Optimizations of 3D MRI Techniques in Brain by Evaluating SENSE Factors

PURPOSE: A parallel imaging method provides us to improve temporal resolution to obtain three-dimensional (3D) MR images. The objective of this study was to optimize three 3D MRI techniques by adjusting 2D SESNE factors of the parallel imaging method in phantom and human brain. MATERIALS AND METHODS: With a 3 Tesla MRI system and an 8-channel phase-array sensitivity-encoding (SENSE) coil, three 3D MRI techniques of 3D T1-weighted imaging (3D T1WI), 3D T2-weighted imaging (3D T2WI) and 3D fluid attenuated inversion recovery (3D FLAIR) imaging were optimized with adjusting SESNE factors in a water phantom and three human brains. The 2D SENSE factor was applied on the phase-encoding and the slice-encoding directions. Signal-to-noise ratio(SNR), percent signal reduction rate(%R), and contrast-to-noise ratio(CNR) were calculated by using signal intensities obtained in specific regions-of-interest (ROI). RESULTS: In the phantom study, SENSE factor=3 was provided in 0.2% reduction of signals against without using SENSE with imaging within 5 minutes for 3D T1WI. SENSE factor=2 was provided in 0.98% signal reduction against without using SENSE with imaging within 5 minutes for 3D T2WI. SENSE factor=4 was provided in 0.2% signal reduction against without using SENSE with imaging around 6 minutes for 3D FLAIR. In the human brain study, SNR and CNR were higher with SENSE factors=3 than 4 for all three imaging techniques. CONCLUSION: This study was performed to optimize 2D SENSE factors in the three 3D MRI techniques that can be scanned in clinical time limitations with minimizing SNR reductions. Without compromising SNR and CNR, the optimum 2D SENSE factors were 3 and 4, yielding the scan time of about 5 to 6 minutes. Further studies are necessary to optimize 3D MRI techniques in other areas in human body.

Segmentation of 3D MRI Image of Children Brain / 医疗卫生装备

Objective To study a semi-automatic segmentation framework for children's brain MRI image ( weighted), which is capable of identifying the different structures of children's MRI images by using the histogram analysis and morphological operations. Methods The framework consisted of four-step segmentation procedures. First, the non-brain structures removal was addressed to obtain the mask of encephalon, then the brain stem and cerebellum were separated respectively from the encephalon mask, and finally, two hemispheres were separated. Results The method behaved well in the segmentation of MRI images of the brain stem, cerebel and two brain hemispheres. Conclusion The method gave the good segmentation results in the children MRI image aging from 5 to 15 years old.