summary and statement of need

paper/citations.bib

@@ -36,20 +36,6 @@ @article{cai_prequal:_2021
 	shorttitle = {Prequal},
 	url = {https://onlinelibrary.wiley.com/doi/10.1002/mrm.28678},
 	doi = {10.1002/mrm.28678},
-	abstract = {Purpose
-              Diffusion weighted MRI imaging (DWI) is often subject to low signal‐to‐noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document.
-            
-            
-              Methods
-              The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter‐scan intensity normalization; susceptibility‐, eddy current‐, and motion‐induced artifact correction; and slice‐wise signal drop‐out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness‐of‐fit and fractional anisotropy analyses.
-            
-            
-              Results
-              Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility‐, eddy current‐, and motion‐induced artifacts; imputed signal‐lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file‐type conversion errors and was shown to be effective on externally available datasets.
-            
-            
-              Conclusions
-              The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI‐focused software packages with intuitive QA.},
 	language = {en},
 	number = {1},
 	urldate = {2024-03-26},
@@ -84,7 +70,7 @@ @article{tournier_mrtrix:_2012
 	shorttitle = {Mrtrix},
 	url = {https://onlinelibrary.wiley.com/doi/10.1002/ima.22005},
 	doi = {10.1002/ima.22005},
-	abstract = {Abstract
+	abstract = {Abstract
             In recent years, diffusion‐weighted magnetic resonance imaging has attracted considerable attention due to its unique potential to delineate the white matter pathways of the brain. However, methodologies currently available and in common use among neuroscientists and clinicians are typically based on the diffusion tensor model, which has comprehensively been shown to be inadequate to characterize diffusion in brain white matter. This is due to the fact that it is only capable of resolving a single fiber orientation per voxel, causing incorrect fiber orientations, and hence pathways, to be estimated through these voxels. Given that the proportion of affected voxels has been recently estimated at 90\%, this is a serious limitation. Furthermore, most implementations use simple “deterministic” streamlines tracking algorithms, which have now been superseded by “probabilistic” approaches. In this study, we present a robust set of tools to perform tractography, using fiber orientations estimated using the validated constrained spherical deconvolution method, coupled with a probabilistic streamlines tracking algorithm. This methodology is shown to provide superior delineations of a number of known white matter tracts, in a manner robust to crossing fiber effects. These tools have been compiled into a software package, called MRtrix, which has been made freely available for use by the scientific community. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 53–66, 2012},
 	language = {en},
 	number = {1},

paper/paper.md

@@ -24,33 +24,20 @@ bibliography: citations.bib
 
 # Summary
 
-Diffusion weighted (DW) neuroimaging is an MR modality that seeks to generate a cohesive mapping of white-matter microstructure in the brain. DWIQC serves as a robust quality assurance tool for DW images as well as a means to facilitate data management and sharing via the XNAT platform. DWIQC utilizes analysis tools developed by FSL [@smith_advances_2004], Prequal [@cai_prequal:_2021], Qsiprep [@cieslak_qsiprep:_2021] and MRtrix [@tournier_mrtrix:_2012] to perform first level preprocessing of DW images and to assess data quality through quantitative metrics. 
+Diffusion weighted neuroimaging is an MR modality that seeks to generate a cohesive mapping of white-matter microstructure in the brain. `DWIQC` serves as a robust quality assurance tool for diffusion weighted images as well as a means to facilitate data management and sharing via the XNAT platform. `DWIQC` utilizes analysis tools developed by FSL [@smith_advances_2004], Prequal [@cai_prequal:_2021], Qsiprep [@cieslak_qsiprep:_2021] and MRtrix [@tournier_mrtrix:_2012] to perform first level preprocessing of diffusion weighted images and to assess data quality through quantitative metrics. `DWIQC` utilizes containerized versions of the aforementioned software to ensure reproducibility of results and portability. `DWIQC` generates an aggregated report of summary metrics and images from the output of analysis software, which can be uploaded to the XNAT data management platform, though upload to XNAT is not necessary.
 
 
 # Statement of need
 
-Diffusion neuroimaging is a burgeoning field with huge potential to deepen our understanding of the brain. While exciting, it also means that acquisition parameters, study designs, and theoretical analysis frameworks vary greatly. DWIQC is an effort to make diffusion imaging analysis and quality assurance accessible to researchers with varying and diverse experimental designs. By 
+Diffusion neuroimaging is a burgeoning field with huge potential to deepen our understanding of the brain. While exciting, it also means that acquisition parameters, study designs, and theoretical analysis frameworks vary greatly. DWIQC is an effort to make diffusion imaging analysis and quality assurance accessible to researchers with diverse experimental designs. By leveraging the respective strengths of various diffusion imaging analysis tools, DWIQC appeals to a broad range of users who employ different data acquisition approaches. Additionally, large, multi-site studies can can use the standardized preprocessing to ensure uniformity in data preprocessing, data quality metrics and downstream analysis.
 
-The purpose of HofstadterTools is to consolidate the fragmented theory and code relevant to the Hofstadter model into one well-documented Python package, which can be used easily by non-specialists as a benchmark or springboard for their own research projects. The Hofstadter model [@Harper55; @Azbel64; @Hofstadter76] is an iconic tight-binding model in physics and famously yields a fractal energy spectrum as a function of flux density, as shown in Figs. \ref{fig:square}, \ref{fig:triangular}, \ref{fig:honeycomb}, and \ref{fig:kagome}. Consequently, it is often treated as an add-on to larger numerical packages, such as WannierTools [@WannierTools], pyqula [@pyqula], and DiagHam [@DiagHam], or simply included as supplementary code together with research articles [@Bodesheim23]. However, the Hofstadter model's generalizability, interdisciplinary appeal, and recent experimental realization, motivates us to create a dedicated package that can provide a detailed analysis of its band structure, in the general case.
+Furthermore, `DWIQC's` integration with the XNAT data management platform facilitates ease of adoption due to XNAT's widespread use at neuroimaging centers. Sites that use `DWIQC` and upload its results to an XNAT instance provide tranparency in analysis practices for collaborating sites. Multi-site studies benefit by seeing diffusion analysis reports in the same format across sites and subjects.
 
-1) **Generalizability.** The Hofstadter model was originally studied in the context of electrons hopping in a periodic potential coupled to a perpendicular magnetic field. However, the model transcends this framework and can be extended in numerous directions. For example, the Peierls phases that arise in the Hamiltonian due to the magnetic field [@Peierls33] can also be generated using artificial gauge fields [@Goldman14] or Floquet modulation [@Eckardt17]. Moreover, the full scope of the Hofstadter model is still being revealed, with papers on its application to hyperbolic lattices [@Stegmaier22], higher-dimensional crystals [@DiColandrea22], and synthesized materials [@Bodesheim23], all published within the last couple of years.
+`DWIQC` benefits researchers by catching problems in data quality in real-time. The robustness `DWIQC's` pipelines allows even minor data quality issues to be caught. By running the diffusion weighted data through large-scale preprocessing pipelines, researchers are privy to data quality problems that would not be caught otherwise. As such, adjustments can be made mid-study to data collection protocols as necessary, rather than waiting until data collection has stopped only to discover data quality issues.
 
-2) **Interdisciplinary appeal.** Owing to its generalizability, interest in the Hofstadter model goes beyond its well-known connection to condensed matter physics and the quantum Hall effect [@Avron03]. In mathematics, for example, the difference relation arising in the solution of the Hofstadter model, known as the Harper equation [@Harper55], is a special case of an "almost Mathieu operator", which is one of the most studied types of ergodic Schrödinger operator [@Simon00; @Avila09]. Moreover, in other branches of physics, the Hofstadter model has growing relevance in a variety of subfields, including: cold atomic gases [@Cooper19], acoustic metamaterials [@Ni19], and photonics [@Zilberberg18].
-
-3) **Recent experimental realization.** Although the Hofstadter model was introduced last century [@Peierls33; @Harper55], it has only been experimentally realized within the last decade. Signatures of the Hofstadter spectrum were first observed in moiré materials [@Dean13] and optical flux lattices [@Aidelsburger13], and they have since been reproduced in several other experimental platforms [@Cooper19; @Ni19; @Zilberberg18; @Roushan17]. Not only does this spur recent theoretical interest, but it also increases the likelihood of experimental groups entering the field, with the need for a self-contained code repository that can be quickly applied to benchmark data and related computations.
-
-A prominent use-case of HofstadterTools is to facilitate the study of a rich landscape of many-body problems. The Hofstadter model is an infinitely-configurable topological flat-band model and hence, is a popular choice among theorists studying strongly-correlated phenomena, such as the fractional quantum Hall effect [@Andrews20; @Andrews21] and superconductivity [@Shaffer21; @Sahay23]. Since there is a relationship between the quantum geometry and topology of single-particle band structures and the stability of exotic strongly-correlated states [@Jackson15; @Andrews23; @Ledwith23; @Lee17; @Tian23; @Wang21], HofstadterTools may be used to guide theorists who are researching quantum many-body systems. More broadly, we hope that HofstadterTools will find many interdisciplinary applications, and we look forward to expanding the package in these directions, with help from the community. 
-
-![\label{fig:square}**Square Lattice** (a) Hofstadter butterfly and (b) Wannier diagram for the Hofstadter model defined with nearest-neighbor hoppings on the square lattice. (a) The energy $E$, and (b) the integrated density of states below the gap $N(E)$, are plotted as a function of flux density $n_\phi=BA_\mathrm{min}/\phi_0=p/499$, where $B$ is the perpendicular field strength, $A_\mathrm{min}$ is the area of a minimal hopping plaquette, $\phi_0$ is the flux quantum, and $p$ is an integer. The $r$-th gap is colored with respect to $t=\sum_{i=0}^r C_i$, where $C_i$ is the Chern number of band $i$. The size of the points in the Wannier diagram is proportional to the size of the gaps. [@DiColandrea22]](butterfly_square_q_499_t_1_col_plane_red-blue_dpi_600_combined-min.png)
-
-![\label{fig:triangular}**Triangular Lattice** (a) Hofstadter butterfly and (b) Wannier diagram for the Hofstadter model defined with nearest-neighbor hoppings on the triangular lattice. [@Avron14]](butterfly_triangular_q_499_t_1_col_plane_jet_period_2_dpi_600_combined-min.png)
-
-![\label{fig:honeycomb}**Honeycomb Lattice** (a) Hofstadter butterfly and (b) Wannier diagram for the Hofstadter model defined with nearest-neighbor hoppings on the honeycomb lattice. [@Agazzi14]](butterfly_honeycomb_q_499_t_1_alpha_1_theta_1_3_col_plane_avron_dpi_1100_combined-min.png)
-
-![\label{fig:kagome}**Kagome Lattice** (a) Hofstadter butterfly and (b) Wannier diagram for the Hofstadter model defined with nearest-neighbor hoppings on the kagome lattice. [@Jing-Min09]](butterfly_kagome_q_499_t_1_alpha_1_theta_1_3_period_8_dpi_600_combined-min.png)
 
 # Acknowledgements
 
-We thank Gunnar Möller, Titus Neupert, Rahul Roy, Alexey Soluyanov, Michael Zaletel, Johannes Mitscherling, Daniel Parker, Stefan Divic, and Mathi Raja, for useful discussions. This project was funded by the Swiss National Science Foundation under Grant No. [P500PT_203168](https://data.snf.ch/grants/grant/203168), and supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Early Career Award No. DE-SC0022716.
+We thank Randy Buckner and Ross Mair for useful discussions during the development of DWIQC. We thank the Center for Brain Science at Harvard and the Dean of Harvard Faculty of Arts and Sciences for the financial support of this project.
 
 # References