Background: The optimal projection data acquisition strategy for myocardial perfusion (MP) single photon emission computed tomography (SPECT) remains controversial.
Methods: We compared MP SPECT using 180 degrees and 360 degrees projection data obtained with the same acquisition time, reconstructed either with filtered back projection (FBP) or the iterative ordered-subsets expectation maximization (OS-EM) algorithm with various combinations of attenuation, detector response, and scatter compensation using mathematical observers and a myocardial defect detection task. We used Monte Carlo-simulated projection data from a population of 3-dimensional nurbs-based cardiac-torso (NCAT) phantoms with ranges of variability in patient anatomy, organ uptake, defect location, defect size, and noise level based on clinical data. Projection data from 180 degrees and 360 degrees acquisitions were generated by assuming the same acquisition time. After iterative or FBP reconstruction, standard postprocessing methods were applied. For each acquisition and reconstruction method, we optimized the number of iterations and cut-off frequency of the Butterworth filter using the Channelized Hotelling Observer methodology. The optimum set of parameters was that which gave the maximum area under the curve.
Results: For both acquisition protocols, OS-EM with compensations provided better performance than FBP or OS-EM without compensation. For FBP, the optimized 180 degrees acquisition provided a statistically significant increase in AUC as compared with optimized 360 degrees acquisition. For OS-EM, the AUCs for 180 degrees were slightly larger than for 360 degrees acquisitions when comparing images reconstructed with the same compensations. However, the differences were smaller and not statistically significant.
Conclusion: With optimized reconstruction and filtering parameters, 180 degrees acquisition provided a statistically significant improvement over 360 degrees acquisition for FBP reconstruction. However, for OS-EM the differences were small and not statistically significant.