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PURPOSE - To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.
METHODS AND MATERIALS - Post-radiation therapy response is modeled using a cell death model (M), a reduced proliferation rate model (M), and cell death and reduced proliferation model (M). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.
RESULTS - For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M and M models compared with the M model. The M model fit, however, had significantly lower sum squared error compared with the M and M models.
CONCLUSIONS - The results of this study indicate that for both doses, the M and M models result in accurate predictions of tumor growth, whereas the M model poorly describes response to radiation therapy.
Copyright © 2017 Elsevier Inc. All rights reserved.
The purpose of this work was to evaluate the feasibility and reproducibility of the spherical mean technique (SMT), a multi-compartmental diffusion model, in the spinal cord of healthy controls, and to assess its ability to improve spinal cord characterization in multiple sclerosis (MS) patients at 3 T. SMT was applied in the cervical spinal cord of eight controls and six relapsing-remitting MS patients. SMT provides an elegant framework to model the apparent axonal volume fraction v , intrinsic diffusivity D , and extra-axonal transverse diffusivity D (which is estimated as a function of v and D ) without confounds related to complex fiber orientation distribution that reside in diffusion MRI modeling. SMT's reproducibility was assessed with two different scans within a month, and SMT-derived indices in healthy and MS cohorts were compared. The influence of acquisition scheme on SMT was also evaluated. SMT's v , D , and D measurements all showed high reproducibility. A decrease in v was observed at the site of lesions and normal appearing white matter (p < 0.05), and trends towards a decreased D and increased D were seen. Importantly, a twofold reduction in acquisition yielded similarly high accuracy with SMT. SMT provides a fast, reproducible, and accurate method to improve characterization of the cervical spinal cord, and may have clinical potential for MS patients.
Copyright © 2018 John Wiley & Sons, Ltd.
Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.
Copyright © 2017 Elsevier Inc. All rights reserved.
It is now widely recognized that voxels with crossing fibers or complex geometrical configurations present a challenge for diffusion MRI (dMRI) reconstruction and fiber tracking, as well as microstructural modeling of brain tissues. This "crossing fiber" problem has been estimated to affect anywhere from 30% to as many as 90% of white matter voxels, and it is often assumed that increasing spatial resolution will decrease the prevalence of voxels containing multiple fiber populations. The aim of this study is to estimate the extent of the crossing fiber problem as we progressively increase the spatial resolution, with the goal of determining whether it is possible to mitigate this problem with higher resolution spatial sampling. This is accomplished using ex vivo MRI data of the macaque brain, followed by histological analysis of the same specimen to validate these measurements, as well as to extend this analysis to resolutions not yet achievable in practice with MRI. In both dMRI and histology, we find unexpected results: the prevalence of crossing fibers increases as we increase spatial resolution. The problem of crossing fibers appears to be a fundamental limitation of dMRI associated with the complexity of brain tissue, rather than a technical problem that can be overcome with advances such as higher fields and stronger gradients.
Copyright © 2017 John Wiley & Sons, Ltd.
We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The combination of intensity templates and image segmentations make this atlas suitable for the fundamental atlas applications of spatial normalization and label propagation. Together, this atlas facilitates 3D anatomical localization and region of interest delineation, and enables comparisons of experimental data across different subjects or across different experimental conditions. This article describes the atlas creation and its contents, and demonstrates the use of the VALiDATe29 atlas in typical applications. The atlas is freely available to the scientific community.
INTRODUCTION - There is a need to develop imaging methods sensitive to axonal injury in multiple sclerosis (MS), given the prominent impact of axonal pathology on disability and outcome. Advanced multi-compartmental diffusion models offer novel indices sensitive to white matter microstructure. One such model, neurite orientation dispersion and density imaging (NODDI), is sensitive to neurite morphology, providing indices of apparent volume fractions of axons (v), isotropic water (v) and the dispersion of fibers about a central axis (orientation dispersion index, ODI). NODDI has yet to be studied for its sensitivity to spinal cord pathology. Here, we investigate the feasibility and utility of NODDI in the cervical spinal cord of MS patients.
METHODS - NODDI was applied in the cervical spinal cord in a cohort of 8 controls and 6 MS patients. Statistical analyses were performed to test the sensitivity of NODDI-derived indices to pathology in MS (both lesion and normal appearing white matter NAWM). Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) analysis were also performed to compare with NODDI.
RESULTS - A decrease in NODDI-derived v was observed at the site of the lesion ( < 0.01), whereas a global increase in ODI was seen throughout white matter ( < 0.001). DKI-derived mean kurtosis (MK) and radial kurtosis (RK) and DTI-derived fractional anisotropy (FA) and radial diffusivity (RD) were all significantly different in MS patients ( < 0.02), however NODDI provided higher contrast between NAWM and lesion in all MS patients.
CONCLUSION - NODDI provides unique contrast that is not available with DKI or DTI, enabling improved characterization of the spinal cord in MS.
OBJECTIVE - Currently, approximately 60-70% of patients with unilateral temporal lobe epilepsy (TLE) remain seizure-free 3 years after surgery. The goal of this work was to develop a presurgical connectivity-based biomarker to identify those patients who will have an unfavorable seizure outcome 1-year postsurgery.
METHODS - Resting-state functional and diffusion-weighted 3T magnetic resonance imaging (MRI) was acquired from 22 unilateral (15 right, 7 left) patients with TLE and 35 healthy controls. A seizure propagation network was identified including ipsilateral (to seizure focus) and contralateral hippocampus, thalamus, and insula, with bilateral midcingulate and precuneus. Between each pair of regions, functional connectivity based on correlations of low frequency functional MRI signals, and structural connectivity based on streamline density of diffusion MRI data were computed and transformed to metrics related to healthy controls of the same age.
RESULTS - A consistent connectivity pattern representing the network expected in patients with seizure-free outcome was identified using eight patients who were seizure-free at 1-year postsurgery. The hypothesis that increased similarity to the model would be associated with better seizure outcome was tested in 14 other patients (Engel class IA, seizure-free: n = 5; Engel class IB-II, favorable: n = 4; Engel class III-IV, unfavorable: n = 5) using two similarity metrics: Pearson correlation and Euclidean distance. The seizure-free connectivity model successfully separated all the patients with unfavorable outcome from the seizure-free and favorable outcome patients (p = 0.0005, two-tailed Fisher's exact test) through the combination of the two similarity metrics with 100% accuracy. No other clinical and demographic predictors were successful in this regard.
SIGNIFICANCE - This work introduces a methodologic framework to assess individual patients, and demonstrates the ability to use network connectivity as a potential clinical tool for epilepsy surgery outcome prediction after more comprehensive validation.
Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.
Quantitative magnetic resonance imaging (qMRI) describes the development and use of MRI to quantify physical, chemical, and/or biological properties of living systems. Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using qMRI methods. The MRI acquisition protocol begins with localizer images (used to locate the position of the body and tissue of interest within the MRI system), quality control measurements of relevant magnetic field distributions, and structural imaging for general anatomical characterization. The qMRI portion of the protocol includes measurements of the longitudinal and transverse relaxation time constants (T1 and T2, respectively). Also acquired are diffusion-tensor MRI data, in which water diffusivity is measured and used to infer pathological processes such as edema. Quantitative magnetization transfer imaging is used to characterize the relative tissue content of macromolecular and free water protons. Lastly, fat-water MRI methods are used to characterize fibro-adipose tissue replacement of muscle. In addition to describing the data acquisition and analysis procedures, this paper also discusses the potential problems associated with these methods, the analysis and interpretation of the data, MRI safety, and strategies for artifact reduction and protocol optimization.
Two diffusion-based approaches, CG (constant gradient) and FEXI (filtered exchange imaging) methods, have been previously proposed for measuring transcytolemmal water exchange rate constant k, but their accuracy and feasibility have not been comprehensively evaluated and compared. In this work, both computer simulations and cell experiments in vitro were performed to evaluate these two methods. Simulations were done with different cell diameters (5, 10, 20μm), a broad range of k values (0.02-30s) and different SNR's, and simulated k's were directly compared with the ground truth values. Human leukemia K562 cells were cultured and treated with saponin to selectively change cell transmembrane permeability. The agreement between measured k's of both methods was also evaluated. The results suggest that, without noise, the CG method provides reasonably accurate estimation of k especially when it is smaller than 10s, which is in the typical physiological range of many biological tissues. However, although the FEXI method overestimates k even with corrections for the effects of extracellular water fraction, it provides reasonable estimates with practical SNR's and more importantly, the fitted apparent exchange rate AXR showed approximately linear dependence on the ground truth k. In conclusion, either CG or FEXI method provides a sensitive means to characterize the variations in transcytolemmal water exchange rate constant k, although the accuracy and specificity is usually compromised. The non-imaging CG method provides more accurate estimation of k, but limited to large volume-of-interest. Although the accuracy of FEXI is compromised with extracellular volume fraction, it is capable of spatially mapping k in practice.
Copyright © 2016 Elsevier Inc. All rights reserved.
BACKGROUND - Diffusion-weighted MRI (DWI) signal attenuation is often not mono-exponential (i.e. non-Gaussian diffusion) with stronger diffusion weighting. Several non-Gaussian diffusion models have been developed and may provide new information or higher sensitivity compared with the conventional apparent diffusion coefficient (ADC) method. However the relative merits of these models to detect tumor therapeutic response is not fully clear.
METHODS - Conventional ADC, and three widely-used non-Gaussian models, (bi-exponential, stretched exponential, and statistical model), were implemented and compared for assessing SW620 human colon cancer xenografts responding to barasertib, an agent known to induce apoptosis via polyploidy. Bayesian Information Criterion (BIC) was used for model selection among all three non-Gaussian models.
RESULTS - All of tumor volume, histology, conventional ADC, and three non-Gaussian DWI models could show significant differences between control and treatment groups after four days of treatment. However, only the non-Gaussian models detected significant changes after two days of treatment. For any treatment or control group, over 65.7% of tumor voxels indicate the bi-exponential model is strongly or very strongly preferred.
CONCLUSION - Non-Gaussian DWI model-derived biomarkers are capable of detecting tumor earlier chemotherapeutic response of tumors compared with conventional ADC and tumor volume. The bi-exponential model provides better fitting compared with statistical and stretched exponential models for the tumor and treatment models used in the current work.
Copyright Â© 2016 Elsevier Inc. All rights reserved.