Browsing by Author "Kopanoğlu, E."

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Open Access
Compressed multi-contrast magnetic resonance image reconstruction using Augmented Lagrangian Method
(IEEE, 2016) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, H. E.
In this paper, a Multi-Channel/Multi-Contrast image reconstruction algorithm is proposed. The method, which is based on the Augmented Lagrangian Method uses joint convex objective functions to utilize the mutual information in the data from multiple channels to improve reconstruction quality. For this purpose, color total variation and group sparsity are used. To evaluate the performance of the method, the algorithm is compared in terms of convergence speed and image quality using Magnetic Resonance Imaging data to FCSA-MT, an alternative approach on reconstructing multi-contrast MRI data.
Open Access
Joint dictionary learning reconstruction of compressed multi-contrast magnetic resonance imaging
(Institute of Electrical and Electronics Engineers, 2018) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, E.; Yarman-Vural, F. T.
This study deals with reconstruction of compressed multicontrast magnetic resonance image (MRI) reconstruction using joint dictionary learning. Usually pre-determined dictionaries are used for compressed sensing reconstructions. Here, we propose an alternating-minimization based algorithm for recovering image and sparsifying transformation from only data itself. The proposed method can also be viewed as a joint multicontrast reconstruction extension of a previous blind compressive sensing algorithm [1]. For evaluation, the algorithm is compared in terms of convergence speed and image quality to both individual dictionary learning based method [1], and a joint reconstruction algorithm using pre-determined dictionaries for MRI [2].
Open Access
Simultaneous use of individual and joint regularization terms in compressive sensing: joint reconstruction of multi‐channel multi‐contrast MRI acquisitions
(Wiley, 2020) Kopanoğlu, E.; Güngör, Alper; Kılıç, Toygan; Sarıtaş, Emine Ülkü; Oğuz, Kader K.; Çukur, Tolga; Güven, H. E.
Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.
Open Access
A synthesis-based approach to compressive multi-contrast magnetic resonance imaging
(IEEE, 2017) Güngör, A.; Kopanoğlu, E.; Çukur, Tolga; Güven, H. E.
In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using Alternating Direction Method of Multipliers (ADMM). We then compare the proposed algorithm to the state-of-the-art Compressive Sensing-MRI algorithms, and show that the proposed method results in better quality images in shorter computation time.