Campuses:

Image Reconstruction

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.
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.
Thursday, September 7, 2017 - 2:55pm - 3:30pm
Jennifer Mueller (Colorado State University)
In Ultrasound Tomography (UST) transducers around the boundary of a medium measure the scattered acoustic waves arising from transmitted pulses emitted from the transducers. In medical applications, the possibilities for transducer placement are constrained by the geometry of the human body. In this talk, optimal transducer placement and excitation patterns will be discussed for several geometries, and their effects on image quality and computation time will be compared for simulated medical imaging data.
Monday, August 14, 2017 - 10:00am - 10:45am
Andreas Menzel (Paul Scherrer Institute)
Ptychography comprises sampling and analyzing the object’s spatial spectrogram by windowed (or “short-space”) Fourier transforms. Its capability to reconstruct both image and illumination, as well as other experimental conditions including instabilities has proven promising for high-resolution X-ray microscopy.
Tuesday, August 15, 2017 - 2:00pm - 2:45pm
James Fienup (University of Rochester)
Phase retrieval has grown in popularity in recent years on account of its success in x-ray coherent diffractive imaging, including ptychography. Our development of phase retrieval for image reconstruction will be reviewed, including both passive and active imaging, in both optics and x-rays, enabling imaging from the nano-scale to the astronomical scale. Iterative transform and nonlinear optimization algorithms will be compared.
Monday, June 6, 2011 - 3:00pm - 4:00pm
Dianne O'Leary (University of Maryland)
Forming the image from a CAT scan and taking the blur out of vacation pictures are problems that are ill-posed. By definition, small changes in the data to an ill-posed problem make arbitrarily large changes in the solution. How can we hope to solve such problems when data are noisy and computer arithmetic is inexact?

In this talk we discuss the use of calibration data, side conditions, and bias constraints to improve the quality of solutions and our confidence in the results.
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