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Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

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Published on: December 9, 2010

Histology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework.

Athanasios Grigoriou^1,2, Carlos Macarro^1,2, Marco Palombo^3,4

¹Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.

Communications Biology

|November 26, 2025

Summary

Histology-informed diffusion MRI simulations (Histo-μSim) enhance body cancer imaging by creating virtual tissues. This approach yields more accurate microstructural biomarkers for precision oncology.

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Published on: October 17, 2018

Area of Science:

Biomedical Imaging
Computational Biology
Oncology

Background:

Diffusion Magnetic Resonance Imaging (dMRI) simulations traditionally focus on brain imaging.
There is a need for novel non-invasive biomarkers in body cancer imaging.
Microscopic tissue complexity is key for developing advanced dMRI biomarkers.

Purpose of the Study:

To introduce Histo-μSim, a Monte Carlo simulation framework for histology-informed dMRI in body cancer imaging.
To generate synthetic dMRI signals from virtual cancer environments linked to tissue properties.
To enable data-driven estimation of microstructural properties like diffusivity and cell size.

Main Methods:

Reconstruction of virtual cancer environments from human liver biopsy stains (hematoxylin-eosin).
Generation of synthetic dMRI signal dictionaries coupled with tissue properties.
Comparison of Histo-μSim metrics with analytical multi-compartment models in silico, ex vivo, and in vivo.

Main Results:

Histo-μSim is feasible for clinical settings.
The framework provides metrics that more accurately reflect histology compared to analytical models.
Histo-μSim successfully estimated properties like extracellular diffusivity, cell size, and membrane permeability.

Conclusions:

Histo-μSim offers histologically-meaningful tissue descriptors for dMRI.
This approach can increase the specificity of dMRI for cancer detection.
Histo-μSim has the potential to significantly contribute to precision oncology.