Magnetic resonance imaging sense reconstruction system using FPGA / Muhammad Faisal Siddiqui - Muhammad Faisal , Siddiqui (2016)
Parallel imaging is a robust method for accelerating the data acquisition in Magnetic Resonance Imaging (MRI). Under-sampled data is acquired in parallel imaging to expedite the MRI scan process, which leads to aliased images. Sensitivity Encoding (SENSE) is a widely used technique to reconstruct the artefact free images from the Parallel MRI (pMRI) aliased data. Reconfigurable hardware based architecture for SENSE has a great potential to provide good quality image reconstruction with significantly less computation time. This thesis aimed to investigate and develop a novel parameterized architecture design for SENSE algorithm. The proposed design is implemented on Field Programmable Gate Arrays (FPGAs) platform, which can provide real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer data to the MRI server. Complex multiplier, complex matrix multiplier and pseudo-inverse modules are designed according to the algorithmic needs to increase the efficiency of the system. Furthermore, variable databus widths are used in the data path of the proposed architecture, which leads to reducing the hardware cost and silicon area. The use of eigenvectors decomposition (E-maps) and pre-scan methods for estimating sensitivity maps are also investigated. The reconstruction results are compared with the multi-core CPU and Graphical Processing Unit (GPU) based reconstructions of SENSE. This research also proposed an intelligent and robust classification technique to classify the MRI scans as normal or abnormal and also for validation purpose. The proposed classifier has been developed by using fast Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and Least Squares Support Vector Machine (LS-SVM). Firstly, fast DWT is employed to extract the salient features of MRI images, followed by PCA, which reduces the dimensions of the features. Finally, LS-SVM is applied to MR image classification using reduced features. The achieved reconstruction results are 850 times faster than the conventional multicore CPU and 85 times faster than the GPU based reconstructions of SENSE, while maintaining the quality of the reconstructed images with significantly less artefact power ( < 2.45 10 4  ) and good mean SNR (35+ dB) values. The proposed system also provides better reconstruction results when using E-maps and achieves <9 10 4  and 29+ dB for artefact power and mean SNR, respectively. Center line profiles comparison also demonstrates the quality of the reconstructed images. The proposed system offers a reconstruction right on the multi-channel data acquisition module which minimizes the transmission cost and memory usage of the MRI system. Furthermore, its low power consumption features can be remarkable especially for portable MRI scanners. Moreover, the proposed classifier technique is significantly faster than the recent well known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. The results indicate that the overall system is capable of reconstructing the high quality images from the pMRI aliased data in real-time and then classify it as normal or abnormal, therefore, it can be used as a significant tool in clinical practice.