Weekend Educational Session
Data Acquisition & Image Reconstruction II
Back to Program-at-a-Glance
Session Topic: Data Acquisition & Image Reconstruction II
Session Sub-Topic: Data Acquisition & Image Reconstruction II
Weekend Course
ORGANIZERS: Mariya Doneva, Kawin Setsompop
| Sunday Parallel 4 Live Q&A | Sunday, 9 August 2020, 14:30 - 15:00 UTC | Moderators: Advanced Image Reconstruction Techniques : Claudia Prieto Artifacts & Corrections: Jongho Lee |
Skill Level: Basic to Advanced
Session Number: WE-16B
Overview
This course will describe MRI acquisition and reconstruction methods. The basic signal encoding principles as well as more advanced acquisition techniques, including non-Cartesian sampling, contrast preparation, and simultaneous multi-slice encoding, will be described. Advanced image reconstruction methods that leverage coil sensitivity information and other constraints will be presented, including compressed sensing, low rank and learned representations. Finally, motion- and off-resonance-related artifacts and methods for their corrections will be presented.
Target Audience
MR scientists who wish to acquire an understanding of advanced MRI acquisition and reconstruction techniques.
Educational Objectives
As a result of attending this course, participants should be able to:
- Define the principles of different methods and pulse sequences for spatial encoding and contrast preparation in MRI;
- Describe the principles and applications of RF pulse design;
- Compare and contrast several current methods for reconstruction of under-sampled data; and
- Explain the off-resonance and motion-related artifacts and current methods for their correction.
| Advanced Image Reconstruction Techniques | ||
| Parallel Imaging
Gastao Cruz
|
||
| Sparsity & Compressed Sensing
Feng Huang, Dong Han, Xinlin Zhang, Aiqi Sun, Xiaobo Qu
Compressed sensing (CS) is a powerful signal processing technique for reconstructing data from highly undersampled measurements. The introduction of CS to magnetic resonance imaging (MRI) has dramatically reduced scan acquisition time, and has demonstrated great success in diverse applications over the last decade. In this talk, we will cover the basic theory of CS, and then give an overview of the combination of CS with fast imaging approaches, such as parallel imaging and partial Fourier. Furthermore, we will also introduce the advanced CS techniques combined with deep learning.
|
||
| Low-Rank Reconstruction Approaches
Frank Ong
|
||
| Learned Representations: Dictionaries, Subspaces, Manifolds
Lei (Leslie) Ying
|
||
| Artifacts & Corrections | ||
| Motion Compensation & Correction
Oliver Speck
Due to the long scan times of seconds or even minutes, MRI is susceptible to subject motion. Such motion can lead to ghosting, blurring and other image artifacts and can result in non diagnostic images or false quantitative results in clinical and scientific studies. Faster imaging is a convenient method to avoid or reduce motion artifacts but has limitations in terms of resolution, and image quality. Motion correction, therefore, is a research field with a long history but only few methods entered clinical routine. A number of approaches have the potential for broader application.
|
||
| Off-Resonance (Static & Dynamic) Artifacts & Corrections
S. Johanna Vannesjo
|
||
| Sensing & Probing for Better Images: MR-Based Markers, Cameras & Other External Devices
Melvyn Ooi
Patient motion can represent a frequent cause of image degradation in MRI examinations. External devices have been employed in both research and clinical settings towards effective motion compensation strategies. Participants will gain an understanding of the basic physics underlying the operation of a range of external devices, and how they can be used to compensate for bulk rigid-body (e.g. head) motion, as well as physiological (e.g. respiration, cardiac cycle) motion. External devices that will be discussed include MR-based markers, optical cameras, and some more traditional devices (e.g. respiratory bellows, EKG).
|
||
Watch the Video
Back to Top