Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior‐based algorithm (PICCS)

Chavarrías, Cristina; Abascal, Juan F P J; Montesinos, Paula; Desco, Manuel

doi:10.1118/1.4921365

Cited by 15 publications

(15 citation statements)

References 60 publications

(108 reference statements)

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“…Sparse reconstruction approaches have been used to accelerate fMRI exams while still providing high quality activation maps in areas of low SNR 156 and improved sensitivity to functional activation 81 . Sparse imaging has also been employed to reduce susceptibility artifacts in fMRI and improve imaging efficiency, making use of the fact that hemodynamic signal changes in fMRI are typically small from frame to frame 157 .…”

Section: Clinical Applications Of Sparse Reconstruction Techniquesmentioning

confidence: 99%

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

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The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed.

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Section: Clinical Applications Of Sparse Reconstruction Techniquesmentioning

confidence: 99%

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Yang

Kretzler

Sudarski

et al. 2016

Investigative Radiology

View full text Add to dashboard Cite

show abstract

“…Various remedies have been proposed to address high spatio-temporal resolution of fMRI such as development of high magnetic field scanner [4,5,6,7], coil sensitivity improvement inside fMRI scanner [8], advancements in pulse sequences [9,10], usage of parallel imaging [11,12], and compressed sensing (CS) based fMRI reconstruction from fewer k-space (spatial Fourier domain) measurements [13,14,15,16,17,18,19,20,21,22]. In this paper, we address the problem of accelerated fMRI reconstruction without the loss of BOLD sensitivity in the CS framework.…”

Section: Introductionmentioning

confidence: 99%

“…In general, it is assumed that the data is sparse in some transform domain and that the chosen samples are incoherent [15]. Compressive sensing framework helps in fMRI reconstruction in two significant ways: 1) It helps in increasing the statistical power of the BOLD signal [16,19] because of its inherent denoising property and 2) it provides improvement in the spatiotemporal resolution of fMRI data [17,21,13,22,14,18,20,15,24,25].…”

Section: Introductionmentioning

confidence: 99%

“…Various CS based reconstruction methods have been proposed so far for accelerated fMRI reconstruction [13,14,15,16,17,18,19,20,21,22,25,24]. These methods can be largely divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

“…These methods reconstruct brain volume at time t using the volume at time t − 1 by assuming causality in the reconstruction framework. Second category includes offline methods that first store k -space data of all fMRI volumes and later, utilize this complete information across both time and space, also called k-t space data, to reconstruct fMRI data [18,19,20,21,22,25,24].…”

Section: Introductionmentioning

confidence: 99%

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Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI

Aggarwal

Gupta

2017

Computers in Biology and Medicine

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A number of reconstruction methods have been proposed recently for accelerated functional Magnetic Resonance Imaging (fMRI) data collection. However, existing methods suffer with the challenge of greater artifacts at high acceleration factors. This paper addresses the issue of accelerating fMRI collection via undersampled k -space measurements combined with the proposed Double Temporal Sparsity based Reconstruction (DTSR) method with the l 1 − l 1 norm constraint. The robustness of the proposed DTSR method has been thoroughly evaluated both at the subject level and at the group level on real fMRI data. Results are presented at various acceleration factors. Quantitative analysis in terms of Peak Signal-to-Noise Ratio (PSNR) and other metrics, and qualitative analysis in terms of reproducibility of brain Resting State Networks (RSNs) demonstrate that the proposed method is accurate and robust. In addition, the proposed DTSR method preserves brain networks that are important for studying fMRI data. Compared to the existing accelerated fMRI reconstruction methods, the DTSR method shows promising potential with an improvement of 10-12dB in PSNR with acceleration factors upto 3.5. Simulation results on real data demonstrate that DTSR method can be used to acquire accelerated fMRI with accurate detection of RSNs.

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PEAR: PEriodic And fixed Rank separation for fast fMRI

et al. 2017

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PurposeIn functional MRI (fMRI), faster acquisition via undersampling of data can improve the spatial‐temporal resolution trade‐off and increase statistical robustness through increased degrees‐of‐freedom. High‐quality reconstruction of fMRI data from undersampled measurements requires proper modeling of the data. We present an fMRI reconstruction approach based on modeling the fMRI signal as a sum of periodic and fixed rank components, for improved reconstruction from undersampled measurements.MethodsThe proposed approach decomposes the fMRI signal into a component which has a fixed rank and a component consisting of a sum of periodic signals which is sparse in the temporal Fourier domain. Data reconstruction is performed by solving a constrained problem that enforces a fixed, moderate rank on one of the components, and a limited number of temporal frequencies on the other. Our approach is coined PEAR ‐ PEriodic And fixed Rank separation for fast fMRI.ResultsExperimental results include purely synthetic simulation, a simulation with real timecourses and retrospective undersampling of a real fMRI dataset. Evaluation was performed both quantitatively and visually versus ground truth, comparing PEAR to two additional recent methods for fMRI reconstruction from undersampled measurements. Results demonstrate PEAR's improvement in estimating the timecourses and activation maps versus the methods compared against at acceleration ratios of R = 8,10.66 (for simulated data) and R = 6.66,10 (for real data).ConclusionsThis paper presents PEAR, an undersampled fMRI reconstruction approach based on decomposing the fMRI signal to periodic and fixed rank components. PEAR results in reconstruction with higher fidelity than when using a fixed‐rank based model or a conventional Low‐rank + Sparse algorithm. We have shown that splitting the functional information between the components leads to better modeling of fMRI, over state‐of‐the‐art methods.

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Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior‐based algorithm (PICCS)

Cited by 15 publications

References 60 publications

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Sparse Reconstruction Techniques in Magnetic Resonance Imaging

Double temporal sparsity based accelerated reconstruction of compressively sensed resting-state fMRI

PEAR: PEriodic And fixed Rank separation for fast fMRI

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