This blog post represents a continuation of the manuals regarding VASO acquisition and VASO signal analysis. It deals with the question of quantifying the VASO signal change with respect to the baseline blood volume at rest. In this post, I try to provide an overview of the values of baseline blood volume in the literature, I hypothesise reasons for their discrepancy and conclude by arguing that one should refrain from analyzing VASO in relative units after all.Continue reading “Baseline CBV and it’s role for the interpretation of layer-dependent VASO signals.”
Title: High resolution fMRI: An introductory course for data acquisition and analysis challenges.
Support: This lecture series is finanzially supported by the FPN-MBIC-school. The session on sequences and sequence artifacts is supported (in kind) by the York-Maastricht-partnership grant. Faruk Omer Gulban works for Brain Innovation.
Coordinators: Laurentius (Renzo) Huber & Omer Faruk Gulban, Cognitive Neuroscience Department
Dates: 7, 14, 21, 28 July 2020 (4 sessions in total), 3pm to 4:30pm.
Video Conference Zoom link (note that these sessions may be recorded): https://maastrichtuniversity.zoom.us/meeting/register/tJAvcu-qpj8sHNVD71Vcu95et-R14QKRs22T
This post lists the background material of the hands-on tutorial about high-resolution EPI on SIEMENS scanners.
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.
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”
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 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.
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.
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.
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.
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.
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.