Image Quality Evaluation of Deep Learning Image Reconstruction and Denoising in Clinical CT
Explore advanced techniques for assessing image quality in CT scans using deep learning, focusing on reconstruction and denoising methods for improved clinical outcomes.
Modeling Prior Information to Guide Image Reconstruction in Radiation Therapy
Explore innovative techniques for incorporating prior knowledge into image reconstruction algorithms, enhancing accuracy and efficiency in radiation therapy planning and delivery.
Deep Learning Guided Reconstruction and Processing for PET, SPECT, and CT
Explore advanced reconstruction techniques using deep learning for medical imaging modalities, enhancing image quality and diagnostic accuracy in PET, SPECT, and CT scans.
AI in Molecular Imaging
Explore AI applications in molecular imaging, focusing on deep reconstruction techniques and their impact on medical diagnostics and research.
Prior Knowledge-Driven Machine Intelligence to Maximize the Potential of Low-Dose Spectral CT
Explore machine intelligence techniques leveraging prior knowledge to enhance low-dose spectral CT imaging, maximizing its potential for improved medical diagnostics and reduced radiation exposure.
Computer Vision and Image Processing - Fundamentals and Applications
Comprehensive exploration of computer vision and image processing, covering fundamentals, techniques, and applications. Includes image formation, enhancement, segmentation, feature extraction, machine learning, and deep learning for visual data analysis.
Behind the Console: Scanning with AIR Recon DL - PROPELLER and 3D Cube
Discover the benefits of MR 30 for SIGNA™ MRI scanner upgrade, featuring AIR™ Recon DL with expanded 3D and PROPELLER sequences for personalized, end-to-end clinical solutions.
Rapid Cardiac MR Using Highly Accelerated Deep-Learning Reconstructed Cine
Explore highly accelerated deep-learning reconstructed cine techniques for rapid cardiac MR imaging, enhancing efficiency in radiology diagnostics.
Tomographic Image Reconstruction with Julia
Explore Julia's capabilities for tomographic image reconstruction, focusing on large-scale inverse problems and matrix-free methods. Learn about package ecosystems for different imaging techniques.
An Introduction to Autoencoders - In Python
Learn to build and implement autoencoders in Python for image compression, reconstruction, and various applications like denoising and anomaly detection.
Рентгеновская компьютерная томография
Изучите принципы и применение рентгеновской компьютерной томографии в геологии и медицине. Освойте методы анализа и интерпретации томографических данных.
Computational Imaging and Mask-Based Techniques - SPACE West Webinar
Explore computational imaging techniques, including mask-based imaging and lensless imaging, with Prof. Salman Asif. Learn about depth reconstruction, simulation results, and practical applications.
Customized CT Workflow and Task-Driven Imaging Techniques
Explore advanced CT imaging techniques, including customized workflows, task-driven imaging, and novel acquisition methods for improved image quality and task performance in medical diagnostics.
Hyperspectral Neutron Imaging and Tomographic Reconstruction - Challenges and Methods
Explore advanced tomographic reconstruction techniques for neutron CT and cryoEM, focusing on challenges, model-based approaches, and applications in materials science and chemical mapping.
SPACE Webinar: X-ray CT Scatter Modeling and Estimation
Explore advanced X-ray CT techniques, including scatter modeling, neural networks, and minimax problems, to enhance image quality and reduce artifacts in medical imaging.
Model Error in CT Reconstruction with Uncertain View Angles
Explore innovative methods for handling model errors in inverse problems, focusing on CT reconstruction with uncertain view angles and improving image quality through uncertainty quantification.
From Data Crimes to Self-Supervised Reconstruction: Advancing AI-based MRI
Explore AI-based MRI advancements, from data crime prevention to self-supervised reconstruction techniques, with insights from UC Berkeley's EECS expert.
Ultra-low-field MRI for Healthcare - A Future Powered by Computing?
Explore the potential of ultra-low-field MRI in healthcare, focusing on computational advancements and future applications in medical imaging.
Noise-Bias and Contrast-Noise Analysis in Medical Imaging
Explore key metrics and concepts in medical imaging analysis, including noise, bias, contrast, and their relationships. Gain insights into image quality assessment and reconstruction methods.
Statistical Methods for Medical Image Processing and Reconstruction
Explore statistical methods for medical image processing and reconstruction, from basic to advanced Bayesian approaches, across various imaging modalities.
Machine Learned Regularisation for Solving Inverse Problems
Machine learned regularization techniques for solving inverse problems, focusing on image reconstruction from tomographic and blurred measurements. Explores data-driven approaches and discusses open mathematical challenges.