27th ISMRM Annual Meeting • 11-16 May 2019 • Montréal, QC, Canada

Weekend Educational Session
MRI Image Reconstruction: Nyquist & Non-Nyquist Techniques

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MRI Image Reconstruction: Nyquist & Non-Nyquist Techniques
Weekend Course

ORGANIZERS: Fernando Boada, Douglas Noll, Neville Gai

Sunday, 12 May 2019
Room 520A-F  13:30 - 16:30 Moderators:  ricardo otazo, Suchandrima Banerjee

Skill Level: Basic to Advanced

Session Number: WE-22

The availability of fast computing power and efficient software development environments has extended the range of image reconstruction algorithms available to the MRI community. In this course, common and cutting-edge image reconstruction algorithms for MRI data reconstruction will be described and demonstrated.

Target Audience
MRI scientists interested in learning and/or implementing image reconstruction algorithms and clinicians interested on learning the essential features of these algorithms.

Educational Objectives
As a result of attending this course, participants should be able to:
- Show a theoretical understanding of the advantages and practical constraints for a broad range of direct and iterative MRI image reconstruction algorithms; and
-Demonstrate steps required for the practical implementation of these algorithms using modern and user-friendly software development platforms.


  Spatial Encoding & Direct Inversion of Cartesian & Non-Cartesian Data
Nicole Seiberlich
Parallel Imaging
Tolga Cukur
A brief survey of the fundamental approaches to parallel imaging will be presented, followed by recent application to multi-acquisition or multi-parametric MRI.

Compressed Sensing
Jonathan Tamir
Compressed sensing takes advantage of sparsity, incoherent sampling, and non-linear reconstruction algorithms to reduce acquisition requirements far below the Nyquist rate. This talk will provide an overview of these concepts and show how they can be used to accelerate MRI. Compressed sensing MRI examples will be discussed, including its combination with parallel imaging and application to dynamic imaging.

  Break & Meet the Teachers
Low Rank Plus Sparse Reconstruction
Jong Chul Ye
In this course, we will review the recent MR researches using low rank and sparse reconstruction. First, basic compressed sensing theory for  MR reconstruction method is first reviewed, after which the low-rank image model is described. Finally, sparse + low rank model is introduced as an novel image modeling for accelerated MRI and artifact removal.

  Using Machine Learning for Image Reconstruction
Florian Knoll
This talk will provide an introduction to the use of machine learning and convolutional neural networks (CNNs) in the area of MR image reconstruction from undersampled acquisitions. We will discuss approaches that are based on iterative reconstruction methods that are commonly used in compressed sensing (CS) as well as purely data driven approaches. Using selected examples, we will discuss both advantages and challenges, covering topics like reconstruction time, design of the training procedure, error metrics and training efficiency and validation of image quality.

  Practical Implementation of Efficient Off-Line Image Reconstruction Pipelines
Hui Xue
  Adjournment & Meet the Teachers
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