TR-FOCI pulse optimisations for SS-SI VASO

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”

Installing ISIS-conv on MAC

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:

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Anatomically informed spatial smoothing

In this blog post, I want to describe the application and working principle of a few spatial smoothing algorithms that are implemented in LAYNII.

  1. Confined smoothing along similar anatomical structures with LN_GRADSMOOTH
  2. Smoothing along the layers with LN_LAYERSMOOTH
  3. Smoothing within columns with LN_LAYERSMOOTH
  4. Smoothing across specific spatial dimensions only with LN_DIRECT_SMOOTH

Continue reading “Anatomically informed spatial smoothing”

3D-printing nii data

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”

How to convert any paper figure into a layer-profile

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.

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