Modeling Diffusion in Cancer and Body
Francesco Grussu1
1Radiomics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain

Synopsis

Diffusion MRI (dMRI) sensitises the MRI signal to the underlying patterns of water diffusion, enabling the inference of salient characteristics of tissue microstructure (e.g., density and size of cells) from multiple signal measurements. Promising dMRI finds an increasing number of applications in several anatomical regions and diseases. This talk will provide an overview of popular approaches for dMRI signal modelling in oncological body imaging. Examples of techniques that will be covered are: multi-compartment models for prostate, liver and breast MRI (e.g., VERDICT, IMPULSED, POMACE); b-tensor encoding in prostate and kidney; apparent exchange rate measurement in the breast.

Introduction

Diffusion Magnetic Resonance Imaging (dMRI) relies on magnetic field gradients to sensitise MRI signals to the patterns of water diffusion within each image voxel (Novikov, 2021). In biological tissues, water diffusion is influenced by the characteristics of the microstructural environments within which diffusion takes place, as for example density and size of cells (Alexander et al., 2019). dMRI modelling techniques aim to infer salient characteristics of such microenvironments from multiple dMRI signal measurements, with applications in several anatomical regions and diseases, including cancer. Popular dMRI model-based approaches rely on multi-compartment modelling of conventional single diffusion encoding (Panagiotaki et al., 2015). Recently, techniques based on more advanced diffusion encodings are also gaining momentum, e.g., oscillating gradient imaging (Jiang et al., 2017), tensor-valued encoding (Nilsson et al., 2021), double diffusion encoding (Duchêne et al., 2020) and filter exchange imaging (Lasič et al., 2016)).

Structure of the talk

This talk will provide an overview of common dMRI modelling approaches relevant in oncology, with a focus on body imaging (i.e., outside the central nervous system). Model design choices will be discussed in the context of the known histological characteristics of tissues where such models find application.

Examples of dMRI techniques that will be covered are listed below.

  • Methods for prostate imaging, such as: multi-compartment Vascular, Extracellular, and Restricted DIffusion for Cytometry in Tumours (VERDICT) (Bailey et al., 2018; Panagiotaki et al., 2015) imaging; joint diffusion-relaxation models (Chatterjee et al., 2018; Lemberskiy et al., 2018); extensions (Jerome et al., 2016) of the Intra-Voxel Incoherent Motion (IVIM) method (Le Bihan et al., 1986); restriction spectrum imaging (Brunsing et al., 2017); b-tensor encoding (Langbein et al., 2021; Nilsson et al., 2021).
  • Breast imaging based on Multiple Tissue Compartment Visualization (Tan et al., 2021); Pulsed and Oscillating gradient MRI for Assessment of Cell Size and Extracellular space (POMACE) (Xu et al., 2021); Apparent Exchange Rate (AXR) measurement (Lasič et al., 2016).
  • Techniques for liver imaging, such as relaxation-IVIM methods (Liu et al., 2020) or Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion (IMPULSED) temporal diffusion spectroscopy (Jiang et al., 2020).
  • Whole-body diffusion MRI for bone metastasis detection (Perez-Lopez et al., 2016).
  • Kidney dMRI based on spectral diffusion analysis (Stabinska et al., 2021) and b-tensor encoding (Nery et al., 2019).

Acknowledgements

FG receives funding from the postdoctoral fellowships programme Beatriu de Pinós (2020 BP 00117), funded by the Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR), Secretary of Universities and Research (Government of Catalonia, Spain).

References

Alexander, D.C., Dyrby, T.B., Nilsson, M., and Zhang, H. (2019). Imaging brain microstructure with diffusion MRI: practicality and applications. NMR Biomed. 32, e3841.

Bailey, C., Collins, D.J., Tunariu, N., Orton, M.R., Morgan, V.A., Feiweier, T., Hawkes, D.J., Leach, M.O., Alexander, D.C., and Panagiotaki, E. (2018). Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings. Front. Oncol. 8.

Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., and Laval-Jeantet, M. (1986). MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161, 401–407.

Brunsing, R.L., Schenker-Ahmed, N.M., White, N.S., Parsons, J.K., Kane, C., Kuperman, J., Bartsch, H., Kader, A.K., Rakow-Penner, R., Seibert, T.M., et al. (2017). Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI. J. Magn. Reson. Imaging 45, 323–336.

Chatterjee, A., Bourne, R.M., Wang, S., Devaraj, A., Gallan, A.J., Antic, T., Karczmar, G.S., and Oto, A. (2018). Diagnosis of Prostate Cancer with Noninvasive Estimation of Prostate Tissue Composition by Using Hybrid Multidimensional MR Imaging: A Feasibility Study. Radiology 287, 864–873.

Duchêne, G., Abarca‐Quinones, J., Leclercq, I., Duprez, T., and Peeters, F. (2020). Insights into tissue microstructure using a double diffusion encoding sequence on a clinical scanner: Validation and application to experimental tumor models. Magn. Reson. Med. 83, 1263–1276.

Jerome, N.P., D’Arcy, J.A., Feiweier, T., Koh, D.-M., Leach, M.O., Collins, D.J., and Orton, M.R. (2016). Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging. Phys. Med. Biol. 61, N667–N680.

Jiang, X., Li, H., Xie, J., McKinley, E.T., Zhao, P., Gore, J.C., and Xu, J. (2017). In vivo imaging of cancer cell size and cellularity using temporal diffusion spectroscopy. Magn. Reson. Med. 78, 156–164.

Jiang, X., Xu, J., and Gore, J.C. (2020). Mapping hepatocyte size in vivo using temporal diffusion spectroscopy MRI. Magn. Reson. Med. 84, 2671–2683.

Langbein, B.J., Szczepankiewicz, F., Westin, C.-F., Bay, C., Maier, S.E., Kibel, A.S., Tempany, C.M., and Fennessy, F.M. (2021). A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer. Invest. Radiol. 56, 845–853.

Lasič, S., Oredsson, S., Partridge, S.C., Saal, L.H., Topgaard, D., Nilsson, M., and Bryskhe, K. (2016). Apparent exchange rate for breast cancer characterization. NMR Biomed. 29, 631–639.

Lemberskiy, G., Fieremans, E., Veraart, J., Deng, F.-M., Rosenkrantz, A.B., and Novikov, D.S. (2018). Characterization of Prostate Microstructure Using Water Diffusion and NMR Relaxation. Front. Phys. 6: 91.

Liu, Y., Wang, X., Cui, Y., Jiang, Y., Yu, L., Liu, M., Zhang, W., Shi, K., Zhang, J., Zhang, C., et al. (2020). Comparative Study of Monoexponential, Intravoxel Incoherent Motion, Kurtosis, and IVIM-Kurtosis Models for the Diagnosis and Aggressiveness Assessment of Prostate Cancer. Front. Oncol. 10: 1763.

Nery, F., Szczepankiewicz, F., Kerkelä, L., Hall, M.G., Kaden, E., Gordon, I., Thomas, D.L., and Clark, C.A. (2019). In vivo demonstration of microscopic anisotropy in the human kidney using multidimensional diffusion MRI. Magn. Reson. Med. 82, 2160–2168.

Nilsson, M., Eklund, G., Szczepankiewicz, F., Skorpil, M., Bryskhe, K., Westin, C., Lindh, C., Blomqvist, L., and Jäderling, F. (2021). Mapping prostatic microscopic anisotropy using linear and spherical b‐tensor encoding: A preliminary study. Magn. Reson. Med. 86, 2025–2033.

Novikov, D.S. (2021). The present and the future of microstructure MRI: From a paradigm shift to normal science. J. Neurosci. Methods 351, 108947.

Panagiotaki, E., Chan, R.W., Dikaios, N., Ahmed, H.U., O’Callaghan, J., Freeman, A., Atkinson, D., Punwani, S., Hawkes, D.J., and Alexander, D.C. (2015). Microstructural Characterization of Normal and Malignant Human Prostate Tissue With Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours Magnetic Resonance Imaging. Invest. Radiol. 50, 218–227.

Perez-Lopez, R., Lorente, D., Blackledge, M.D., Collins, D.J., Mateo, J., Bianchini, D., Omlin, A., Zivi, A., Leach, M.O., de Bono, J.S., et al. (2016). Volume of Bone Metastasis Assessed with Whole-Body Diffusion-weighted Imaging Is Associated with Overall Survival in Metastatic Castration-resistant Prostate Cancer. Radiology 280, 151–160.

Stabinska, J., Ljimani, A., Zöllner, H.J., Wilken, E., Benkert, T., Limberg, J., Esposito, I., Antoch, G., and Wittsack, H. (2021). Spectral diffusion analysis of kidney intravoxel incoherent motion MRI in healthy volunteers and patients with renal pathologies. Magn. Reson. Med. 85, 3085–3095.

Tan, E.T., Wilmes, L.J., Joe, B.N., Onishi, N., Arasu, V.A., Hylton, N.M., Marinelli, L., and Newitt, D.C. (2021). Denoising and Multiple Tissue Compartment Visualization of Multi‐b‐Valued Breast Diffusion MRI. J. Magn. Reson. Imaging 53, 271–282.

Xu, J., Jiang, X., Devan, S.P., Arlinghaus, L.R., McKinley, E.T., Xie, J., Zu, Z., Wang, Q., Chakravarthy, A.B., Wang, Y., et al. (2021). MRI‐cytometry: Mapping nonparametric cell size distributions using diffusion MRI. Magn. Reson. Med. 85, 748–761.

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)