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24.03.15 08:30

Who will pay my pension someday...?

Information event on pension rights for Internationally Mobile Researchers.

23.03.15 17:14

PhD Funding through the TUM Graduate School

STIBET Program of the DAAD // Internationalization voucher of the TUM-GS

20.03.15 16:23

Biomedical Applications for Cancer Treatment

April 23rd, 2015 I 04.00-06.00pm I Klinikum rechts der Isar I Pavillon Lecture Hall


March 2015


Golkov, Vladimir, M.Sc. Computer Science

Associated member of GSISH

Contact Details:

Technische Universität München
Department of Computer Science
Informatik 9
Boltzmannstrasse 3
85748 Garching


Phone: +49-89-289-17777
Fax: +49-89-289-17757
Office: 02.09.061

E-mail: golkov(at)

Project Details:


Denoising of Diffusion MRI Data Using Prior Knowledge



Prof. Dr. Axel Haase
Prof. Dr. Daniel Cremers
Dr. Marion I. Menzel
Dr. Jonathan Sperl

Start of PhD-work at GSISH:

June 2013

Project Description: 
Diffusion magnetic resonance imaging (diffusion MRI) is an emerging non-invasive imaging method which provides a novel characterization of biological tissue. Notably, it allows examining the orientation and integrity of nerve fiber tracts in the human brain. Diffusion-MRI-based quantification of nerve fiber integrity allows improved diagnosis of diseases such as multiple sclerosis, whereas information about nerve fiber orientation can be used for treatment planning. Moreover, diffusion MRI has a wide variety of additional potential applications in biology and medicine, both inside and outside the central nervous system. This biomedical imaging method has been established for early stroke diagnosis, and many other applications are still in development and have great innovation potential. Diffusion MRI is feasible on most clinical MR scanners.

The obtained data is in most cases six-dimensional, and the major problem is the trade-off between scan time, image resolution and signal-to-noise ratio. The goal of the ongoing project is to investigate whether combinations of available and novel image denoising methods can improve the quality of diffusion MRI data and e.g. allow a more reliable diagnosis of multiple sclerosis.


V. Golkov, J.I. Sperl, T. Sprenger, H.-J. Bungartz, M. Sedlacek, E.T. Tan, L. Marinelli, C.J. Hardy, K.F. King, M.I. Menzel. “Comparison of Diffusion Kurtosis Tensor Estimation Methods in an Advanced Quality Assessment Framework”, Proc. ESMRMB 2012.

T. Sprenger, B. Fernandez, M. Bach, J.I. Sperl, V. Golkov, E.T. Tan, L. Marinelli, K.F. King, C.J. Hardy, Q. Zhu, M. Czisch, P. Sämann, A. Haase, M.I. Menzel. “Evaluation of DSI Imaging with Compressed Sensing under the Presence of Different Noise Levels on a Diffusion Phantom”, Proc. ESMRMB 2012.

V. Golkov
, T. Sprenger, M.I. Menzel, E.T. Tan, K.F. King, C.J. Hardy, L. Marinelli, D. Cremers, J.I. Sperl. “Noise Reduction in Accelerated Diffusion Spectrum Imaging through Integration of SENSE Reconstruction into Joint Reconstruction in Combination with q-Space Compressed Sensing”, Proc. ISMRM 2013.

J.I. Sperl, E.T. Tan, T. Sprenger, V. Golkov, K.F. King, C.J. Hardy, L. Marinelli, M.I. Menzel. “Phase Sensitive Reconstruction in Diffusion Spectrum Imaging Enabling Velocity Encoding and Unbiased Noise Distribution”, Proc. ISMRM 2013.

T. Sprenger, B. Fernandez, J.I. Sperl, V. Golkov, M. Bach, E.T. Tan, K.F. King, C.J. Hardy, L. Marinelli, M. Czisch, P. Sämann, A. Haase, M.I. Menzel. “SNR-dependent Quality Assessment of Compressed-Sensing-Accelerated Diffusion Spectrum Imaging Using a Fiber Crossing Phantom”, Proc. ISMRM 2013.

V. Golkov
, M.I. Menzel, T. Sprenger, A. Menini, D. Cremers, J.I. Sperl. “Reconstruction, Regularization, and Quality in Diffusion MRI Using the Example of Accelerated Diffusion Spectrum Imaging”, 16th Annual Meeting of the German Chapter of the ISMRM, 2013, Freiburg, Germany.

V. Golkov
, T. Sprenger, A. Menini, M.I. Menzel, D. Cremers, J.I. Sperl. “Effects of Low-Rank Constraints, Line-Process Denoising, and q-Space Compressed Sensing on Diffusion MR Image Reconstruction and Kurtosis Tensor Estimation”, Proc. ESMRMB 2013.
Certficiate of Merit Award.

V. Golkov
, T. Sprenger, M.I. Menzel, D. Cremers, J.I. Sperl. “Line-Process-Based Joint SENSE Reconstruction of Diffusion Images with Intensity Inhomogeneity Correction and Noise Non-Stationarity Correction”, Proc. ESMRMB 2013.

V. Golkov
, M.I. Menzel, T. Sprenger, A. Haase, D. Cremers, and J.I. Sperl. “Semi-Joint Reconstruction for Diffusion MRI Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images”, Proc. ISMRM-ESMRMB 2014.

V. Golkov
, M.I. Menzel, T. Sprenger, M. Souiai, A. Haase, D. Cremers, and J.I. Sperl. “Direct Reconstruction of the Average Diffusion Propagator with Simultaneous Compressed-Sensing-Accelerated Diffusion Spectrum Imaging and Image Denoising by Means of Total Generalized Variation Regularization”, Proc. ISMRM-ESMRMB 2014.

J.I. Sperl, T. Sprenger, E.T. Tan, V. Golkov, M.I. Menzel, C.J. Hardy, and L. Marinelli. “Total Variation-Regularized Compressed Sensing Reconstruction for Multi-Shell Diffusion Kurtosis Imaging”, Proc. ISMRM-ESMRMB 2014.

T. Sprenger, J.I. Sperl, B. Fernandez, V. Golkov, E.T. Tan, C.J. Hardy, L. Marinelli, M. Czisch, P. Sämann, A. Haase, and M.I. Menzel. “Novel Acquisition Scheme for Diffusion Kurtosis Imaging Based on Compressed-Sensing Accelerated DSI Yielding Superior Image Quality”, ISMRM-ESMRMB 2014.

V. Golkov
, M.I. Menzel, T. Sprenger, M. Souiai, A. Haase, D. Cremers, and J.I. Sperl. “Improved Diffusion Kurtosis Imaging and Direct Propagator Estimation Using 6-D Compressed Sensing”, OHBM 2014.

V. Golkov, J.I. Sperl, M.I. Menzel, T. Sprenger, E.T. Tan, L. Marinelli, C.J. Hardy, A. Haase, and D. Cremers. “Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate”, MICCAI CDMRI 2014 / Springer Mathematics and Visualization.


06/2012 - present:
Ph.D. student at GE Global Research Munich and TU München
“Denoising of Diffusion MRI Data Using A-Priori Knowledge”

10/2007 - 05/2012:
B.Sc. & M.Sc. Computer Science at TU München

Minor subject: Medicine

09/2010 - 03/2011:

Semester abroad at Ecole Polytechnique, France
Areas of specialization: Imaging and C++ Programming

Practical experience:

10/2011 - 05/2012:
Master’s Thesis at GE Global Research Munich and TU München
“Kurtosis Estimation in Diffusion Spectrum Imaging Using Non-Gaussian Noise Models”

10/2007 - 03/2012:
Programming projects in medical imaging, machine learning, data mining, computer graphics, computer vision, constraint programming, bioinformatics

04/2010 - 08/2010:

Bachelor Thesis at TU München

“MRI-Based Tissue Classification for Non-Invasive Histology of Atherosclerotic Plaques”


10/2009 - 03/2010:

Bachelor practical course: system development project at Munich Airport