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joss.06269/10.21105.joss.06269.crossref.xml | 138 ++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 joss.06269/10.21105.joss.06269.crossref.xml diff --git a/joss.06269/10.21105.joss.06269.crossref.xml b/joss.06269/10.21105.joss.06269.crossref.xml new file mode 100644 index 0000000000..c08fcdaa6d --- /dev/null +++ b/joss.06269/10.21105.joss.06269.crossref.xml @@ -0,0 +1,138 @@ + + + + 20240321T104922-b7f5524b8af2964c3b14f226d50177989cf20d13 + 20240321104922 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 03 + 2024 + + + 9 + + 95 + + + + MRdataset : A unified and user-friendly interface to +medical imaging datasets + + + + Harsh + Sinha + https://orcid.org/0000-0002-8498-8559 + + + Pradeep Reddy + Raamana + https://orcid.org/0000-0003-4662-0558 + + + + 03 + 21 + 2024 + + + 6269 + + + 10.21105/joss.06269 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10805061 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6269 + + + + 10.21105/joss.06269 + https://joss.theoj.org/papers/10.21105/joss.06269 + + + https://joss.theoj.org/papers/10.21105/joss.06269.pdf + + + + + + Solving the pervasive problem of protocol +non-compliance in MRI using an open-source tool mrQA + Sinha + bioRxiv + 10.1101/2023.07.17.548591 + 2023 + Sinha, H., & Raamana, P. R. +(2023). Solving the pervasive problem of protocol non-compliance in MRI +using an open-source tool mrQA. bioRxiv. +https://doi.org/10.1101/2023.07.17.548591 + + + Nipy/nibabel: 5.1.0 + Brett + 10.5281/zenodo.7795644 + 2023 + Brett, M., Markiewicz, C. J., Hanke, +M., Côté, M.-A., Cipollini, B., McCarthy, P., Jarecka, D., Cheng, C. P., +Halchenko, Y. O., Cottaar, M., Larson, E., Ghosh, S., Wassermann, D., +Gerhard, S., Lee, G. R., Baratz, Z., Wang, H.-T., Kastman, E., +Kaczmarzyk, J., … freec84. (2023). Nipy/nibabel: 5.1.0 (Version 5.1.0). +Zenodo. https://doi.org/10.5281/zenodo.7795644 + + + PyBIDS: Python tools for BIDS +datasets + Yarkoni + Journal of Open Source +Software + 40 + 4 + 10.21105/joss.01294 + 2019 + Yarkoni, T., Markiewicz, C. J., Vega, +A. de la, Gorgolewski, K. J., Salo, T., Halchenko, Y. O., McNamara, Q., +DeStasio, K., Poline, J.-B., Petrov, D., Hayot-Sasson, V., Nielson, D. +M., Carlin, J., Kiar, G., Whitaker, K., DuPre, E., Wagner, A., Tirrell, +L. S., Jas, M., … Blair, R. (2019). PyBIDS: Python tools for BIDS +datasets. Journal of Open Source Software, 4(40), 1294. +https://doi.org/10.21105/joss.01294 + + + + + + From c323bbbc76842675c0e7a9086976fbeb9dfa57d1 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Thu, 21 Mar 2024 10:50:03 +0000 Subject: [PATCH 3/7] Creating 10.21105.joss.06269.jats --- joss.06269/10.21105.joss.06269.jats | 395 ++++++++++++++++++++++++++++ 1 file changed, 395 insertions(+) create mode 100644 joss.06269/10.21105.joss.06269.jats diff --git a/joss.06269/10.21105.joss.06269.jats b/joss.06269/10.21105.joss.06269.jats new file mode 100644 index 0000000000..f238ea0de3 --- /dev/null +++ b/joss.06269/10.21105.joss.06269.jats @@ -0,0 +1,395 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6269 +10.21105/joss.06269 + +MRdataset : A unified and user-friendly interface to +medical imaging datasets + + + +https://orcid.org/0000-0002-8498-8559 + +Sinha +Harsh + + + + +https://orcid.org/0000-0003-4662-0558 + +Raamana +Pradeep Reddy + + + + + + + +Intelligent Systems Program, School of Computing and +Information, University of Pittsburgh, United States of +America + + + + +Department of Biomedical Informatics, University of +Pittsburgh, United States of America + + + + +Department of Radiology, University of Pittsburgh, United +States of America + + + + +22 +11 +2023 + +9 +95 +6269 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +neuroimaging + + + + + + Summary +

Large-scale medical imaging datasets (e.g., MR, PET, CT) play a + pivotal role in healthcare research. However, the reliability of their + statistical results is contingent on the quality of the acquired data. + These datasets are often acquired at multiple sites using diverse + scanners and formats. Divergent practices at individual sites and + scanners lead to differences in acquisition and data organization. + Manually reconciling these variations is not only time-consuming and + labor-intensive but also error-prone. To address this need, we + developed MRdataset, a Python package that serves as a seamless + interface to access diverse medical imaging dataset formats. MRdataset + eliminates the burden of extensive re-formatting by accounting for + differences in data organization, scanner specifications and various + imaging formats.

+
+ + Statement of need + +

MRdataset uses modular classes to read various dataset + formats to produce a common data structure. Its hierarchical + representation of subjects, sessions, sequences and runs enables a + consistent API for accessing elements such as subjects/sequences in + the dataset. +

+ +
+

Medical imaging data is shared in various file formats (such as + DICOM, NIfTI, Analyze) and organized differently for distinct study + designs, and data capturing platforms (such as XNAT). The lack of + standardized interfaces adds unnecessary complexity to the otherwise + straightforward task of retrieving imaging sequences. This leads to + significant inefficiencies and delays, hindering the seamless flow + between data acquisition and analysis. MRdataset includes separate + modules for handling each format (e.g., DICOM, NifTi) and the user can + simply specify the format while reading the dataset. MRdataset + provides a canonical data structure to offer researchers with a + consistent API, regardless of the underlying file format or dataset + structure on disk, thereby acting as a unified interface across + various dataset formats + ([fig:hierarchy]).

+

Prior works + (Brett + et al., 2023; + Yarkoni + et al., 2019) have acknowledged the complexity of working with + large scale, heterogeneous datasets. However, there has been limited + emphasis on providing a unified interface for accessing various + subjects and sequences without reformatting, that goes beyond one file + format and dataset structure. It is important to note that in the + normal course of a typical research project that involves many open + datasets, importing, reformatting and validation of datasets typically + occurs years after the data acquisition process has been completed. + Relying on a specialized tool designed exclusively for a specific + format is not practical. A unified interface helps decouple the + analyses from the constraints and idiosyncrasies of various formats. + Besides being user-friendly for accessing multiple formats, MRdataset + enables direct access to sessions/subjects to facilitate + sequence-level and subject-level analyses. MRdataset captures the + broader perspective of reading the dataset as a whole and provides + intuitive methods for horizontal traversal (across + + + N + subjects, and + + S + sites for a given sequence) and vertical traversal (across multiple + sequences for a given subject within a session) as shown in + [fig:schematic]. + [fig:loops] shows + an example of a script accessing subjects and sequences from a DICOM + dataset.

+ +

Illustration of horizontal and vertical traversal for + accessing subjects/sequences of a medical imaging + dataset

+ +
+

In addition to the differences in study design and imaging formats, + imaging scans also exhibit differences in MR physics parameters due to + scanner vendors (such as Siemens, GE, and Philips) and + hardware/software version updates. MRdataset accommodates such + variations through custom classes for each acquisition parameter (via + the protocol library we developed). These classes encapsulate + contextual information, including their physical units, value range + and their level of criticality to subsequent analyses. Utilizing + specific classes for each acquisition parameter provides a rich, + native and unambiguous representation of these parameters. They would + also allow easy extensibility to accommodate distinct study designs + and requirements. For example, we capture multiple echo times and flip + angles in an array-like fashion although they are scattered in + different DICOM slices. Subsequently, they are stored in a manner + tailored to align with the needs of researchers.

+

Imaging datasets are often large (N>10,000), and parsing DICOM + slice headers is slow, given each imaging session may have about 400 + DICOM slices each. In order to efficiently process such large numbers + and high I/O activity, we support partitioning for easy parallel + processing e.g., on a HPC cluster and later merging them. Subsets can + be processed individually using parallel processing and then can be + merged together. A dataset may be divided based on subject groups, + time of acquisition, acquisition protocol, etc. By dividing the data + into partitions, researchers can choose to work with specific subsets + or merge only the relevant subsets for their analyses. For example, + MRdataset captures scanner/site metadata. Researchers can analyze + individual subsets from different sites to understand if any + variations in image quality are site/scanner-specific, aiding in the + identification of potential sources of confounding factors.

+ +

Examples showing how MRdataset can be used to access + subjects, and sequences for horizontal and vertical + traversal.

+ +
+

Healthcare research involves diverse equipment and evolving + community standards that utilize distinct formats and acquisition + protocols. Therefore, we would have to continue dealing with diverse + formats and scanners. Hence, a well-designed interface with rich + native representations for different parameters and formats is + essential to simplify the traversal and manipulation of complex + medical imaging datasets, allowing researchers to focus on scientific + questions as opposed to details deep on the disk. Its versatility has + already been demonstrated in facilitating a large study on over 20 + open neuroimaging datasets with different formats for automatic + protocol compliance assessment + (Sinha + & Raamana, 2023).

+
+ + Acknowledgments +

We acknowledge contributions from Tanupat Boonchalermvichien for + their help with parsing some parts of the DICOM format in the initial + stages.

+
+ + + + + + + SinhaHarsh + RaamanaPradeep Reddy + + Solving the pervasive problem of protocol non-compliance in MRI using an open-source tool mrQA + bioRxiv + Cold Spring Harbor Laboratory + 2023 + 10.1101/2023.07.17.548591 + + + + + + BrettMatthew + MarkiewiczChristopher J. + HankeMichael + CôtéMarc-Alexandre + CipolliniBen + McCarthyPaul + JareckaDorota + ChengChristopher P. + HalchenkoYaroslav O. + CottaarMichiel + LarsonEric + GhoshSatrajit + WassermannDemian + GerhardStephan + LeeGregory R. + BaratzZvi + WangHao-Ting + KastmanErik + KaczmarzykJakub + GuidottiRoberto + DanielJonathan + DuekOr + RokemAriel + MadisonCindee + Papadopoulos OrfanosDimitri + SólonAnibal + MoloneyBrendan + MorencyFélix C. + GoncalvesMathias + MarkelloRoss + RiddellCameron + BurnsChristopher + MillmanJarrod + GramfortAlexandre + LeppäkangasJaakko + BoschJasper J. F. van den + VincentRobert D. + BraunHenry + SubramaniamKrish + VanAndrew + GorgolewskiKrzysztof J. + RaamanaPradeep Reddy + KlugJulian + NicholsB. Nolan + BakerEric M. + HayashiSoichi + PinsardBasile + HaselgroveChristian + HymersMark + EstebanOscar + KoudoroSerge + Pérez-GarcíaFernando + DockèsJérôme + OosterhofNikolaas N. + AmirbekianBago + ChristianHorea + Nimmo-SmithIan + NguyenLy + ReddigariSamir + St-JeanSamuel + PanfilovEgor + GaryfallidisEleftherios + VaroquauxGael + LegarretaJon Haitz + HahnKevin S. + WallerLea + HindsOliver P. + FauberBennet + PerezFabian + RobertsJacob + PolineJean-Baptiste + StuttersJon + JordanKesshi + CieslakMatthew + MorenoMiguel Estevan + HrnčiarTomáš + HaenelValentin + SchwartzYannick + DarwinBenjamin C + ThirionBertrand + GauthierCarl + SoloveyIgor + GonzalezIvan + PalasubramaniamJath + LecherJustin + LeinweberKatrin + RaktivanKonstantinos + CalábkováMarkéta + FischerPeter + GervaisPhilippe + GaddeSyam + BallingerThomas + RoosThomas + ReddamVenkateswara Reddy + freec84 + + Nipy/nibabel: 5.1.0 + Zenodo + 202304 + https://doi.org/10.5281/zenodo.7795644 + 10.5281/zenodo.7795644 + + + + + + YarkoniTal + MarkiewiczChristopher J. + VegaAlejandro de la + GorgolewskiKrzysztof J. + SaloTaylor + HalchenkoYaroslav O. + McNamaraQuinten + DeStasioKrista + PolineJean-Baptiste + PetrovDmitry + Hayot-SassonValérie + NielsonDylan M. + CarlinJohan + KiarGregory + WhitakerKirstie + DuPreElizabeth + WagnerAdina + TirrellLee S. + JasMainak + HankeMichael + PoldrackRussell A. + EstebanOscar + AppelhoffStefan + HoldgrafChris + StadenIsla + ThirionBertrand + KleinschmidtDave F. + LeeJohn A. + CastelloMatteo Visconti Oleggio di + NotterMichael P. + BlairRoss + + PyBIDS: Python tools for BIDS datasets + Journal of Open Source Software + The Open Journal + 2019 + 4 + 40 + https://doi.org/10.21105/joss.01294 + 10.21105/joss.01294 + 1294 + + + + + +
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