QML Powered Interface for Diffusion Imaging

Rupali Jadhav; Ajay Jadhav; Vinay Ghate; Gitesh Mahadik; Praneeth Shetty

doi:10.32628/CSEIT2410329

Authors

Rupali Jadhav Assistant Professor, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Ajay Jadhav BE Scholar, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Vinay Ghate BE Scholar, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Gitesh Mahadik BE Scholar, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India Author
Praneeth Shetty BE Scholar, Department of Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India Author

DOI:

https://doi.org/10.32628/CSEIT2410329

Keywords:

Diffusion Imaging, Query Markup Language, Python technology, user-friendly interface, Medical Imaging, Accessibility, Technology Adoption

Abstract

In the field of medical imaging, Diffusion Imaging (DI) has emerged as a powerful technique for investigating the microstructural properties of biological tissues. However, the complexity of DI analysis software often poses a significant barrier to its widespread adoption, as it typically requires proficiency in Python programming and command-line interactions. This technical barrier can limit the accessibility of DI technology to individuals without extensive technical expertise, hindering its potential impact in various medical and research applications To address this challenge, we propose a novel solution that leverages the capabilities of Query Markup Language (QML) to develop a user-friendly interface for Diffusion Imaging. By combining the power of Python technology, which forms the core of DI analysis, with the intuitive interface design capabilities of QML, our project aims to democratize DI analysis and make it accessible to a broader audience, including medical professionals, researchers, and students. Our research focuses on bridging the gap between the technical complexities of DI analysis and user accessibility. The proposed QML-powered interface will feature modern UI elements with fluid animations, ensuring a seamless and engaging user experience. Crucially, it will abstract away the intricacies of Python programming and command-line interactions, allowing users to concentrate on the analysis and interpretation of DI data without the burden of technical hurdles.

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