A multi-contrast MRI approach to thalamus segmentation.

Thalamic alterations occur in many neurological disorders including Alzheimer's disease, Parkinson's disease and multiple sclerosis. Routine interventions to improve symptom severity in movement disorders, for example, often consist of surgery or deep brain stimulation to diencephalic nuclei. Therefore, accurate delineation of grey matter thalamic subregions is of the upmost clinical importance. MRI is highly appropriate for structural segmentation as it provides different views of the anatomy from a single scanning session. Though with several contrasts potentially available, it is also of increasing importance to develop new image segmentation techniques that can operate multi-spectrally. We hereby propose a new segmentation method for use with multi-modality data, which we evaluated for automated segmentation of major thalamic subnuclear groups using T1 -weighted, T2* -weighted and quantitative susceptibility mapping (QSM) information. The proposed method consists of four steps: Highly iterative image co-registration, manual segmentation on the average training-data template, supervised learning for pattern recognition, and a final convex optimisation step imposing further spatial constraints to refine the solution. This led to solutions in greater agreement with manual segmentation than the standard Morel atlas based approach. Furthermore, we show that the multi-contrast approach boosts segmentation performances. We then investigated whether prior knowledge using the training-template contours could further improve convex segmentation accuracy and robustness, which led to highly precise multi-contrast segmentations in single subjects. This approach can be extended to most 3D imaging data types and any region of interest discernible in single scans or multi-subject templates.

Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution.

Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L2 fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods while keeping low CPU time. Our improvements are appraised on both clinical assessment and statistical analysis.

Proteínas que intervienen en la homeostasis bucal / Proteins that intervene in bucal homoestasis

Se presentan algunos aspectos generales de un grupo de proteínas que recientemente han sido descritas y cuya función es determinante para el mantenimiento de la homeostasis de la cavidad bucal. Estas proteínas son las histatinas, antibióticos naturales; las cistatinas, que inhiben la acción de las proteasas bacterianas en la enfermedad periodontal; y la estaterina, que regula la precipitación del dosfato de calcio en la saliva. Las histatinas, cistatinas y estaterina han sido aisladas, caracterizadas y secuenciadas. Se piensa que en un futuro no muy lejano estas moléculas podrán ser utilizadas para dar una mejor calidad de vida a pacientes con infecciones bucales causadas por hongos o con otras enfermedades relacionadas