Eleftheria Panagiotaki

Compartment models of the diffusion MR signal in brain white matter: A taxonomy and comparison

This paper aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a taxonomy of multi-compartment models of white matter from combinations of simple models for the intra- and the extra-axonal spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of imaging parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from two fixed rat brains, which allows us to fit, study and compare the different models. The study examines a total of 47 analytic models, including several well-used models from the literature, which we place within the taxonomy. The results show that models that incorporate intra-axonal restriction, such as ball and stick or CHARMED, generally explain the data better than those that do not, such as the DT or the biexponential models. However, three-compartment models which account for restriction parallel to the axons and incorporate pore size explain the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-axonal space with a cylindrical intra-axonal component of single radius and a third spherical compartment of non-zero radius. We also measure the stability of the non-zero radius intra-axonal models and find that single radius intra-axonal models are more stable than gamma distributed radii models with similar fitting performance.

Noninvasive Quantification of Solid Tumor Microstructure Using VERDICT MRI

There is a need for biomarkers that are useful for noninvasive imaging of tumor pathophysiology and drug efficacy. Through its use of endogenous water, diffusion-weighted MRI (DW-MRI) can be used to probe local tissue architecture and structure. However, most DW-MRI studies of cancer tissues have relied on simplistic mathematical models, such as apparent diffusion coefficient (ADC) or intravoxel incoherent motion (IVIM) models, which produce equivocal results on the relation of the model parameter estimate with the underlying tissue microstructure. Here, we present a novel technique called VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) to quantify and map histologic features of tumors in vivo. VERDICT couples DW-MRI to a mathematical model of tumor tissue to access features such as cell size, vascular volume fraction, intra- and extracellular volume fractions, and pseudo-diffusivity associated with blood flow. To illustrate VERDICT, we used two tumor xenograft models of colorectal cancer with different cellular and vascular phenotypes. Our experiments visualized known differences in the tissue microstructure of each model and the significant decrease in cell volume resulting from administration of the cytotoxic drug gemcitabine, reflecting the apoptotic volume decrease. In contrast, the standard ADC and IVIM models failed to detect either of these differences. Our results illustrate the superior features of VERDICT for cancer imaging, establishing it as a noninvasive method to monitor and stratify treatment responses.

Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging.

ObjectiveThe aim of this study was to demonstrate the feasibility of the recently introduced Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours (VERDICT) framework for imaging prostate cancer with diffusion-weighted magnetic resonance imaging (DW-MRI) within a clinical setting. Materials and MethodsThe VERDICT framework is a noninvasive microstructure imaging technique that combines an in-depth diffusion MRI acquisition with a mathematical model to estimate and map microstructural tissue parameters such as cell size and density and vascular perfusion. In total, 8 patients underwent 3-T MRI using 9 different b values (100–3000 s/mm2). All patients were imaged before undergoing biopsy. Experiments with VERDICT analyzed DW-MRI data from patients with histologically confirmed prostate cancer in areas of cancerous and benign peripheral zone tissue. For comparison, we also fitted commonly used diffusion models such as the apparent diffusion coefficient (ADC), the intravoxel incoherent motion (IVIM), and the kurtosis model. We also investigated correlations of ADC and kurtosis with VERDICT parameters to gain some biophysical insight into the various parameter values. ResultsEight patients had prostate cancer in the peripheral zone, with Gleason score 3 + 3 (n = 1), 3 + 4 (n = 6), and 4 + 3 (n = 1). The VERDICT model identified a significant increase in the intracellular and vascular volume fraction estimates in cancerous compared with benign peripheral zone, as well as a significant decrease in the volume of the extracellular-extravascular space (EES) (P = 0.05). This is in agreement with manual segmentation of the biopsies for prostate tissue component analysis, which found proliferation of epithelium, loss of surrounding stroma, and an increase in vasculature. The standard ADC and kurtosis parameters were also significantly different (P = 0.05) between tissue types. There was no significant difference in any of the IVIM parameters (P = 0.11 to 0.29). The VERDICT parametric maps from voxel-by-voxel fitting clearly differentiated cancer from benign regions. Kurtosis and ADC parameters correlated most strongly with VERDICT’s intracellular volume fraction but also moderately with the EES and vascular fractions. ConclusionsThe VERDICT model distinguished tumor from benign areas, while revealing differences in microstructure descriptors such as cellular, vascular, and EES fractions. The parameters of ADC and kurtosis models also discriminated between cancer and benign regions. However, VERDICT provides more specific information that disentangles the various microstructural features underlying the changes in ADC and kurtosis. These results highlight the clinical potential of the VERDICT framework and motivate the construction of a shorter, clinically viable imaging protocol to enable larger trials leading to widespread translation of the method.

A ranking of diffusion MRI compartment models with in vivo human brain data.

Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter.

Information Theoretic Ranking of Four Models of Diffusion Attenuation in Fresh and Fixed Prostate Tissue Ex Vivo

To compare the theoretical information content of four popular models of diffusion‐weighted signal attenuation.

Two-Compartment Models of the Diffusion MR Signal in Brain White Matter

This study aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a hierarchy of two-compartment models of white matter from combinations of simple models for the intra and extracellular spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from a fixed rat brain, which allows us to fit, study and compare the different models. The results show that models which incorporate pore size describe the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-cellular space with a cylindrical intra-cellular component.

Limitations and Prospects for Diffusion-Weighted MRI of the Prostate.

Diffusion-weighted imaging (DWI) is the most effective component of the modern multi-parametric magnetic resonance imaging (mpMRI) scan for prostate pathology. DWI provides the strongest prediction of cancer volume, and the apparent diffusion coefficient (ADC) correlates moderately with Gleason grade. Notwithstanding the demonstrated cancer assessment value of DWI, the standard measurement and signal analysis methods are based on a model of water diffusion dynamics that is well known to be invalid in human tissue. This review describes the biophysical limitations of the DWI component of the current standard mpMRI protocol and the potential for significantly improved cancer assessment performance based on more sophisticated measurement and signal modeling techniques.

Viable and fixed white matter: diffusion magnetic resonance comparisons and contrasts at physiological temperature.

Fixed samples have been used extensively in diffusion MRI (dMRI) studies. However, fixation causes significant structural changes in tissue. The purpose of this study was to evaluate fixed white matter as a surrogate for viable white matter during development and validation of dMRI methods.

High-fidelity meshes from tissue samples for diffusion MRI simulations

This paper presents a method for constructing detailed geometric models of tissue microstructure for synthesizing realistic diffusion MRI data. We construct three-dimensional mesh models from confocal microscopy image stacks using the marching cubes algorithm. Random-walk simulations within the resulting meshes provide synthetic diffusion MRI measurements. Experiments optimise simulation parameters and complexity of the meshes to achieve accuracy and reproducibility while minimizing computation time. Finally we assess the quality of the synthesized data from the mesh models by comparison with scanner data as well as synthetic data from simple geometric models and simplified meshes that vary only in two dimensions. The results support the extra complexity of the three-dimensional mesh compared to simpler models although sensitivity to the mesh resolution is quite robust.

Information-based ranking of 10 compartment models of diffusion-weighted signal attenuation in fixed prostate tissue

This study compares the theoretical information content of single‐ and multi‐compartment models of diffusion‐weighted signal attenuation in prostate tissue. Diffusion‐weighted imaging (DWI) was performed at 9.4 T with multiple diffusion times and an extended range of b values in four whole formalin‐fixed prostates. Ten models, including different combinations of isotropic, anisotropic and restricted components, were tested. Models were ranked using the Akaike information criterion. In all four prostates, two‐component models, comprising an anisotropic Gaussian component and an isotropic restricted component, ranked highest in the majority of voxels. Single‐component models, whether isotropic (apparent diffusion coefficient, ADC) or anisotropic (diffusion tensor imaging, DTI), consistently ranked lower than multi‐component models. Model ranking trends were independent of voxel size and maximum b value in the range tested (1.6–16 mm3 and 3000–10 000 s/mm2). This study characterizes the two major water components previously identified by biexponential models and shows that models incorporating both anisotropic and restricted components provide more information‐rich descriptions of DWI signals in prostate tissue than single‐ or multi‐component anisotropic models and models that do not account for restricted diffusion. Copyright