Compressed Sensing

Friday, October 18, 2019 - 9:05am - 9:50am
Mujdat Cetin (University of Rochester)
We present synthetic aperture radar (SAR) as a computational imaging modality, emphasizing aspects of radar that differentiate it from other imaging problems. In this context, we present samples of work resulting from two related lines of inquiry in our group: (1) sparsity-driven radar imaging, and (2) machine learning for radar imaging.
Thursday, October 17, 2019 - 4:15pm - 5:00pm
Florian Knoll (NYU Langone Medical Center)
In this talk, I will provide an introduction to the use of machine learning and convolutional neural networks (CNNs) in the area of MR image reconstruction. Building on a general framework of inverse problems and variational optimization, I will focus on application examples from image reconstruction for accelerated Magnetic Resonance (MR) imaging. I will cover both methodological developments as well as clinical translation and validation.
Monday, October 14, 2019 - 2:55pm - 3:45pm
Mariya Doneva (Philips Research Laboratory)
This lecture gives an overview of methods for scan time reduction in quantitative MRI based on regularized image reconstruction. Besides the generic constraints that can be used for image series, the known signal model in quantitative MRI permits designing a model-based constraint tailored to the specific application. This is a much stronger prior knowledge, which, provided that the model is accurate, enables even higher accelerations and improved image quality.
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