TAG SEPARATION IN CARDIAC
TAGGED MRI
J. HUANG
Presenter: Lin Zhong
MICCAI 2008
MRI

Magnetic resonance imaging(MRI)
 primarily a noninvasive medical
imaging technique used in radiology to
visualize detailed internal structure and
limited function of the body
 MRI provides much greater contrast
between the different soft tissues of the body
than computed tomography (CT)
TAGGED MRI
Tagging consists in tattooing the myocardium
with a geometrical pattern (lines or grid), using
selective spatial pulses.
 the magnetization of the myocardial (or any
other) tissue will persist for times (e.g. one
cardiac circle) and will create a visible mark in
the MR images that moves precisely with the
underlying tissue.
 can noninvasively render visible the internal
motions of tissues.
 Pose great challenges to the cardiac image
processing

RELATED WORK

Morphological operations [Guttman 1994]
Fill in the region between removed tagging lines
 Bad generalization


Band pass [osman 1999]
Enhance tag pattern region and increase contrast
 Performance depend on the designed filters.

RELATED WORK

Band stop [Qian,Z. 2007]




Images are processed in the spectral domain
Low frequency peak at the origin is from
actual tissue
Other energy peaks are introduced by tag
patterns
Abandon all frequency in the localized regions
will cause some artifacts in the recovered
images after tag removal
MOTIVATION

The tag patterns have a regular texture


Discrete Cosine Transform (DCT)
The Cardiac images without tag patterns are
piecewise smooth with sparse gradients

Wavelet Transform (WT)
Two dictionaries can be built. Tag pattern and
piecewise smooth image can be represented
sparsely by these two dictionaries.
 Tag-only image can be used for tag tracking,
the remaining image can be used for accurately
localizing the cardiac boundaries.

PROBLEM FORMULATION

I=S+T
 I : tagged cardiac MR image
 S: piecewise smooth cardiac image without tags
 T: tag-only image
PROBLEMS TO BE SOLVED
Build dictionary A for cardiac images S
 Undecimated Wavelet Transforms (UWT)
 Suitable for the sparse representation of natural
scene
 Build dictionary B for tag pattern T
 Discrete Cosine Transform (DCT)
 Deal images in the frequency domain, and is
appropriate for a sparse representation of periodic or
smooth behaviors.
 Find optimal coefficients for

Image decomposition via the combination of sparse representations and a variational approach.
ITIP, 2005
OPTIMIZATION OF COEFFICIENTS

Original Objective function

Modified objective function

TV penalty guide S to be a piece wise smooth image
with sparse gradient.

Block-coordinate Relaxation algorithm[Bruce,1998]
RESULTS