Synopsis
Fast imaging
techniques are crucial for abdominal MRI. This presentation will first cover
the basic concepts of parallel imaging techniques and their usage in accelerating
abdominal scans. We will further discuss recent advances in fast imaging
techniques and how these techniques enable quantitative perfusion measurement
in the abdomen.
Target Audience
Basic scientists and
clinicians who are interested in fast imaging and perfusion imaging in body MRI.Objectives
- Understand the basic concepts of k-space
sampling and how parallel imaging techniques can be used to accelerate data
acquisition in body MRI
-
Discuss recent advances in fast body imaging
including non-Cartesian parallel imaging and compressed sensing
- Discuss how quantitative perfusion imaging is
performed with various fast imaging techniques in the abdomen
Introduction
MR imaging plays an important role in abdominal imaging, including
anatomical and functional evaluation of solid organs such as the liver, kidneys
and pancreas. The current clinical protocols often contain dozens of scans with
different contrast weightings and orientations and most of the scans require a
breath-hold to limit motion artifacts. When successful, abdominal MRI can
provide exquisite images for lesion diagnosis and evaluation. All too often,
the breath-holds are too long and a significant proportion of patients
(especially sicker individuals) cannot provide the requisite breath-holds,
which results in motion corrupted examinations. To overcome these limitations,
fast imaging techniques have been developed to speed up data acquisition and
are routinely applied in clinical examinations.Parallel Imaging
In this section, we
will first briefly introduce the basics of k-space sampling and factors
influencing image acquisition time. The standard approach to accelerate image
acquisition is to skip k-space lines in the phase-encoding direction, which
will introduce fold-over artifacts. With the aid of coil arrays, various
parallel imaging techniques have been developed to reconstruct the undersampled
images and eliminate the fold-over artifacts. In this section, we will review the
concepts of two frequently used parallel imaging techniques (SENSE, Sensitivity
Encoding; GRAPPA, Generalized Auto calibrating Partially Parallel Acquisition)
and their applications in accelerating body imaging [1,2].Recent Advances in Fast Abdominal Imaging
This section will
review recent developments in fast abdominal imaging, which include view-sharing
techniques, non-Cartesian parallel imaging, and compressed sensing.
View-sharing methods are widely used to accelerate dynamic imaging through more
frequent sampling of the center of k-space and reducing the sampling of the
periphery [3,4]. Non-Cartesian sampling schemes are increasingly
favored due to the intrinsic motion robustness and high scan efficiency [5,6]. Compressed sensing is introduced to achieve
higher acceleration factors as compared to standard parallel imaging methods
with pseudorandom data sampling [7,8]. All these advanced fast imaging methods are
often combined in recent developments to largely reduce the breath-hold
duration or enable completely free-breathing scans for body imaging.Quantitative Perfusion Imaging
There has been significant recent
interest in quantitative perfusion imaging in the abdomen [9]. Compared to stationary brain
imaging, perfusion measurement in the body is particularly challenging due to
the requirement of high spatiotemporal resolution, large-volume coverage, and
minimization of motion artifacts. To meet all these requirements, multiple long
breath-holds are often used, which could exhaust the patient and result in
motion degradation of images [10]. Recent development in fast
MRI techniques provide a solution to overcome all of these problems. In this
section, we will first introduce the quantitative modeling for perfusion quantification
[11,12] and then discuss how
quantitative perfusion imaging is performed with various fast imaging
techniques in the abdomen [6,13–15].Acknowledgements
No acknowledgement found.References
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