Evaluation of reconstruction algorithms in SPECT neuroimaging: I. Comparison of statistical noise in SPECT neuroimages with 'naive' and 'realistic' predictions.
In the presence of statistical noise, an iterative reconstruction algorithm (IRA) for the quantitative reconstruction of single-photon-emission computed tomographic (SPECT) brain images overcomes major limitations of applying the standard filtered back projection (FBP) reconstruction algorithm to projection data which have been degraded by convolution of the true radioactivity distribution with a finite-resolution distance-dependent detector response: (a) the non-uniformity within the grey (or white) matter voxels which results even though the true model is uniform within these voxels; (b) a significantly lower ratio of grey/white matter voxel values than in the true model; and (c) an inability to detect an altered radioactivity value within the grey (or white) matter voxels. It is normally expected that an algorithm which improves spatial resolution and quantitative accuracy might also increase the magnitude of the statistical noise in the reconstructed image. However, the noise properties in the IRA images are very similar to those in the FBP images. In fact, the noise magnitude in both the FBP and IRA images is only slightly greater than that computed by the 'naive prediction', which presumably is a lower limit to the amount of statistical noise in a reconstructed image. Thus, the IRA should provide the potential for quantitative SPECT imaging of normal physiological responses or diseases involving both the brain grey and white matter.
Department of Radiology, George Washington University, Washington, DC.
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Kim
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Zeeberg
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D000465:Algorithms; D001927:Brain Diseases / Q000000981:diagnostic imaging*; D005069:Evaluation Studies as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015899:Tomography, Emission-Computed, Single-Photon