Reconstruction by calibration over tensors for multi‐coil multi‐acquisition balanced SSFP imaging

Bıyık, Erdem; Ilicak, Efe; Çukur, Tolga

doi:10.1002/mrm.26902

Cited by 18 publications

(40 citation statements)

References 51 publications

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“…Second, an initial acquisition combination was performed here to estimate a common set of coil sensitivities across acquisitions. While this is a computationally efficient choice, accuracy of the estimates could be enhanced by using a tensor‐based decomposition across the coil and acquisition dimensions of bSSFP datasets . Lastly, the scan times for multi‐acquisition bSSFP scans may prove to be impractical in applications with demanding requirements for spatial resolution and coverage.…”

Section: Discussionmentioning

confidence: 99%

“…), where a joint combination was performed over coils and acquisitions. For sensitivity‐based combination, joint sensitivity profiles across coils and acquisitions were initially estimated via ESPIRiT as described in Ref . After estimation of joint sensitivities (

Ĵ_{n, d}

), we used the optimal linear combination described in Equation to obtain a banding‐suppressed image

Ŝ_{o}

(Joint‐SBC).…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we consider the joint encoding model for multi‐coil, multi‐acquisition bSSFP images used in Ref . The joint encoding model casts the bSSFP image captured by the d th coil element in the n th phase‐cycled acquisition as:

S_{n, d} false(r false) = P_{n} false(r false) C_{d} false(r false) S_{o} false(r false)

where r is the spatial location.…”

Section: Theorymentioning

confidence: 99%

“…A main‐field inhomogeneity yielding 0±62 Hz (mean ± std across volume) off‐resonance, and an array of D =8 coils arranged in a circular configuration were assumed . Tissue parameters were adapted from Ref …”

Section: Simulationsmentioning

confidence: 99%

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Factorized sensitivity estimation for artifact suppression in phase‐cycled bSSFP MRI

et al. 2020

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Objective: 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.

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Section: Discussionmentioning

confidence: 99%

Ĵ_{n, d}

), we used the optimal linear combination described in Equation to obtain a banding‐suppressed image

Ŝ_{o}

(Joint‐SBC).…”

Section: Methodsmentioning

confidence: 99%

S_{n, d} false(r false) = P_{n} false(r false) C_{d} false(r false) S_{o} false(r false)

where r is the spatial location.…”

Section: Theorymentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

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Factorized sensitivity estimation for artifact suppression in phase‐cycled bSSFP MRI

et al. 2020

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“…Lin‐PE was selected because of its widespread usage in clinical protocols and robustness to eddy current effects. Rnd‐PE was selected because it resembles the relatively large jumps in k‐space that are commonly seen in highly undersampled acquisitions for compressed sensing, low‐high profile ordering for low latency imaging or k − t sampling patterns for dynamic imaging. GA‐Rad was selected to represent non‐Cartesian with widespread utility in dynamic imaging .…”

Section: Methodsmentioning

confidence: 99%

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Bruijnen

Stemkens

Berg

et al. 2019

Magnetic Resonance in Med

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Purpose To propose an explicit Balanced steady‐state free precession (bSSFP) signal model that predicts eddy current‐induced steady‐state disruptions and to provide a prospective, practical, and general eddy current compensation method. Theory and Methods Gradient impulse response functions (GIRF) were used to simulate trajectory‐specific eddy current‐induced phase errors at the end of a repetition block. These phase errors were included in bloch simulations to establish a bSSFP signal model to predict steady‐state disruptions and their corresponding image artifacts. The signal model was embedded in the MR system and used to compensate the phase errors by prospectively modifying the phase cycling scheme of the RF pulse. The signal model and eddy current compensation method were validated in phantom and in vivo experiments. In addition, the signal model was used to analyze pre‐existing eddy current mitigation methods, such as 2D tiny golden angle radial and 3D paired phase encoded Cartesian acquisitions. Results The signal model predicted eddy current‐induced image artifacts, with the zeroth‐order GIRF being the primary factor to predict the steady‐state disruption. Prospective RF phase cycling schemes were automatically computed online and considerably reduced eddy current‐induced image artifacts. The signal model provides a direct relationship for the smoothness of k‐space trajectories, which explains the effectiveness of phase encode pairing and tiny golden angle trajectory. Conclusions The proposed signal model can accurately predict eddy current‐induced steady‐state disruptions for bSSFP imaging. The signal model can be used to derive the eddy current‐induced phase errors required for trajectory‐specific RF phase cycling schemes, which considerably reduce eddy current‐induced image artifacts.

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Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks

et al. 2021

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Purpose Synthetic magnetic resonance imaging (MRI) requires the acquisition of multicontrast images to estimate quantitative parameter maps, such as T1, T2, and proton density (PD). The study aims to develop a multicontrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI. Methods The JPI and JDL methods are extended and combined to improve reconstruction for better‐quality, synthesized images. JPI is performed as a first step to estimate the missing k‐space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS‐Net) is modified and extended to form a joint variable splitting network (JVS‐Net) to apply to multicontrast reconstructions. The proposed method is designed and tested for multidynamic multiecho (MDME) images with Cartesian uniform under‐sampling using acceleration factors between 4 and 8. Results It is demonstrated that the normalized root‐mean‐square error (nRMSE) is lower and the structural similarity index measure (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast‐weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts. Conclusion Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.

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Reconstruction by calibration over tensors for multi‐coil multi‐acquisition balanced SSFP imaging

Cited by 18 publications

References 51 publications

Factorized sensitivity estimation for artifact suppression in phase‐cycled bSSFP MRI

Factorized sensitivity estimation for artifact suppression in phase‐cycled bSSFP MRI

Prospective GIRF‐based RF phase cycling to reduce eddy current‐induced steady‐state disruption in bSSFP imaging

Accelerated multicontrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks

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