TY - GEN
T1 - Temporal Diffeomorphic Free Form Deformation (TDFFD) applied to motion and deformation quantification of tagged MRI sequences
AU - De Craene, Mathieu
AU - Tobon-Gomez, Catalina
AU - Butakoff, Constantine
AU - Duchateau, Nicolas
AU - Piella, Gemma
AU - Rhode, Kawal S.
AU - Frangi, Alejandro F.
PY - 2012
Y1 - 2012
N2 - This paper presents strain quantification results obtained from the Tagged Magnetic Resonance Imaging (TMRI) sequences acquired for the 1 st cardiac Motion Analysis Challenge (cMAC). We applied the Temporal Diffeomorphic Free Form Deformation (TDFFD) algorithm to the phantom and the 15 healthy volunteers of the cMAC database. The TDFFD was modified in two ways. First, we modified the similarity metric to incorporate frame to frame intensity differences. Second, on volunteer sequences, we performed the tracking backward in time since the first frames did not show the contrast between blood and myocardium, making these frames poor choices of reference. On the phantom, we propagated a grid adjusted to tag lines to all frames for visually assessing the influence of the different algorithmic parameters. The weight between the two metric terms appeared to be a critical parameter for making a compromise between good tag tracking while preventing drifts and avoiding tag jumps. For each volunteer, a volumetric mesh was defined in the Steady-State Free Precession (SSFP) image, at the closest cardiac time from the last frame of the tagging sequence. Uniform strain patterns were observed over all myocardial segments, as physiologically expected.
AB - This paper presents strain quantification results obtained from the Tagged Magnetic Resonance Imaging (TMRI) sequences acquired for the 1 st cardiac Motion Analysis Challenge (cMAC). We applied the Temporal Diffeomorphic Free Form Deformation (TDFFD) algorithm to the phantom and the 15 healthy volunteers of the cMAC database. The TDFFD was modified in two ways. First, we modified the similarity metric to incorporate frame to frame intensity differences. Second, on volunteer sequences, we performed the tracking backward in time since the first frames did not show the contrast between blood and myocardium, making these frames poor choices of reference. On the phantom, we propagated a grid adjusted to tag lines to all frames for visually assessing the influence of the different algorithmic parameters. The weight between the two metric terms appeared to be a critical parameter for making a compromise between good tag tracking while preventing drifts and avoiding tag jumps. For each volunteer, a volumetric mesh was defined in the Steady-State Free Precession (SSFP) image, at the closest cardiac time from the last frame of the tagging sequence. Uniform strain patterns were observed over all myocardial segments, as physiologically expected.
UR - http://www.scopus.com/inward/record.url?scp=84858328073&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28326-0_7
DO - 10.1007/978-3-642-28326-0_7
M3 - Conference contribution
AN - SCOPUS:84858328073
SN - 9783642283253
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 68
EP - 77
BT - Statistical Atlases and Computational Models of the Heart
T2 - 2nd International Workshop on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM 2011, Held in Conjunction with MICCAI 2011
Y2 - 22 September 2011 through 22 September 2011
ER -