Head motion during Computed Tomographic (CT) brain imaging studies can adversely affect the reconstructed image through distortion, loss of resolution and other related artifacts. In this paper, we propose a marker based innovative approach to detect and mitigate motion artifacts in three dimensional cone-beam brain CT systems without using any external motion tracking sensor. Motion is detected using correlations between the adjacent projections. Once motion is detected, motion parameters (i.e. six degrees-of-freedom of motions) are estimated using a numerical optimization technique. Artifacts, caused by motions, are mitigated by using a modified form Feldkemp-Davis-Kress (FDK) algorithm which uses the estimated motion parameters in back-projection stage. The proposed approach has been evaluated on a modified three-dimensional Shepp-Logan phantom with a range of simulated motions. Simulation results demonstrate a quantitative and qualitative validation of motion detection and artifacts mitigation technique.

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Head motion during brain CT studies can degrade the reconstructed image through distortion and other artifacts such as blurring, doubling and thereby contributing to misdiagnosis of diseases. Estimation of motion parameters is essential for mitigating motion artifacts. In this paper, we propose a marker based numerical optimization method to measure six degrees of freedom of head motion in three-dimensional cone-beam CT system without using any external motion sensors. Simulation results demonstrate that our method has prerequisite accuracy, linearity and range compared to the existing external sensor based method.

In this paper, a Euclidean distance based minutia matching algorithm is proposed to improve the matching accuracy in fingerprint verification system. This algorithm extracts matched minutia pairs from input and template fingerprints by using the smallest minimum sum of closest Euclidean distance (SMSCED), corresponding rotation angle and empirically chosen statistical threshold values. Instead of using the minutia type and orientation angle, which are widely employed in existing algorithms, the proposed algorithm uses only the minutia location, to reduce the effect of non-linear distortion. Experimental results show that the proposed method has higher accuracy with improved verification rate and rejection rate.