Efficient Reconstruction Technique for Multi-Slice CS-MRI Using Novel Interpolation and 2D Sampling Scheme

TLDR: A novel fast interpolated compressed sensing technique based on 2D variable density under-sampling (VRDU) scheme that has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content.

Abstract: Compressed Sensing (CS) theory breaks the Nyquist theorem through random under-sampling and enables us to reconstruct a signal from 10%-50% samples Magnetic Resonance Imaging (MRI) is a good candidate for application of compressed sensing techniques due to i) implicit sparsity in MR images and ii) inherently slow data acquisition process In multi-slice MRI, strong inter-slice correlation has been exploited for further scan time reduction through interpolated compressed sensing (iCS) In this paper, a novel fast interpolated compressed sensing (FiCS) technique is proposed based on 2D variable density under-sampling (VRDU) scheme The 2D-VRDU scheme improves the result by sampling the high energy central part of the k-space slices The novel interpolation technique takes two consecutive slices and estimates the missing samples of the target slice (T slice) from its left slice (L slice) Compared to the previous methods, slices recovered with the proposed FiCS technique have a maximum correlation with their corresponding original slices The proposed FiCS technique is evaluated by using both subjective and objective assessment In subjective assessment, our proposed technique shows less partial volume loss compared to existing techniques For objective assessment different performance metrics, such as structural similarity index measurement (SSIM), peak signal to noise ratio (PSNR), mean square error (MSE) and correlation, are used and compared with existing interpolation techniques Simulation results on knee and brain dataset shows that the proposed FiCS technique has improved image quality and performance with even reduced scan time, lower computational complexity and maximum information content