Parallel Imaging: Basic and Advanced Transmission and Reception Concepts

Date and Location: June 25-27, 2008, Instituto Superior Técnico, Physics Department, Lisbon/PT
Course Organisers: David J. Larkman and Rita G. Nunes, Imaging Sciences Department, Imperial College London, London/UK
Local Organiser: Patricia Figueiredo, Instituto Superior Técnico, Physics Department, Lisbon/PT
Faculty: David Atkinson, Peter Boernert, Felix Breuer, Jo Hajnal, Michael Hansen, David Larkman, Shaihan Malik, Klass Pruessman

Programme:
Click here to download the programme as PDF.

Computing facilities sponsored by Philips MRI

Course description:
This course is a new course designed to provide a strong practical foundation in the principles of parallel magnetic resonance imaging. Parallel Imaging (PI) is now an integral part of many clinical MRI exams. The concepts and methods of PI are informing research in many disparate aspects of MRI. This course is aimed at PhD students and scientists new to Parallel Imaging who wish to gain a working knowledge of parallel magnetic resonance to underpin their work. The course will be split in two parts, with approximately half the time spent attending lectures and the other half doing practical MATLAB tutorial exercises. We will provide computers and software licenses for the duration of the course.

The course will cover image reconstruction from multiple coils starting with an image domain view and moving quickly to a k-space perspective. We will then look at more advanced methods; non-cartesian and consistency based reconstruction and many of the mathematical tools used in these reconstruction algorithms. We will look at allied methods in particular spatio-temporal undersampling and subsequent reconstructions (e.g. k-t SENSE) along with the use of multiple transmit coils (Parallel Transmit). Finally we will look to the future and discuss how how multi channel MRI may impact on future directions in MRI.

An integral part of the course will be the MATLAB tutorials where attendees will be able to work through example code provided for them. These examples will demonstrate and enhance their understanding of the concepts discussed throughout the course. Exercises will be set where attendees will modify this code to develop new examples and functionality. At the end of the course they will be free to take this code away with them.

Some previous exposure to MATLAB is preferable but not mandatory. Those participants who have not used MATLAB should have some programming experience. All participants and will be expected to know essential MR physics. A working knowledge of image acquisition methods and k-space is essential.

Some previous exposure to MATLAB is preferable but not mandatory. Those participants who have not used MATLAB should have some programming experience. All participants and will be expected to know essential MR physics. A working knowledge of image acquisition methods and k-space is essential.

Learning Objectives:

Image domain parallel imaging
– Define the basic reconstruction problem.
– Reconstruct full images from aliased images
– Explore the effects of coil coupling on the reconstruction
– Calculate and measure reconstruction quality.

k-space parallel imaging
– Compare SMASH, GRAPPA and a “big matrix” reconstruction method
– Relate Image domain and k-space methods.
– Assess costs and benefits of image domain and k-space methods

Coils and calibration
– Understand how coil calibration is achieved.
– Compare auto-calibration and pre-calibration approaches (assessing costs and benefits)
– Establish design criteria for parallel imaging array coils
– Demonstrate how coil calibration errors affect reconstruction

Non-cartesian – iterative reconstruction
– Define the reconstruction problem
– Review mathematical methods used in reconstruction
– Reconstruct non-uniformly sampled data

Regularisation and pre-conditioning in PPI
– Summarize regularisation approaches.
– When is regularisation appropriate and at what level.
– Compare regularised and non regularised reconstructions

Consistency based reconstruction
– Understand how coils provide a consistency metric.
– Iterative reconstruction using consistency cost functions.
– Repairing motion damaged images using array coil information.

Spatio-temproal undersampling and reconstruction
– Compare reconstruction techniques e.g. k-t SENSE, k-t GRAPPA, TSENSE, x-f choice
– Properties of calibration data
– Calculating and measuring reconstruction quality.

Parallel transmission
– The small tip angle approximation
– Generating spatially modulated excitations using array coils
– Coil calibration for parallel transmission
– Costs and benefits of parallel transmission

Future Directions in multi channel MRI
– Where is multi channel MRI taking us?