Monalisa is a Matlab/C++ MRI reconstruction toolbox aiming to redefine MR imaging by delivering fast, robust, and high-quality reconstructions.

The document is provided on the website.

The package is available on GitHub.

The official codes are available on GitHub for the paper: 

Machine learning algorithms on eye tracking trajectories to classify patients with spatial neglect

Recent fMRI acceleration techniques have allowed for sub-second temporal resolution, but are still impacted by decreased SNR, inter-slice crosstalk, and eddy current artifacts. A novel acquisition and reconstruction approach, dubbed Hi-Fi fMRI, combines a spiral phyllotaxis sampling trajectory, a compressed-sensing algorithm, and application of motion correction directly to k-space to obtain minimal distortions and high SNR, as well as a 250 msec temporal resolution (chosen during image reconstruction). Here are the protocols for the experiments to activate visual cortices, auditory cortices, and the cortices for resting-state.

• Hi-Fi fMRI visual

• Hi-Fi fMRI audio

• Hi-Fi resting state

The protocols for the projects MR-Eye and MR-Eye-Track.

MR-Eye 2.0: Motion poses significant challenges to diagnostic accuracy and treatment planning in Ophthalmology. The project investigates a clinical 3-Tesla MRI protocol for eyes that achieves high spatial resolution and resilience to eye motion. To achieve this, a motion artifact correction strategy is integrated into a compressed sensing (CS) image reconstruction that leverages eye-tracking signals. This motion-robust reconstruction is demonstrated on a continuous gradient recalled echo (GRE) sequence with fat suppression (LIBRE). 

MR-Eye-Track: The project aims to introduce a novel approach relying on the hypothesis that supervised machine learning (ML) algorithms can learn from the raw MRI data the patterns associated with eye motion and specific eye positions, thereby eliminating the need for an ET to obtain 3D motion-resolved eye images. 

The dataset of Eye-Tracking trajectories of patients with spatial neglect

Patients with right hemisphere damage and visual neglect have severe problems to orient attention towards left-sided objects, often associated with the tendency to produce inappropriate rightward saccades. In its most severe form, this tendency can assume the compulsive character of a rightward deviation of gaze as soon as the visual scene deploys (so-called “magnetic attraction of gaze”). However, little is known about the exact nature of inappropriate rightward saccades, their relation with impaired conscious perception of left-sided stimuli, and their lesional correlates. To explore these issues, we studied three groups of patients with right brain damage: patients with signs of left visual neglect associated to left homonymous hemianopia, neglect patients without hemianopia, and patients without neglect or hemianopia. Participants searched for a gap missing within a target, presented among distractors. Manual responses for target detection were required, while participants were encouraged to move their eyes during search. Endogenous attention could be summoned to the target location by a central cue. 

MR-Eye atlas is a novel digital atlas constructed from MR images (T1-weighted MRI acquired at 1.5T) of a large-scale population of healthy volunteers. It gathers a male and female structural atlas constructed from 594 males and 616 females, with their corresponding probability maps of the different labels projected onto the average respective male and female templates. The atlases include 9 regions of interesting lens, globe, optic nerve, intraconal and extraconal fat, and four rectus muscles (lateral, medial, inferior, and superior). 

Cardiac 2D Cine MRI Dataset is 2D CINE MRI data acquired once with a cartesian trajectory and once with a radial trajectory in four chamber view on helthy volunteers. Each data set is accompanied with an example of a reconstructed image produced with the Monalisa reconstruction toolbox.