Computational Methods in Medical Image Reconstruction
( 30 Modules )
Module #1 Introduction to Medical Image Reconstruction Overview of medical imaging modalities, importance of image reconstruction, and course objectives
Module #2 Mathematical Foundations of Image Reconstruction Review of linear algebra, Fourier transform, and optimization techniques
Module #3 Image Formation Models Physical principles of X-ray, CT, MRI, and PET imaging; forward models for each modality
Module #4 Image Reconstruction Problem Formulation Inverse problem formulation, ill-posedness, and regularization techniques
Module #5 Algebraic Reconstruction Techniques (ART) ART algorithm, its variants, and applications in X-ray and CT
Module #6 Filter Backprojection (FBP) FBP algorithm, its variants, and applications in X-ray and CT
Module #7 Iterative Reconstruction Methods Overview of iterative methods, including Expectation-Maximization (EM) and Maximum Likelihood (ML)
Module #8 Statistical Image Reconstruction Bayesian inference, Markov Chain Monte Carlo (MCMC), and applications in PET and SPECT
Module #9 Compressed Sensing in Medical Imaging Introduction to compressed sensing, application in MRI and CT
Module #10 Machine Learning in Medical Image Reconstruction Introduction to machine learning, application in image reconstruction, and deep learning techniques
Module #11 Image Reconstruction in X-ray CT Specific challenges and solutions in X-ray CT reconstruction, including beam hardening and scatter correction
Module #12 Image Reconstruction in MRI Specific challenges and solutions in MRI reconstruction, including parallel imaging and compressed sensing
Module #13 Image Reconstruction in PET and SPECT Specific challenges and solutions in PET and SPECT reconstruction, including attenuation correction and scatter correction
Module #14 Image Reconstruction in Ultrasound Specific challenges and solutions in ultrasound image reconstruction, including beamforming and artifacts correction
Module #15 Image Reconstruction in Optical Imaging Specific challenges and solutions in optical imaging reconstruction, including diffuse optical imaging and optical coherence tomography
Module #16 Image Reconstruction for Motion Correction Methods for motion correction in medical imaging, including registration and motion modeling
Module #17 Image Reconstruction for Limited-View and Incomplete Data Methods for reconstruction from limited-view and incomplete data, including interpolation and extrapolation techniques
Module #18 Image Reconstruction for Multi-Modality Imaging Methods for combining data from multiple modalities, including fusion and registration techniques
Module #19 Computational Challenges in Medical Image Reconstruction Discussion of computational challenges, including parallelization, GPU acceleration, and big data handling
Module #20 Validation and Evaluation of Image Reconstruction Algorithms Methods for evaluating image reconstruction algorithms, including metrics and benchmarking datasets
Module #21 Clinical Applications of Image Reconstruction Examples of clinical applications of image reconstruction, including cancer diagnosis and treatment planning
Module #22 Current Trends and Future Directions Discussion of current trends and future directions in computational methods for medical image reconstruction
Module #23 Case Studies in Medical Image Reconstruction Real-world case studies in medical image reconstruction, including examples from industry and academia
Module #24 Practical Implementation of Image Reconstruction Algorithms Hands-on experience with implementing image reconstruction algorithms using popular programming languages and software packages
Module #25 Image Reconstruction for Emerging Applications Discussion of emerging applications of image reconstruction, including image-guided therapy and personalized medicine
Module #26 Multi-Disciplinary Collaboration in Medical Image Reconstruction Importance of collaboration between clinicians, engineers, and computer scientists in medical image reconstruction
Module #27 Ethical Considerations in Medical Image Reconstruction Discussion of ethical considerations in medical image reconstruction, including data privacy and bias
Module #28 Image Reconstruction for Big Data Analytics Methods for handling large datasets in medical image reconstruction, including cloud computing and distributed processing
Module #29 Image Reconstruction for Real-Time Imaging Methods for real-time image reconstruction, including GPU acceleration and parallel processing
Module #30 Course Wrap-Up & Conclusion Planning next steps in Computational Methods in Medical Image Reconstruction career
