Image coregistration: quantitative processing framework for the assessment of brain lesions.

Area of Science:

• Medical Imaging
• Neuroscience
• Biomedical Engineering

Background:

• Quantitative assessment of brain lesions requires coregistering MRI parameters.
• Existing frameworks lack generalization and adaptability for diverse clinical and research needs.
• There is a need for an intuitive, automated quantitative processing framework.

Purpose of the Study:

• To construct a generalized and adaptable quantitative post-processing framework for brain lesion analysis.
• To enable intuitive, near real-time, in-depth voxel-wise analysis of multiparametric MRI data.
• To validate the framework's functionality using brain tumor MRI cases.

Main Methods:

• Developed a framework using Matlab and SPM software components.
• Created Matlab routines for coregistration transform extraction, ROI specification, and voxel value storage.
• Input coregistered MRI sequences for multiparametric analysis and model derivation.

Main Results:

• Successfully implemented an intuitive quantitative post-processing framework.
• Validated framework functionality with brain tumor MRI cases.
• Demonstrated increased usage of post-processing and simultaneous research activities.

Conclusions:

• Common software components can create an intuitive, real-time quantitative post-processing framework.
• The framework improves scalability and adoption for diagnostic question answering.
• Facilitates multiparametric voxel-wise analysis for improved clinical and research insights.