Diffusion imaging in neurological disease

White Matter Magnetic Resonance Diffusion Measures in Multiple Sclerosis with Overactive Bladder

BACKGROUND

Lower urinary tract (LUT) symptoms are reported in more than 80% of patients with multiple sclerosis (MS), most commonly an overactive bladder (OAB). The relationship between brain white matter (WM) changes in MS and OAB symptoms is poorly understood.

OBJECTIVES

We aim to evaluate (i) microstructural WM differences across MS patients (pwMS) with OAB symptoms, patients without LUT symptoms, and healthy subjects using diffusion tensor imaging (DTI), and (ii) associations between clinical OAB symptom scores and DTI indices.

METHODS

Twenty-nine female pwMS [mean age (SD) 43.3 years (9.4)], including seventeen with OAB [mean age (SD) 46.1 years (8.6)] and nine without LUT symptoms [mean age (SD) 37.5 years (8.9)], and fourteen healthy controls (HCs) [mean age (SD) 48.5 years (20)] were scanned in a 3T MRI with a DTI protocol. Additionally, clinical scans were performed for WM lesion segmentation. Group differences in fractional anisotropy (FA) were evaluated using tract-based spatial statistics. The Urinary Symptom Profile questionnaire assessed OAB severity.

RESULTS

A statistically significant reduction in FA (p = 0.004) was identified in microstructural WM in pwMS, compared with HCs. An inverse correlation was found between FA in frontal and parietal WM lobes and OAB scores (p = 0.021) in pwMS. Areas of lower FA, although this did not reach statistical significance, were found in both frontal lobes and the rest of the non-dominant hemisphere in pwMS with OAB compared with pwMS without LUT symptoms (p = 0.072).

CONCLUSIONS

This study identified that lesions affecting different WM tracts in MS can result in OAB symptoms and demonstrated the role of the WM in the neural control of LUT functions. By using DTI, the association between OAB symptom severity and WM changes were identified, adding knowledge to the current LUT working model. As MS is predominantly a WM disease, these findings suggest that regional WM involvement, including of the anterior corona radiata, anterior thalamic radiation, superior longitudinal fasciculus, and superior frontal-occipital fasciculus and a non-dominant prevalence in WM, can result in OAB symptoms. OAB symptoms in MS correlate with anisotropy changes in different white matter tracts as demonstrated by DTI. Structural impairment in WM tracts plays an important role in LUT symptoms in MS.

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging

PURPOSE

To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI).

METHODS

We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed.

RESULTS

In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle.

CONCLUSIONS

White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.

Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images

This paper presents a scientific foundation for automated stroke severity classification. We have constructed and assessed a system which extracts diagnostically relevant information from Magnetic Resonance Imaging (MRI) images. The design was based on 267 images that show the brain from individual subjects after stroke. They were labeled as either Lacunar Syndrome (LACS), Partial Anterior Circulation Syndrome (PACS), or Total Anterior Circulation Stroke (TACS). The labels indicate different physiological processes which manifest themselves in distinct image texture. The processing system was tasked with extracting texture information that could be used to classify a brain MRI image from a stroke survivor into either LACS, PACS, or TACS. We analyzed 6475 features that were obtained with Gray-Level Run Length Matrix (GLRLM), Higher Order Spectra (HOS), as well as a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM) methods. The resulting features were ranked based on the p-value extracted with the Analysis Of Variance (ANOVA) algorithm. The ranked features were used to train and test four types of Support Vector Machine (SVM) classification algorithms according to the rules of 10-fold cross-validation. We found that SVM with Radial Basis Function (RBF) kernel achieves: Accuracy (ACC) = 93.62%, Specificity (SPE) = 95.91%, Sensitivity (SEN) = 92.44%, and Dice-score = 0.95. These results indicate that computer aided stroke severity diagnosis support is possible. Such systems might lead to progress in stroke diagnosis by enabling healthcare professionals to improve diagnosis and management of stroke patients with the same resources.

Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.

SEGMENTATION AND CLASSIFICATION OF ISCHEMIC STROKE USING OPTIMIZED FEATURES IN BRAIN MRI

Detection of ischemic stroke using brain magnetic resonance imaging (MRI) images is vital and a challenging task in clinical practice. We propose a novel method based on optimization technique to identify stroke lesion in diffusion-weighted imaging (DWI) MRI sequences of the brain. The algorithm was tested in a specific slice having large area of stroke region from a series of 292 real-time images obtained from different stroke affected subjects from IMS and SUM Hospital. The proposed method consists of pre-processing, segmentation, extraction of important features and classification of stroke type. The particle swarm optimization (PSO) and Darwinian particle swarm optimization (DPSO) algorithms were applied in segmenting the stroke lesions. The important features were extracted with the gray-level co-occurrence matrix (GLCM) algorithm and in decision making process, the feature set is classified into three types of stroke according to The Oxfordshire Community Stroke Project (OCSP) classification using support vector machine (SVM) classifier. The lesion area was segmented effectively with DPSO process with classification weighted accuracy of 90.23%, which is higher than PSO method having weighted accuracy of 85.19%. Similarly, the values of different measured parameters were high in DPSO technique, the computational time was also higher in DPSO method for segmenting the stroke lesions. These results confirm that the DPSO-based approach with SVM classifier is an effective way to identify the decision making process of ischemic stroke lesion in MRI images of the brain.

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

In albinism, the number of ipsilaterally projecting retinal ganglion cells (RGCs) is significantly reduced. The retina and optic chiasm have been proposed as candidate sites for misrouting. Since a correlation between the number of lateral geniculate nucleus (LGN) relay neurons and LGN size has been shown, and based on previously reported reductions in LGN volumes in human albinism, we suggest that fiber projections from LGN to the primary visual cortex (V1) are also reduced. Studying structural differences in the visual system of albinism can improve the understanding of the mechanism of misrouting and subsequent clinical applications. Diffusion data and tractography are useful for mapping the OR (optic radiation). This manuscript describes two algorithms for OR reconstruction in order to compare brain connectivity in albinism and controls.An MRI scanner with a 32-channel head coil was used to acquire structural scans. A T1-weighted 3D-MPRAGE sequence with 1 mm(3) isotropic voxel size was used to generate high-resolution images for V1 segmentation. Multiple proton density (PD) weighted images were acquired coronally for right and left LGN localization. Diffusion tensor imaging (DTI) scans were acquired with 64 diffusion directions. Both deterministic and probabilistic tracking methods were run and compared, with LGN as the seed mask and V1 as the target mask. Though DTI provides relatively poor spatial resolution, and accurate delineation of OR may be challenging due to its low fiber density, tractography has been shown to be advantageous both in research and clinically. Tract based spatial statistics (TBSS) revealed areas of significantly reduced white matter integrity within the OR in patients with albinism compared to controls. Pairwise comparisons revealed a significant reduction in LGN to V1 connectivity in albinism compared to controls. Comparing both tracking algorithms revealed common findings, strengthening the reliability of the technique.

Automatic detection and quantification of acute cerebral infarct by fuzzy clustering and histographic characterization on diffusion weighted MR imaging and apparent diffusion coefficient map

Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.