Tomographic image quality of rotating slat versus parallel hole-collimated SPECT

Parallel and converging hole collimators are most frequently used in nuclear medicine. Less common is the use of rotating slat collimators for single photon emission computed tomography (SPECT). The higher photon collection efficiency, inherent to the geometry of rotating slat collimators, results in much lower noise in the data. However, plane integrals contain spatial information in only one direction, whereas line integrals provide two-dimensional information. It is not a trivial question whether the initial gain in efficiency will compensate for the lower information content in the plane integrals. Therefore, a comparison of the performance of parallel hole and rotating slat collimation is needed. This study compares SPECT with rotating slat and parallel hole collimation in combination with MLEM reconstruction with accurate system modeling and correction for scatter and attenuation. A contrast-to-noise study revealed an improvement of a factor 2-3 for hot lesions and more than a factor of 4 for cold lesion. Furthermore, a clinically relevant case of heart lesion detection is simulated for rotating slat and parallel hole collimators. In this case, rotating slat collimators outperform the traditional parallel hole collimators. We conclude that rotating slat collimators are a valuable alternative for parallel hole collimators

Medical image tomography: A statistically tailored neural network approach

In medical computed tomography (CT) the tomographic images are reconstructed from planar information collected 180∘ to 360∘ around the patient. In clinical applications, the reconstructions are typically produced using a filtered backprojection algorithm. Filtered backprojection methods have limitations that create a high percentage of statistical uncertainty in the reconstructed images. Many techniques have been developed which produce better reconstructions, but they tend to be computationally expensive, and thus, impractical for clinical use;Artificial neural networks (ANN) have been shown to be adept at learning and then simulating complex functional relationships. For medical tomography, a neural network can be trained to produce a reconstructed medical image given the planar data as input. Once trained an ANN can produce an accurate reconstruction very quickly;A backpropagation ANN with statistically derived activation functions has been developed to improve the trainability and generalization ability of a network to produce accurate reconstructions. The tailored activation functions are derived from the estimated probability density functions (p.d.f.s) of the ANN training data set. A set of sigmoid derivative functions are fitted to the p.d.f.s and then integrated to produce the ANN activation functions, which are also estimates of the cumulative distribution functions (c.d.f.s) of the training data. The statistically tailored activation functions and their derivatives are substituted for the logistic function and its derivative that are typically used in backpropagation ANNs;A set of geometric images was derived for training an ANN for cardiac SPECT image reconstruction. The planar projections for the geometric images were simulated using the Monte Carlo method to produce sixty-four 64-quadrant planar views taken 180 about each image. A 4096 x 629 x 4096 architecture ANN was simulated on the MasPar MP-2, a massively parallel single-instruction multiple-data (SIMD) computer. The ANN was trained on the set of geometric tomographic images. Trained on the geometric images, the ANN was able to generalize the input-to-output function of the planar data-to-tomogram and accurately reconstruct actual cardiac SPECT images

Monte Carlo tomographic reconstruction in SPECT impact of bootstrapping and number of generated events

In Single Photon Emission Computed Tomography (SPECT), 3D images usually reconstructed by performing a set of bidimensional (2D) analytical or iterative reconstructions can also be reconstructed using an iterative reconstruction algorithm involving a 3D projector. Accurate Monte Carlo (MC) simulations modeling all the physical effects that affect the imaging process can be used to estimate this projector. However, the accuracy of the projector is affected by the stochastic nature of MC simulations. In this paper, we study the accuracy of the reconstructed images with respect to the number of simulated histories used to estimate the MC projector. Furthermore, we study the impact of applying the bootstrapping technique when estimating the projectorComment: 15 pages, 9 figures, 2 table

The Performance of MLEM for Dynamic Imaging From Simulated Few-View, Multi-Pinhole SPECT

Stationary small-animal SPECT systems are being developed for rapid dynamic imaging from limited angular views. This work quantified, through simulations, the performance of Maximum Likelihood Expectation Maximization (MLEM) for reconstructing a time-activity curve (TAC) with uptake duration of a few seconds from a stationary, three-camera multi-pinhole SPECT system. The study also quantified the benefits of a heuristic method of initializing the reconstruction with a prior image reconstructed from a conventional number of views, for example from data acquired during the late-study portion of the dynamic TAC. We refer to MLEM reconstruction initialized by a prior-image initial guess (IG) as MLEMig. The effect of the prior-image initial guess on the depiction of contrast between two regions of a static phantom was quantified over a range of angular sampling schemes. A TAC was modeled from the experimentally measured uptake of 99mTc-hexamethylpropyleneamine oxime (HMPAO) in the rat lung. The resulting time series of simulated images was quantitatively analyzed with respect to the accuracy of the estimated exponential washin and washout parameters. In both static and dynamic phantom studies, the prior-image initial guess improved the spatial depiction of the phantom, for example improved definition of the cylinder boundaries and more accurate quantification of relative contrast between cylinders. For example in the dynamic study, there was ~ 50% error in relative contrast for MLEM reconstructions compared to ~ 25-30% error for MLEMig. In the static phantom study, the benefits of the initial guess decreased as the number of views increased. The prior-image initial guess introduced an additive offset in the reconstructed dynamic images, likely due to biases introduced by the prior image. MLEM initialized with a uniform initial guess yielded images that faithfully reproduced the time dependence of the simulated TAC; there were no s- atistically significant differences in the mean exponential washin/washout parameters estimated from MLEM reconstructions compared to the true values. Washout parameters estimated from MLEMig reconstructions did not differ significantly from the true values, however the estimated washin parameter differed significantly from the true value in some cases. Overall, MLEM reconstruction from few views and a uniform initial guess accurately quantified the time dependance of the TAC while introducing errors in the spatial depiction of the object. Initializing the reconstruction with a late-study initial guess improved spatial accuracy while decreasing temporal accuracy in some cases