Zimu Huo

Zimu Huo

I’m interested in developing advanced data acquisition methods and reconstruction algorithms in magnetic resonance imaging (MRI).

Simulatenous Mulit-slice reconstruction tutorial 4

less than 1 minute read

Published:

The slice grappa tries to reduce the total artefact without placing constraints on the intraslice and interslice artifacts. So the artefacts can be arbitrarily large(like vectors pointing in opposite directions can cancel out each other, also called artefact cancellation). The split slice trades interslice artefact with intraslice artefact (lower interslice artefact and higher intraslice artefact) by forming a “correlation matrix” that contrains the interslice leakage to be minimum.

View the split slice slice GRAPPA tutorial here

You May Also Enjoy

Burst imaging simulation

less than 1 minute read

Published:

An ultrafast excitation and acquisition technique.

Simulatenous Mulit-slice reconstruction tutorial 5

less than 1 minute read

Published:

The slice-leakage is an artefact where the information from one slice is unintentionally transmitted to another slice during reconstruction. Any information from slice A that appears in slice B at the end of the reconstruction is considered slice leakage. The current method involves using a Monte Carlo simulation to impose unique frequency modulations on each slice. After reconstructions, the slice leakage can be determined by quantifying the frequency modulations on each individual slice. For example, if we add a 4 Hz modulation on slice A and a 6 Hz modulation on slice B, any 6 Hz component found on slice A after the reconstruction could be used to indicate slice leakage.

NUFFT tutorial

less than 1 minute read

Published:

Challenges arise when reconstructing from non-uniform sampling patterns in the Fourier domain. Performing a direct Fourier transform in such cases incurs a quadratic computation cost of O($N^2$), rendering it impractical for standard practices at high resolutions. This requires transforming non-Cartesian samples back into a uniformly spaced Cartesian grid, enabling the utilization of more efficient FFT algorithms. Typical regridding procedures involve convolving the acquired data with a predefined gridding kernel weighted by the pre-calculated density compensation function, followed by a resampling process onto the Cartesian grid. The ideal gridding kernel is a sinc function, as its Fourier transform results in a rectangular function. However, since the optimal convolution function is of infinite extent, implementing it directly is impractical. To address this, the convolution kernel is truncated and windowed. A popular choice for this purpose is the use of Kaiser-Bessel functions. These methodologies are commonly known as “gridding” and is a special case of non-uniform Fourier transforms (NUFFT).

bSSFP simulation tutorial 2

less than 1 minute read

Published:

Here, I present various variations of bSSFP: standard bSSFP, fluctuating equilibrium, and alternating steady states. Each variation manipulates sequence parameters such as TR, flip angles, and RF phase to generate unique profiles optimized for specific tasks.