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.
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.