Infarct density distribution by MRI in the porcine model of acute and chronic myocardial infarction as a potential method transferable to the clinic.
To study the feasibility of a myocardial infarct (MI) quantification method [signal intensity-based percent infarct mapping (SI-PIM)] that is able to evaluate not only the size, but also the density distribution of the MI. In 14 male swine, MI was generated by 90 min of closed-chest balloon occlusion followed by reperfusion. Seven (n = 7) or 56 (n = 7) days after reperfusion, Gd-DTPA-bolus and continuous-infusion enhanced late gadolinium enhancement (LGE) MRI, and R1-mapping were carried out and post mortem triphenyl-tetrazolium-chloride (TTC) staining was performed. MI was quantified using binary [2 or 5 standard deviation (SD)], SI-PIM and R1-PIM methods. Infarct fraction (IF), and infarct-involved voxel fraction (IIVF) were determined by each MRI method. Bias of each method was compared to the TTC technique. The accuracy of MI quantification did not depend on the method of contrast administration or the age of the MI. IFs obtained by either of the two PIM methods were statistically not different from the IFs derived from the TTC measurements at either MI age. IFs obtained from the binary 2SD method overestimated IF obtained from TTC. IIVF among the three different PIM methods did not vary, but with the binary methods the IIVF gradually decreased with increasing the threshold limit. The advantage of SI-PIM over the conventional binary method is the ability to represent not only IF but also the density distribution of the MI. Since the SI-PIM methods are based on a single LGE acquisition, the bolus-data-based SI-PIM method can effortlessly be incorporated into the clinical image post-processing procedure.
Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, MCLM 556, Birmingham, AL, 35294-0005, USA.
Akos
A
Varga-Szemes
Tamas
T
Simor
Zsofia
Z
Lenkey
Rob J
RJ
van der Geest
Robert
R
Kirschner
Levente
L
Toth
Brigitta C
BC
Brott
Ada
A
Elgavish
Gabriel A
GA
Elgavish
D000818:Animals; D003287:Contrast Media; D004195:Disease Models, Animal; D019786:Gadolinium DTPA; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009203:Myocardial Infarction / Q000473:pathology*; D011897:Random Allocation; D013194:Staining and Labeling; D013552:Swine
D003287:Contrast Media; D019786:Gadolinium DTPA