Author: renzohuber

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:

Continue reading “Installing ISIS-conv on MAC” →

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
  5. Smoothing long the time domain with LN_TEMPSMOOTH

Continue reading “Anatomically informed spatial smoothing” →

Negative Voxels in VASO

In the last years, I and multiple other VASO users have encountered many occasions of voxels that show negative CBV change (positive VASO signal change) while the BOLD suggests that the activation should be positive. In this blog post, I want to list potential sources of this surprising effect.

Continue reading “Negative Voxels in VASO” →

How send raw k-space data to any given server with Twix or with a modified ICE-chain

In this blog-post I want to describe a setup, how you can include an additional ICE functor in your reconstruction pipeline that sends raw Twix files from the Reconstruction computer to any given custom server.  Continue reading “How send raw k-space data to any given server with Twix or with a modified ICE-chain” →

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.

Continue reading “How to convert any paper figure into a layer-profile” →

Documentation of Installing an IDEA VirtualBox for VE11 from OVA

This post documents the installation of an IDEA VE11 virtual box on a mac as done on May 14th 2018

Big thanks to Andy for figuring out how this works

Prerequisites

  • Here I start with a already built images of IDEA on windows vista and mars on Ubuntu. the images from FMRIF can be taken from erbium.nimh.nih.gov:/fmrif/projects/SiemensIdea/virtual_machines/OVF/): IDEA_ve11c-mars.ova and IDEA_ve11c+vd13d+vd13a.ova
  • Virtual box software can be downloaded here.

Continue reading “Documentation of Installing an IDEA VirtualBox for VE11 from OVA” →

EPI phase correction algorithms

At high resolution EPI, the gradients are pushed to their limits and the ramp sampling ratio is particularly large. This means that the ghosting is increased and the Nyquist ghost correction is getting more important. In this post, I describe how to change the Nyquist ghost correction algorithm.

Phase_correction.gif
The high ramp sampling ratio in high-resolution EPI results in larger ghosts. Changing the correction algorithm from “normal” to “local” can help a lot.

Continue reading “EPI phase correction algorithms” →

SS-SI VASO pitfalls in visual cortex

image125.gif
Activation maps for BOLD and VASO. At about 0.8 mm resolution, one starts to see that VASO is less sensitive to large draining veins.

With respect to high-resolution VASO application, visual cortex is very unique. I found it to be a challenging area. However, because of its high demand, I have been working on is with multiple collaborators. The most important pitfalls of SS-SI VASO in visual cortex that I came across in these collaborations are discussed below.

The take home message tat I learned from manny experiments is:

  1. Use axial slices with the phase encoding direction A>>P.
  2. Watch out for negative voxels.
  3. Invest a lot of effort in optimizing GRAPPA parameters, its worth it.

Continue reading “SS-SI VASO pitfalls in visual cortex” →

Unwanted spatial blurring during resampling

In layer-fMRI, we spend so much time and effort to achieve high spatial resolutions and small voxel sizes during the acquisition. However, during the evaluation pipeline much of this spatial resolution can be lost during multiple resampling steps.

In this post, I want to discuss sources of signal blurring during spatial resampling steps and potential strategies to account for them.

Continue reading “Unwanted spatial blurring during resampling” →

Partial-Fourier imaging at High Resolutions

This blog post discusses the resolution loss when applying partial-Fourier imaging in GE-EPI in the presence of strong T2*-decay.

I found that that when I was aiming for high-resolutions, it is beneficial to refrain from the application of partial Fourier (PF) imaging as much as possible. For the long readout durations at high-resolutions and the fast T2/T2*-decay at high field strengths results in even stronger blurring of partial-Fourier.

Continue reading “Partial-Fourier imaging at High Resolutions” →

Smoothing within layers

Smoothing within layers can be advantageous for multiple reasons:

  • Increasing the CNR without loosing spatial information across cortical depths.
  • Visualization of striping pattern across columnar structures.
  • Avoiding leakage of physiological noise from CSF space into GM tissue.
BOLD smoothing
VASO smoothing

image88.gif Continue reading “Smoothing within layers” →

Finding ROI of the double layers in M1

In this post I want to describe the guidelines that helped me to find the right spot of primary motor cortex (M1) that has a double-layer pattern during a conventional finger tapping task.

The motor cortex is an excellent model system to debug-layer fMRI methodology for multiple reasons:

  1. It has a consistent folding pattern across people.
  2. Its folding pattern is convoluted across one axis only. Hence, it is possible to use thicker slices with higher in-plane resolution.
  3. With 4mm, its is the thickest part of the cortex compared to all other areas. Hence, layer analysis can be done even with 1.2 mm voxels.
  4. It has an expected double layer structure, with two separate peaks. The separability of the peaks can be used as a measure of functional specificity.
  5. It is very close to the RF-receive coils and has high tSNR.
  6. It is easy to shim.

One tricky part, however, is to find the right location of the double layer feature.

Continue reading “Finding ROI of the double layers in M1” →

Quick analysis pipeline of getting layer fMRI profiles without anatomical reference data

This is a step-by-step description on how to obtain layer profiles from any high-resolution fMRI dataset. It is based on manual delineated ROIs and does not require the tricky analysis steps including distortion correction, registration to whole brain “anatomical” datasets, or automatic tissue type segmentation. Hence this is a very quick way for a first glance of the freshly acquired data.

This post shows how you can get from activation maps to layer-profiles in 10 min. In a quick and dirty way.

The important steps are: 1.) Upscaling, 2.) Manual delineation of GM, 3.) Calculation of cortical depths in ROI, 4.) Extracting functional data based on calculated cortical depths.

Continue reading “Quick analysis pipeline of getting layer fMRI profiles without anatomical reference data” →

Gradient Temperature

tSNR changes across time

I got interested in gradient temperature because of the weird effect that tSNR seemed to increase over time.

participant 1, tSNR maps refer to a 30 min fMRI run

Upon posing this effect on Twitter, PracitalfMRI and Ben Poser suggested that it might be due to gradient temperature. So I learned how to track it with as described below.

Continue reading “Gradient Temperature” →