Persistent Reeb Graph Matching for Fast Brain Search.

Area of Science:

• Neuroimaging
• Computational Anatomy
• Medical Image Analysis

Background:

• Large-scale human neuroimaging datasets present significant computational challenges for analysis.
• Efficiently searching and comparing brain structures is crucial for understanding neurological conditions and development.
• Current methods for comparing cortical surfaces may lack efficiency or robustness to topological variations.

Purpose of the Study:

• To develop a novel, efficient algorithm for searching similar brains within large magnetic resonance (MR) imaging collections.
• To represent and quantify differences in cortical surface geometry using Reeb graphs derived from Laplace-Beltrami eigenfunctions.
• To address topological noise in Reeb graphs through progressive pruning and matching based on critical point persistence.

Main Methods:

• Construction of Reeb graphs from Laplace-Beltrami eigenfunctions of cortical surfaces.
• Development of a progressive pruning and matching algorithm utilizing critical point persistence to handle topological noise.
• Calculation of distances between Reeb graphs of cortical surfaces in under 10 milliseconds on a standard PC.

Main Results:

• The proposed algorithm achieves highly efficient distance calculation between cortical surfaces.
• Demonstrated successful application on a dataset of 1326 brains for searching, clustering, and automated labeling.
• The method proves valuable for 'Big Data' challenges in human neuroimaging research.

Conclusions:

• The novel Reeb graph-based algorithm offers an efficient and robust solution for comparing cortical surfaces.
• This approach significantly advances the capability to analyze large-scale neuroimaging datasets.
• The method holds promise for accelerating discoveries in human brain research through Big Data analytics.