Accurate scatter compensation in SPECT can be performed by modeling the scatter response function during the reconstruction process. This method is called reconstruction-based scatter compensation (RBSC). It has been shown that RBSC has a number of advantages over other methods of compensating for scatter, but using RBSC for fully 3D compensation has resulted in prohibitively long reconstruction times. In this work we propose two new methods that can be used in conjunction with existing methods to achieve marked reductions in RBSC reconstruction times. The first method, Coarse-Grid Scatter Modeling, significantly accelerates the scatter model by exploiting the fact that scatter is dominated by low frequency information. The second method, Intermittent RBSC, further accelerates the reconstruction process by limiting the number of iterations during which scatter is modeled. The fast implementations were evaluated using a Monte Carlo simulated experiment of the 3D MCAT phantom with Tc-99m tracer, and also using experimentally acquired data with Tl-201 tracer. Results indicated that these fast methods can reconstruct, with fully 3D compensation, images very similar to those obtained using conventional RBSC methods, and in reconstruction times that are an order of magnitude shorter. Using these methods, fully 3D iterative reconstruction with RBSC can be performed well within the realm of clinically realistic times (under 10 minutes for 64 × 64 × 24 image reconstruction).

Metals produce predictable artifacts in CT images of the head. Using the new method, metal artifacts can be discriminated from anatomy, and the discrimination can be used to reduce metal artifacts.

We evaluate fast reconstruction algorithms including ordered subsets-EM (OS-EM) and Rescaled Block Iterative EM (RBI-EM) in fully 3D SPECT applications on the basis of their convergence and resolution recovery properties as iterations proceed. Using a 3D computer-simulated phantom consisting of 3D Gaussian objects, we simulated projection data that includes only the effects of sampling and detector response of a parallel-hole collimator. Reconstructions were performed using each of the three algorithms (ML-EM, OS-EM, and RBI-EM) modeling the 3D detector response in th:, projection function. Resolution recovery was evaluated by fitting Gaussians to each of the four objects in the iterated image estimates at selected intervals. Results show that OS-EM and RBI-EM behave identically in this case; their resolution recovery results are virtually indistinguishable.Their resolution behavior appears to be very similar to that of ML-EM, but accelerated by a factor of twenty. For all three algorithms, smaller objects take more iterations to converge. Next, we consider the effect noise has on convergence. For both noise-free and noisy data, we evaluate the log likelihood function at each subiteration of OS-EM and RBI-EM, and at each iteration of ML-EM. With noisy data, both OS-EM and RBI-EM give results for which the log-likelihood function oscillates. Especially for 180-degree acquisitions, RBI-EM oscillates less than OS-EM. Both OS-EM and RBI-EM appear to converge to solutions, but not to the ML solution.We conclude that both OS-EM and RBI-EM can be effective algorithms for fully 3 0 SPECT reconstruction. Both recover resolution similarly to ML-EM, only more quickly.

In aviation security, checked luggage is screened by computed tomography (CT) scanning, followed by automatic target recognition from the CT images. Metal objects in the bags cause image artifacts that degrade object representation, leading to increased false alarms. We develop a new method, which isolates and reduces artifacts in an intermediate image, based on a numerical optimization that de-emphasizes metal and has a novel constraint for beam hardening and scatter. Results on test bags showed excellent artifact reduction, even for multiple metal objects.

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