Evaluation of corpus callosum morphometry in patients with mesial temporal lobe epilepsy with hippocampal sclerosis

Automatic Segmentation of Corpus Callosum in Midsagittal Based on Bayesian Inference Consisting of Sparse Representation Error and Multi-Atlas Voting

In this paper, we introduce a novel automatic method for Corpus Callosum (CC) in midsagittal plane segmentation. The robust segmentation of CC in midsagittal plane is key role for quantitative study of structural features of CC associated with various neurological disorder such as epilepsy, autism, Alzheimer's disease, and so on. Our approach is based on Bayesian inference using sparse representation and multi-atlas voting which both methods are used in various medical imaging, and show outstanding performance. Prior information in the proposed Bayesian inference is obtained from probability map generated from multi-atlas voting. The probability map contains the information of shape and location of CC of target image. Likelihood in the proposed Bayesian inference is obtained from gamma distribution function, generated from reconstruction errors (or sparse representation error), which are calculated in sparse representation of target patch using foreground dictionary and background dictionary each. Unlike the usual sparse representation method, we added gradient magnitude and gradient direction information to the patches of dictionaries and target, which had better segmentation performance than when not added. We compared three main segmentation results as follow: (1) the joint label fusion (JLF) method which is state-of-art method in multi-atlas voting based segmentation for evaluation of our method; (2) prior information estimated from multi-atlas voting only; (3) likelihood estimated from comparison of the reconstruction errors from sparse representation error only; (4) the proposed Bayesian inference. The methods were evaluated using two data sets of T1-weighted images, which one data set consists of 100 normal young subjects and the other data set consist of 25 normal old subjects and 22 old subjects with heavy drinker. In both data sets, the proposed Bayesian inference method has significantly the best segmentation performance than using each method separately.

Planimetry investigation of the corpus callosum in temporal lobe epilepsy patients

Objective:

To evaluate the effects of temporal lobe epilepsy (TLE) on corpus callosum (CC) morphometry in patients with TLE.

Methods:

This retrospective study was conducted at the Faculty of Medicine, Tekirdag Namik Kemal University, Tekirdag, Turkey between November 2010 and December 2013. The epileptic syndrome diagnosis was based on International League Against Epilepsy criteria, and this study was conducted on the MRIs of 25 epilepsy patients and 25 control subjects. We classified the patients according to their duration of epilepsy <10 and ≥10 years. The projection area length (PAL) of the CC was also estimated. Total brain volumes (TBV) were measured on CT images.

Results:

The mean values of TBV for patients with TLE and the control group were not statistically different, but the CC PAL values were statistically different. The mean CC PAL values of under and over 25 years of age in patients with TLE were statistically different. The mean values of TBV of under and over 10 years duration of TLE were small statistically, but the CC PAL values were statistically different.

Conclusion:

The results indicate a clear influence of TLE on the structure of the CC rather than TBV.

DTI-based response-driven modeling of mTLE laterality

Purpose

To develop lateralization models for distinguishing between unilateral and bilateral mesial temporal lobe epilepsy (mTLE) and determining laterality in cases of unilateral mTLE.

Background

mTLE is the most common form of medically refractory focal epilepsy. Many mTLE patients fail to demonstrate an unambiguous unilateral ictal onset. Intracranial EEG (icEEG) monitoring can be performed to establish whether the ictal origin is unilateral or truly bilateral with independent bitemporal ictal origin. However, because of the expense and risk of intracranial electrode placement, much research has been done to determine if the need for icEEG can be obviated with noninvasive neuroimaging methods, such as diffusion tensor imaging (DTI).

Methods

Fractional anisotropy (FA) was used to quantify microstructural changes reflected in the diffusivity properties of the corpus callosum, cingulum, and fornix, in a retrospective cohort of 31 patients confirmed to have unilateral (n = 24) or bilateral (n = 7) mTLE. All unilateral mTLE patients underwent resection with an Engel class I outcome. Eleven were reported to have hippocampal sclerosis on pathological analysis; nine had undergone prior icEEG. The bilateral mTLE patients had undergone icEEG demonstrating independent epileptiform activity in both right and left hemispheres. Twenty-three nonepileptic subjects were included as controls.

Results

In cases of right mTLE, FA showed significant differences from control in all callosal subregions, in both left and right superior cingulate subregions, and in forniceal crura. Comparison of right and left mTLE cases showed significant differences in FA of callosal genu, rostral body, and splenium and the right posteroinferior and superior cingulate subregions. In cases of left mTLE, FA showed significant differences from control only in the callosal isthmus. Significant differences in FA were identified when cases of right mTLE were compared with bilateral mTLE cases in the rostral and midbody callosal subregions and isthmus. Based on 11 FA measurements in the cingulate, callosal and forniceal subregions, a response-driven lateralization model successfully differentiated all cases (n = 54) into groups of unilateral right (n = 12), unilateral left (n = 12), and bilateral mTLE (n = 7), and nonepileptic control (23).

Conclusion

The proposed response-driven DTI biomarker is intended to lessen diagnostic ambiguity of laterality in cases of mTLE and help optimize selection of surgical candidates. Application of this model shows promise in reducing the need for invasive icEEG in prospective cases.

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Develop response-driven lateralization model using diffusion tensor imaging

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Distinguish between unilateral and bilateral mesial temporal lobe epilepsy (mTLE)

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Determine or lessen diagnostic ambiguity of laterality in cases of unilateral mTLE

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Optimize selection of surgical candidates

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Reduction of the need for intracranial EEG