Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

in-text citation syntax; terminology fix #23

Merged
merged 1 commit into from
Oct 11, 2019
Merged

in-text citation syntax; terminology fix #23

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions paper.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,7 @@ advantages. Firstly, unlike existing C++ frameworks such as ODIN [@jochimsen2004
fees that are otherwise associated with other scientific research platforms such as MATLAB. Thirdly, there has been a
proliferation of deep learning projects developed in Python in recent years. These advantages allow `PyPulseq` to be
integrated with projects related to various stages of the MRI pipeline. For example - deep learning techniques for
acquisition (intelligent slice planning in [@ravi2018amri]) and related downstream reconstruction. Finally, the
acquisition (intelligent slice planning in @ravi2018amri) and related downstream reconstruction. Finally, the
standard Python package manager - PyPI - enables convenient installs on multiple OS platforms. These Python-derived
benefits ensure that `PyPulseq` can reach a wider audience.

Expand All @@ -93,7 +93,7 @@ MATLAB [@layton2017pulseq].
# Target audience

`PyPulseq` is aimed at MRI researchers focusing on pulse sequence design, image reconstruction, and MRI physics. We also
envisage PyPulseq to be utilized for repeatability and reproducibility studies such as those for functional MRI
envisage PyPulseq to be utilized for replicability and reproducibility studies such as those for functional MRI
(multi-site, multi-vendor). The package could also serve as a hands-on teaching aid for MRI faculty and students.
Beginners can get started with the bundled example pulse sequences. More familiar users can import the appropriate
packages to construct and deploy custom pulse sequences.
Expand Down