Daniel Herzka

William Grissom

Craig Meyer

This educational talk will cover the advantages and disadvantages of Cartesian and non-Cartesian sampling techniques.  Cartesian, radial, and spiral k-space scanning methods will be compared with respect to scan efficiency, hardware considerations, off-resonance effects, motion sensitivity, and scan acceleration.  

Mitsue Miyazaki

Motion sensitization techniques are used in various applications, such as flow imaging, black blood imaging, bright blood imaging, etc. Technical differences in motion/flow sensitization methods are discussed in this presentation. 

René Botnar

Over the past decade Magnetic Resonance Imaging (MRI) has become an increasingly important non-invasive tool in risk assessment and treatment monitoring of cardiovascular disease. However, despite ongoing progress and developments in MR acquisition and reconstruction technology, physiological motion remains a major problem in many cardiovascular MRI applications. Since MR acquisition is slow compared to physiological motion, the extensive cardiac and respiratory induced motion of the heart during the acquisition period can degrade image quality by introducing ghosting and blurring like motion artifacts. Several cardiac and respiratory motion compensation techniques have been proposed over the last two decades to overcome this problem. These techniques are based on minimizing or correcting the motion during the acquisition. This part of the Image Acquisition & Reconstruction Course at ISMRM 2017 will include an overview of some of these methods, discussing their strengths and limitations.

Xiaohong Joe Zhou

This lecture focuses on three pulse sequence strategies to increase spatial resolution, accelerate acquisition, and improve image quality while reducing artifacts.  First, strategies for reducing the field-of-view (FOV) are described using examples of spatial saturation, multi-dimensional RF excitation, and selective RF refocusing.  Second, reference scans are presented for measuring errors in k-space and enabling various phase corrections in echo-train pulse sequences.  Third, gradient pre-emphasis is discussed as an effective method to reduce the adverse effects caused by eddy currents in a variety of pulse sequences. Although these three topics may appear isolated, together they reflect a central theme of how to improve image quality and/or speed while avoiding artifacts.

Alexey Samsonov

Incomplete data sampling is an attractive approach to accelerate MRI but it requires prior knowledge-driven image reconstruction. Sparsity is a powerful concept that allows linking many different types of prior knowledge to the mathematical apparatus adopted in MR image reconstruction. Compressed sensing theory establishes conditions for optimal use of sparse representations for high quality MR image reconstruction from undersampled data. In this talk, we will cover the aforementioned concepts of advanced image reconstruction and demonstrate real examples of accelerated structural and dynamic MRI. We will also discuss both theoretical requirements of compressed sensing and essential aspects of its practical implementation.

Dan Ma

Quantitative MR measurements are essential to assess complex changes in the brain and monitor treatment outcomes. Although full quantitative multi-parametric acquisition has long been the goal of research in MR, the conventional methods typically provide information on a single parameter at a time, thus requiring significant scan time. The purpose of Magnetic Resonance Fingerprinting (MRF) is to introduce a new framework to data acquisition and post-processing that permits the simultaneous quantification of multiple tissue properties in a time efficient manner.

Mariya Doneva

This lecture explains the principles of model-based reconstruction methods and their linearization using dictionaries for MR parameter mapping.  

Steen Moeller

Sajan Goud Lingala

Mathews Jacob

Novel image and patch manifold models that can exploit the non-linear and non-local redundancies in a dynamic dataset will be introduced. Specifically, the collection of images/patches in the dataset is assumed to be on a smooth manifold. I will introduce novel iterative algorithms to exploit the structure of the data. The use of these algorithms enables implicit motion compensation and motion resolution, and hence is a good alternative to current strategies that perform these operations explicity.