Introduction: Motion of the patient during myocardial perfusion SPECT could potentially results in false perfusion defects. The effect of different reconstruction methods on these artifacts is not studied. Clarification of the relation between the extent, severity and duration of motion with the resultant artifacts may be helpful in designing special soft wares for motion correction. This study is a preliminary evaluation of motion artifacts in different reconstruction methods. Materials and Methods: Normal myocardial perfusion SPECT from a patient with low risk of CAD and no motion during imaging according to the review of cinematic images by 3 nuclear medicine specialists is selected. The scan was acquired one hour after IV injection of 740MBq of Tc-99m-MIBI through 180 degrees from RAO 45 to LPO 45, in 32 frames and 30 seconds per frame. The original images were moved artificially in frames 7, 16 and 24 (as initial, mid and late portion of imaging) from 1 to 3 pixels and for 30-90 seconds using VISION software. Also this artificial motion was applied in X and Y axis and in negative and positive directions. One hundred forty four images were produced and all were reconstructed using filtered back projection (FBP) and iterative (OSEM) reconstructions and interpreted visually and semi-quantitatively (using 17 segment model with 5 point scoring). Results: Applying one, two and three pixels motion converted normal scan to abnormal image in 2.8%, 25% and 39.6% respectively using FBP and in 18.8% , 43.2% and 68.8% respectively using OSEM reconstruction.(P<0.001). Correlation of interpretation (Kappa) for two sets of images (FBP vs OSEM) was 0.488. Summed score (SS) for each scan reflects the intensity and severity of the defects. Overall, mean summed score was 5.25 in FBP and 8.08 in OSEM reconstruction (P<0.05). Mean SS in returning motions for 1, 2 and 3 frames(corresponding to 30, 60 and 90 seconds) were 2.52, 6 and 8.1 using FBP respectively and 3.85 , 8.77 and 11.58 in OSEM respectively (ANOVA, P<0.001). With non-returning motions no normal image was noted in both reconstruction methods. Conclusion: This study showed that OSEM reconstruction is more sensitive for motion artifacts compared to FBP. Also severity and extent of the defects were larger in OSEM reconstruction compared to FBP. Severity of the artifacts is increased with increasing duration of motion (or number of frames involved).

This paper studies the problem of Simultaneous Sparse Approximation (SSA). This problem arises in many applications that work with multiple signals maintaining some degree of dependency, e. g., radar and sensor networks. We introduce a new method towards joint recovery of several independent sparse signals with the same support. We provide an analytical discussion of the convergence of our method, called Simultaneous Iterative Method (SIM). In this study, we compared our method with other group-sparse reconstruction techniques, namely Simultaneous Orthogonal Matching Pursuit (SOMP) and Block Iterative Method with Adaptive Thresholding (BIMAT), through numerical experiments. The simulation results demonstrated that SIM outperformed these algorithms in terms of the metrics Signal to Noise Ratio (SNR) and Success Rate (SR). Moreover, SIM is considerably less complicated than BIMAT, which makes it feasible for practical applications such as implementation in MIMO radar systems.

This paper presents a new filtering approach based on fuzzy-logic which has high performance in mixed noise environments. This filter is mainly based on the idea that each pixel is not allowed to be uniformly fired by each of the fuzzy rules. In the proposed filtering algorithm, the rule membership functions are tuned iteratively in order to preserve the image edges. Several test experiments were performed in order to hlight the merit of the proposed method. The results are very promising and indicating the high performance of the proposed filter in image restoration compared with those of the filters which have been recently cited in the image processing literature.

Let $ H$ be a Hilbert space and $C$ be a closed, convex and nonempty subset of $H$. Let $T:C \rightarrow H$ be a non-self and non-expansive mapping. V. Colao and G. Marino with particular choice of the sequence  $\{\alpha_{n}\}$ in Krasonselskii-Mann algorithm, ${x}_{n+1}={\alpha}_{n}{x}_{n}+(1-{\alpha}_{n})T({x}_{n}),$ proved both weak and strong converging results. In this paper, we generalize their algorithm and result, imposing some conditions upon the set $C$ and finite many mappings from $C$ in to $H$, to obtain a converging sequence to a common fixed point for these non-self and non-expansive mappings.

In the present work, by applying known Bernstein polynomials and their advantageous properties, we establish an efficient iterative algorithm to approximate the numerical solution of fuzzy Fredholm integral equations of the second kind. The convergence of the proposed method is given and the numerical examples illustrate that the proposed iterative algorithm are valid.