On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization

Bilen, Çağdaş; Selesnick, Ivan; Wang, Y.; Otazo, Ricardo; Kim, Danny; Axel, Leon; Sodickson, Daniel K.

doi:10.1109/icassp.2010.5495163

Cited by 21 publications

(25 citation statements)

References 12 publications

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“…L 1 regularization enables a large reduction in the sampling and computation costs for sensing signals that have a compressible representation, thus enabling dramatic under-sampling of data while maintaining image quality [21]. CMR has emerged as a prime candidate for applying L 1 regularization, as CMR images are almost exclusively compressible [20][21][22]. A novel iterative reconstruction strategy employing an investigational sequence with L 1 regularization, iterative Sparse SENSE (IS-SENSE), has shown promise reducing image noise, potentially enabling clinical applications of highly accelerated parallel imaging at CMR [23].…”

Section: Introductionmentioning

confidence: 99%

Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an IS-SENSE reconstruction

Bogachkov¹,

Ayache²,

Allen³

et al. 2016

Int J Cardiovasc Imaging

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Cardiac MR is considered the gold standard in assessing RV function. The purpose of this study is to evaluate the clinical utility of an investigational iterative reconstruction algorithm in the quantitative assessment of RV function. This technique has the potential to improve the clinical utility of CMR in the evaluation of RV pathologies, particularly in patients with dyspnea, by shortening acquisition times without adversely influencing imaging performance. Segmented cine images were acquired on 9 healthy volunteers and 29 patients without documented RV pathologies using conventional GRAPPA acquisition with factor 2 acceleration (GRAPPA 2), a spatio-temporal TSENSE acquisition with factor 4 acceleration (TSENSE 4), and iteratively reconstructed Sparse SENSE acquisition with factor 4 acceleration (IS-SENSE 4). 14 subjects were re-analyzed and intraclass correlation coefficients (ICC) were calculated and Bland-Altman plots generated to assess agreement. Two independent reviewers qualitatively scored images. Comparison of acquisition techniques was performed using univariate analysis of variance (ANOVA). Differences in RV EF, BSA-indexed ESV (ESVi), BSA-indexed EDV (EDVi), and BSA-indexed SV (SVi) were shown to be statistically insignificant via ANOVA testing. R(2) values for linear regression of TSENSE 4 and IS-SENSE 4 versus GRAPPA 2 were 0.34 and 0.72 for RV-EF, and 0.61 and 0.76 for RV-EDVi. ICC values for intraobserver and interobserver quantification yielded excellent agreement, and Bland-Altman plots assessing agreement were generated as well. Qualitative review yielded small, but statistically significant differences in image quality and noise between TSENSE 4 and IS-SENSE 4. All three techniques were rated nearly artifact free. Segmented imaging acquisitions with IS-SENSE reconstruction and an acceleration factor of 4 accurately and reliably quantitates RV systolic function parameters, while maintaining image quality. TSENSE-4 accelerated acquisitions showed poorer correlation to standard imaging, and inferior interobserver and intraobserver agreement. IS-SENSE has the potential to shorten cine acquisition times by 50 %, improving imaging options in patients with intermittent arrhythmias or difficulties with breath holding.

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Section: Introductionmentioning

confidence: 99%

Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an IS-SENSE reconstruction

Bogachkov¹,

Ayache²,

Allen³

et al. 2016

Int J Cardiovasc Imaging

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“…For TwIST, each iteration depends on the two previous estimates [12]. Its solution is (26) where ζ and β are the positive relaxation factors that control the convergence of the iteration. …”

Section: Algorithmsmentioning

confidence: 99%

“…The mean square error (MSE), Stein's unbiased risk estimation (SURE) and generalized cross validation (GCV) methods are tested in both L 1 and L 2 problems, where the predictive risk and the signal estimation errors are minimized to select λ [24,25]. As shown in [26], minimizing root mean square error (RMSE) with the original signal may not always be a good method. In this paper, we propose a new approach that is called data-driven sparsity learning based on an LMMSE equalizer to tune the regularization parameter.…”

Section: Introductionmentioning

confidence: 99%

Comparison of sparse recovery algorithms for channel estimation in underwater acoustic OFDM with data-driven sparsity learning

Huang

Wan

Zhou

et al. 2014

Physical Communication

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“…One approach taken in reference [89] presented a four-fold accelerated cardiac perfusion MRI through exploiting the sparsity of the dynamic image set in x-f space and employing k-t random undersampling by CS. Combined with PI, a joint reconstruction approach, named k-t JOCS (joint CS) can highly accelerate the acquisition (more than six-fold) by using Fourier transform in time and spatial TV [89].…”

Section: Cardiac Imagingmentioning

confidence: 99%

“…Combined with PI, a joint reconstruction approach, named k-t JOCS (joint CS) can highly accelerate the acquisition (more than six-fold) by using Fourier transform in time and spatial TV [89]. Another approach also merges CS into PI in Cardiac MRI to evaluate left ventricular volumes and function with high accuracy in patients with the acceleration rate up to 11 [90].…”

Section: Cardiac Imagingmentioning

confidence: 99%

Improving the image quality in compressed sensing MRI by the exploitation of data properties

Yang¹

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Minimizing scan time without compromising image quality has been the main thrust of magnetic resonance imaging (MRI) since the establishment of the field. Tremendous effort has been put into MRI systems in pursuit of faster MR imaging techniques, which can substantially facilitate clinical diagnoses and improve the patient experience. Traditional MRI accelerated approaches mainly focused on hardware improvements to improve the speed of scanning. These methods are still governed by Nyquist-Shannon sampling rate. Hence, when the acceleration rate is pushed beyond the theoretical limit, aliasing artefacts are introduced which degrade the reconstructed image quality and are difficult to eliminate via current methods. Recently, it has been found that a newly developed signal processing theory, compressed sensing (CS), can be used to create high-resolution signal data sets from sparse samples, despite violating the classical Nyquist-Shannon sampling criteria. The application of CS to MRI opens up an appealing avenue to offer potentially significant scan time reductions. CS reconstructs MR images from incomplete k-space measurements. Taking advantage of the fact that MR images have sparse representations under certain mathematical transformations, CS overcomes the distortions resulting from the under-sampled k-space data. Significant progress has been made in the development of CS MRI. However, there are two major remaining challenges to its full adoption in clinical applications: (1) the small amount of under-sampled data (for example, less than 1/8 of the whole k-space data) will inherently introduce artefacts in the reconstructed image and compromise diagnosis accuracy for the reconstructed images; (2) owing to hardware limits, it is difficult to realise two-dimension random under-sampling, which is favoured by CS operation.Practically, the random phase-encode under-sampling pattern has been widely implemented.However, it introduces coherent aliasing artefacts that are hard to eliminate using the CS theory.In this thesis, several techniques are proposed that can improve the quality of images reconstructed by CS MRI. These techniques have the potential to facilitate faithful reconstruction of CS MRI in clinical applications. A novel two-stage reconstruction scheme is introduced in Chapter 3. In this method, the under-sampled k-space data is segmented into low-frequency and high-frequency parts.Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the under-sampled high-frequency k-space data to complete the second stage reconstruction of the whole image. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate than conventional approaches. Because the restricted isometric property is easier to satisfy than conventional CS method, the reconstruction consequently ii produces lower residual errors in each step...

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On compressed sensing in parallel MRI of cardiac perfusion using temporal wavelet and TV regularization

Cited by 21 publications

References 12 publications

Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an IS-SENSE reconstruction

Right ventricular assessment at cardiac MRI: initial clinical experience utilizing an IS-SENSE reconstruction

Comparison of sparse recovery algorithms for channel estimation in underwater acoustic OFDM with data-driven sparsity learning

Improving the image quality in compressed sensing MRI by the exploitation of data properties

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