MP2RAGE: challenges and artifacts of its use for layer-fMRI

The MP2RAGE sequence is very popular for 7T anatomical imaging and is very commonly used to acquire 0.7-1 mm resolution whole brain anatomical reference data. Aside of this common application, it can also be very helpful for layer-fMRI studies to obtain even higher resolution T1 maps in the range of 0.5mm iso. However, when optimizing MP2RAGE sequence parameters for layer-fMRI studies, there are a few things that might be helpful to keep in mind.

In this post, I would like to discuss the challenges of using the popular MP2RAGE sequence in layer-fMRI studies. Specifically I will discuss challenges/features regarding:

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Example analysis pipeline of layer-VASO

In this blog post I want to go through the analysis pipeline of layer-dependent VASO.

I will go through the all the analysis steps that need to be done to go from raw data from the scanner to final layer profiles. The entire thing will take about 30 min (10 min analysis and 20 min explaining and browsing through data).

During the entire analysis pipeline I am using the following software packages: SPM, AFNI, and LAYNII, and gnuplot (if you want fancier plotting tools)

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How many layers should I extract?

In this blog post, I want to share my thoughts on the number of layers that should be extracted from any given dataset. I will try to give an overview of how many layers are usually extracted in the field, I’ll describe my personal choices of layer numbers, and I will try to discuss the challenges of layer signal extraction along the way.

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Registration of high-resolution data

In this blog post Sri Kashyap and I describe how to deal with the registration of high-resolution datasets across days, across different resolutions, and across different sequences.

I am particularly fond of the following two tools: Firstly, ITK-SNAP for visually-guided manual alignment and secondly, using ANTs programs: antsRegistration and antsApplyTransforms.

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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.

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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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