Tissue microstructure features derived from anomalous diffusion measurements in magnetic resonance imaging

Characterization of breast lesions using multi-parametric diffusion MRI and machine learning

Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.

Three-dimensional micro-structurally informed in silico myocardium-Towards virtual imaging trials in cardiac diffusion weighted MRI

In silico tissue models (viz. numerical phantoms) provide a mechanism for evaluating quantitative models of magnetic resonance imaging. This includes the validation and sensitivity analysis of imaging biomarkers and tissue microstructure parameters. This study proposes a novel method to generate a realistic numerical phantom of myocardial microstructure. The proposed method extends previous studies by accounting for the variability of the cardiomyocyte shape, water exchange between the cardiomyocytes (intercalated discs), disorder class of myocardial microstructure, and four sheetlet orientations. In the first stage of the method, cardiomyocytes and sheetlets are generated by considering the shape variability and intercalated discs in cardiomyocyte-cardiomyocyte connections. Sheetlets are then aggregated and oriented in the directions of interest. The morphometric study demonstrates no significant difference (p>0.01) between the distribution of volume, length, and primary and secondary axes of the numerical and real (literature) cardiomyocyte data. Moreover, structural correlation analysis validates that the in-silico tissue is in the same class of disorderliness as the real tissue. Additionally, the absolute angle differences between the simulated helical angle (HA) and input HA (reference value) of the cardiomyocytes (4.3°±3.1°) demonstrate a good agreement with the absolute angle difference between the measured HA using experimental cardiac diffusion tensor imaging (cDTI) and histology (reference value) reported by (Holmes et al., 2000) (3.7°±6.4°) and (Scollan et al. 1998) (4.9°±14.6°). Furthermore, the angular distance between eigenvectors and sheetlet angles of the input and simulated cDTI is much smaller than those between measured angles using structural tensor imaging (as a gold standard) and experimental cDTI. Combined with the qualitative results, these results confirm that the proposed method can generate richer numerical phantoms for the myocardium than previous studies.

Generalisation of continuous time random walk to anomalous diffusion MRI models with an age-related evaluation of human corpus callosum

Diffusion MRI measures of the human brain provide key insight into microstructural variations across individuals and into the impact of central nervous system diseases and disorders. One approach to extract information from diffusion signals has been to use biologically relevant analytical models to link millimetre scale diffusion MRI measures with microscale influences. The other approach has been to represent diffusion as an anomalous transport process and infer microstructural information from the different anomalous diffusion equation parameters. In this study, we investigated how parameters of various anomalous diffusion models vary with age in the human brain white matter, particularly focusing on the corpus callosum. We first unified several established anomalous diffusion models (the super-diffusion, sub-diffusion, quasi-diffusion and fractional Bloch-Torrey models) under the continuous time random walk modelling framework. This unification allows a consistent parameter fitting strategy to be applied from which meaningful model parameter comparisons can be made. We then provided a novel way to derive the diffusional kurtosis imaging (DKI) model, which is shown to be a degree two approximation of the sub-diffusion model. This link between the DKI and sub-diffusion models led to a new robust technique for generating maps of kurtosis and diffusivity using the sub-diffusion parameters β_SUB and D_SUB. Superior tissue contrast is achieved in kurtosis maps based on the sub-diffusion model. 7T diffusion weighted MRI data for 65 healthy participants in the age range 19-78 years was used in this study. Results revealed that anomalous diffusion model parameters α and β have shown consistent positive correlation with age in the corpus callosum, indicating α and β are sensitive to tissue microstructural changes in ageing.

Review of fractional epidemic models

The global impact of corona virus (COVID-19) has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 influenza A(H1N1) pandemic. In this paper, we have focused on reviewing the results of epidemiological modelling especially the fractional epidemic model and summarized different types of fractional epidemic models including fractional Susceptible-Infective-Recovered (SIR), Susceptible-Exposed-Infective-Recovered (SEIR), Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) models and so on. Furthermore, we propose a general fractional SEIAR model in the case of single-term and multi-term fractional differential equations. A feasible and reliable parameter estimation method based on modified hybrid Nelder-Mead simplex search and particle swarm optimisation is also presented to fit the real data using fractional SEIAR model. The effective methods to solve the fractional epidemic models we introduced construct a simple and effective analytical technique that can be easily extended and applied to other fractional models, and can help guide the concerned bodies in preventing or controlling, even predicting the infectious disease outbreaks.

Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges

Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.

Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design

Axon diameter mapping using diffusion MRI in the living human brain has attracted growing interests with the increasing availability of high gradient strength MRI systems. A systematic assessment of the consistency of axon diameter estimates within and between individuals is needed to gain a comprehensive understanding of how such methods extend to quantifying differences in axon diameter index between groups and facilitate the design of neurobiological studies using such measures. We examined the scan-rescan repeatability of axon diameter index estimation based on the spherical mean technique (SMT) approach using diffusion MRI data acquired with gradient strengths up to 300 mT/m on a 3T Connectom system in 7 healthy volunteers. We performed statistical power analyses using data acquired with the same protocol in a larger cohort consisting of 15 healthy adults to investigate the implications for study design. Results revealed a high degree of repeatability in voxel-wise restricted volume fraction estimates and tract-wise estimates of axon diameter index derived from high-gradient diffusion MRI data. On the region of interest (ROI) level, across white matter tracts in the whole brain, the Pearson’s correlation coefficient of the axon diameter index estimated between scan and rescan experiments was r = 0.72 with an absolute deviation of 0.18 μm. For an anticipated 10% effect size in studies of axon diameter index, most white matter regions required a sample size of less than 15 people to observe a measurable difference between groups using an ROI-based approach. To facilitate the use of high-gradient strength diffusion MRI data for neuroscientific studies of axonal microstructure, the comprehensive multi-gradient strength, multi-diffusion time data used in this work will be made publicly available, in support of open science and increasing the accessibility of such data to the greater scientific community.

In vivo assessment of Lauren classification for gastric adenocarcinoma using diffusion MRI with a fractional order calculus model

OBJECTIVES

To evaluate the performance of a fractional order calculus (FROC) diffusion model for imaging-based assessment of Lauren classification in gastric adenocarcinoma.

METHODS

In this study, 43 patients (15 females, 28 males) with gastric adenocarcinoma underwent MRI at 1.5 T. According to pathology-based Lauren classification, 10 patients had diffuse-type, 20 had intestinal-type, and 13 had mixed-type lesions. The diffuse and mixed types were combined as diffuse-and-mixed type to be differentiated from the intestinal type using diffusion MRI. Diffusion-weighted images were acquired by using eleven b-values (0-2000 s/mm²). Three FROC model parameters comprising diffusion coefficient D, intravoxel diffusion heterogeneity β, and a microstructural quantity μ, together with a conventional apparent diffusion coefficient (ADC), were estimated. The mean parameter values in the tumour were computed by using a percentile histogram analysis. Individual or linear combinations of the mean parameters in the tumour were used to differentiate the diffuse-and-mixed type from the intestinal type using descriptive statistics and receiver operating characteristic (ROC) analyses.

RESULTS

Significant differences were observed between diffuse-and-mixed-type and intestinal-type lesions in D (0.99 ± 0.20 μm²/ms vs. 1.11 ± 0.23 μm²/ms; p = 0.036), β (0.37 ± 0.08 vs. 0.43 ± 0.11; p = 0.043), μ (7.92 ± 2.79 μm vs. 9.87 ± 1.52 μm; p = 0.038), and ADC (0.81 ± 0.34 μm²/ms vs. 0.96 ± 0.19 μm²/ms; p = 0.033). Among the individual parameters, μ produced the largest area under the ROC curve (0.739). The combinations of (D, β, μ) and (β and μ) produced the best overall performance with a sensitivity of 0.739, specificity of 0.750, accuracy of 0.744, and area under the curve of 0.793 (95% confidence interval: 0.657-0.929).

CONCLUSION

Diffusion MRI with the FROC model holds promise for non-invasive assessment of Lauren classification for gastric adenocarcinoma.

KEY POINTS

• High b-value diffusion MRI with a FROC model that is sensitive to tissue microstructures can differentiate the diffuse-and-mixed type from intestinal type of gastric adenocarcinoma. • The combination of FROC parameters produced the best result for distinguishing the diffuse-and-mixed type from the intestinal type with an area under the receiver operating characteristic curve of 0.793. • The FROC model parameters, individually or conjointly, hold promise for repeated, non-invasive evaluations of gastric adenocarcinoma at various time points throughout disease progression or regression to complement conventional Lauren classification.

Detection of axonal degeneration in a mouse model of Huntington's disease: comparison between diffusion tensor imaging and anomalous diffusion metrics

OBJECTIVE

The goal of this work is to study the changes in white matter integrity in R6/2, a well-established animal model of Huntington's disease (HD) that are captured by ex vivo diffusion imaging (DTI) using a high field MRI (17.6 T).

MATERIALS AND METHODS

DTI and continuous time random walk (CTRW) models were used to fit changes in the diffusion-weighted signal intensity in the corpus callosum of controls and in R6/2 mice.

RESULTS

A significant 13% decrease in fractional anisotropy, a 7% increase in axial diffusion, and a 33% increase in radial diffusion were observed between R6/2 and control mice. No change was observed in the CTRW beta parameter, but a significant decrease in the alpha parameter (- 21%) was measured. Histological analysis of the corpus callosum showed a decrease in axonal organization, myelin alterations, and astrogliosis. Electron microscopy studies demonstrated ultrastructural changes in degenerating axons, such as an increase in tortuosity in the R6/2 mice.

CONCLUSIONS

DTI and CTRW diffusion models display quantitative changes associated with the microstructural alterations observed in the corpus callosum of the R6/2 mice. The observed increase in the diffusivity and decrease in the alpha CTRW parameter providing support for the use of these diffusion models for non-invasive detection of white matter alterations in HD.

A fractional motion diffusion model for a twice-refocused spin-echo pulse sequence

The purpose of this study was to develop an analytical expression for a fractional motion (FM) diffusion model to characterize diffusion-induced signal attenuation in a twice-refocused spin-echo (TRSE) sequence that is resilient to eddy currents, and to demonstrate its applicability to human brain imaging in vivo. Based on the FM theory, which provides a unified statistical description for Langevin motions, the diffusion-weighted (DW) MR signal was measured with a TRSE sequence that balances the concomitant gradients. The analytical expression was fitted to a set of DW images acquired with 14 b-values (0-4000 s/mm² ) from a total of 10 healthy human subjects at 3 T, yielding three FM parameter maps based on anomalous diffusion coefficient D_{φ, ψ} , diffusion increment variance φ, and diffusion correlation ψ, respectively. These parameters were used to characterize different brain regions in gray matter (GM), white matter (WM), and cerebrospinal fluid. The analytical expression for the TRSE-based FM model accurately described diffusion signal attenuation in healthy brain tissues at high b-values. TRSE's robustness against eddy currents was illustrated by comparing results from an expression for a conventional Stejskal-Tanner sequence. The TRSE-based FM model also produced consistent GM-WM contrast (p < 0.01) across all brain regions studied, whereas the consistency was not observed with the Stejskal-Tanner-based FM model. This new analytical expression is expected to enable further investigations to probe tissue structures by exploiting anomalous diffusion properties without being hindered by eddy-current perturbations at high b-values.

Assessment of microstructural signal compartments across the corpus callosum using multi-echo gradient recalled echo at 7 T

Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders in which white matter is implicated, as it has been linked to demyelination, re-myelination, and axonal damage in clinical conditions. Ultra-high field magnetic resonance imaging data obtained using a multi-echo gradient echo sequence has been shown to contain information on myelin, axonal and extracellular compartments in white matter. In this study, we aimed to assess the sensitivity of a three-compartment model to estimate the variation of corresponding compartment parameters (water fraction, relaxation time and frequency shift) of the corpus callosum sub-regions, which are known to have different tissue structure. Additionally, we computed the g-ratio using myelin and axonal water fractions and performed a voxel-by-voxel analysis in the corpus callosum. Based on data acquired for ten participants, we show that the myelin compartment water fraction and T₂^∗ is consistent across the corpus callosum sub-regions, whilst myelin frequency shift varies. The results show that the variation in water fraction, T₂^∗ and frequency shift for the myelin signal compartment across the corpus callosum is smaller than for the axonal and extracellular signal compartments. The computed g-ratio was comparable to previously published studies in the corpus callosum. Our study suggests that a multi-echo GRE approach in vivo combined with a complex three-compartment model is sensitive to microstructural parameter variations across the human corpus callosum.