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s: Phase Contrast MR Angiography(PC MRA) is excellent MRA technique for measuring the velocity ofvessels in the human body. PC MRA need to at least four images for angiogram reconstruction and it caused longerscan time. Therefore, we used keyhole imaging combined PC MRA to reduce the scan time. However, keyhole imag-ing can lead the erroneous effects as loss of phase information or frequency discontinuous. In this study, we appliedthe keyhole imaging combined 2D PC MRA for improving the temporal resolution and also measured the velocityto evaluate the accuracy of phase information. We used 0.32T MRI scanner(Magfinder II, Scimedix, Korea). Usingthe 2D PC MRA pulse sequence, the vascular images for a human brain targeted on the Superior Sagittal Sinus(SSS)were obtained. We applied tukey window function for keyhole images to minimize the ringing artifact and erroneousfactors that are induced frequency discontinuous and phase information loss. We also applied zero-padded algorithmto peripheral missing k-space lines to compare keyhole imaging results and the artifact power(AP) value was mea-sured on the complex difference images to validate the image quality. Consider as based on our results, heavy imagedistortions and artifacts were shown until using at least 50% keyhole factor. Using above the 50% keyhole factorsare shown well reconstructed and matched for magnitude images and velocity information measurements. In con-clusion, we confirmed the image quality and velocity information of keyhole technique combined 2D PC MRA. Espe-cially, measured velocity information through the keyhole imaging combination was similar to the velocity informationof full sampled k-space image despite of frequency discontinuous and phase information loss in the keyhole imagingreconstruction process. Consequently, the keyhole imaging combined 2D PC MRA will give some clinical usefulnessand advantages as improving the temporal resolution and measuring the velocity information via selecting the appro-priate keyhole factor at low tesla MRI system.Key words: keyhole imaging, PC MRA, velocity measurement, zero-padding

s: Parallel imaging technique can provide several advantages for a multitude of MRI applications. Especially,in SENSE technique, sensitivity maps were always required in order to determine the reconstruction matrix, there-fore, a number of difference approaches using sensitivity information from coils have been demonstrated to improveof image quality. Moreover, many filtering methods were proposed such as adaptive matched filter and nonlinear dif-fusion technique to optimize the suppression of background noise and to improve of image quality. In this study, weperformed SENSE reconstruction using computer simulations to confirm the most suitable method for the feasibilityof filtering effect and according to changing order of polynomial fit that were applied on variation of spatial resolutionof sensitivity map. The image was obtained at 0.32T(Magfinder II, Genpia, Korea) MRI system using spin-echo pulsesequence(TR/TE = 500/20 ms, FOV = 300 mm, matrix = 128×128, thickness = 8 mm). For the simulation, obtainedimage was multiplied with four linear-array coil sensitivities which were formed of 2D-gaussian distribution and theimage was complex white gaussian noise was added. Image processing was separated to apply two methods whichwere polynomial fitting and filtering according to spatial resolution of sensitivity map and each coil image was sub-sampled corresponding to reduction factor(r-factor) of 2 and 4. The results were compared to mean value of geometyfactor(g-factor) and artifact power(AP) according to r-factor 2 and 4. Our results were represented while changingof spatial resolution of sensitivity map and r-factor, polynomial fit methods were represented the better results com-pared with general filtering methods. Although our result had limitation of computer simulation study instead ofapplying to experiment and coil geometric array such as linear, our method may be useful for determination of optimalsensitivity map in a linear coil array.Key words: SENSE, sensitivity map, polynomial fitting, low pass filtering, spatial resolution