Automatic quantitation of regional myocardial wall motion and thickening from gated technetium-99m sestamibi myocardial perfusion single-photon emission computed tomography.
OBJECTIVE
We developed an automatic quantitative algorithm for the measurement of regional myocardial wall motion and wall thickening from three-dimensional gated technetium-99m sestamibi myocardial perfusion single-photon emission computed tomographic images.


BACKGROUND
The algorithm measures the motion of the three-dimensional endocardial surface using a modification of the centerline method, as well as wall thickening using both geometry (gaussian fit) and partial volume (counts).


METHODS
The algorithm was tested using a "variable thickness" heart phantom, and the quantitative results were compared with visual segmental assessment of myocardial motion and thickening in 79 clinical patients with a wide range of ejection fractions (6% to 87%).


RESULTS
Phantom measurements of simulated motion and thickening were accurate regardless of the camera used (dual or triple detector), the angular span of reconstructed data (180 degrees or 360 degrees), the amount of motion (3 or 6 mm) or the amount of thickening (33%, 50% or 100%). Quantitative measurements of segmental motion and thickening in the patients were correlated with visual scores (r = 0.668, exact agreement 72.6%, kappa 0.433 and r = 0.550, exact agreement 74.7%, kappa 0.408, respectively). Significant inverse linear relations exist between the global (summed) visual motion score and the average quantitative motion, and between the global (summed) visual thickening score and the average quantitative thickening. Automatic quantitative ejection fraction measurements correlated extremely well with average quantitative motion (r = 0.929) and thickening (r = 0.959).


CONCLUSIONS
Our algorithm is accurate and may be the first automatic technique for the quantitative three-dimensional assessment of regional ventricular function in cardiology.
Germano
G
G
Department of Medicine, Cedars-Sinai Research Institute, Cedars-Sinai Medical Center, University of California Los Angeles School of Medicine, 90048, USA. germano@csmc.edu
Erel
J
J
Lewin
H
H
Kavanagh
P B
PB
Berman
D S
DS
D000368:Aged; D000465:Algorithms; D005260:Female; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000000981:diagnostic imaging*; D019047:Phantoms, Imaging; D011446:Prospective Studies; D019275:Radiopharmaceuticals; D017256:Technetium Tc 99m Sestamibi; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D016277:Ventricular Function, Left; D015646:Ventriculography, First-Pass
D019275:Radiopharmaceuticals; D017256:Technetium Tc 99m Sestamibi