Cookie Settings

We use cookies to optimize our website. These include cookies that are necessary for the operation of the site, as well as those that are only used for anonymous statistic. You can decide for yourself which categories you want to allow. Further information can be found in our data privacy protection .


These cookies are necessary to run the core functionalities of this website and cannot be disabled.

Name Webedition CMS
Purpose This cookie is required by the CMS (Content Management System) Webedition for the system to function correctly. Typically, this cookie is deleted when the browser is closed.
Name econda
Purpose Session cookie emos_jcsid for the web analysis software econda. This runs in the “anonymized measurement” mode. There is no personal reference. As soon as the user leaves the site, tracking is ended and all data in the browser are automatically deleted.

These cookies help us understand how visitors interact with our website by collecting and analyzing information anonymously. Depending on the tool, one or more cookies are set by the provider.

Name econda
Purpose Statistics
External media

Content from external media platforms is blocked by default. If cookies from external media are accepted, access to this content no longer requires manual consent.

Name YouTube
Purpose Show YouTube content
Name Twitter
Purpose activate Twitter Feeds
Microstructural Imaging Research Group

Microstructural Imaging Research Group (Kurz)

Microstructural Imaging

The research group "Microstructural imaging" focuses on the development of methods and models for the non-invasive quantification of microstructural changes in biological tissue and their implementation into clinical routine diagnostics. The group develops, tests, and implements novel MRI techniques such as vessel architectural imaging, arterial spin labeling, and relaxometry-based mapping techniques for pre-clinical (9.4T) and clinical MRI (3T, 7T), as well as numerical and analytical models to quantify spatial and functional parameters of microscopically small structures (e.g., vessels, cells) well below the resolution of MR images. These structures change during tumor growth and may serve as biomarkers for tumor grading, treatment planning, and therapy response assessment. Their changing characteristics scale to imaging signatures that form the basis of machine learning approaches in advanced medical image analysis, including deep learning and radiomics techniques.

Research focus:
Vascular network architecture and topology
Diffusion effects in MR imaging
Joint modelling of MRI and microscopy
Validation of biophysical models in microstructural imaging
Quantitative medical image analysis

Dr. Ke Zhang
Freya Garhöfer
Katharina Holz
Christian Neelsen
Clara Meinzer
Lukas Rotkopf
Julian Schroers
Sabine Wolf
Yeong-Chul Yun


Upper panel: modular network structure in glioblastoma. (a) Schematic graphs of a community unfolding process on an entire vascular network in a healthy mouse brain hemisphere (top) and full glioblastoma (bottom). Each level of partitioning represents a local maximum in modularity, attained with increasing community sizes. The rightmost graph shows the clustering scheme with global maximum modularity over a central slice of the original microscopy image. Communities are depicted by circles with diameter and brightness (blue) proportional to cluster size, while the weight of a connection (the number of intercommunity vessel segments) is encoded in the edge thickness and brightness (red). To the right of the partitioning chains, projections of 100 μm thick sections of the skeletonized vasculature show community affiliation (at global modularity maximum) through the color of each branch segment. Adapted from Hahn A et al., Scientific Reports 2019, Hahn A et al., J Theor Biol 2020.

Middle panel: Schematic flowchart of the numerical processing conducted on masked, segmented ultramicroscopy datasets of 3D vascular structure (left image). Each volume partition of the grid that lies within the mask was modeled as a virtual NMR voxel, containing the known microvasculature. Following a determination of the blood vessel induced off-resonance frequency distribution within the virtual voxel (color-coded in a 2D cut through a cubic voxel with 100 μm side length; third image), the extravascular water proton signal was numerically simulated in free induction decay conditions. The magnetization decay accountable to spin dephasing was parametrized using different fit functions and a differentiation between short- and long-time decay. Adapted from Hahn A et al., NMR Biomed 2020.

Lower panel: Vascular mapping for mouse and human glioblastoma. Mouse imaging at 9.4 Tesla MRI: (a) T1-weighted image with gadolinium-based contrast agent, (b) spin echo relaxation rate map with increased relaxation rates in the tumor periphery, (c) gradient echo relaxation rate map, (d) map of the dipole field strength, (e) capillary radius map, showing increasing radii in the tumor region. Human imaging at 3.0 Tesla MRI: (f) T1-weighted image with gadolinium-based contrast agent, (g) map of cerebral blood volume indicating increased blood volume in the tumor region, (h,i) parameters from vascular architecture imaging (slope length and slope, respectively) that provide additional information to intravoxel vascular arrangement, (j) vessel size index representing a measure for average vessel intravoxel diameter, (k) microvessel density map. Adapted from Zhang K et al., PloS One 2019, Buschle LR et al., Magn Reson Mater Phy 2018.

Selected publications:

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines. Hahn A, Bode J, Schuhegger S, Krüwel T, Sturm VJF, Zhang K, Jende JME, Tews B, Heiland S, Bendszus M, Breckwoldt MO, Ziener CH, Kurz FT. NMR Biomed. 2020:e4307. doi: 10.1002/nbm.4307.

Gibbs point field model quantifies disorder in microvasculature of U87-glioblastoma. Hahn A, Bode J, Krüwel T, Kampf T, Buschle LR, Sturm VJF, Zhang K, Tews B, Schlemmer HP, Heiland S, Bendszus M, Ziener CH, Breckwoldt MO, Kurz FT. J Theor Biol. 2020;494:110230.

Jende JME, Groener JB, Kender Z, Rother C, Hahn A, Hilgenfeld T, Juerchott A, Preisner F, Heiland S, Kopf S, Nawroth P, Bendszus M, Kurz FT. Structural nerve remodeling on 3-T MR neurography differs between painful and painless diabetic polyneuropathy in either type 1 or type 2 diabetes. Radiology. 2020;294(2):405-414. doi: 10.1148/radiol.2019191347.

Venkataramani V, Tanev DI, Strahle C, Studier-Fischer A, Fankhauser L, Kessler T, Körber C, Kardorff M, Ratliff M, Xie R, Horstmann H, Messer M, Paik SP, Knabbe J, Sahm F, Kurz FT, Acikgöz AA, Herrmannsdörfer F, Agarwal A, Bergles DE, Chalmers A, Miletic H, Turcan S, Mawrin C, Hänggi D, Liu HK, Wick W, Winkler F, Kuner T. Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature. 2019; 573(7775):532-538.

Glioblastoma multiforme restructures the topological connectivity of cerebrovascular networks. Hahn A, Bode J, Krüwel T, Solecki G, Heiland S, Bendszus M, Tews B, Winkler F, Breckwoldt MO, Kurz FT. Sci Rep. 2019;13;9(1):11757.

Dual-contrast pCASL using simultaneous gradient-echo/spin-echo multiband EPI. Zhang K, Sturm VJ, Buschle LR, Hahn A, Yun SD, Jon Shah N, Bendszus M, Heiland S, Schlemmer HP, Ziener CH, Kurz FT. Magn Reson Imaging. 2019;57:359-367.

Diffusion effects in myelin sheath free induction decay. Kurz FT, Buschle LR, Hahn A, Jende JME, Bendszus M, Heiland S, Ziener CH. J Magn Reson. 2018;297:61-75.

Vessel radius mapping in an extended model of transverse relaxation. Buschle LR, Ziener CH, Zhang K, Sturm VJF, Kampf T, Hahn A, Solecki G, Winkler F, Bendszus M, Heiland S, Schlemmer HP, Kurz FT. Magn Reson Mater Phy. 2018;31(4):531-551.

The influence of spatial patterns of capillary networks on transverse relaxation. Kurz FT, Ziener CH, Rückl M, Hahn A, Sturm VJF, Zhang K, Buschle LR, Bendszus M, Heiland S, Schlemmer HP, Bauer WR, Kampf T. Magn Reson Imaging. 2017;40:31-47.

Spin dephasing in a magnetic dipole field around large capillaries: Approximative and exact results. Kurz FT, Buschle LR, Kampf T, Zhang K, Schlemmer HP, Heiland S, Bendszus M, Ziener CH. J Magn Reson. 2016;273:83-97.

to top
powered by webEdition CMS