TY - JOUR
T1 - A MAP framework for tag line detection in SPAMM data using Markov random fields on the B-spline solid
AU - Chen, Yasheng
AU - Amini, Amir A.
N1 - Funding Information:
Manuscript received November 4, 2001; revised July 6, 2002. This work was supported in part by the National Institutes of Health (NIH) under Grant HL57628 and Grant HL64217. Asterisk indicates corresponding author. Y. Chen is with the Cardiovascular Image Analysis Laboratory, Washington University, St. Louis, MO 63110 USA. *A. A. Amini is with the Cardiovascular Image Analysis Laboratory, Washington University, St. Louis, MO 63110 USA (e-mail: amini@cauchy. wustl.edu). Digital Object Identifier 10.1109/TMI.2002.804430
PY - 2002/9
Y1 - 2002/9
N2 - Magnetic resonance (MR) tagging is a technique for measuring heart deformations through creation of a stripe grid pattern on cardiac images. In this paper, we present a maximum a posteriori (MAP) framework for detecting tag lines using a Markov random field (MRF) defined on the lattice generated by three-dimensional (3-D) and four-dimensional (4-D) (3-D+t) uniform sampling of B-spline models. In the 3-D case, MAP estimation is cast for detecting present tag features in the current image given an initial solid from the previous frame (the initial undeformed solid is manually positioned by clicking on corner points of a cube). The method also allows the parameters of the solid model, including the number of knots and the spline order, to he adjusted within the same framework. Fitting can start with a solid with less knots and lower spline order and proceed to one with more knots and/or higher order so as to achieve more accuracy and/or higher order of smoothness. In the 4-D case, the initial model is considered to be the linear interpolation of a sequence of optimal solids obtained from 3-D tracking. The same framework proposed for the 3-D case can once again be applied to arrive at a 4-D B-spline model with a higher temporal order.
AB - Magnetic resonance (MR) tagging is a technique for measuring heart deformations through creation of a stripe grid pattern on cardiac images. In this paper, we present a maximum a posteriori (MAP) framework for detecting tag lines using a Markov random field (MRF) defined on the lattice generated by three-dimensional (3-D) and four-dimensional (4-D) (3-D+t) uniform sampling of B-spline models. In the 3-D case, MAP estimation is cast for detecting present tag features in the current image given an initial solid from the previous frame (the initial undeformed solid is manually positioned by clicking on corner points of a cube). The method also allows the parameters of the solid model, including the number of knots and the spline order, to he adjusted within the same framework. Fitting can start with a solid with less knots and lower spline order and proceed to one with more knots and/or higher order so as to achieve more accuracy and/or higher order of smoothness. In the 4-D case, the initial model is considered to be the linear interpolation of a sequence of optimal solids obtained from 3-D tracking. The same framework proposed for the 3-D case can once again be applied to arrive at a 4-D B-spline model with a higher temporal order.
KW - B-splines
KW - Deformable models
KW - Markov random fields
KW - Tagged magnetic resonance imaging (MRI)
UR - http://www.scopus.com/inward/record.url?scp=0036770499&partnerID=8YFLogxK
U2 - 10.1109/TMI.2002.804430
DO - 10.1109/TMI.2002.804430
M3 - Article
C2 - 12564879
AN - SCOPUS:0036770499
SN - 0278-0062
VL - 21
SP - 1110
EP - 1122
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 9
ER -