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157 results on '"Omer, Hammad"'

5. Efficient Reconstruction of Free Breathing Under-Sampled Cardiac Cine MRI

Author

Ahmed, Abdul Haseeb, Qureshi, Ijaz M., Shah, Jawad Ali, and Omer, Hammad

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Respiratory motion can cause strong blurring artifacts in the reconstructed image during MR acquisition. These artifacts become more prominent when use in the presence of undersampled data. Recently, compressed sensing (CS) is developed as an MR reconstruction technique, to recover good quality images from the compressive k-space samples. To maximize the benefits of CS in free breathing data, it is understandable to use CS with the motion corrected images. In this paper, we have developed a new CS based motion corrected image reconstruction technique. In this two-stage technique, we use similarity measure to sort the motion corrupted data into different respiratory states. Then, we use a new reconstruction algorithm, which iteratively performs reconstruction and motion correction. The performance of the proposed method is qualitatively and quantitively evaluated using simulated data and clinical data. Results depict that this method performs the better reconstruction of respiratory motion corrected cardiac cine images as compared to the CS based reconstruction method.

Published
2019

19. Parallel MRI : tools and applications

Author

Omer, Hammad and Dickinson, Robert

Subjects
615.84
Abstract

Magnetic Resonance Imaging (MRI) is a non-ionising imaging modality which can provide excellent soft-tissue contrast because of a large number of flexible contrast parameters. One major limitation of MRI is its long acquisition time. Parallel MRI provides a framework to reduce the scan time. The aim of this thesis is to investigate and develop new methods to improve the performance of Parallel MRI. A new GUI (Graphical User Interface) based platform is developed using Matlab which provides an interactive environment to apply different Parallel MRI algorithms as well as helps to analyse the results. Regularization based reconstruction in Parallel MRI utilizes some prior information about the image to achieve better reconstruction results. The use of regularization in Parallel MRI is investigated and a new algorithm is proposed which uses wavelet-denoising of the coil sensitivity estimates before applying SENSE (a Parallel MRI algorithm). The results show that the proposed method is computationally efficient and offers a good alternative to regularization for lower acceleration factors (AF) in Parallel MRI. A good choice of the regularization parameter in regularization based Parallel MRI reconstructions plays a pivotal role to have good results. A new algorithm to choose the regularization parameter efficiently has been developed. This method uses the g-Factor (noise amplification parameter in Parallel MRI) as a regularization parameter and provides better reconstruction results than the contemporary methods. The proposed algorithm improves the computational efficiency of regularization based reconstructions in Parallel MRI. The use of Parallel MRI in interventional imaging can greatly reduce the time required for imaging. A novel catheter based phased array coil, composed of two independent coil elements has been developed. This phased array receiver coil can implement Parallel MRI. Some initial imaging experiments using this coil system have been performed and the results show a successful implementation of Parallel MRI on the acquired data.

Published
2012
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26. Compressed SVD-based L plus S model to reconstruct undersampled dynamic MRI data using parallel architecture

Author

Shafique, Muhammad, Qazi, Sohaib, Omer, Hammad, Shafique, Muhammad, Qazi, Sohaib, and Omer, Hammad

Abstract

Background Magnetic Resonance Imaging (MRI) is a highly demanded medical imaging system due to high resolution, large volumetric coverage, and ability to capture the dynamic and functional information of body organs e.g. cardiac MRI is employed to assess cardiac structure and evaluate blood flow dynamics through the cardiac valves. Long scan time is the main drawback of MRI, which makes it difficult for the patients to remain still during the scanning process.Objective By collecting fewer measurements, MRI scan time can be shortened, but this undersampling causes aliasing artifacts in the reconstructed images. Advanced image reconstruction algorithms have been used in literature to overcome these undersampling artifacts. These algorithms are computationally expensive and require a long time for reconstruction which makes them infeasible for real-time clinical applications e.g. cardiac MRI. However, exploiting the inherent parallelism in these algorithms can help to reduce their computation time.Methods Low-rank plus sparse (L+S) matrix decomposition model is a technique used in literature to reconstruct the highly undersampled dynamic MRI (dMRI) data at the expense of long reconstruction time. In this paper, Compressed Singular Value Decomposition (cSVD) model is used in L+S decomposition model (instead of conventional SVD) to reduce the reconstruction time. The results provide improved quality of the reconstructed images. Furthermore, it has been observed that cSVD and other parts of the L+S model possess highly parallel operations; therefore, a customized GPU based parallel architecture of the modified L+S model has been presented to further reduce the reconstruction time.Results Four cardiac MRI datasets (three different cardiac perfusion acquired from different patients and one cardiac cine data), each with different acceleration factors of 2, 6 and 8 are used for experiments in this paper. Experimental results demonstrate that using the proposed parallel archit

Published
2023
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49. FPGA-Based Pipelined Architecture for Real-Time Estimation of Sensitivity Maps Using Pre-Scan Method in Parallel MRI.

Author

Khan, Tooba, Siddiqui, Muhammad Faisal, and Omer, Hammad

Subjects
FIELD programmable gate arrays, IMAGE reconstruction algorithms, DATA acquisition systems, IMAGE reconstruction
Abstract

Parallel MRI (pMRI) is a widely used technique for increasing the MRI acquisition speed, by using multiple receiver coils with spatially discriminated sensitivities. Sensitivity encoding (SENSE) is a parallel MRI technique used for reconstructing the aliasing free images from the under-sampled MRI data using the spatial information obtained from multiple receiver coils. Precise receiver coil sensitivity estimation is critical for the correct image reconstruction from the under-sampled data in SENSE. Pre-scan method is a quick and conventionally used method for correct estimation of the receiver coil sensitivities, to be used in SENSE. In this work, an application-specific hardware implemented on FPGA (Field Programmable Gate Array) for real-time sensitivity maps estimation using the pre-scan method is proposed. The proposed architecture has the potential to be installed on the receiver coil data acquisition system, which would provide sensitivity maps for image reconstruction, without moving the raw data to the MRI workstation. Parallelism is utilized in the proposed pipelined architecture to make it even faster. From the experimental results, it is shown that the proposed architecture estimates the sensitivity maps in only 1.466 ms for eight receiver coils. Furthermore, high mean SNR (30 + dB), low root-mean-square-error (< 6. 5 × 1 0 − 5 ) and low artifact power (< 6 × 1 0 − 4 ) are achieved for the under-sampled human head, cardiac and phantom data sets (acceleration factor = 2), reconstructed with SENSE algorithm utilizing the sensitivity maps estimated by the proposed architecture. [ABSTRACT FROM AUTHOR]

Published
2020
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