Magnetic resonance image reconstruction using analytic image representation

Njiwa, J. Yankam; Zhu, Yuemin; Robini, Marc; Magnin, Isabelle E.

doi:10.1016/j.nima.2006.10.032

Cited by 6 publications

(9 citation statements)

References 5 publications

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“…The analytic image concept has already been used to reconstruct partial k-space data both from static [8] and dynamic [9] objects. In this work we demonstrated that the combination of this concept to SENSE successfully and robustly reconstructs images while achieving higher acceleration factors.…”

Section: Discussionmentioning

confidence: 99%

“…Analytic image reconstruction has been described earlier [8,9]; for simplicity, we will illustrate the principles as far as they are required for the study presented.…”

Section: Analytic Image Reconstructionmentioning

confidence: 99%

“…Both parallel imaging [1][2][3][4][5] and partial Fourier techniques [6][7][8][9], which sample only a fraction of the k-space data to perform phase-constrained reconstruction [6,7], have been proposed for accelerating data acquisition. 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].…”

Section: Introductionmentioning

confidence: 99%

“…The method uses the benefits of the analytic image concept [8,9], which exploits data redundancies in k-space, for reconstructing the aliased images, which in a second step will be processed by SENSE reconstruction. The analytic image SENSE (AI_SENSE) algorithm illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

“…Analytic image reconstruction has been described earlier [8,9]; for simplicity, we will illustrate the principles as far as they are required for the study presented.…”

Section: Analytic Image Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analytic image concept combined to SENSE reconstruction

Njiwa

Baltes

Rudin

2011

Magn Reson Mater Phy

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“…Much effort has focused on the reconstruction from the partial k-space truncated in the phaseencoding direction (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). To correctly reconstruct the spatial MR image from such 1D partial k-space data, a basic idea is to correct the phase drift.…”

mentioning

confidence: 99%

MRI reconstruction from 2D truncated k‐space

Luo¹,

Zhu²,

Li³

et al. 2011

Magnetic Resonance Imaging

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Purpose: To shorten acquisition time by means of both partial scanning and partial echo acquisition and to reconstruct images from such 2D partial k-space acquisitions. Materials and Methods:We propose an approach to reconstructing magnetic resonance images from 2D truncated k-space in which the k-space is truncated in both phase-and frequency-encoding directions. Unlike conventional reconstruction techniques, the proposed approach is based on a newly developed 2D singularity function analysis (SFA) model and a sparse representation of an image whose parameters can be estimated from the 2D partial k-space data. Such a sparse representation leads to an accurate recovery of the missing k-space data and, hence, an accurate reconstruction of the image.Results: The proposed approach can reconstruct an image from as little as 20%-30% of the k-space data and the quality of the reconstructed image is comparable to the reference image that is reconstructed from the complete k-space data. Conclusion:Despite the high asymmetry of a 2D truncated k-space, the proposed approach allows for accurate reconstruction without the need of phase correction and, thus, overcomes the assumption of slow phase variations that is usually required by the existing reconstruction methods. It provides a new way of fast imaging for applications that require a significant reduction of the acquisition time.

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