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.

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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.

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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.

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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.

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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.

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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.

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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.

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Gradient Temperature

tSNR changes across time

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

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.

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GRAPPA kernel size

Almost every modern fMRI protocol (at SIEMENS scanners) uses GRAPPA. However, only very few people pay a lot of attention on optimal usage of the GRAPPA auto-callibration data. I realized the importance of optimizing GRAPPA parameters when doing high-resolution EPI. At high resolutions, GRAPPA-related noise can become an increasingly important limitation.  This is especially true with the low bandwidth that the body gradient coils force us to use.

In this blog-post I will explain how the GRAPPA kernel-size affects the fMRI data quality, how you can change it, how you can find out which kernel-size was used, and I will descrive simple software tools to identify regions that might benefit from adaptations of the GRAPPA-kernel size.

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layer-fMRI software repositories

There is a long list of software packages that are capable of performing high-resolution MRI analysis.

Some of them are used by multiple groups and some of them are customized for specific studies only.

In this post, I want to give an overview over the most important software packages, their advantages and disadvantages, and their popularity in the field.

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Layer fMRI papers

This page depicts a collection of all layer-fMRI papers.

This list solely includes papers that fulfil the following inclusion criteria:

  • focus on functional imaging
  • cortical layers
  • human imaging
  • preprints are included

Suggestions and corrections are welcome layerfMRI@gmail.com

For non-cortical, non-fMRI, non-human, or non-layer high resolution MRI, please see reviews in special issues 1, 2, 3 (and references therein). The raw data used in this infographic are available here.

Click on image to enlarge.

Overview-01

2021

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2015

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2011

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1999

Bias field correction

This post shows the overview of doing bias field correction in in SPM. Doing this helps me a lot to improve the accuracy of FreeSurfer with MP2RAGE data. The members of Polimeni’s group also use it as an additional per-processing step before giving the data to Freesurfer.

The SPM bis field correction is part of the segmentation pipeline in SPM. I use the following bash script and the following matlab stript:

Bias_field_script_job.m and  start_bias_field.sh.

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Using a standalone Nifti I/O in C++

In this blog post, I describe how you can build your own standalone C++ program to analyze nii data without any dependencies beyond C++.  Hence, it will work without the  libraries from other fMRI software packages, e.g. odin, afni. The only tricky part is to use a stand alone nii I/O to write your own C++ evaluation programs. Here, I do it by incorporating the few files that i borrowed from the afni source code.

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How to deal with Ghosts in high-res EPI

Background

Ghost are the biggest limitation in high res fMRI. Similar to low resolution fMRI, ghosts in sub-millimeter EPI are arising from mismatch of k-space lines. This mismatch can be associated with (A) the actual readout itself or (B) inappropriate GRAPPA auto calibration data.

Here I try to make notes of strategies that I found helpful to minimize ghosts.

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Getting layers in EPI space

Overview

Big steps:

  1. Manual aligment of MP2RAGE with EPI (optional when MP2RAGE is acquired in same session)
  2. ANTS alignment of MP2RAGE and EPI. (part of anatomical_maser.sh, see github)
  3. Running Freesurfer on MP2RAGE data in EPI space. (part of anatomical_maser.sh, see github)
  4. Using SUMA to get fine samples tissue borders in EPI-voxel space (in oblique space) (part of anatomical_maser.sh, see github)
  5. Manual correction of Freesurfer GM-ribbon
  6. calculating layers from GM-ribbon in neuroDebian.

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Motion and BOLD correction in SS-SI-VASO

Intro

This is a description on how to use SPM and/or AFNI for high-resolution SS-SI-VASO data evaluations.

Here it is explained what to do with the raw fMTR data from the scanner to obtain CBV and BOLD signal time courses .

Sequence

Every other image has a different contrast.

Screenshot 2017-11-25 15.27.14.png

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