Sparse Coding and Auto-encoders for Advanced and Robust Processing of Biomedical Images

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Application of DSP in medical image processing has become an integral part of nearly every procedure, machine and system utilized in medical centers today. The overall objective of the proposed research program is to develop novel signal and image processing algorithms and implement robust and reliable techniques for medical imaging systems. Deep convolutional neural networks and compressed sensing approaches will be investigated throughout this research. The fundamental and theoretical elements of the research include image analysis, formation, enhancement, registration, reconstruction and modeling. In dynamic cardiac cine Magnetic Resonance Imaging (MRI), scan times are lengthy, because the spatial frequency information is retrieved sequentially and the image will be formed only after all the frequency information is gathered. Therefore, in these acquired images, the spatio-temporal resolution is limited by the low imaging speed. It is a demanding task to obtain cardiac sequences with better spatio-temporal resolution to capture the dynamic activity of the heart. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatio-temporal resolution. Another main purpose of this research proposal is to improve CS reconstruction of under-sampled data by exploiting spatio-temporal sparsity and efficient spiral trajectories. In addition, in the reconstruction of highly accelerated dynamic MRI data corresponding to cardiac perfusion and cardiac cine, the main challenge is the sensitivity of the methods to large inter-frame motion. Specifically, the compactness of the signal representation decreases with inter-frame motion, thus restricting the maximum possible acceleration. The reconstructions often suffer from temporal blurring and motion-related artifacts at higher acceleration factors. To address the above challenge, several motion-estimation and compensation schemes will be developed. We aim to address the problem of motion artifacts including respiratory and cardiac motion in compressed sensing reconstructions. Computed Tomography (CT) is considered to be a high-dose application and is harmful to patient's health specially in repeated follow ups and in pediatrics. To reduce the exposure effect, the imaging dosage can be lowered, which leads to reduced signal to noise ratio. The second part of this research proposal will focus on developing signal processing techniques for denoising low-dose CT images, utilizing the sparse representations. Different sparse coding and dictionary learning techniques will be proposed. Deep learning and reinforcement learning approaches will be investigated and novel algorithms will be developed to learn directly from the normal-dose CT images. By learning various low-level to high-level features from low-dose and normal-dose images, we will generate high-quality noise free CT images from low-dose images. We are collaborating with the Dept. of Medical Imaging, Univ. of Saskatchewan.