Zimu Huo

Zimu Huo

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

SENSE reconstruction tutorial

less than 1 minute read

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SENSE is a parallel imaging technique used to accelerate data acquisition in MRI.

In the original implementation of SENSE, the full-resolution coil sensitivity maps are estimated based on low-resolution calibration data. Consequently, high-resolution information is inherently absent. This contrasts with GRAPPA reconstruction, where high-resolution information is retained.

The advantage of SENSE lies in its simplicity, allowing for easy formulation as a forward model that can be solved through iterative reconstruction. See the next post for CG-SENSE (iterative reconstruction)

View the SENSE reconstruction tutorial here

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Burst imaging simulation

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An ultrafast excitation and acquisition technique.

This demo demonstrates how you can implement a sequence with just a few lines of code (in under 10 seconds) and visualize both the EPG and isochromat simulations with my simulation toolbox

View the BURST demo here

Simulatenous Mulit-slice reconstruction tutorial 5

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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.

View the SMS tutorial 5 here

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).

View the NUFFT tutorial here