Noise reduction in FLAIR2 images using total generalized variation, Gaussian and Wiener filtering

MRI-quality and morphometric MRI analysis to identify focal cortical dysplasia: An exploratory study

BACKGROUND

In the pre-surgical evaluation of people with focal epilepsy and a normal MRI, Morphometric Analysis Program v2018 (MAP18) aids in detecting visually inconspicuous focal cortical dysplasia (FCD). We investigated the impact of MRI scans with reduced signal-to-noise ratio (SNR) and spatial resolution (SR) on FCD detection by MAP18, aiming to improve the chances of achieving seizure freedom through epilepsy surgery.

METHODS

Thirty MRI scans with the identified lesion using MAP18 radiologically confirmed as FCD by a neuroradiologist, were retrospective analysed. SNR and SR were artificially reduced in ten steps, and their impact on MAP18 outcomes was assessed using multilevel analysis.

RESULTS

There was a significant effect after reducing SR and SNR for z-score and volume of the FCD cluster, the total number of detected clusters, and volume of these clusters. After SNR reduction, there was also a significant effect for z-score of the total number of detected clusters. FCD became undetectable by MAP18 after six steps of SR reduction (voxel size 2.8 × 2.8 × 2.8 mm³) and after two steps of SNR reduction.

CONCLUSIONS

This exploratory study suggests that reduced SR and SNR negatively affect FCD detection with MRI post-processing (MAP18). The MAP18 evaluator should screen MRI quality before post-processing, particularly for scans with significant visual noise or voxel sizes of 2.8 × 2.8 × 2.8 mm³ and upwards, as repeating a low-quality MRI scan is less burdensome than the adverse effects of continued seizures due to failure to detect FCD.

Three-dimensional MRI sequences in MS diagnosis and research

The most recent guidelines for magnetic resonance imaging (MRI) in multiple sclerosis (MS) recommend three-dimensional (3D) MRI sequences over their two-dimensional (2D) counterparts. This development has been made possible by advances in MRI scanner hardware and software. In this article, we review the 3D versions of conventional sequences, including T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR), as well as more advanced scans, including double inversion recovery (DIR), FLAIR2, FLAIR*, phase-sensitive inversion recovery, and susceptibility weighted imaging (SWI).

A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features

Detection of masses and micro calcifications are a stimulating task for radiologists in digital mammogram images. Radiologists using Computer Aided Detection (CAD) frameworks to find the breast lesion. Micro calcification may be the early sign of breast cancer. There are different kinds of methods used to detect and recognize micro calcification from mammogram images. This paper presents an ELM (Extreme Learning Machine) algorithm for micro calcification detection in digital mammogram images. The interference of mammographic image is removed at the pre-processing stages. A multi-scale features are extracted by a feature generation model. The performance did not improve by all extracted feature, therefore feature selection is performed by nature-inspired optimization algorithm. At last, the hybridized ELM classifier taken the selected optimal features to classify malignant from benign micro calcifications. The proposed work is compared with various classifiers and it shown better performance in training time, sensitivity, specificity and accuracy. The existing approaches considered here are SVM (Support Vector Machine) and NB (Naïve Bayes classifier). The proposed detection system provides 99.04% accuracy which is the better performance than the existing approaches. The optimal selection of feature vectors and the efficient classifier improves the performance of proposed system. Results illustrate the classification performance is better when compared with several other classification approaches.