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Performance of ordered-subset reconstruction algorithms under conditions of extreme attenuation and truncation in myocardial SPECT.

UNLABELLED
We studied the bias and variance characteristics of the ordered-subset expectation maximization (OSEM) and rescaled block-iterative EM (RBIEM) iterative reconstruction algorithms in myocardial SPECT under extreme, but realistic, conditions.


METHOD
We used the 2-dimensional mathematic cardiac torso phantom to simulate 2 patient anatomies: a large male with a raised diaphragm and a female with large breast size, approximating extreme cases of attenuation conditions found in the clinic. For each anatomy, realistic 201Tl projection data were simulated for a 180 degrees acquisition arc. Three cases of truncation for a 90 degrees-configured dual detector system were simulated: no truncation, moderate truncation, and extreme truncation. For each case, an ensemble of 250 noise simulations was generated, and each noisy dataset was reconstructed with the OSEM and RBIEM algorithms. The reconstructions modeled only the effects of nonuniform attenuation and used a range of subset configurations. Over the ensemble, we computed means and variances of activity in 8 regions of interest (ROIs) in the heart as a function of iteration.


RESULTS
Under conditions of no truncation and moderate truncation, the results from OSEM and RBIEM were very close to those from maximum-likelihood EM (MLEM); in all cases, the difference in ROI means was <2.5%. For extreme truncation, the errors increased to as much as 11% with OSEM, but these were no greater than the errors for MLEM under the same conditions. The OSEM algorithm with 2 views per subset was found to result in much higher variance of ROI estimates for the same bias as compared with RBIEM or OSEM with 4 or more views per subset.


CONCLUSION
The OSEM and RBIEM algorithms are at least as robust to highly attenuating patients and truncation as MLEM algorithm and can be adequate substitutes for MLEM, even in extreme cases. Clinical users should apply the smallest number of subsets that can be accommodated by allowable processing time to reduce image noise and variance in quantitative estimates.