In vivo MRI signatures of hippocampal subfield pathology in intractable epilepsy.

OBJECTIVES: Our aim is to assess the subfield-specific histopathological correlates of hippocampal volume and intensity changes (T1, T2) as well as diff!usion MRI markers in TLE, and investigate the efficacy of quantitative MRI measures in predicting histopathology in vivo. EXPERIMENTAL DESIGN: We correlated in vivo volumetry, T2 signal, quantitative T1 mapping, as well as diffusion MRI parameters with histological features of hippocampal sclerosis in a subfield-specific manner. We made use of on an advanced co-registration pipeline that provided a seamless integration of preoperative 3 T MRI with postoperative histopathological data, on which metrics of cell loss and gliosis were quantitatively assessed in CA1, CA2/3, and CA4/DG. PRINCIPAL OBSERVATIONS: MRI volumes across all subfields were positively correlated with neuronal density and size. Higher T2 intensity related to increased GFAP fraction in CA1, while quantitative T1 and diffusion MRI parameters showed negative correlations with neuronal density in CA4 and DG. Multiple linear regression analysis revealed that in vivo multiparametric MRI can predict neuronal loss in all the analyzed subfields with up to 90% accuracy. CONCLUSION: Our results, based on an accurate co-registration pipeline and a subfield-specific analysis of MRI and histology, demonstrate the potential of MRI volumetry, diffusion, and quantitative T1 as accurate in vivo biomarkers of hippocampal pathology.

Prostate cancer detection using residual networks.

PURPOSE: To automatically identify regions where prostate cancer is suspected on multi-parametric magnetic resonance images (mp-MRI). METHODS: A residual network was implemented based on segmentations from an expert radiologist on T2-weighted, apparent diffusion coefficient map, and high b-value diffusion-weighted images. Mp-MRIs from 346 patients were used in this study. RESULTS: The residual network achieved a hit or miss accuracy of 93% for lesion detection, with an average Jaccard score of 71% that compared the agreement between network and radiologist segmentations. CONCLUSION: This paper demonstrated the ability for residual networks to learn features for prostate lesion segmentation.

Individual feature maps: a patient-specific analysis tool with applications in temporal lobe epilepsy.

PURPOSE: MRI-based diagnosis of temporal lobe epilepsy (TLE) can be challenging when pathology is not visually evident due to low image contrast or small lesion size. Computer-assisted analyses are able to detect lesions common in a specific patient population, but most techniques do not address clinically relevant individual pathologies resulting from the heterogeneous etiology of the disease. We propose a novel method to supplement the radiological inspection of TLE patients (n = 15) providing patient-specific quantitative assessment. METHOD: Regions of interest are defined across the brain and volume, relaxometry, and diffusion features are extracted from them. Statistical comparisons between individual patients and a healthy control group (n = 17) are performed on these features, identifying and visualizing significant differences through individual feature maps. Four maps are created per patient showing differences in intensity, asymmetry, and volume. RESULTS: Detailed reports were generated per patient. Abnormal hippocampal intensity and volume differences were detected in all patients diagnosed with mesial temporal sclerosis (MTS). Abnormal intensities in the temporal cortex were identified in patients with no MTS. A laterality score correctly distinguished left from right TLE in 12 out of 15 patients. CONCLUSION: The proposed focus on subject-specific quantitative changes has the potential of improving the assessment of TLE patients using MRI techniques, possibly even redefining current imaging protocols for TLE.

Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging.

The detection of MRI abnormalities that can be associated to seizures in the study of temporal lobe epilepsy (TLE) is a challenging task. In many cases, patients with a record of epileptic activity do not present any discernible MRI findings. In this domain, we propose a method that combines quantitative relaxometry and diffusion tensor imaging (DTI) with support vector machines (SVM) aiming to improve TLE detection. The main contribution of this work is two-fold: on one hand, the feature selection process, principal component analysis (PCA) transformations of the feature space, and SVM parameterization are analyzed as factors constituting a classification model and influencing its quality. On the other hand, several of these classification models are studied to determine the optimal strategy for the identification of TLE patients using data collected from multi-parametric quantitative MRI. A total of 17 TLE patients and 19 control volunteers were analyzed. Four images were considered for each subject (T1 map, T2 map, fractional anisotropy, and mean diffusivity) generating 936 regions of interest per subject, then 8 different classification models were studied, each one comprised by a distinct set of factors. Subjects were correctly classified with an accuracy of 88.9%. Further analysis revealed that the heterogeneous nature of the disease impeded an optimal outcome. After dividing patients into cohesive groups (9 left-sided seizure onset, 8 right-sided seizure onset) perfect classification for the left group was achieved (100% accuracy) whereas the accuracy for the right group remained the same (88.9%). We conclude that a linear SVM combined with an ANOVA-based feature selection+PCA method is a good alternative in scenarios like ours where feature spaces are high dimensional, and the sample size is limited. The good accuracy results and the localization of the respective features in the temporal lobe suggest that a multi-parametric quantitative MRI, ROI-based, SVM classification could be used for the identification of TLE patients. This method has the potential to improve the diagnostic assessment, especially for patients who do not have any obvious lesions in standard radiological examinations.

Modelamiento en 3D de patologías vasculares a partir de imágenes de resonancia magnética sustraída / 3D Modeling of Vascular Pathologies from contrast enhanced magnetic resonance images (MRI)

Este artículo presenta un método para la generación de modelos vasculares en 3D, a partir de imágenes de resonancia magnética (IRM), usando un algoritmo de fast marching. Los principales aportes del método propuesto en este artículo son la utilización de la imagen original como base para la definición de la función de velocidad que rige el desplazamiento de la interfaz y la selección automática del tiempo en el cual la interfaz logra segmentar la arteria. El método fue validado en imágenes de arterias carótidas patológicas y de fantasmas vasculares. Una apreciación cualitativa de los modelos vasculares obtenidos muestra una extracción adecuada de la pared vascular. Una validación cuantitativa demostró que los modelos generados dependen de la escogencia de los parámetros del algoritmo, al inducir un error máximo de 1,34 vóxeles en el diámetro de las estenosis medidas.