In recent years there has been an increased thrust to understand and quantify the complex information conveyed by medical images. Developing modern computational techniques that offer the potential for extracting diverse and complex information from imaging data and applying these to a plethora of clinical studies is crucial. These techniques not only support precise quantification but also overcome the limitations of subjective visual interpretation. Furthermore, these methods can facilitate finding specific markers that relate to pathologies as well as aid in treatment planning.
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Optimal definitions for computing HKA angle in caos: an in-vitro comparison study Authors: Guillaume Dardenne, Bhushan Borotikar, Hoel Letissier, Ahmed Zemirline, Eric Stindel
Assessment and comparison of image quality between two real-time sequences for dynamic MRI of distal joints at 3.0 Tesla Authors: Marc Garetier, Jean Rousset, Karim Makki, Sylvain Brochard, François Rousseau, Douraïed Ben Salem, Bhushan Borotikar
Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning Authors: Arnaud Boutillon, Asma Salhi, Valérie Burdin, Bhushan Borotikar
Multimodal Brain Connectomics-Based Prediction of Parkinson’s Disease Using Graph Attention Networks Authors: Apoorva Safai, Nirvi Vakharia, Shweta Prasad, Jitender Saini, Apurva Shah, Abhishek Lenka, Pramod Kumar Pal, Madhura Ingalhalikar
Neurite orientation dispersion and density imaging in cocaine use disorder Authors: Jalil Rasgado-Toledo, Apurva Shah, Madhura Ingalhalikar, Eduardo A Garza-Villarreal
