The Corpus callosum (CC) is a massive white matter structure in the brain, and changes in its shape and volume are associated with subject characteristics, several diseases, and clinical conditions. The CC is mostly studied in magnetic resonance imaging (MRI), where it is segmented to extract valuable information. With the increasing availability of MRI data and the proliferation of automated algorithms to perform CC segmentation, quality control (QC) verification is mandatory to assure reliability in the entire analysis pipeline. We propose a convolutional neural network (CNN) for QC of CC segmentations. The CNN gets information on the mask and contextual information on the image and performs deep feature extraction using a pre-trained model. The CNN model was fine-tuned using T1-weighted MR images with CC masks, in the task of classifying correct or incorrect segmentations. The CNN-based approach got an area under the curve (AUC) of 97.98% on the test set. We used an additional test set of patients with tumor to test generalization capability of the trained model to other domains.
Magnetic resonance spectroscopic imaging (MRSI) has been widely used for studying metabolic alterations in brain-related pathologies, especially due to its non-invasiveness. Even though some software for MRSI data analysis have been developed, only a few are used by biomedical researchers and in a clinical setting routine, as their use still poses a challenge in keeping the trade-off between information content and ease of implementation. Aiming to increase MRSI analysis automation, our study proposes an open-source toolbox for analysis, automatic spectra quality control and MRSI data visualization. The proposed toolbox allows the visual inspection of all spectra. It makes possible the automated selection of spectra of interest using two different approaches: by clustering them using Pearson’s correlation coefficient or by discarding spectra based on spectral quality metrics. Using the magnetic resonance imaging (MRI) content, the toolbox provides information about the region from which the MRSI grid were acquired, such as the brain tissue ratio in each voxel: white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). Once spatial and spectral information is combined, spatial averaging over anatomically defined regions of interest (ROIs) can be applied, for instance, by averaging the spectra and fitting the result. The proposed toolbox aims to simplify and automate MRSI analysis, being easy to install and to use.
Magnetic resonance spectroscopy (MRS) has been widely used for studying metabolic changes in rheumatic, neurodegenerative diseases and several other types of pathologies. Nevertheless, the accurate measurement of brain metabolite concentrations is still problematic and challenging, specially for multivoxel MR Spectroscopic Imaging (MRSI) data. There is a collection of artifacts and spectra are acquired from a region containing mixed tissues: white matter (WM), grey matter (GM) and cerebrospinal uid (CSF) composition. However, the studies are interested in analyzing metabolite changes in a particular brain tissue or structure. Therefore, our work proposes a pipeline for automatic selection of spectra of interest, a subset of spectra from MRSI acquisitions based on MRI content analysis and spectral quality metrics. The proposed pipeline helps to improve multivoxel spectroscopy analysis and estimates of metabolite concentrations, by eliminating spectra outside the tissue or structure of interest and identifying noisy spectra.
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