Abstract
This work presents preliminary results on the development, characterisation, and use of a novel physical phantom designed as a simple mimic of tumour cellular structure, for diffusion-weighted magnetic resonance imaging (DW-MRI) applications. The phantom consists of a collection of roughly spherical, micron-sized core–shell polymer ‘cells’, providing a system whose ground truth microstructural properties can be determined and compared with those obtained from modelling the DW-MRI signal. A two-compartment analytic model combining restricted diffusion inside a sphere with hindered extracellular diffusion was initially investigated through Monte Carlo diffusion simulations, allowing a comparison between analytic and simulated signals. The model was then fitted to DW-MRI data acquired from the phantom over a range of gradient strengths and diffusion times, yielding estimates of ‘cell’ size, intracellular volume fraction and the free diffusion coefficient. An initial assessment of the accuracy and precision of these estimates is provided, using independent scanning electron microscope measurements and bootstrap-style simulations. Such phantoms may be useful for testing microstructural models relevant to the characterisation of tumour tissue.
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References
Roberts, T.P.L., Rowley, H.A.: Diffusion weighted magnetic resonance imaging in stroke. Eur. J. Radiol. 45, 185–194 (2003)
Padhani, A.R., Liu, G., Koh, D.M., Chenevert, T.L., Thoeny, H.C., Takahara, T., Dzik-Jurasz, A., Ross, B.D., Van Cauteren, M., Collins, D., Hammoud, D.A., Rustin, G.J.S., Taouli, B., Choyke, P.L.: Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11, 102–125 (2009)
Stanisz, G.J., Szafer, A., Wright, G.A., Henkelman, R.M.: An analytical model of restricted diffusion in bovine optic nerve. Magn. Reson. Med. 37, 103–111 (1997)
Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y., Basser, P.J.: AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn. Reson. Med. 59, 1347–1354 (2008)
Barazany, D., Basser, P.J., Assaf, Y.: In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. Brain 132, 1210–1220 (2009)
Alexander, D.C., Hubbard, P.L., Hall, M.G., Moore, E.A., Ptito, M., Parker, G.J.M., Dyrby, T.B.: Orientationally invariant indices of axon diameter and density from diffusion MRI. NeuroImage 52, 1374–1389 (2010)
Panagiotaki, E., Walker-Samuel, S., Siow, B., Johnson, S.P., Rajkumar, V., Pedley, R.B., Lythgoe, M.F., Alexander, D.C.: Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res. 74, 1902–1912 (2014)
Hall, M.G., Alexander, D.C.: Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI. IEEE Trans. Med. Imaging 28, 1354–1364 (2009)
Yeh, C.H., Schmitt, B., Le Bihan, D., Li-Schlittgen, J.R., Lin, C.P., Poupon, C.: Diffusion microscopist simulator: a general Monte Carlo simulation system for diffusion magnetic resonance imaging. PLoS ONE 8, e76626 (2013)
Fieremans, E., De Deene, Y., Delputte, S., Özdemir, M.S., Achten, E., Lemahieu, I.: The design of anisotropic diffusion phantoms for the validation of diffusion weighted magnetic resonance imaging. Phys. Med. Biol. 53, 5405–5419 (2008)
Hubbard, P.L., Zhou, F.L., Eichhorn, S.J., Parker, G.J.M.: Biomimetic phantom for the validation of diffusion magnetic resonance imaging. Magn. Reson. Med. 73, 299–305 (2015)
Siow, B., Drobnjak, I., Chatterjee, A., Lythgoe, M.F., Alexander, D.C.: Estimation of pore size in a microstructure phantom using the optimised gradient waveform diffusion weighted NMR sequence. J. Magn. Reson. 214, 51–60 (2012)
Dietrich, O., Hubert, A., Heiland, S.: Imaging cell size and permeability in biological tissue using the diffusion-time dependence of the apparent diffusion coefficient. Phys. Med. Biol. 59, 3081–3096 (2014)
Zhou, F.L., Hubbard, P.L., Eichhorn, S.J., Parker, G.J.M.: Jet deposition in near-field electrospinning of patterned polycaprolactone and sugar-polycaprolactone core-shell fibres. Polymer 52, 3603–3610 (2011)
Zhou, F.L., Hubbard, P.L., Eichhorn, S.J., Parker, G.J.M.: Coaxially electrospun axon-mimicking fibers for diffusion magnetic resonance imaging. ACS Appl. Mater. Interfaces 4, 6311–6316 (2012)
Malyarenko, D., Galbán, C.J., Londy, F.J., Meyer, C.R., Johnson, T.D., Rehemtulla, A., Ross, B.D., Chenevert, T.L.: Multi-system repeatability and reproducibility of apparent diffusion coefficient measurement using an ice-water phantom. J. Magn. Reson. Imaging 37, 1238–1246 (2013)
Zhang, L., Huang, J., Si, T., Xu, R.X.: Coaxial electrospray of microparticles and nanoparticles for biomedical applications. Expert Rev. Med. Devices 9, 595–612 (2012)
Murday, J.S., Cotts, R.M.: Self-diffusion coefficient of liquid lithium. J. Chem. Phys. 48, 4938–4945 (1968)
Neuman, C.H.: Spin echo of spins diffusing in a bounded medium. J. Chem. Phys. 60, 4508–4511 (1974)
Price, W.S., Barzykin, A.V., Hayamizu, K., Tachiya, M.: A model for diffusive transport through a spherical interface probed by pulsed-field gradient NMR. Biophys. J. 74, 2259–2271 (1998)
Bland, J.M., Altman, D.G.: Measuring agreement in method comparison studies. Stat. Methods Med. Res. 8, 135–160 (1999)
Walker-Samuel, S., Orton, M., McPhail, L.D., Robinson, S.P.: Robust estimation of the apparent diffusion coefficient (ADC) in heterogeneous solid tumors. Magn. Reson. Med. 62, 420–429 (2009)
Kristoffersen, A.: Optimal estimation of the diffusion coefficient from non-averaged and averaged noisy magnitude data. J. Magn. Reson. 187, 293–305 (2007)
Yao, J., Lim, L.K., Xie, J., Hua, J., Wang, C.H.: Characterization of electrospraying process for polymeric particle fabrication. J. Aerosol Sci. 39, 987–1002 (2008)
Acknowledgments
The authors thank Matt Hall for assistance with the simulations, and acknowledge the assistance given by IT Services and the use of the Computational Shared Facility at The University of Manchester. This work was supported by the MRC and AstraZeneca, and used facilities funded by the BBSRC. This work was supported by CRUK [C8742/A18097]. This is a contribution from the Cancer Imaging Centre in Cambridge and Manchester, which is funded by the EPSRC and Cancer Research UK.
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McHugh, D.J., Zhou, F., Hubbard Cristinacce, P.L., Naish, J.H., Parker, G.J.M. (2015). Ground Truth for Diffusion MRI in Cancer: A Model-Based Investigation of a Novel Tissue-Mimetic Material. In: Ourselin, S., Alexander, D., Westin, CF., Cardoso, M. (eds) Information Processing in Medical Imaging. IPMI 2015. Lecture Notes in Computer Science(), vol 9123. Springer, Cham. https://doi.org/10.1007/978-3-319-19992-4_14
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