Chen X, Dallmeier-Tiessen S, Dasler R, Feger S, Fokianos P, Gonzalez JB, Hirvonsalo H, Kousidis D, Lavasa A, Mele S, Rodriguez DR, Šimko T, Smith T, Trisovic A, Trzcinska A, Tsanaktsidis I, Zimmermann M, Cranmer K, Heinrich L, Watts G, Hildreth M, Lloret Iglesias L, Lassila-Perini K, Neubert S (2019) Open is not enough. Nat Phys 15(2):113–119

Article  CAS  Google Scholar 

Niso G, Botvinik-Nezer R, Appelhoff S, De La Vega A, Esteban O, Etzel JA, Finc K, Ganz M, Gau R, Halchenko YO et al (2022) Open and reproducible neuroimaging: from study inception to publication. Neuroimage 263:119623

Article  PubMed  Google Scholar 

Stikov N, Trzasko JD, Bernstein MA (2019) Reproducibility and the future of MRI research. Magn Reson Med 82(6):1981–1983. https://doi.org/10.1002/mrm.27939

Article  PubMed  Google Scholar 

Bell LC, Shimron E (2023) Sharing data is essential for the future of ai in medical imaging. Radiol Artifi Intell 6(1):230337

Article  Google Scholar 

Baker M (2016) 1,500 scientists lift the lid on reproducibility. Nature 533(7604):452–454. https://doi.org/10.1038/533452a. (Accessed 2024-12-09)

Article  CAS  PubMed  Google Scholar 

Rougier NP. R-words \(\cdot\) Issue #5 \(\cdot\) ReScience/ReScience-article. https://github.com/ReScience/ReScience-article/issues/5. Accessed 9 Dec 2024

Goodman SN, Fanelli D, Ioannidis JPA (2016) What does research reproducibility mean? Sci Transl Med 8(341):341–1234112. https://doi.org/10.1126/scitranslmed.aaf5027. (Accessed 2024-12-09)

Article  Google Scholar 

Sciences E, Affairs PaG, Science E, Information BoRD, Sciences DoEaP, Statistics CoAaT, Analytics BoMS, Studies DoEaL, Board NaRS, Education DoBaSS, Statistics CoN, Behavioral C, Science CoRaRi (2019) Understanding Reproducibility and Replicability. In: Reproducibility and Replicability in Science. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK547546/. Accessed 9 Dec 20249

Claerbout JF, Karrenbach M (1992) Electronic documents give reproducible research a new meaning. In: SEG technical program expanded abstracts 1992, pp 601–604. Society of Exploration Geophysicists. https://doi.org/10.1190/1.1822162. Accessed 9 Dec 2024

Buckheit JB, Donoho DL (1995) WaveLab and reproducible research. In: Bickel P, Diggle P, Fienberg S, Krickeberg K, Olkin I, Wermuth N, Zeger S, Antoniadis A, Oppenheim G (eds) Wavelets and statistics, vol 103. Springer, New York, pp 55–81. https://doi.org/10.1007/978-1-4612-2544-7_5

Chapter  Google Scholar 

Donoho DL, Maleki A, Rahman IU, Shahram M, Stodden V (2009) Reproducible research in computational harmonic analysis. Comput Sci Eng 11(1):8–18. https://doi.org/10.1109/MCSE.2009.15. Conference name: computing in science and engineering. Accessed 9 Dec 2024

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. arXiv:1409.0575 [cs]. https://doi.org/10.48550/arXiv.1409.0575. Accessed 9 Dec 2024

Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, Platen Pv, Ma C, Jernite Y, Plu J, Xu C, Scao TL, Gugger S, Drame M, Lhoest Q, Rush AM (2020) HuggingFace’s transformers: state-of-the-art natural language processing. arXiv:1910.03771 [cs]. https://doi.org/10.48550/arXiv.1910.03771. Accessed 12 Dec 2024

Karakuzu A, Boudreau M, Stikov N (2024) Reproducible research practices in magnetic resonance neuroimaging: a review informed by advanced language models. Magn Reson Med Sci 23(3):252–267. https://doi.org/10.2463/mrms.rev.2023-0174

Article  PubMed  PubMed Central  Google Scholar 

Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS (2011) Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front Neuroinform 5:13. https://doi.org/10.3389/fninf.2011.00013

Article  PubMed  PubMed Central  Google Scholar 

Esteban O, Markiewicz CJ, Blair RW, Moodie CA, Isik AI, Erramuzpe A, Kent JD, Goncalves M, DuPre E, Snyder M, Oya H, Ghosh SS, Wright J, Durnez J, Poldrack RA, Gorgolewski KJ (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16(1):111–116. https://doi.org/10.1038/s41592-018-0235-4. (Accessed 2024-12-12)

Article  CAS  PubMed  Google Scholar 

Cieslak M, Cook PA, He X, Yeh F-C, Dhollander T, Adebimpe A, Aguirre GK, Bassett DS, Betzel RF, Bourque J, Cabral LM, Davatzikos C, Detre JA, Earl E, Elliott MA, Fadnavis S, Fair DA, Foran W, Fotiadis P, Garyfallidis E, Giesbrecht B, Gur RC, Gur RE, Kelz MB, Keshavan A, Larsen BS, Luna B, Mackey AP, Milham MP, Oathes DJ, Perrone A, Pines AR, Roalf DR, Richie-Halford A, Rokem A, Sydnor VJ, Tapera TM, Tooley UA, Vettel JM, Yeatman JD, Grafton ST, Satterthwaite TD (2021) QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nat Methods 18(7):775–778. https://doi.org/10.1038/s41592-021-01185-5. (Accessed 2024-12-12)

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hayashi S, Caron BA, Heinsfeld AS, Vinci-Booher S, McPherson B, Bullock DN, Bertò G, Niso G, Hanekamp S, Levitas D, Ray K, MacKenzie A, Avesani P, Kitchell L, Leong JK, Nascimento-Silva F, Koudoro S, Willis H, Jolly JK, Pisner D, Zuidema TR, Kurzawski JW, Mikellidou K, Bussalb A, Chaumon M, George N, Rorden C, Victory C, Bhatia D, Aydogan DB, Yeh F-CF, Delogu F, Guaje J, Veraart J, Fischer J, Faskowitz J, Fabrega R, Hunt D, McKee S, Brown ST, Heyman S, Iacovella V, Mejia AF, Marinazzo D, Craddock RC, Olivetti E, Hanson JL, Garyfallidis E, Stanzione D, Carson J, Henschel R, Hancock DY, Stewart CA, Schnyer D, Eke DO, Poldrack RA, Bollmann S, Stewart A, Bridge H, Sani I, Freiwald WA, Puce A, Port NL, Pestilli F, (2024) brainlife.io: A decentralized and open-source cloud platform to support neuroscience research. Nat Methods 21(5):809–813. https://doi.org/10.1038/s41592-024-02237-2. (Accessed 2024-12-10)

Chung AW, Seunarine KK, Clark CA (2016) NODDI reproducibility and variability with magnetic field strength: a comparison between 1.5 T and 3 T. Hum Brain Mapp 37(12):4550–4565. https://doi.org/10.1002/hbm.23328

Article  PubMed  PubMed Central  Google Scholar 

Peters DC, Derbyshire JA, McVeigh ER (2003) Centering the projection reconstruction trajectory: reducing gradient delay errors. Magn Reson Med 50(1):1–6. https://doi.org/10.1002/mrm.10501

Article  PubMed  PubMed Central  Google Scholar 

Karakuzu A, Biswas L, Cohen-Adad J, Stikov N (2022) Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. Magn Reson Med 88(3):1212–1228. https://doi.org/10.1002/mrm.29292

Article  PubMed  Google Scholar 

Jie S, Qin X, Ying L, Gengying L (2005) Home-built magnetic resonance imaging system (0.3 T) with a complete digital spectrometer. Rev Sci Instrum 76(10):105101. https://doi.org/10.1063/1.2069707

Article  CAS  Google Scholar 

Boudreau M, Karakuzu A, Cohen-Adad J et al (2024) Repeat it without me: crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge. Magn Reson Med 92(3):1115–1127. https://doi.org/10.1002/mrm.30111

Article  PubMed  Google Scholar 

Stupic KF, Ainslie M, Boss MA, Charles C, Dienstfrey AM, Evelhoch JL, Finn P, Gimbutas Z, Gunter JL, Hill DLG, Jack CR, Jackson EF, Karaulanov T, Keenan KE, Liu G, Martin MN, Prasad PV, Rentz NS, Yuan C, Russek SE (2021) A standard system phantom for magnetic resonance imaging. Magn Reson Med 86(3):1194–1211. https://doi.org/10.1002/mrm.28779

Article  PubMed  PubMed Central  Google Scholar 

Cieślak A, Piórkowski A, Obuchowicz R (2022) Comparison of interpolation methods for MRI images acquired with different matrix sizes. In: Pietka E, Badura P, Kawa J, Wieclawek W (eds) Information technology in biomedicine. Springer, Cham, pp 119–131. https://doi.org/10.1007/978-3-031-09135-3_11

Chapter  Google Scholar 

Hajianfar G, Hosseini SA, Bagherieh S, Oveisi M, Shiri I, Zaidi H (2024) Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: A phantom study. Med Biol Eng Comput 62(8):2319–2332. https://doi.org/10.1007/s11517-024-03071-6

Article  PubMed  PubMed Central  Google Scholar 

Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H (2006) Intensity non-uniformity correction in MRI: existing methods and their validation. Med Image Anal 10(2):234–246. https://doi.org/10.1016/j.media.2005.09.004

Article  PubMed  Google Scholar 

Newitt DC, Tan ET, Wilmes LJ, Chenevert TL, Kornak J, Marinelli L, Hylton N (2015) Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the American College of Radiology imaging network 6698 breast cancer trial. J Magn Reson Imaging JMRI 42(4):908–919. https://doi.org/10.1002/jmri.24883

Article  PubMed  Google Scholar 

Shimron E, Tamir JI, Wang K, Lustig M (2022) Implicit data crimes: machine learning bias arising from misuse of public data. Proc Natl Acad Sci 119(13):2117203119

Article  Google Scholar 

Maier O, Baete SH, Fyrdahl A, Hammernik K, Harrevelt S, Kasper L, Karakuzu A, Loecher M, Patzig F, Tian Y, Wang K, Gallichan D, Uecker M, Knoll F (2021) CG-SENSE revisited: results from the first ISMRM reproducibility challenge. Magn Reson Med 85(4):1821–1839. https://doi.org/10.1002/mrm.28569. (Accessed 2024-12-13)

Article  PubMed  Google Scholar 

Pruessmann KP, Weiger M, Börnert P, Boesiger P (2001) Advances in sensitivity encoding with arbitrary k-space trajectories. Magn Reson Med 46(4):638–651. https://doi.org/10.1002/mrm.1241

Article  CAS  PubMed  Google Scholar 

Inati SJ, Naegele JD, Zwart NR, Roopchansingh V, Lizak MJ, Hansen DC, Liu C-Y, Atkinson D, Kellman P, Kozerke S, Xue H, Campbell-Washburn AE, Sørensen TS, Hansen MS (2017) ISMRM Raw data format: a proposed standard for MRI raw datasets. Magn Reson Med 77(1):411–421. https://doi.org/10.1002/mrm.26089. (Accessed 2024-12-11)

Article  PubMed  Google Scholar 

Bachant P. I failed to reproduce my own results from a decade ago. https://petebachant.me/failed-to-repro/. Accessed 13 Dec 2024

Tamburri DA, Blincoe K, Palomba F, Kazman R (2020) “The Canary in the Coal Mine...’’ A cautionary tale from the decline of SourceForge. Softw Pract Exp 50(10):1930–1951. https://doi.org/10.1002/spe.2874. (Accessed 2024-12-13)

Article  Google Scholar 

Barker M, Chue Hong NP, Katz DS, Lamprecht A-L, Martinez-Ortiz C, Psomopoulos F, Harrow J, Castro LJ, Gruenpeter M, Martinez PA, Honeyman T (2022) Introducing the FAIR principles for research software. Sci Data 9(1):622. https://doi.org/10.1038/s41597-022-01710-x. (Accessed 2024-12-14)

Article  PubMed  PubMed Central