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Numerous simulation techniques have been developed, however, few of them have been directly compared, thus limiting the assessment of the accuracy and interchangeability of these methods as well as the accuracy of the quantitative techniques derived from them. In this work, we compared the accuracy and computational demands of eight previously published simulation approaches that adopt different geometries (ranging from infinite cylinders to synthetic vascular anatomical networks (VANs)), field offset calculations (analytical and Fourier-based), and water diffusion implementations (Monte Carlo and convolution-based), all of which are available in an open-source Python toolkit, BOLDs\u03c9imsuite. The reference simulation approach for comparison used three-dimensional infinite cylinders, analytical field offsets, and Monte Carlo diffusion. When compared with the reference approach, most of the simulations, including two- and three-dimensional geometries, were in excellent agreement when assuming the intravascular signal contribution was small. Two commonly employed simulation approaches were notably biased; both used two-dimensional geometries with overly simplified vasculature or field offset calculations. In general, the simulated intravascular signal was the least consistent across approaches, thus potentially resulting in larger errors when the intravascular signal contribution is large. Lastly, the VAN results were in good agreement with the reference but they diverged slightly, yet systematically, from each other at smaller radii (\u22723 \u03bcm), primarily driven by intravascular signal differences. We conclude, therefore, that the reference approach is an attractive option for exploratory simulations in the many cases where anatomical and haemodynamic realism is not needed, balancing ease of implementation, accessibility, versatility, computational efficiency, accuracy of results, and interpretability. These findings help pave the way for a broader adoption of forward modelling of the BOLD signal and more reliable interpretations of biophysical simulations aiming to develop quantitative models of the BOLD signal.<\/jats:p>","DOI":"10.1162\/imag.a.1247","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T20:01:56Z","timestamp":1777579316000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating BOLD functional MRI biophysical simulation approaches: Impact of vascular geometry, magnetic field calculations, and water diffusion models"],"prefix":"10.1162","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7631-1049","authenticated-orcid":false,"given":"Avery J.L.","family":"Berman","sequence":"first","affiliation":[{"name":"Department of Physics, Carleton University, Ottawa, ON, Canada"},{"name":"University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Chauss\u00e9","sequence":"additional","affiliation":[{"name":"Rotman Research Institute, Baycrest Health Sciences, North York, ON, Canada"},{"name":"Department of Chemistry, University of Waterloo, Waterloo, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grant","family":"Hartung","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States"},{"name":"Department of Radiology, Harvard Medical School, Boston, MA, United States"},{"name":"Institute for Mechanics, Computational Mechanics Group, Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan R.","family":"Polimeni","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States"},{"name":"Department of Radiology, Harvard Medical School, Boston, MA, United States"},{"name":"Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Jean","family":"Chen","sequence":"additional","affiliation":[{"name":"Rotman Research Institute, Baycrest Health Sciences, North York, ON, Canada"},{"name":"Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2026,5,26]]},"reference":[{"key":"2026052615563047200_IMAG.a.1247-b1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neuroimage.2017.09.015","article-title":"The impact of vessel size, orientation and intravascular contribution on the neurovascular fingerprint of BOLD bSSFP fMRI","volume":"163","author":"Baez-Yanez","year":"2017","journal-title":"NeuroImage"},{"issue":"2\u20133","key":"2026052615563047200_IMAG.a.1247-b2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1002\/ima.1850060203","article-title":"Effects of biophysical and physiological-parameters on brain activation-induced R(2) asterisk and R(2) changes\u2014Simulations using a deterministic diffusion-model","volume":"6","author":"Bandettini","year":"1995","journal-title":"International Journal of Imaging Systems and Technology"},{"key":"2026052615563047200_IMAG.a.1247-b3","doi-asserted-by":"crossref","unstructured":"Bandettini, P. 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