Microstructural parameters from DW‐MRI for tumour characterization and local recurrence prediction in particle therapy of skull‐base chordoma

Morelli, Letizia; Palombo, Marco; Buizza, Giulia; Riva, Giulia; Pella, Andrea; Fontana, Giulia; Imparato, Sara; Iannalfi, Alberto; Orlandi, Ester; Paganelli, Chiara; Baroni, Guido

doi:10.1002/mp.16202

Cited by 3 publications

(2 citation statements)

References 56 publications

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“…Recently, the numerical simulation of dMRI signals within histologically-realistic voxel models is being increasingly used to inform parameter estimation [Nilsson et al, 2010, Nguyen et al, 2014, Fieremans and Lee, 2018, Buizza et al, 2021, Morelli et al, 2023. Simulation-informed approaches increase the realism of signal models, and may thus improve the biological fidelity of dMRI parametric maps [Nedjati-Gilani et al, 2017, Palombo et al, 2019.…”

Section: Introductionmentioning

confidence: 99%

SpinFlowSim: a blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer

Voronova,

Grigoriou,

Bernatowicz

et al. 2024

Preprint

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Diffusion Magnetic Resonance Imaging (dMRI) sensitises the MRI signal to spin motion. This includes Brownian diffusion, but also flow across intricate networks of capillaries. This effect, the intra-voxel incoherent motion (IVIM), enables microvasculature characterisation with dMRI, through metrics such as the vascular signal fractionfVor Apparent Diffusion Coefficient (ADC)D*. The IVIM metrics, while sensitive to perfusion, are in general protocol-dependent, and their interpretation can change depending on the flow regime spins experience during the dMRI measurements (e.g., diffusive vs ballistic), which is in general not known — facts that hamper their clinical utility. Innovative vascular dMRI models are needed to enable thein vivocalculation of biologically meaningful markers of capillary flow. These could have relevant applications in cancer, for instance assessing responses to anti-angiogenic therapies targeting tumor vessels. This paper tackles this need by introducingSpinFlowSim, an open-source simulator of dMRI signals arising from blood flow within pipe networks. SpinFlowSim, tailored for the laminar flow patterns in capillaries, enables the synthesis of highly-realistic microvascular dMRI signals, given networks reconstructed from histology. We showcase the simulator by generating synthetic signals for 15 networks, reconstructed from liver biopsies, and containing cancerous and non-cancerous tissue. Signals exhibit com-plex, non-mono-exponential behaviours, pointing towards the co-existence of different flow regimes within the same network, and diffusion time dependence. We also demonstrate the potential utility of SpinFlowSim by devising a strategy for microvascular property mapping informed by the synthetic signals, focussing on the quantification of blood velocity distribution moments, and of anapparent network branchingindex. These were estimatedin silicoandin vivo, in healthy volunteers and in 13 cancer patients, scanned at 1.5T. In conclusion, realistic flow simulations, as those enabled by SpinFlowSim, may play a key role in the development of the next-generation of dMRI methods for microvascular mapping, with immediate applications in oncology.

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Section: Introductionmentioning

confidence: 99%

SpinFlowSim: a blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer

Voronova,

Grigoriou,

Bernatowicz

et al. 2024

Preprint

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“…increasing the biological specificity of parameter estimation [Nilsson et al, 2010, Palombo et al, 2016, Palombo et al, 2019, Buizza et al, 2021, Morelli et al, 2023. In particular, Monte Carlo (MC) diffusion simulations within 3D meshes derived from histology have enabled the characterisation of fine, sub-cellular microstructural details, such as axonal beading/undulation [Lee et al, 2020b, Lee et al, 2024, or neural process complexity [Palombo et al, 2016, Palombo et al, 2019.…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo simulation framework for histology-informed diffusion MRI cancer characterisation and microstructural parameter estimation

Grigoriou,

Macarro,

Palombo

et al. 2024

Preprint

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Computer simulations within substrates that mimic the complexity of biological tissues are key to the development of biophysical diffusion Magnetic Resonance Imaging (dMRI) models. Realistic simulations have enabled, for example, the non-invasive estimation of fine neuronal sub-structures, which is playing an increasingly key role in neurology and neuro-science. However, biologically-realistic, simulation-informed dMRI techniques are also needed in other applications, as for example in oncological imaging of body tumours. This article aims to fill this gap by presenting a Monte Carlo (MC) framework tailored for histology-informed simulations in body imaging applications. The framework, which combines free software with custom-written routines, is demonstrated on substrates reconstructed from hematoxylin-eosin (HE) stains of human liver biopsies, including non-cancerous liver and primary/metastatic liver cancer tissues. The article has four main contributions. Firstly, it provides practical guidelines on how to conduct realistic MC diffusion simulations informed by HE histology. Secondly, it reports reference values on cell size (CS), cell density and on other cellular properties in non-cancerous and cancerous liver — information not easily found in the literature, yet essential to inform the design of innovative dMRI techniques. Thirdly, it presents a detailed characterisation of synthetic signals generated for clinically feasible dMRI protocols, shedding light onto patterns of intra-/extra-cellular (IC/EC) water diffusion in liver. Finally, it illustrates the utility of the framework, by devising a strategy where synthetic signals inform the estimation of unexplored microstructural properties, as the EC intrinsic diffusivity and CS distribution skewness. The strategy is demonstrated on actual dMRI scans, acquired onex vivomouse tissue and in cancer patientsin vivo.

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Advanced Diffusion‐Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology

Fokkinga,

Hernandez‐Tamames,

Ianus

et al. 2023

Magnetic Resonance Imaging

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Diffusion‐weighted magnetic resonance imaging (DW‐MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW‐MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state‐of‐the‐art of DW‐MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW‐MRI method is essential. In this article, we review the current landscape of DW‐MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b‐tensor diffusion encoding or joint diffusion‐relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter‐/intra‐scanner robustness, and on assessing histological validity in broader contexts.Level of EvidenceNATechnical EfficacyStage 2

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Microstructural parameters from DW‐MRI for tumour characterization and local recurrence prediction in particle therapy of skull‐base chordoma

Cited by 3 publications

References 56 publications

SpinFlowSim: a blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer

SpinFlowSim: a blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer

A Monte Carlo simulation framework for histology-informed diffusion MRI cancer characterisation and microstructural parameter estimation

Advanced Diffusion‐Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology

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