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Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense)
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| Zeitschriftentitel: | Concepts in Magnetic Resonance Part A |
|---|---|
| Personen und Körperschaften: | , |
| In: | Concepts in Magnetic Resonance Part A, 40A, 2012, 6, S. 269-280 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
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| Schlagwörter: |
| author_facet |
Majumdar, Angshul Ward, Rabab K. Majumdar, Angshul Ward, Rabab K. |
|---|---|
| author |
Majumdar, Angshul Ward, Rabab K. |
| spellingShingle |
Majumdar, Angshul Ward, Rabab K. Concepts in Magnetic Resonance Part A Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) Spectroscopy |
| author_sort |
majumdar, angshul |
| spelling |
Majumdar, Angshul Ward, Rabab K. 1546-6086 1552-5023 Wiley Spectroscopy http://dx.doi.org/10.1002/cmr.a.21244 <jats:title>Abstract</jats:title><jats:p>SENSitivity Encoding (SENSE) is a parallel MR image reconstruction technique that yields optimal results when the sensitivity maps are accurately known. Unfortunately, in practical scenarios, obtaining accurate estimates of the sensitivity maps is not possible. In this work, we propose a technique that iteratively reconstructs the image and refines the sensitivity maps (from initial estimates). Our technique is named <jats:italic>i</jats:italic>SENSE (iterative SENSE). Our proposed technique exploits the sparsity of the MR image in some transform domains or the rank deficiency characteristic of the matrix representing the MRI image; the former leads to a compressed sensing‐based reconstruction method, whereas the latter leads to an image reconstruction method that minimizes the nuclear norm (NN) of the image matrix. The sensitivity maps are assumed to be rank‐deficient matrices, and thus the refinement of the sensitivity maps is achieved via the NN minimization. To evaluate the performance of the proposed method, we have carried out the experiments on real and one simulated datasets. We have compared our method with three state‐of‐the‐art image domain methods—SparSENSE (Sparse SENSE), NNSENSE (NN Regularized SENSE), and JSENSE (Joint SENSE reconstruction)—and one widely used frequency domain method—GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition). Our method yields the best reconstruction results both in quantitative (normalized mean‐squared error) and qualitative (visual inspection of reconstructed and difference images) evaluation. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 269–280, 2012.</jats:p> Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (<i>i</i>sense) Concepts in Magnetic Resonance Part A |
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Concepts in Magnetic Resonance Part A |
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| title |
Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_unstemmed |
Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_full |
Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_fullStr |
Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_full_unstemmed |
Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_short |
Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_sort |
iterative estimation of mri sensitivity maps and image based on sense reconstruction method (<i>i</i>sense) |
| topic |
Spectroscopy |
| url |
http://dx.doi.org/10.1002/cmr.a.21244 |
| publishDate |
2012 |
| physical |
269-280 |
| description |
<jats:title>Abstract</jats:title><jats:p>SENSitivity Encoding (SENSE) is a parallel MR image reconstruction technique that yields optimal results when the sensitivity maps are accurately known. Unfortunately, in practical scenarios, obtaining accurate estimates of the sensitivity maps is not possible. In this work, we propose a technique that iteratively reconstructs the image and refines the sensitivity maps (from initial estimates). Our technique is named <jats:italic>i</jats:italic>SENSE (iterative SENSE). Our proposed technique exploits the sparsity of the MR image in some transform domains or the rank deficiency characteristic of the matrix representing the MRI image; the former leads to a compressed sensing‐based reconstruction method, whereas the latter leads to an image reconstruction method that minimizes the nuclear norm (NN) of the image matrix. The sensitivity maps are assumed to be rank‐deficient matrices, and thus the refinement of the sensitivity maps is achieved via the NN minimization. To evaluate the performance of the proposed method, we have carried out the experiments on real and one simulated datasets. We have compared our method with three state‐of‐the‐art image domain methods—SparSENSE (Sparse SENSE), NNSENSE (NN Regularized SENSE), and JSENSE (Joint SENSE reconstruction)—and one widely used frequency domain method—GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition). Our method yields the best reconstruction results both in quantitative (normalized mean‐squared error) and qualitative (visual inspection of reconstructed and difference images) evaluation. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 269–280, 2012.</jats:p> |
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| author | Majumdar, Angshul, Ward, Rabab K. |
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| description | <jats:title>Abstract</jats:title><jats:p>SENSitivity Encoding (SENSE) is a parallel MR image reconstruction technique that yields optimal results when the sensitivity maps are accurately known. Unfortunately, in practical scenarios, obtaining accurate estimates of the sensitivity maps is not possible. In this work, we propose a technique that iteratively reconstructs the image and refines the sensitivity maps (from initial estimates). Our technique is named <jats:italic>i</jats:italic>SENSE (iterative SENSE). Our proposed technique exploits the sparsity of the MR image in some transform domains or the rank deficiency characteristic of the matrix representing the MRI image; the former leads to a compressed sensing‐based reconstruction method, whereas the latter leads to an image reconstruction method that minimizes the nuclear norm (NN) of the image matrix. The sensitivity maps are assumed to be rank‐deficient matrices, and thus the refinement of the sensitivity maps is achieved via the NN minimization. To evaluate the performance of the proposed method, we have carried out the experiments on real and one simulated datasets. We have compared our method with three state‐of‐the‐art image domain methods—SparSENSE (Sparse SENSE), NNSENSE (NN Regularized SENSE), and JSENSE (Joint SENSE reconstruction)—and one widely used frequency domain method—GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition). Our method yields the best reconstruction results both in quantitative (normalized mean‐squared error) and qualitative (visual inspection of reconstructed and difference images) evaluation. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 269–280, 2012.</jats:p> |
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| spelling | Majumdar, Angshul Ward, Rabab K. 1546-6086 1552-5023 Wiley Spectroscopy http://dx.doi.org/10.1002/cmr.a.21244 <jats:title>Abstract</jats:title><jats:p>SENSitivity Encoding (SENSE) is a parallel MR image reconstruction technique that yields optimal results when the sensitivity maps are accurately known. Unfortunately, in practical scenarios, obtaining accurate estimates of the sensitivity maps is not possible. In this work, we propose a technique that iteratively reconstructs the image and refines the sensitivity maps (from initial estimates). Our technique is named <jats:italic>i</jats:italic>SENSE (iterative SENSE). Our proposed technique exploits the sparsity of the MR image in some transform domains or the rank deficiency characteristic of the matrix representing the MRI image; the former leads to a compressed sensing‐based reconstruction method, whereas the latter leads to an image reconstruction method that minimizes the nuclear norm (NN) of the image matrix. The sensitivity maps are assumed to be rank‐deficient matrices, and thus the refinement of the sensitivity maps is achieved via the NN minimization. To evaluate the performance of the proposed method, we have carried out the experiments on real and one simulated datasets. We have compared our method with three state‐of‐the‐art image domain methods—SparSENSE (Sparse SENSE), NNSENSE (NN Regularized SENSE), and JSENSE (Joint SENSE reconstruction)—and one widely used frequency domain method—GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition). Our method yields the best reconstruction results both in quantitative (normalized mean‐squared error) and qualitative (visual inspection of reconstructed and difference images) evaluation. © 2012 Wiley Periodicals, Inc. Concepts Magn Reson Part A 40A: 269–280, 2012.</jats:p> Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (<i>i</i>sense) Concepts in Magnetic Resonance Part A |
| spellingShingle | Majumdar, Angshul, Ward, Rabab K., Concepts in Magnetic Resonance Part A, Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense), Spectroscopy |
| title | Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_full | Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_fullStr | Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_full_unstemmed | Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_short | Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| title_sort | iterative estimation of mri sensitivity maps and image based on sense reconstruction method (<i>i</i>sense) |
| title_unstemmed | Iterative estimation of MRI sensitivity maps and image based on sense reconstruction method (isense) |
| topic | Spectroscopy |
| url | http://dx.doi.org/10.1002/cmr.a.21244 |