add paper and citations

paper/citations.bib

@@ -0,0 +1,34 @@
+
+@article{cox_afni:_1996,
+	title = {{AFNI}: {Software} for {Analysis} and {Visualization} of {Functional} {Magnetic} {Resonance} {Neuroimages}},
+	volume = {29},
+	copyright = {https://www.elsevier.com/tdm/userlicense/1.0/},
+	issn = {00104809},
+	shorttitle = {{AFNI}},
+	url = {https://linkinghub.elsevier.com/retrieve/pii/S0010480996900142},
+	doi = {10.1006/cbmr.1996.0014},
+	language = {en},
+	number = {3},
+	urldate = {2025-02-18},
+	journal = {Computers and Biomedical Research},
+	author = {Cox, Robert W.},
+	month = jun,
+	year = {1996},
+	pages = {162--173},
+}
+
+@article{li_first_2016,
+	title = {The first step for neuroimaging data analysis: {DICOM} to {NIfTI} conversion},
+	volume = {264},
+	issn = {01650270},
+	shorttitle = {The first step for neuroimaging data analysis},
+	url = {https://linkinghub.elsevier.com/retrieve/pii/S0165027016300073},
+	doi = {10.1016/j.jneumeth.2016.03.001},
+	language = {en},
+	urldate = {2025-02-18},
+	journal = {Journal of Neuroscience Methods},
+	author = {Li, Xiangrui and Morgan, Paul S. and Ashburner, John and Smith, Jolinda and Rorden, Christopher},
+	month = may,
+	year = {2016},
+	pages = {47--56},
+}

paper/paper.md

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+---
+title: 'Scanbuddy: fMRI motion plotting and SNR estimation at scan acquisition'
+tags:
+  - Python
+  - neuroimaging
+  - fMRI
+  - motion detection
+  - data quality
+  - afni
+  - snr
+authors:
+  - name: Daniel J. Asay
+    orcid: 0000-0002-6691-7706
+    affiliation: 1
+  - name: Timothy M. O'Keefe
+    affiliation: 1
+  - name: Randy L. Buckner
+    affiliation: "1, 2, 3" 
+affiliations:
+ - name: Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States
+   index: 1
+ - name: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
+   index: 2
+ - name: Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts, United States
+   index: 3
+date: 18 February 2025
+bibliography: citations.bib
+---
+
+# Summary
+
+Functional magnetic resonance imaging (fMRI) is a powerful research and clinical tool for in-vivo imaging of human brain function. `Scanbuddy` is containerized software that produces motion plots at time of scan acquisition. Users should have a machine separate from the scanner host pc with its own display monitor that is capable of running linux and docker. Users can set up auto exporting from the scanner host pc via a Samba share mount for BOLD scans. With auto exporting, the scanner will automatically send reconstructed dicoms to both the scanner host pc and the `Scanbuddy` machine. `Scanbuddy` uses the excellent dcm2niix [@li_first_2016] and AFNI [cox_afni:_1996] packages for dicom conversion and motion plotting. `Scanbuddy` was developed in a linux environment and can handle Repetition Times as fast as 0.6 seconds. 
+
+`Scanbuddy` also provides an estimate of the Signal-to-Noise Ratio (SNR) with the motion plots to estimate data quality. Scanbuddy does not save motion plots by default and does not store data on its host machine. `Scanbuddy` will create a new motion plot and compute a new SNR metric for every fMRI scan acquired. `Scanbuddy` also supports multi-echo BOLD scans with the assumption that the second echo time (TE2) is the TE of interest. `Scanbuddy` is containerized with Docker and is available on Github Container Repository. `Scanbuddy` also checks headcoil elements and notifies users if a headcoil is missing expected elements (e.g. the headcoil is not plugged in properly).
+
+
+# Statement of need
+
+One of the biggest challenges facing fMRI data quality is subject movement during data acquisition. Even subtle actions, such as swallowing or yawning, can have large impacts on data quality. To combat subject motion and optimize data quality, motion-correcting software algorithms can be employed in the post-processing stage, as well as data deletion and imputation methods. However, there are instances of subject motion being severe enough to make the dataset unusable. `Scanbuddy` attempts to address subject motion by producing motion plots to be viewed by researchers at the time of data acquisition, appearing on screen at the conclusion of fMRI scans. By seeing motion plots at scan acquisition, researchers determine if a scan should be re-acquired due to excessive motion rather than lose part or all of an expensive dataset. With `Scanbuddy`, researchers no longer have to wait until data processing to find out how much a participant moved and can avoid the steep cost of data loss.
+
+`Scanbuddy` helps researchers avoid data loss or acquisition of poor data with the SNR estimation and checking headcoil elements. SNR estimation helps researchers set data quality expectations at the piloting stage of an fMRI experiment. SNR estimation at later phases of an fMRI experiment keeps users apprised to on-going data quality and of any sudden dip in data quality. Additionally, by checking headcoil elements, `Scanbuddy` provides a safety net to researcher mistakes that could result in loss of data at a significant cost.
+
+# Acknowledgements
+
+We thank the Center for Brain Science at Harvard and the Harvard Faculty of Arts and Sciences for the financial support of this project.
+
+# References