Browsing by Subject "Joint reconstruction"
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Item Open Access Factorized sensitivity estimation for artifact suppression in phase‐cycled bSSFP MRI(Wiley, 2020) Bıyık, Erdem; Keskin, Kübra; Dar, Salman Ul Hassan; Koç, Aykut; Çukur, TolgaObjective: Balanced steady‐state free precession (bSSFP) imaging suffers from banding artifacts in the presence of magnetic field inhomogeneity. The purpose of this study is to identify an efficient strategy to reconstruct banding‐free bSSFP images from multi‐coil multi‐acquisition datasets. Method: Previous techniques either assume that a naïve coil‐combination is performed a priori resulting in suboptimal artifact suppression, or that artifact suppression is performed for each coil separately at the expense of significant computational burden. Here we propose a tailored method that factorizes the estimation of coil and bSSFP sensitivity profiles for improved accuracy and/or speed. Results: In vivo experiments show that the proposed method outperforms naïve coil‐combination and coil‐by‐coil processing in terms of both reconstruction quality and time. Conclusion: The proposed method enables computationally efficient artifact suppression for phase‐cycled bSSFP imaging with modern coil arrays. Rapid imaging applications can efficiently benefit from the improved robustness of bSSFP imaging against field inhomogeneity.Item Open Access Rapid multi-contrast magnetic resonance imaging and time-of-flight angiography(2019-01) Kılıç, ToyganMagnetic resonance imaging (MRI) is a frequently used imaging modality for examining soft tissue structures. Long scanning time is the most crucial constraint that limits the use of MRI in the clinics. Partial Fourier (PF), parallel imaging (PI), and compressed sensing (CS) methods have been proposed to accelerate acquisitions by undersampling the data in k-space. However, further increase in acceleration factor as well as image quality are needed in certain applications of MRI, such as T2-weighted imaging and time-of- flight (TOF) angiography. Previous studies have adopted SPIRiT, a popular CS method, to the problem of multi-contrast image reconstruction. However, the mutual information across different contrast images were not utilized in these studies. In this thesis, a new method is proposed to benefit from the correlated structural information among the images by emphasizing high-spatial frequencies during joint reconstruction. The results obtained from in vivo brain scans and numerical phantom show that the proposed method is more robust against parameter selection when compared to conventional methods. For TOF angiography images, the goal of this thesis is to increase the signal-to-noise-ratio and shorten the scanning time, simultaneously. For this purpose, a combination of 2D acceleration in the phase-encode dimensions via CS and 1D PF data acquisition in the frequency-encode dimension to reduce echo time is proposed. Following this data acquisition, a joint reconstruction that iteratively alternates between CS and PF is introduced. In vivo angiography results in the brain show that the proposed time-efficient TOF method improves the vessel-background contrast, while decreasing the scanning time.Item Open Access Reconstruction by calibration over tensors for multi-coil multi-acquisition balanced SSFP imaging(John Wiley & Sons, 2018) Bıyık, Erdem; Ilıcak, Efe; Çukur, TolgaPurpose: To develop a rapid imaging framework for balanced steady-state free precession (bSSFP) that jointly reconstructs undersampled data (by a factor of R) across multiple coils (D) and multiple acquisitions (N). To devise a multi-acquisition coil compression technique for improved computational efficiency. Methods: The bSSFP image for a given coil and acquisition is modeled to be modulated by a coil sensitivity and a bSSFP profile. The proposed reconstruction by calibration over tensors (ReCat) recovers missing data by tensor interpolation over the coil and acquisition dimensions. Coil compression is achieved using a new method based on multilinear singular value decomposition (MLCC). ReCat is compared with iterative self-consistent parallel imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions. Results: Compared to parallel imaging or profile-encoding methods, ReCat attains sensitive depiction of high-spatial-frequency information even at higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.1 ± 1.0 dB over SPIRiT and by 0.9 ± 0.3 dB over PE-SSFP (mean ± SD across subjects; average for N = 2-8, R = 8-16). Furthermore, reconstructions based on MLCC achieve 0.8 ± 0.6 dB higher PSNR compared to those based on geometric coil compression (GCC) (average for N = 2-8, R = 4-16). Conclusion: ReCat is a promising acceleration framework for banding-artifact-free bSSFP imaging with high image quality; and MLCC offers improved computational efficiency for tensor-based reconstructions. Magn Reson Med 79:2542-2554, 2018.Item 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.