Publication

NiGSP: Graph Signal Processing on multimodal MRI data

2023

Résumé

NiGSP is a python-based toolbox aimed at facilitating the adoption of graph signal processing with an emphasis on multimodal brain imaging data. We present its standard workflow, that allows basic filtering operations and metrics computations, and we introduce a novel application to cerebral stroke consisting in the creation of a subject-specific anatomical lesion-based filter to be applied on functional MRI timeseries.

Source officielle

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2023

Résumé

Source officielle

Concepts associés (19)

Medical image computing

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics and medicine. This field develops computational and mathematical methods for solving problems pertaining to medical images and their use for biomedical research and clinical care. The main goal of MIC is to extract clinically relevant information or knowledge from medical images.

Computational anatomy

Computational anatomy is an interdisciplinary field of biology focused on quantitative investigation and modelling of anatomical shapes variability. It involves the development and application of mathematical, statistical and data-analytical methods for modelling and simulation of biological structures. The field is broadly defined and includes foundations in anatomy, applied mathematics and pure mathematics, machine learning, computational mechanics, computational science, biological imaging, neuroscience, physics, probability, and statistics; it also has strong connections with fluid mechanics and geometric mechanics.

Large deformation diffeomorphic metric mapping

Large deformation diffeomorphic metric mapping (LDDMM) is a specific suite of algorithms used for diffeomorphic mapping and manipulating dense imagery based on diffeomorphic metric mapping within the academic discipline of computational anatomy, to be distinguished from its precursor based on diffeomorphic mapping. The distinction between the two is that diffeomorphic metric maps satisfy the property that the length associated to their flow away from the identity induces a metric on the group of diffeomorphisms, which in turn induces a metric on the orbit of shapes and forms within the field of Computational Anatomy.

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