Fundamentals and applications of medical image reconstruction and processing. Reconstruction from non-uniformly sampled data, projection data, regularly/randomly undersampled data. Parallel imaging and compressed sensing for medical imaging. Improving image quality, denoising, deconvolution, off-resonance correction. Post-processing of images, image registration, image segmentation. Examples from magnetic resonance imaging (MRI), X-ray computed tomography (CT), and magnetic particle imaging (MPI).
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All homework assignments should be completed and submitted. Grades, except for the final project, should justify a letter grade of D or better.
Introduction, Multi-dimensional Fourier Transform Imaging overview: MRI, CT, MPI Cartesian Sampling and Reconstruction, Data sampling and reconstruction in 1D, Data sampling and reconstruction in 2D/3D Image and Frequency Domain Reconstruction: Projection reconstruction (CT, MPI), Partial Fourier reconstruction in MRI Image and Frequency Domain Reconstruction: Non-Cartesian reconstructions: gridding, NUFFT, Examples from MRI, MPI and CT Improving Image Quality: Image denoising, Image deconvolution Improving Image Quality: Off-resonance correction in MRI, Correction of timing errors Parallel Imaging: Encoding in Image and Frequency Domains, Phased-arrays in MRI Parallel Imaging: SENSE and GRAPPA algorithms, Coil compression Compressed Sensing: Random undersampling Compressed Sensing: Sparsity/compressibility and nonlinear recovery, Model-based reconstructions Medical Image Registration: Rigid and non-rigid registration, Surface-based registration Medical Image Registration: Multi-modal registration and image fusion Medical Image Segmentation: Edge-based and region-based segmentation, Atlas-based segmentation, Computer-aided diagnosis ECTS - Workload Table: Activities Number Hours Workload Homework 5 10 50 Project (including preparation, interview and presentation) 1 58 58 Course hours 14 3 42 Total Workload: 150 Total Workload / 30: 150 / 30 5 ECTS Credits of the Course: 5 Type of Course: Lecture Course Material: Written - PP Teaching Methods: Lecture - Presentations