MRI-cytometry: Mapping nonparametric cell size distributions using diffusion MRI

Junzhong Xu, Xiaoyu Jiang, Sean P. Devan, Lori R. Arlinghaus, Eliot T. McKinley, Jingping Xie, Zhongliang Zu, Qing Wang, A. Bapsi Chakravarthy, Yong Wang, John C. Gore

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Purpose: This report introduces and validates a new diffusion MRI-based method, termed MRI-cytometry, which can noninvasively map intravoxel, nonparametric cell size distributions in tissues. Methods: MRI was used to acquire diffusion MRI signals with a range of diffusion times and gradient factors, and a model was fit to these data to derive estimates of cell size distributions. We implemented a 2-step fitting method to avoid noise-induced artificial peaks and provide reliable estimates of tumor cell size distributions. Computer simulations in silico, experimental measurements on cultured cells in vitro, and animal xenografts in vivo were used to validate the accuracy and precision of the method. Tumors in 7 patients with breast cancer were also imaged and analyzed using this MRI-cytometry approach on a clinical 3 Tesla MRI scanner. Results: Simulations and experimental results confirm that MRI-cytometry can reliably map intravoxel, nonparametric cell size distributions and has the potential to discriminate smaller and larger cells. The application in breast cancer patients demonstrates the feasibility of direct translation of MRI-cytometry to clinical applications. Conclusion: The proposed MRI-cytometry method can characterize nonparametric cell size distributions in human tumors, which potentially provides a practical imaging approach to derive specific histopathological information on biological tissues.

Original languageEnglish
Pages (from-to)748-761
Number of pages14
JournalMagnetic resonance in medicine
Issue number2
StatePublished - Feb 1 2021


  • MRI
  • cell size
  • cytometry
  • diffusion
  • distribution
  • oscillating gradient
  • volume fraction


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