Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a...

Area of Science:

• Biomedical Engineering
• Medical Imaging Analysis
• Signal Processing

Background:

• Biomedical image analysis often uses specialized techniques for different modalities like terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
• Current methods for signal pre-processing and classification are often developed independently within each imaging community, leading to a lack of standardization and potential for missed synergistic insights.
• Advancements in statistical signal processing, multi-resolution analysis, and fractional order calculus offer opportunities for cross-modal integration.

Purpose of the Study:

• To present a comprehensive review of recent advances in biomedical image analysis and classification for THz pulse imaging and DCE-MRI.
• To identify commonalities in data structures between THz and DCE-MRI to facilitate the development of a unified multi-channel data fusion framework.
• To explore the potential for improved software standardization and enhanced disease inference through cross-modal signal processing.

Main Methods:

• Discussion of signal pre-processing techniques including multi-resolution analysis and fractional order calculus.
• Review of statistical signal processing methods such as principal component analysis (PCA) and independent component analysis (ICA).
• Exploration of feature selection, extraction, and classification methods, including support vector machines (SVM), extreme learning machines (ELM), Clifford algebra classifiers, and deep learning.

Main Results:

• Identified commonalities in data structures between THz pulse imaging and DCE-MRI.
• Highlighted the importance of preserving textural information during feature selection.
• Presented advanced classification techniques suitable for multi-modal biomedical image data.

Conclusions:

• There is significant scope to develop a common multi-channel framework for signal processing in biomedical imaging, integrating techniques from THz and DCE-MRI.
• A unified framework can lead to better software standardization, particularly in the pre-processing and de-noising stages.
• Future work should focus on developing this unified framework to leverage synergies between imaging modalities for more accurate disease proliferation inference.