Computational Modelling of B₀ Field Strength Effects on Stroke MRI Image Quality: 0.5T vs 1.5T

Authors

  • Dauda Biodun Amuda
    Department of Pure and Applied Physics, LAUTECH, Ogbomoso, Nigeria.
  • Oluwole Ogundoyin
  • Olanike Funke Amuda
  • Samson Damilola
  • Ismail Oyeleke Olarinoye
  • Adesola Emmanuel Adepoju
  • Godwin Inalegwu Ogbole
  • Victor Olufemi Oyedepo
  • Temitope Olugbenga Bello
  • Oluwasegun Daniel Oyinloye
  • Omowonuola Ogundoyin

Keywords:

Magnetic Resonance Imaging, Ischemic Stroke, Magnetic Field Strength, Computational Simulation, Image Quality

Abstract

Ischemic stroke outcomes depend on rapid, accurate neuroimaging, and magnetic resonance imaging (MRI) performance is governed by static magnetic field strength. Low-field 0.5 Tesla systems form a substantial share of installed MRI infrastructure in Nigeria and similar resource-limited settings, yet their comparative performance for stroke imaging has not previously been examined using a physics-based computational framework. This study modeled the effects of field strength on MRI image quality in ischemic stroke imaging, comparing 0.5 Tesla and 1.5 Tesla. A digital brain phantom (256 by 256 pixels) was constructed with six tissue compartments, including cerebrospinal fluid and a simulated ischemic stroke lesion in the left middle cerebral artery territory. Magnetic field distributions were computed using a susceptibility-perturbation finite element approximation in Python, followed by spin-echo signal generation, k-space synthesis, and image reconstruction, evaluated using signal-to-noise ratio, contrast-to-noise ratio, normalized mutual information, and image sharpness across thirty noise realizations per field strength. At 1.5 Tesla, signal-to-noise ratio increased by 72.41% and contrast-to-noise ratio by 73.07% relative to 0.5 Tesla, while image sharpness decreased by 32.38% relative to 0.5 Tesla; both metrics changed monotonically and consistently between the two field strengths examined. The simulated stroke lesion satisfied the Rose detectability criterion at both field strengths, with a substantially larger safety margin at 1.5 Tesla (+169.8%) than at 0.5 Tesla (+55.9%). These findings indicate that 1.5 Tesla is the superior field strength for stroke MRI across most evaluated criteria, and the two-point interpolation relationships derived between 0.5T and 1.5T provide a preliminary computational reference for protocol comparison between these two field strengths in resource-limited radiology practice.

Dimensions

Al-Salahat, A. (2025). Advances in neuroimaging for acute ischemic stroke. Journal of the Neurological Sciences, 460, 122981.

Barrett, H. H., & Myers, K. J. (2004). Foundations of image science. John Wiley & Sons.

Bhat, S. S., Fernandes, T. T., Poojar, P., da Silva Ferreira, M., Rao, P. C., & Hanumantharaju, M. C. (2021). Low-field MRI of stroke: Challenges and opportunities. Journal of Magnetic Resonance Imaging, 54(2), 372–390. https://doi.org/10.1002/jmri.27324

Campbell-Washburn, A. E., Ramasawmy, R., Restivo, M. C., Bhattacharya, I., Basar, B., & Herzka, D. A. (2019). Opportunities in interventional and diagnostic imaging by using high-performance low-field-strength MRI. Radiology, 293(2), 384–393. https://doi.org/10.1148/radiol.2019190452

Deistung, A., Schweser, F., & Reichenbach, J. R. (2017). Overview of quantitative susceptibility mapping. NMR in Biomedicine, 30(4), e3569. https://doi.org/10.1002/nbm.3569

Edelstein, W. A., Glover, G. H., Hardy, C. J., & Redington, R. W. (1986). The intrinsic signal-to-noise ratio in NMR imaging. Magnetic Resonance in Medicine, 3(4), 604–618. https://doi.org/10.1002/mrm.1910030413

Gagliardo, C., Marrale, M., Agnello, F., Cutaia, G., Lo Casto, A., & Midiri, M. (2025). Potential of stroke imaging using a new prototype of low-field MRI: A prospective direct 0.55T/1.5T scanner comparison. Journal of Clinical Medicine, 14(1), 100. https://doi.org/10.3390/jcm14010100

Gracien, R. M., Jurcoane, A., Wagner, M., Reitz, S. C., Mayer, C., & Volz, S. (2016). Multimodal quantitative MRI assessment of cortical damage in relapsing-remitting multiple sclerosis. Journal of Magnetic Resonance Imaging, 44(6), 1600–1607. https://doi.org/10.1002/jmri.25297

Gudbjartsson, H., & Patz, S. (1995). The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine, 34(6), 910–914. https://doi.org/10.1002/mrm.1910340618

Kravchenko, M., Kravchenko, D., Rennert, J., Sieren, M. M., Juergens, K. U., & Niehoff, J. H. (2025). Enhanced detection of acute ischemic stroke with low-field MRI. Stroke and Vascular Interventional Neurology, 6, e002110. https://doi.org/10.1161/SVIN.125.002110

Mahendran, A. K., Saoji, A. P., & Thakur, D. G. (2022). Impact of magnetic field inhomogeneity on the quality of magnetic resonance images and compensation techniques: A review. Research Reports in Medical Imaging, 15, 21–37. https://doi.org/10.2147/RMI.S369491

Pohmann, R., Speck, O., & Scheffler, K. (2016). Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 Tesla using current receive coil arrays. Magnetic Resonance in Medicine, 75(2), 801–809. https://doi.org/10.1002/mrm.25677

Rusche, M., Niehoff, J. H., Haberland, U., Hanning, U., Langenbach, M. C., & Spiesecke, U. (2022). Potential of stroke imaging using a new prototype of low-field MRI: A prospective direct 0.55T/1.5T scanner comparison. Journal of Clinical Medicine, 11(10), 2798. https://doi.org/10.3390/jcm11102798

Salaudeen, M. A., Musa, A. A., & Bello, A. (2024). Epidemiology and burden of stroke in sub-Saharan Africa: Implications for neuroimaging access. African Journal of Neurological Sciences, 43(2), 15–28.

Siemonsen, S., Mouridsen, K., Holst, B., Heit, J. J., Zaharchuk, G., & Fiehler, J. (2017). Quantitative T2 values predict time from symptom onset in acute stroke patients. Stroke, 40(5), 1612–1616. https://doi.org/10.1161/STROKEAHA.108.542548

Stockmann, J. P., & Wald, L. L. (2018). In vivo B0 field shimming methods for MRI at 7T. NeuroImage, 168, 71–87. https://doi.org/10.1016/j.neuroimage.2017.06.013

Wansapura, J. P., Holland, S. K., Dunn, R. S., & Ball, W. S., Jr. (1999). NMR relaxation times in the human brain at 3.0 Tesla. Journal of Magnetic Resonance Imaging, 9(4), 531–538.

Yuen, M. M., Prabhat, A. M., Mazurek, M. H., Chavva, I. R., Crawford, A., & Cahn, B. A. (2022). Portable, low-field magnetic resonance imaging enables highly accessible and dynamic bedside evaluation of ischemic stroke. Science Advances, 8(15), eabm3952. https://doi.org/10.1126/sciadv.abm3952

Published

2026-09-14

How to Cite

Amuda, D. B., Ogundoyin, O., Amuda, O. F., Damilola, S., Olarinoye, I. O., Adepoju, A. E., Ogbole, G. I., Oyedepo, V. O., Bello, T. O., Oyinloye, O. D., & Ogundoyin, O. (2026). Computational Modelling of B₀ Field Strength Effects on Stroke MRI Image Quality: 0.5T vs 1.5T. Nigerian Journal of Physics, 35(S), 234-241. https://doi.org/10.62292/njp.v35(s).2026.727

Issue

Vol. 35 No. S (2026): Nigerian Journal of Physics - Vol. 35(S)

Section

Articles
Copyright & Licensing

Copyright (c) 2026 Dauda Biodun Amuda, Oluwole Ogundoyin, Olanike Funke Amuda, Samson Damilola, Ismail Oyeleke Olarinoye, Adesola Emmanuel Adepoju, Godwin Inalegwu Ogbole, Victor Olufemi Oyedepo, Temitope Olugbenga Bello, Oluwasegun Daniel Oyinloye, Omowonuola Ogundoyin

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

Amuda, D. B., Ogundoyin, O., Amuda, O. F., Damilola, S., Olarinoye, I. O., Adepoju, A. E., Ogbole, G. I., Oyedepo, V. O., Bello, T. O., Oyinloye, O. D., & Ogundoyin, O. (2026). Computational Modelling of B₀ Field Strength Effects on Stroke MRI Image Quality: 0.5T vs 1.5T. Nigerian Journal of Physics, 35(S), 234-241. https://doi.org/10.62292/njp.v35(s).2026.727

Most read articles by the same author(s)