CBV-fMRI with VASO is highly dependent on a good inversion contrast. It gives it its CBV sensitivity and is also responsible for most of the VASO specific pitfalls (e.g. inflow, CSF etc. ). And thus, it should be optimized as much as possible.
In this blog post, I want to describe the most important features of a reliable inversion pulse for the application of VASO at 7T with a head transmit coil.
Continue reading “TR-FOCI pulse optimisations for SS-SI VASO”
ISIS-conv is a very useful dicom to nii converter from Enrico Reimer. ISIS-conv gets along with a lot of challenging data sets that no other converter (that I know of) can handle so conveniently:
- SMS-data, where individual slices have a non-constant inter-slice distances.
- VASO data with non-constant TRs
- Multi-echo, multi-coil, and Magnitude/Phase data.
There is a Mac-installation package of ISIS-conv. Unfortunately, however, with every IOS update, it has become more complicated to install it.
Since, I spend too much time figuring out how to install it after every update, I am collecting the necessary steps in this Blog post for future reference:
Continue reading “Installing ISIS-conv on MAC”
In this blog post, I want to describe the application and working principle of a few spatial smoothing algorithms that are implemented in LAYNII.
- Confined smoothing along similar anatomical structures with
- Smoothing along the layers with
- Smoothing within columns with
- Smoothing across specific spatial dimensions only with
Continue reading “Anatomically informed spatial smoothing”
In this Blog post, I seek to describe a quick example of how to analyze high-resolution data across layers and columns with LAYNII.
Continue reading “Quick example of cortical unfolding in LAYNII”
In this blogpost I describe how to install the DTI-TK NIfTI quicklook plugin for nii files for mac OS.
Continue reading “How to install nifty QuickLook”
In this blog post, I want to write about pipelines on how to prepare Nifti-brain data and make them printable by a 3D-printer.
Two pipelines are shown. One pipeline describes the 3D-printing the cortical folding structure that is estimated with Freesurfer and subsequently corrected with Meshlab. And another pipeline describes how you can 3D-print any binary nii-volume by using the AFNI-program IsoSurface and correct the output with netfabb. Continue reading “3D-printing nii data”
Often we would like to normalize depth-dependent fMRI signals and assign it to specific cytoarchitectonially defined cortical layers. However, we often only have access to cytoarchitectonial histology data in the form to figures in papers. But since we only have the web-view or the PDF available, we cannot easily extract those data as a layer-profile. Since most layering tools are designed for nii data only, paper figures (e.g. jpg or GNP) are not straight-forwardly transformed to layer profiles.
In this blob post, I describe a set of steps on how to convert any paper figure into a nii-file that allows the extraction of layer profiles.
Continue reading “How to convert any paper figure into a layer-profile”