POCSENSE: POCS‐based reconstruction for sensitivity encoded magnetic resonance imaging

Samsonov, Alexey; Kholmovski, Eugene; Parker, Dennis L.; Johnson, Chris R.

doi:10.1002/mrm.20285

Cited by 133 publications

(148 citation statements)

References 36 publications

(63 reference statements)

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“…The effects of combining SENSE with partial Fourier techniques that employ the POCS and homodyne reconstruction methods have been explored (12). In addition, POCSENSE (13), which uses POCS as an alternative method to reconstruct images acquired with parallel imaging, has been proposed. POCSENSE has the flexibility to incorporate partial Fourier sampling.…”

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confidence: 99%

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SENSE phase‐constrained magnitude reconstruction with iterative phase refinement

Lew

Pineda

Clayton

et al. 2007

Magnetic Resonance in Med

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Conventional sensitivity encoding (SENSE) reconstruction is based on equations in the complex domain. However, for many MRI applications only the magnitude is relevant. If there exists an estimate of the underlying phase information, a magnitudeonly phase-constrained reconstruction can help to improve the conditioning of the SENSE reconstruction problem. Consequently, this reduces g-factor-related noise enhancement. In previous attempts at phase-constrained SENSE reconstruction, image quality was hampered by strong aliasing artifacts resulting from inadequate phase estimates and high sensitivity to phase errors. If a full-resolution phase image is used, a significant reduction in aliasing errors and better noise properties compared to SENSE can be obtained. An iterative scheme that improves the phase estimate to better approximate the phase is presented. The mathematical framework of the new approach is provided together with comparisons of conventional SENSE, phase-constrained SENSE, and the new phase-refinement method. Both theory and experimental verification demonstrate significantly better noise performance at high reduction factors, i.e., close to the theoretical limit. For applications that need only magnitude data, an iterative phase-constrained SENSE reconstruction can provide substantial SNR improvement over SENSE reconstruction and less artifacts than phaseconstrained SENSE. Magn Reson Med 58:910 -921, 2007.

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confidence: 99%

“…Some initial investigations (13)(14)(15)(16)(17) have explored the use of phase-constrained reconstruction. Here, an estimate of the underlying image phase is used to improve the SENSE reconstruction.…”

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confidence: 99%

SENSE phase‐constrained magnitude reconstruction with iterative phase refinement

Lew

Pineda

Clayton

et al. 2007

Magnetic Resonance in Med

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“…Step 1 the combined-channels image is modulated by the coil sensitivity profile of each channel in order to make the combined-channels estimate consistent with the coil data before the data projection in Step 2, which enforces the data consistency constraint for each channel by projecting the current estimate onto the corresponding coil data [28]. In Step 3 data from multiple channels are combined to obtain a combined-channels estimate image.…”

Section: Methodsmentioning

confidence: 99%

Stationary wavelet transform for under-sampled MRI reconstruction

Kayvanrad

McLeod

Baxter

et al. 2014

Magnetic Resonance Imaging

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In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional -penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions.

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“…In view of the independence of the two approaches, combining partial k-space and pMRI should allow further reductions of the data acquisition time [10][11][12][13][14][15][16][17][18]. In fact, recent studies have already demonstrated feasibility of this concept such as techniques combining SENSE and partial Fourier [10][11][12][13][14] and homodyne detection SENSE (HM_SENSE) [11,12], which has been claimed to constitute a robust algorithm. An important aspect of these methods is the estimation of the underlying image phase, which is required for improving the SENSE reconstruction also with regard to the acceleration factor.…”

Section: Introductionmentioning

confidence: 99%

Analytic image concept combined to SENSE reconstruction

Njiwa

Baltes

Rudin

2011

Magn Reson Mater Phy

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Object Two approaches of reconstructing undersampled partial k-space data, acquired with multiple coils are compared: homodyne detection combined with SENSE (HM_SENSE) and analytic image reconstruction combined with SENSE (AI_SENSE). The latter overcomes limitations of HM_ SENSE by considering aliased images as analytic thus avoiding the need for phase correction required for HM_SENSE. Materials and methods In vivo imaging experiments were carried out in male Lewis rats using both gradient echo and spin echo sequences. Accelerated images obtained by using the various reconstruction algorithms were compared to fully sampled reference images both qualitatively and quantitatively. Results For the various sampling patterns evaluated, both HM_SENSE and AI_SENSE were found to yield robust image reconstruction with small deviations from the reference image. Even for high acceleration factors AI_SENSE still provided useful results and was found superior compared to HM_SENSE. Conclusion Combination of partial k-space sampling and parallel image acquisition allows for further acceleration of data acquisition as compared to each method alone. Image reconstruction from undersampled data sets using the AI_SENSE algorithm was found to considerably reduce reconstruction errors and artifacts observed for HM_SENSE reconstruction caused by errors in phase estimation.

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POCSENSE: POCS‐based reconstruction for sensitivity encoded magnetic resonance imaging

Cited by 133 publications

References 36 publications

SENSE phase‐constrained magnitude reconstruction with iterative phase refinement

SENSE phase‐constrained magnitude reconstruction with iterative phase refinement

Stationary wavelet transform for under-sampled MRI reconstruction

Analytic image concept combined to SENSE reconstruction

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