Key research themes
1. What advanced algorithmic approaches improve artifact detection and removal in scalp EEG signals and what are their application-specific challenges?
This research theme focuses on developing and reviewing advanced methods for detecting and removing artifacts from scalp EEG recordings. Accurate artifact handling is critical because artifacts can significantly misrepresent neural signals and affect clinical and research applications such as epilepsy monitoring, brain-computer interfaces, and neurological disorder diagnosis. The challenge lies in the complexity and variability of artifact sources (e.g., ocular, muscular, motion artifacts) and the overlap of artifacts with brain signals in temporal, spectral, and spatial domains, requiring sophisticated preprocessing beyond simple filtering or thresholding. Application-specific constraints such as real-time processing, single-channel data, and lack of reference channels further complicate method development.
2. How can deep learning and hybrid methodologies enhance metal artifact reduction (MAR) in X-ray computed tomography imaging?
This research area investigates advanced algorithmic techniques, particularly deep learning models such as convolutional neural networks (CNNs), to tackle the challenging problem of metal-induced artifacts in X-ray CT images. Metal artifacts arise due to beam hardening, photon starvation, and scattering from metallic implants, severely degrading image quality and clinical interpretability. Traditional MAR methods include physical effect correction, interpolation, and iterative reconstruction, but each has limitations. Recent approaches combine multiple techniques (hybrid methods) and leverage CNNs to fuse information, reduce artifacts, and preserve anatomical structures, demonstrating enhanced performance and potential for generalization.
3. What signal processing and transform-based methods effectively reduce noise and artifacts in imaging modalities such as CT, MRI, and seismic data while preserving structural fidelity?
This theme encompasses approaches leveraging mathematical transforms, interpolation, and statistical modeling for image denoising and artifact suppression across a diverse set of imaging applications, including computed tomography, magnetic resonance imaging, and seismic reflection data. The goal is to attenuate unwanted noise or artifacts such as ring artifacts, blurring, and incoherent noise without compromising essential structural details or contour preservation. Techniques include wavelet and curvelet transforms, Laplace-based interpolation, measurement reduction methods based on tomographic inversion theory, residual signal recovery via pattern-based filters, and image quality evaluation metrics. The tradeoff between noise reduction and detail preservation motivates the development of specialized algorithms and evaluation methodologies.
