Yuchi Liu1
1Radiology, University of Michigan, Ann Arbor, MI, United States
Synopsis
MR
Fingerprinting (MRF) is a novel approach for simultaneous multi-parameter
quantification. This course will introduce MRF basics, including pulse sequence
design, data acquisition, dictionary generation and reconstruction. In
particular, recent advances in model-based reconstruction such as low rank
methods exploiting spatial and temporal sparsity in the acquired data will be
reviewed.
Target Audience
MRI
researchers interested in simultaneous multi-parameter quantification such as
relaxation time quantification using MR Fingerprinting approaches, and
researchers interested in exploring advanced model-based reconstruction methods
for MRF. Objectives
By the end of this session, the attendees should be able to understand
the basic concept of MRF and its difference from traditional relaxation time
quantification methods, and the recent advances in model-based reconstruction
approaches for MRF.Principles
In MRF, a pulse sequence with continuously varying
timings and/or RF strengths is used which sensitizes the signal to multiple
tissue properties simultaneously. The goal of the MRF pulse sequence is to
force tissues with different tissue properties, i.e. T1 and T2
values, to produce distinguishable signals. Following data collection, a dictionary is generated by specifying ranges for the
T1 and T2 values which could appear in the tissue, and
using the Bloch equations to calculate the signal time course for each
combination of T1 and T2. For each pixel, the signal time
course from the measured data is matched to the dictionary to find the closest
dictionary entry. The pair of T1 and T2 values used to
generate the selected dictionary entry is then assigned to this pixel to
generate quantitative maps.
In the simplest matching approach, the dot
product (inner product) between the acquired signal time course for a pixel and
all the dictionary entries are calculated. The
dictionary entry with the highest inner product value is selected as the best
match. Direct dot pattern matching is straightforward, rapid, and relatively
easy to implement. However, incorporating advanced model-based reconstruction
algorithms could improve the precision of MRF in mapping
relaxation times. These techniques exploit the low rank properties of
the dictionary and the spatial/temporal sparsity of the acquired data. Acknowledgements
No acknowledgement found.References
No reference found.