@misc{9248516,
  abstract     = {{Magnetic resonance imaging (MRI) is a medical imaging modality that provides morphologi-
cal and anatomical information about soft tissues. The development of new MRI methods,
such as diffusion MRI (dMRI), has resulted in contrast mechanisms that further characterize
the properties of tissue, such as microstructure.

The development of a new contrast mechanism is typically associated with the development
of new pulse sequences. Specifically, probing different properties of tissue microstructure
require adaptations to diffusion encoding gradients. Therefore, researchers are highly inter-
ested in quick prototyping of sequences for testing using clinical MRI systems. However,
designing new sequences or adaptations to available ones has traditionally been a cumber-
some process due to a high entry bar-programming language and limited access to the source
code in vendor’s native operating framework for MRI systems. A promising alternative is
the open-source pulse sequence programming framework called Pulseq. However, using
the open-source sequence programming framework limits the ability to reconstruct images
using software provided by the MRI system vendor.

The aim of this work is to implement and evaluate methods for reconstruction of MRI images
acquired with a spin-echo pulse sequence built in Pulseq using echo planar imaging (EPI)
readout in combination with partial Fourier (PF) imaging and generalized autocalibrating
partial parallel acquisition (GRAPPA) acceleration techniques.

A GRAPPA reconstruction method was implemented alongside zero filling, homodyne and
POCS methods for PF imaging. Additionally, a navigator-based method for the correction of
Nyquist N/2 ghost artifacts was implemented.

All implemented methods were tested with simulated data using the Shepp-Logan phantom
and a spherical homogeneous physical water phantom. In vivo brain MRI acquisition was
performed in a healthy volunteer. All data were acquired using pulse sequences built in
Pulseq with varying acceleration factors. The reconstruction results were visually evaluated
for all data sets.

Reconstruction of both digital and physical phantom data showed that the POCS algorithm
was the superior reconstruction method for PF imaging. All data were successfully recon-
structed using the GRAPPA algorithm. The ghost correction method was successful in
reducing N/2 ghosts for the digital phantom, while it did not yield clear image improvements
for other data.}},
  author       = {{Steger Larsson, Joakim}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Image reconstruction for diffusion MRI with EPI in Pulseq}},
  year         = {{2026}},
}