Radiation therapy effects on white matter fiber tracts of the limbic circuit

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134834/1/mp5560.pd

Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy

PURPOSE: This study systematically investigates the predictive power of volumetric imaging feature sets extracted from select neuroanatomical sites in lateralizing the epileptogenic focus in mesial temporal lobe epilepsy (mTLE) patients. METHODS: A cohort of 68 unilateral mTLE patients who had achieved an Engel class I outcome postsurgically was studied retrospectively. The volumes of multiple brain structures were extracted from preoperative magnetic resonance (MR) images in each. The MR image data set consisted of 54 patients with imaging evidence for hippocampal sclerosis (HS-P) and 14 patients without (HS-N). Data mining techniques (i.e., feature extraction, feature selection, machine learning classifiers) were applied to provide measures of the relative contributions of structures and their correlations with one another. After removing redundant correlated structures, a minimum set of structures was determined as a marker for mTLE lateralization. RESULTS: Using a logistic regression classifier, the volumes of both hippocampus and amygdala showed correct lateralization rates of 94.1%. This reflected about 11.7% improvement in accuracy relative to using hippocampal volume alone. The addition of thalamic volume increased the lateralization rate to 98.5%. This ternary-structural marker provided a 100% and 92.9% mTLE lateralization accuracy, respectively, for the HS-P and HS-N groups. CONCLUSIONS: The proposed tristructural MR imaging biomarker provides greater lateralization accuracy relative to single- and double-structural biomarkers and thus, may play a more effective role in the surgical decision-making process. Also, lateralization of the patients with insignificant atrophy of hippocampus by the proposed method supports the notion of associated structural changes involving the amygdala and thalamus

Segmentation of corpus callosum using diffusion tensor imaging: validation in patients with glioblastoma

Abstract Background This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma. Methods Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the proposed segmentation method in such cases. Results Dice coefficients, estimated to compare automatic and manual segmentation results for Witelson subdivisions, ranged from 94% to 98% for control subjects and from 81% to 95% for tumor patients, illustrating closeness of automatic and manual segmentations. Studying the effect of corpus callosum rotation by different Euler angles showed that although segmentation results were more sensitive to azimuth and elevation than skew, rotations caused by brain tumors do not have major effects on the segmentation results. Conclusions The proposed method and similarity measure segment corpus callosum by propagating a hyper-surface inside the structure (resulting in high sensitivity), without penetrating into neighboring fiber bundles (resulting in high specificity)

Simultaneous optimization of power and duration of radio-frequency pulse in PARACEST MRI

Chemical exchange saturation transfer (CEST) MRI is increasingly used to probe mobile proteins and microenvironment properties, and shows great promise for tumor and stroke diagnosis. The CEST effect is complex and depends not only on the CEST agent concentration, exchange rates, the characteristic of the magnetization transfer (MT), and the relaxation properties of the tissue, but also varies with the experimental conditions such as radio-frequency (RF) pulse power and duration. The RF pulse is one of the most important factors that promote the CEST effect for biological properties such as pH, temperature and protein content, especially for contrast agents with intermediate to fast exchange rates. The CEST effect is susceptible to the RF duration and power. The present study aims at determining the optimal power and the corresponding optimal duration (that maximize the CEST effect) using an off-resonance scheme through a new definition of the CEST effect. This definition is formulated by solving the Bloch-McConnell equation through the R1ρ method (based on the eigenspace solution) for both of the MT and CEST effects as well as their interactions. The proposed formulations of the optimal RF pulse power and duration are the first formulations in which the MT effect is considered. The extracted optimal RF pulse duration and power are compared with those of the MTR asymmetry model in two- and three-pool systems, using synthetic data that are similar to the muscle tissue. To validate them further, the formulations are compared with the empirical formulation of the CEST effect and other findings of the previous researches. By extending our formulations, the optimal power and the corresponding optimal duration (in the biological systems with many chemical exchange sites) can be determined

A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications

Current segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available results, they heavily require high memory GPUs. In this paper, we customize a memory-efficient (GPU) brain structure segmentation framework, named FLBS, based on nnU-nets which enables our framework to adapt its architecture based on memory constraints dynamically. To further reduce the need for memory, we also reduce multi-label brain segmentation to the fusion of sequential single-label segmentations. In the first step, single label patches are extracted from the T1w and segmentation maps by locating the approximate area of each structure on the MNI305 template, including the safety margin. These considerations not only decrease the hardware usage but also maintains comparable computational time. Moreover, the target brain structures are customizable based on the specific clinical applications. We evaluate the performance in terms of Dice coefficient, runtime, and GPU requirement on OASIS-3 and CoRR-BNU1 datasets. The validation results show our comparable accuracies with state-of-the-arts and confirm the generalizability on unseen datasets while significantly reducing GPU requirements and maintaining runtime duration. Our framework is also executable on a budget GPU with a minimum requirement of 4G RAM. We develop a memory-efficient deep Brain MRI segmentation tool that significantly reduces the hardware requirement of MRI segmentation while maintaining comparable accuracy and time. These advantages make FLBS suitable for widespread use in clinical applications, especially for clinics with a limited budget. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available on https://github.com/arnejad/FLBS

A Fast and Memory-Efficient Brain MRI Segmentation Framework for Clinical Applications

Current segmentation tools of brain MRI provide quantitative structural information for diagnosing neurological disorders. However, their clinical application is generally limited due to high memory usage and time consumption. Although 3D CNN-based segmentation methods have recently achieved the state-of-the-art and come up with timely available results, they heavily require high memory GPUs. In this paper, we customize a memory-efficient (GPU) brain structure segmentation framework, named FLBS, based on nnU-nets which enables our framework to adapt its architecture based on memory constraints dynamically. To further reduce the need for memory, we also reduce multi-label brain segmentation to the fusion of sequential single-label segmentations. In the first step, single label patches are extracted from the T1w and segmentation maps by locating the approximate area of each structure on the MNI305 template, including the safety margin. These considerations not only decrease the hardware usage but also maintains comparable computational time. Moreover, the target brain structures are customizable based on the specific clinical applications. We evaluate the performance in terms of Dice coefficient, runtime, and GPU requirement on OASIS-3 and CoRR-BNU1 datasets. The validation results show our comparable accuracies with state-of-the-arts and confirm the generalizability on unseen datasets while significantly reducing GPU requirements and maintaining runtime duration. Our framework is also executable on a budget GPU with a minimum requirement of 4G RAM. We develop a memory-efficient deep Brain MRI segmentation tool that significantly reduces the hardware requirement of MRI segmentation while maintaining comparable accuracy and time. These advantages make FLBS suitable for widespread use in clinical applications, especially for clinics with a limited budget. We plan to release the framework as a part of a free clinical brain imaging analysis tool. The code for this framework is publicly available on https://github.com/arnejad/FLBS

A neuroimaging model based on MRI, DTI, and spect findings for lateralization of temporal lobe epilepsy

Purpose: Temporal lobe epilepsy (TLE) is the most widespread type of epilepsy with the most successful resection outcome. Interhemispheric variations detected in the images of T1-weighted and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI), and ictal and interictal single photon emission computed tomography (SPECT), and in the indices of mean diffusivity (MD) and fractional anisotropy (FA) of diffusion tensor imaging (DTI), are within the established markers ofTLE laterality. However, current non-quantitative imaging evaluations may not optimally incorporate the imaging information into the decision-making process prior to resection of mesial temporalstructures. We hypothesize that quantitative TLE lateralization response models of MRI, DTI, and SPECTneuroimaging attributes will optimize the selection ofsurgical candidates and reduce, in some cases, the need for extraoperative electrocorticography (eECoG). Method: Neuroimaging features of 138 retrospective TLE patients with Engel class l surgical outcomes were extracted, including the hippocampal volumes, normalized ictal-interictal SPECT and FLAIR intensities, and mean diffusivity, along with the cingulate and forniceal fractional anisotropy (FA). Using logistic function regression, univariate and multivariate models were developed. Results: The model incorporating all multivariate attributes for138 TLE cases that had at least one imaging attribute and imputing the missing attributes with the mean values of the corresponding attributes measured oncontrol cohort reached the probability of detection and false alarm of 0.83 and 0.17 for all cases, and 0.90 and 0.10 for the patients who underwent eECoG. Conclusion: Increased reliability in lateralizing TLE cases using the proposed response model involving the incorporation of the multivariate attributes reinforces the notion that eECoG in a number ofcases may be circumvented. The proposed response model can be further generalized by integrating attributes of additional neuroclinical, neurophysiological, neuropsychological, and neuroimaging attributes into the presurgical decision making process