Segmentation of Gated SPECT Images for Automatic Computation of Myocardial Volume and Ejection Fraction

We describe an image processing system for the automatic assessment of ejection fraction (EF) from noisy SPECT left ventricular myocardial perfusion images. A segmentation scheme consisting of an elliptical coordinate transformation, matched filtering and dynamic contour tracking detects the endoand epicardial boundaries. Computation of EF is performed based on the epirather than the endo-cardial boundary, which proves to be more robust for images with low signal-to-noise ratios. The computation incorporates anatomical constraints of constant myocardial mass and smooth cardiac variation. The algorithm was tested on different image modalities and shows good linear agreement with EFs obtained from conventional approaches based on planar gated blood pool imaging (PET: y=8.7+1.07x, r=0.84 , Technetium-99m MIBI SPECT: y=5.0+0.80x, r=0.90 , Thallium-201 SPECT: y=23.5+0.82x, r=0.77, RMS error = 10.0). The proposed scheme may be an alternative for EF computation without the need for additional image acquisitions.