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

Abstract

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.

Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study

“…Low E[ D ∆ 2 ] and FA values throughout healthy breast tissue may indicate that the abundance of elongated structures such as ducts, lobules, and stroma in FGT have larger diameters than the mean displacement of water molecules during the diffusion time [ 55 ] probed by our acquisition gradient waveforms. While theoretical tools are currently being developed to further investigate diffusion time-dependent effects [ 18 , 19 ] in the context of MDD acquisitions [ 27 , 66 ], such investigations go beyond the scope of the present work.…”

“…Although MK has been shown to be superior to ADC or MD for lesion differentiation in the breast [ 15 , 16 , 17 ], it still confounds two sources of tissue heterogeneity, namely the variance of cell densities over the voxel scale and the presence of anisotropic cells, whether these are aligned or not over the voxel scale. Although other approaches have revealed nonparametric cell size distribution, this limitation has not been addressed yet [ 18 , 19 ].…”

Test-retest reproducibility of in vivo oscillating gradient and microscopic anisotropy diffusion MRI in mice at 9.4 Tesla

“…OGSE and µA dMRI have recently been gaining attention in various disease and injury models and their feasibility has been shown in both preclinical and clinical settings. Importantly, OGSE dMRI can provide measures of mean cell size [19,20] and axonal diameter [21,22], while µA dMRI can provide estimates of cell shape [4]. The OGSE ΔMD metric has shown increased sensitivity, compared to MD alone, in the assessment of hypoxia-ischemia [23] and radiation therapy treatment response [24] in rodents, and in various pathologies in humans, including muscle contraction abnormalities [25], high- and low-grade brain tumor differentiation [26], and neonatal hypoxic-ischemic encephalopathy [27].…”

“…OGSE and µA dMRI have recently been gaining attention in various disease and injury models and their feasibility has been shown in both preclinical and clinical settings. Importantly, OGSE dMRI can provide measures of mean cell size [19,20] and axonal diameter [21,22], while µA . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.…”

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“…Most tensor-valued encoding protocols are based on double diffusion encoding (DDE) techniques [16,[20][21][22][23] or a combination of linear tensor encoding (LTE) and spherical tensor encoding (STE) [4,15,17,24]. As DDE sequences are implemented via two consecutive diffusion encoding pulses separated by a mixing time, in some cases they may require longer TEs than standard LTE/STE sequences to achieve equal bvalues [25]. Conventional DTI and DKI utilize only LTE, in which all gradients are along the same axis, so that diffusion is encoded along a single direction at a time.…”