods are compared via the quantitative analysis of AC PET images using volumes of interest in normal organs and on lesions. We assume the PET/CT values after CT-based AC to be the reference standard. RESULTS: In regions of normal physiologic uptake, the average error of the mean standardized uptake value was 14.1% ± 10.2% and 7.7% ± 8.4% for the segmentation and the AT&PR methods, respectively. Lesion-based errors were 7.5% ± 7.9% for the segmentation method and 5.7% ± 4.7% for the AT&PR method. CONCLUSION: The MRAC method using AT&PR provided better overall PET quantification accuracy than the basic MR image segmentation approach. This better quantification was due to the significantly reduced volume of errors made regarding volumes of interest within or near bones and the slightly reduced volume of errors made regarding areas outside the lungs.

Web DOI Project Page [BibTex]


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Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Seeger, M., Nickisch, H.

SIAM Journal on Imaging Sciences, 4(1):166-199, March 2011 (article)

Abstract
Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher-order Bayesian decision-making problems, such as optimizing image acquisition in magnetic resonance scanners, can be addressed by querying the SLM posterior covariance, unrelated to the density‘s mode. We propose a scalable algorithmic framework, with which SLM posteriors over full, high-resolution images can be approximated for the first time, solving a variational optimization problem which is convex iff posterior mode finding is convex. These methods successfully drive the optimization of sampling trajectories for real-world magnetic resonance imaging through Bayesian experimental design, which has not been attempted before. Our methodology provides new insight into similarities and differences between sparse reconstruction and approximate Bayesian inference, and has important implications for compressive sensing of real-world images.

Web DOI Project Page [BibTex]


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The effect of patient positioning aids on PET quantification in PET/MR imaging

Mantlik, F., Hofmann, M., Werner, M., Sauter, A., Kupferschläger, J., Schölkopf, B., Pichler, B., Beyer, T.

European Journal of Nuclear Medicine and Molecular Imaging, 38(5):920-929, May 2011 (article)

Abstract
Objectives Clinical PET/MR requires the use of patient positioning aids to immobilize and support patients for the duration of the combined examination. Ancillary immobilization devices contribute to overall attenuation of the PET signal, but are not detected with conventional MR sequences and, hence, are ignored in standard MR-based attenuation correction (MR-AC). We report on the quantitative effect of not accounting for the attenuation of patient positioning aids in combined PET/MR imaging. Methods We used phantom and patient data acquired with positioning aids on a PET/CT scanner (Biograph 16, HI-REZ) to mimic PET/MR imaging conditions. Reference CT-based attenuation maps were generated from measured (original) CT transmission images (origCT-AC). We also created MR-like attenuation maps by following the same conversion procedure of the attenuation values except for the prior delineation and subtraction of the positioning aids from the CT images (modCT-AC). First, a uniform 68Ge cylinder was positioned centrally in the PET/CT scanner and fixed with a vacuum mattress (10 cm thick) and, in a repeat examination, with MR positioning foam pads. Second, 16 patient datasets were selected for subsequent processing. All patients were regionally immobilized with positioning aids: a vacuum mattress for head/neck imaging (nine patients) and a foam mattress for imaging of the lower extremities (seven patients). PET images were reconstructed following CT-based attenuation and scatter correction using the original and modified (MR-like) CT images: PETorigCT-AC and PETmodCT-AC, respectively. PET images following origCT-AC and modCT-AC were compared visually and in terms of mean differences of voxels with a standardized uptake value of at least 1.0. In addition, we report maximum activity concentration in lesions for selected patients. Results In the phantom study employing the vacuum mattress the average voxel activity in PETmodCT-AC was underestimated by 6.4% compared to PETorigCT-AC, with 3.4% of the PET voxels being underestimated by 10% or more. When the MR foam pads were not accounted for during AC, PETmodCT-AC was underestimated by 1.1% on average, with none of the PET voxels being underestimated by 10% or more. Evaluation of the head/neck patient data showed a decrease of 8.4% ([68Ga]DOTATOC) and 7.4% ([18F]FDG) when patient positioning aids were not accounted for during AC, while the corresponding decrease was insignificant for the lower extremities. Conclusion Depending on the size and density of the positioning aids used, a regionally variable underestimation of PET activity following AC is observed when positioning aids are not accounted for. This underestimation may become relevant in combined PET/MR imaging of patients with neuropsychiatric indications, but appears to be of no clinical relevance in imaging the extremities.

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PDF DOI Project Page [BibTex]

2010


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Optimization of k-Space Trajectories for Compressed Sensing by Bayesian Experimental Design

Seeger, M., Nickisch, H., Pohmann, R., Schölkopf, B.

Magnetic Resonance in Medicine, 63(1):116-126, January 2010 (article)

Abstract
The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as a Bayesian experimental design problem. Bayesian inference is approximated by a novel relaxation to standard signal processing primitives, resulting in an efficient optimization algorithm for Cartesian and spiral trajectories. On clinical resolution brain image data from a Siemens 3T scanner, automatically optimized trajectories lead to significantly improved images, compared to standard low-pass, equispaced, or variable density randomized designs. Insights into the nonlinear design optimization problem for MRI are given.

Web DOI Project Page [BibTex]

2009


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Towards quantitative PET/MRI: a review of MR-based attenuation correction techniques

Hofmann, M., Pichler, B., Schölkopf, B., Beyer, T.

European Journal of Nuclear Medicine and Molecular Imaging, 36(Supplement 1):93-104, March 2009 (article)

Abstract
Introduction Positron emission tomography (PET) is a fully quantitative technology for imaging metabolic pathways and dynamic processes in vivo. Attenuation correction of raw PET data is a prerequisite for quantification and is typically based on separate transmission measurements. In PET/CT attenuation correction, however, is performed routinely based on the available CT transmission data. Objective Recently, combined PET/magnetic resonance (MR) has been proposed as a viable alternative to PET/CT. Current concepts of PET/MRI do not include CT-like transmission sources and, therefore, alternative methods of PET attenuation correction must be found. This article reviews existing approaches to MR-based attenuation correction (MR-AC). Most groups have proposed MR-AC algorithms for brain PET studies and more recently also for torso PET/MR imaging. Most MR-AC strategies require the use of complementary MR and transmission images, or morphology templates generated from transmission images. We review and discuss these algorithms and point out challenges for using MR-AC in clinical routine. Discussion MR-AC is work-in-progress with potentially promising results from a template-based approach applicable to both brain and torso imaging. While efforts are ongoing in making clinically viable MR-AC fully automatic, further studies are required to realize the potential benefits of MR-based motion compensation and partial volume correction of the PET data.

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2009

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Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

Seeger, M., Nickisch, H., Pohmann, R., Schölkopf, B.

In Advances in neural information processing systems 21, pages: 1441-1448, (Editors: D Koller and D Schuurmans and Y Bengio and L Bottou), Curran, Red Hook, NY, USA, 22nd Annual Conference on Neural Information Processing Systems (NIPS), June 2009 (inproceedings)

Abstract
We show how improved sequences for magnetic resonance imaging can be found through automated optimization of Bayesian design scores. Combining recent advances in approximate Bayesian inference and natural image statistics with high-performance numerical computation, we propose the first scalable Bayesian experimental design framework for this problem of high relevance to clinical and brain research. Our solution requires approximate inference for dense, non-Gaussian models on a scale seldom addressed before. We propose a novel scalable variational inference algorithm, and show how powerful methods of numerical mathematics can be modified to compute primitives in our framework. Our approach is evaluated on a realistic setup with raw data from a 3T MR scanner.

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PDF Web Project Page [BibTex]

2008


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MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration

Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Schölkopf, B., Pichler, B.

Journal of Nuclear Medicine, 49(11):1875-1883, October 2008 (article)

Abstract
For quantitative PET information, correction of tissue photon attenuation is mandatory. Generally in conventional PET, the attenuation map is obtained from a transmission scan, which uses a rotating radionuclide source, or from the CT scan in a combined PET/CT scanner. In the case of PET/MRI scanners currently under development, insufficient space for the rotating source exists; the attenuation map can be calculated from the MR image instead. This task is challenging because MR intensities correlate with proton densities and tissue-relaxation properties, rather than with attenuation-related mass density. METHODS: We used a combination of local pattern recognition and atlas registration, which captures global variation of anatomy, to predict pseudo-CT images from a given MR image. These pseudo-CT images were then used for attenuation correction, as the process would be performed in a PET/CT scanner. RESULTS: For human brain scans, we show on a database of 17 MR/CT image pairs that our method reliably enables e stimation of a pseudo-CT image from the MR image alone. On additional datasets of MRI/PET/CT triplets of human brain scans, we compare MRI-based attenuation correction with CT-based correction. Our approach enables PET quantification with a mean error of 3.2% for predefined regions of interest, which we found to be clinically not significant. However, our method is not specific to brain imaging, and we show promising initial results on 1 whole-body animal dataset. CONCLUSION: This method allows reliable MRI-based attenuation correction for human brain scans. Further work is necessary to validate the method for whole-body imaging.

Web DOI Project Page [BibTex]

2008