Comparison of manual and automated fiber quantification tractography in patients with temporal lobe epilepsy.

OBJECTIVE: To investigate the agreement between manually and automatically generated tracts from diffusion tensor imaging (DTI) in patients with temporal lobe epilepsy (TLE). Whole and along-the-tract diffusivity metrics and correlations with patient clinical characteristics were analyzed with respect to tractography approach. METHODS: We recruited 40 healthy controls and 24 patients with TLE who underwent conventional T1-weighted imaging and 60-direction DTI. An automated (Automated Fiber Quantification, AFQ) and manual (TrackVis) deterministic tractography approach was used to identify the uncinate fasciculus (UF) and parahippocampal white matter bundle (PHWM). Tract diffusion scalar metrics were analyzed with respect to agreement across automated and manual approaches (Dice Coefficient and Spearman correlations), to side of onset of epilepsy and patient clinical characteristics, including duration of epilepsy, age of onset and presence of hippocampal sclerosis. RESULTS: Across approaches the analysis of tract morphology similarity revealed Dice coefficients at moderate to good agreement (0.54 - 0.6) and significant correlations between diffusion values (Spearman's Rho=0.4-0.9). However, within bilateral PHWM, AFQ yielded significantly lower FA (left: Z = 4.4, p<0.001; right: Z = 5.1, p<0.001) and higher MD values (left: Z=-4.7, p<0.001; right: Z=-3.7, p<0.001) compared to the manual approach. Whole tract DTI metrics determined using AFQ were significantly correlated with patient characteristics, including age of epilepsy onset in FA (R = 0.6, p = 0.02) and MD of the ipsilateral PHWM (R=-0.6, p = 0.02), while duration of epilepsy corrected for age correlated with MD in ipsilateral PHWM (R = 0.7, p<0.01). Correlations between clinical metrics and diffusion values extracted using the manual whole tract technique did not survive correction for multiple comparisons. Both manual and automated along-the-tract analyses demonstrated significant correlations with patient clinical characteristics such as age of onset and epilepsy duration. The strongest and most widespread localized ipsi- and contralateral diffusivity alterations were observed in patients with left TLE and patients with HS compared to controls, while patients with right TLE and patients without HS did not show these strong effects. CONCLUSIONS: Manual and AFQ tractography approaches revealed significant correlations in the reconstruction of tract morphology and extracted whole and along-tract diffusivity values. However, as non-identical methods they differed in the respective yield of significant results across clinical correlations and group-wise statistics. Given the absence of excellent agreement between manual and AFQ techniques as demonstrated in the present study, caution should be considered when using AFQ particularly when used without reference to benchmark manual measures.

Optimization of data acquisition and analysis for fiber ball imaging.

The inverse Funk transform of high angular resolution diffusion imaging (HARDI) data provides an estimate for the fiber orientation density function (fODF) in white matter (WM). Since the inverse Funk transform is a straightforward linear transformation, this technique, referred to as fiber ball imaging (FBI), offers a practical means of calculating the fODF that avoids the need for a response function or nonlinear numerical fitting. Nevertheless, the accuracy of FBI depends on both the choice of b-value and the number of diffusion-encoding directions used to acquire the HARDI data. To inform the design of optimal scan protocols for its implementation, FBI predictions are investigated here with in vivo data from healthy adult volunteers acquired at 3 T for b-values spanning 1000 to 10,000 s/mm2, for diffusion-encoding directions varying in number from 30 to 256 and for TE ranging from 90 to 120 ms. Our results suggest b-values above 4000 s/mm2 with at least 64 diffusion-encoding directions are adequate to achieve reasonable accuracy with FBI for calculating axon-specific diffusion measures and for performing WM fiber tractography (WMFT).

Structural plasticity of the ventral stream and aphasia recovery.

Restrengthening of the residual language network is likely to be crucial for speech recovery in poststroke aphasia. Eight participants with chronic aphasia received intensive speech therapy for 3 weeks, with standardized naming tests and brain magnetic resonance imaging before and after therapy. Kurtosis-based diffusion tensor tractography was used to measure mean kurtosis (MK) along a segment of the inferior longitudinal fasciculus (ILF). Therapy-related reduction in the number of semantic but not phonemic errors was associated with strengthening (renormalization) of ILF MK (r = -0.90, p < 0.05 corrected), suggesting that speech recovery is related to structural plasticity of language-specific components of the residual language network. Ann Neurol 2017;82:147-151.

Evaluating kurtosis-based diffusion MRI tissue models for white matter with fiber ball imaging.

In order to quantify well-defined microstructural properties of brain tissue from diffusion MRI (dMRI) data, tissue models are typically employed that relate biological features, such as cell morphology and cell membrane permeability, to the diffusion dynamics. A variety of such models have been proposed for white matter, and their validation is a topic of active interest. In this paper, three different tissue models are tested by comparing their predictions for a specific microstructural parameter to a value measured independently with a recently proposed dMRI method known as fiber ball imaging (FBI). The three tissue models are all constructed with the diffusion and kurtosis tensors, and they are hence compatible with diffusional kurtosis imaging. Nevertheless, the models differ significantly in their details and predictions. For voxels with fractional anisotropies (FAs) exceeding 0.5, all three are reasonably consistent with FBI. However, for lower FA values, one of these, called the white matter tract integrity (WMTI) model, is found to be in much better accord with FBI than the other two, suggesting that the WMTI model has a broader range of applicability.

Dependence on b-value of the direction-averaged diffusion-weighted imaging signal in brain.

PURPOSE: The dependence of the direction-averaged diffusion-weighted imaging (DWI) signal in brain was studied as a function of b-value in order to help elucidate the relationship between diffusion weighting and brain microstructure. METHODS: High angular resolution diffusion imaging (HARDI) data were acquired from two human volunteers with 128 diffusion-encoding directions and six b-value shells ranging from 1000 to 6000s/mm2 in increments of 1000s/mm2. The direction-averaged signal was calculated for each shell by averaging over all diffusion-encoding directions, and the signal was plotted as a function of b-value for selected regions of interest. As a supplementary analysis, similar methods were also applied to retrospective DWI data obtained from the human connectome project (HCP), which includes b-values up to 10,000s/mm2. RESULTS: For all regions of interest, a simple power law relationship accurately described the observed dependence of the direction-averaged signal as a function of the diffusion weighting. In white matter, the characteristic exponent was 0.56±0.05, while in gray matter it was 0.88±0.11. Comparable results were found with the HCP data. CONCLUSION: The direction-averaged DWI signal varies, to a good approximation, as a power of the b-value, for b-values between 1000 and 6000s/mm2. The exponents characterizing this power law behavior were markedly different for white and gray matter, indicative of sharply contrasting microstructural environments. These results may inform the construction of microstructural models used to interpret the DWI signal.

Preoperative automated fibre quantification predicts postoperative seizure outcome in temporal lobe epilepsy.

Approximately one in every two patients with pharmacoresistant temporal lobe epilepsy will not be rendered completely seizure-free after temporal lobe surgery. The reasons for this are unknown and are likely to be multifactorial. Quantitative volumetric magnetic resonance imaging techniques have provided limited insight into the causes of persistent postoperative seizures in patients with temporal lobe epilepsy. The relationship between postoperative outcome and preoperative pathology of white matter tracts, which constitute crucial components of epileptogenic networks, is unknown. We investigated regional tissue characteristics of preoperative temporal lobe white matter tracts known to be important in the generation and propagation of temporal lobe seizures in temporal lobe epilepsy, using diffusion tensor imaging and automated fibre quantification. We studied 43 patients with mesial temporal lobe epilepsy associated with hippocampal sclerosis and 44 healthy controls. Patients underwent preoperative imaging, amygdalohippocampectomy and postoperative assessment using the International League Against Epilepsy seizure outcome scale. From preoperative imaging, the fimbria-fornix, parahippocampal white matter bundle and uncinate fasciculus were reconstructed, and scalar diffusion metrics were calculated along the length of each tract. Altogether, 51.2% of patients were rendered completely seizure-free and 48.8% continued to experience postoperative seizure symptoms. Relative to controls, both patient groups exhibited strong and significant diffusion abnormalities along the length of the uncinate bilaterally, the ipsilateral parahippocampal white matter bundle, and the ipsilateral fimbria-fornix in regions located within the medial temporal lobe. However, only patients with persistent postoperative seizures showed evidence of significant pathology of tract sections located in the ipsilateral dorsal fornix and in the contralateral parahippocampal white matter bundle. Using receiver operating characteristic curves, diffusion characteristics of these regions could classify individual patients according to outcome with 84% sensitivity and 89% specificity. Pathological changes in the dorsal fornix were beyond the margins of resection, and contralateral parahippocampal changes may suggest a bitemporal disorder in some patients. Furthermore, diffusion characteristics of the ipsilateral uncinate could classify patients from controls with a sensitivity of 98%; importantly, by co-registering the preoperative fibre maps to postoperative surgical lacuna maps, we observed that the extent of uncinate resection was significantly greater in patients who were rendered seizure-free, suggesting that a smaller resection of the uncinate may represent insufficient disconnection of an anterior temporal epileptogenic network. These results may have the potential to be developed into imaging prognostic markers of postoperative outcome and provide new insights for why some patients with temporal lobe epilepsy continue to experience postoperative seizures.

Epilepsy-related cytoarchitectonic abnormalities along white matter pathways.

OBJECTIVE: Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy. Unfortunately, the clinical outcomes of TLE cannot be determined based only on current diagnostic modalities. A better understanding of white matter (WM) connectivity changes in TLE may aid the identification of network abnormalities associated with TLE and the phenotypic characterisation of the disease. METHODS: We implemented a novel approach for characterising microstructural changes along WM pathways using diffusional kurtosis imaging (DKI). Along-the-tract measures were compared for 32 subjects with left TLE and 36 age-matched and gender-matched controls along the left and right fimbria-fornix (FF), parahippocampal WM bundle (PWMB), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF) and cingulum bundle (CB). Limbic pathways were investigated in relation to seizure burden and control with antiepileptic drugs. RESULTS: By evaluating measures along each tract, it was possible to identify abnormalities localised to specific tract subregions. Compared with healthy controls, subjects with TLE demonstrated pathological changes in circumscribed regions of the FF, PWMB, UF, AF and ILF. Several of these abnormalities were detected only by kurtosis-based and not by diffusivity-based measures. Structural WM changes correlated with seizure burden in the bilateral PWMB and cingulum. CONCLUSIONS: DKI improves the characterisation of network abnormalities associated with TLE by revealing connectivity abnormalities that are not disclosed by other modalities. Since TLE is a neuronal network disorder, DKI may be well suited to fully assess structural network abnormalities related to epilepsy and thus serve as a tool for phenotypic characterisation of epilepsy.

Optimization of white matter fiber tractography with diffusional kurtosis imaging.

Diffusional kurtosis imaging (DKI) is a clinically feasible diffusion MRI technique for white matter (WM) fiber tractography (FT) with the ability to directly resolve intra-voxel crossing fibers by means of the kurtosis diffusion orientation distribution function (dODF). Here we expand on previous work by exploring properties of the kurtosis dODF and their subsequent effects on WM FT for in vivo human data. For comparison, the results are contrasted with fiber bundle orientation estimates provided by the diffusion tensor, which is the primary quantity obtained from diffusion tensor imaging. We also outline an efficient method for performing DKI-based WM FT that can substantially decrease the computational requirements. The recommended method for implementing the kurtosis ODF is demonstrated to optimize the reproducibility and sensitivity of DKI for detecting crossing fibers while reducing the occurrence of non-physically-meaningful, negative values in the kurtosis dODF approximation. In addition, DKI-based WM FT is illustrated for different protocols differing in image acquisition times from 48 to 5.3 min.

Quantitative assessment of diffusional kurtosis anisotropy.

Diffusional kurtosis imaging (DKI) measures the diffusion and kurtosis tensors to quantify restricted, non-Gaussian diffusion that occurs in biological tissue. By estimating the kurtosis tensor, DKI accounts for higher order diffusion dynamics, when compared with diffusion tensor imaging (DTI), and consequently can describe more complex diffusion profiles. Here, we compare several measures of diffusional anisotropy which incorporate information from the kurtosis tensor, including kurtosis fractional anisotropy (KFA) and generalized fractional anisotropy (GFA), with the diffusion tensor-derived fractional anisotropy (FA). KFA and GFA demonstrate a net enhancement relative to FA when multiple white matter fiber bundle orientations are present in both simulated and human data. In addition, KFA shows net enhancement in deep brain structures, such as the thalamus and the lenticular nucleus, where FA indicates low anisotropy. Thus, KFA and GFA provide additional information relative to FA with regard to diffusional anisotropy, and may be particularly advantageous for the assessment of diffusion in complex tissue environments.

CT Evaluation of Small-Diameter Coronary Artery Stents: Effect of an Integrated Circuit Detector with Iterative Reconstruction.

PURPOSE: To use suitable objective methods of analysis to assess the influence of the combination of an integrated-circuit computed tomographic (CT) detector and iterative reconstruction (IR) algorithms on the visualization of small (≤3-mm) coronary artery stents. MATERIALS AND METHODS: By using a moving heart phantom, 18 data sets obtained from three coronary artery stents with small diameters were investigated. A second-generation dual-source CT system equipped with an integrated-circuit detector was used. Images were reconstructed with filtered back-projection (FBP) and IR at a section thickness of 0.75 mm (FBP75 and IR75, respectively) and IR at a section thickness of 0.50 mm (IR50). Multirow intensity profiles in Hounsfield units were modeled by using a sum-of-Gaussians fit to analyze in-plane image characteristics. Out-of-plane image characteristics were analyzed with z upslope of multicolumn intensity profiles in Hounsfield units. Statistical analysis was conducted with one-way analysis of variance and the Student t test. RESULTS: Independent of stent diameter and heart rate, IR75 resulted in significantly increased xy sharpness, signal-to-noise ratio, and contrast-to-noise ratio, as well as decreased blurring and noise compared with FBP75 (eg, 2.25-mm stent, 0 beats per minute; xy sharpness, 278.2 vs 252.3; signal-to-noise ratio, 46.6 vs 33.5; contrast-to-noise ratio, 26.0 vs 16.8; blurring, 1.4 vs 1.5; noise, 15.4 vs 21.2; all P < .001). In the z direction, the upslopes were substantially higher in the IR50 reconstructions (2.25-mm stent: IR50, 94.0; IR75, 53.1; and FBP75, 48.1; P < .001). CONCLUSION: The implementation of an integrated-circuit CT detector provides substantially sharper out-of-plane resolution of coronary artery stents at 0.5-mm section thickness, while the use of iterative image reconstruction mostly improves in-plane stent visualization.

A simple noise correction scheme for diffusional kurtosis imaging.

PURPOSE: Diffusional kurtosis imaging (DKI) is sensitive to the effects of signal noise due to strong diffusion weightings and higher order modeling of the diffusion weighted signal. A simple noise correction scheme is proposed to remove the majority of the noise bias in the estimated diffusional kurtosis. METHODS: Weighted linear least squares (WLLS) fitting together with a voxel-wise, subtraction-based noise correction from multiple, independent acquisitions are employed to reduce noise bias in DKI data. The method is validated in phantom experiments and demonstrated for in vivo human brain for DKI-derived parameter estimates. RESULTS: As long as the signal-to-noise ratio (SNR) for the most heavily diffusion weighted images is greater than 2.1, errors in phantom diffusional kurtosis estimates are found to be less than 5 percent with noise correction, but as high as 44 percent for uncorrected estimates. In human brain, noise correction is also shown to improve diffusional kurtosis estimates derived from measurements made with low SNR. CONCLUSION: The proposed correction technique removes the majority of noise bias from diffusional kurtosis estimates in noisy phantom data and is applicable to DKI of human brain. Features of the method include computational simplicity and ease of integration into standard WLLS DKI post-processing algorithms.