Assessment of geometrical distortion and activity distribution after attenuation correction: A SPECT phantom study.

BACKGROUND

If single photon emission computed tomography (SPECT) images are reconstructed with filtered backprojection (FBP), not accounting for photon attenuation, artifacts can occur related to geometrical distortion and inaccurate estimation of regional distribution of radioactivity. By reconstructing the images with an iterative algorithm such as the maximum likelihood-expectation maximization (ML-EM) that incorporates the attenuation distribution information, it is possible to compensate for nonuniform attenuation. The aim of this study was to assess whether correction for nonuniform attenuation in SPECT can reduce the geometrical distortion and improve the activity quantitation.

METHODS AND RESULTS

Three capillary sources containing the same amount of technetium 99m were imaged by a dual-headed SPECT system provided with two gadolinium 153 scanning transmission line sources, in nonuniform attenuation conditions. The images were reconstructed (1) with the use of FBP, (2) with the iterative ML-EM algorithm, and (3) with the iterative ML-EM algorithm incorporating attenuation maps. The geometrical distortion was estimated by comparing the spread that occurred in 2 orthogonal directions in the reconstructed transverse slices, expressed by full width at half maximum related to the x-axis and y-axis line spread functions. The accuracy of activity quantitation was analyzed by comparing the counts in regions of interest placed over the transverse slices of the 3 sources, located in different attenuating areas. The FBP-reconstructed slices showed a spread of image intensity toward the direction of minor attenuation; the source shape improved in the iterative ML-EM images, as well as in the iterative attenuation-corrected ML-EM images. The sources located deep in the phantom showed an apparent decrease in image intensity in both FBP and ML-EM images, which became less evident in the iterative attenuation-corrected ML-EM images.

CONCLUSIONS

Image reconstruction with the iterative ML-EM algorithm, without the use of attenuation maps, can reduce geometrical distortion and eliminate streak artifacts, leading to an improvement in the object's shape and size, but does not reduce activity underestimation and inaccurate quantitation. In the iterative attenuation-corrected ML-EM images, there was a significant improvement in the accurate quantitation of activity distribution and a further reduction in geometrical distortion. In conclusion, nonisotropic attenuation correction with iterative ML-EM reduced the geometrical distortion of images and improved the accuracy of activity quantitation.