Maximum intensity projection and minimum intensity projection can be insightful for mapping of vessels in 3D-slabs. In this post, I describe the application of intensity projections with LAYNII.
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.
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:
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
- Smoothing long the time domain with
In this Blog post, I seek to describe a quick example of how to analyze high-resolution data across layers and columns with LAYNII.
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.
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”
Recently, my adviser Peter Bandettini asked me to compile a list of all potential applications for layer-fMRI. It was a fun exercise to think about it. Below, you find a list of all potential applications that I could think of. Continue reading “List of potential layer-fMRI application studies”
In this blogpost I describe how to install the DTI-TK NIfTI quicklook plugin for nii files for mac OS.
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.
In this blog post I want to discuss how the tSNR in sub-millimeter fMRI can be substantially improved by optimizing the GRAPPA regularization. Adjusting one single GRAPPA reconstruction parameter can almost double the tSNR of your fMRI time series. With almost no penalty.
This post contains a collection of recently presented powerpoint slides.
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
- 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.
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.
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:
- Use axial slices with the phase encoding direction A>>P.
- Watch out for negative voxels.
- Invest a lot of effort in optimizing GRAPPA parameters, its worth it.
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.
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.
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.
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:
- It has a consistent folding pattern across people.
- Its folding pattern is convoluted across one axis only. Hence, it is possible to use thicker slices with higher in-plane resolution.
- 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.
- 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.
- It is very close to the RF-receive coils and has high tSNR.
- It is easy to shim.
One tricky part, however, is to find the right location of the double layer feature.