Update media.md

media.md

@@ -9,9 +9,9 @@ Below, you find the following highlights:
 - [Topology aware learning for medical imaging](#topology-aware-learning-for-medical-imaging)
 - [Shape and appearance modelling](#shape-and-appearance-modelling)
 - [Geometric machine learning](#geometric-machine-learning)
-- [Cross-disciplinary applications](#Cross-disciplinary-applications )
-- [Synchrotron imaging for validation and prediction models of diffusion MRI](#Synchrotron-imaging-for-validation-and-prediction-models-of-diffusion-MRI)
-- [Light-sheet Fluorescence microscopy is crosslinked with MRI ](#Light-sheetFluorescence-microscopy-is-crosslinked-with-MRI)
+- [Cross-disciplinary applications](#cross-disciplinary-applications )
+- [Synchrotron imaging for validation and prediction models of diffusion MRI](#synchrotron-imaging-for-validation-and-prediction-models-of-diffusion-MRI)
+- [Light-sheet Fluorescence microscopy is crosslinked with MRI ](#light-sheet-fluorescence-microscopy-is-crosslinked-with-MRI)
 
 ## Magnetic resonance imaging of the brain
 Magnetic resonance imaging (MRI) is an extremely versatile modality that allows us to acquire 3D images of the **brain** with many different “contrasts” leading to the collection of 4D or sometimes N-D volumes. **Modeling** these images means applying and developing **signal/image processing**, **biophysical models**, and **mathematical procedures** to report summary parameters that can reveal the presence of pathology like stroke, edema, trauma, neurodegeneration,  demyelination, etc. There is a variety of MRI techniques like functional and structural MRI (relaxometry, susceptibility, etc.).