Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model
State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/13/12/16714 |
| _version_ | 1811187524948721664 |
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| author | Angshul Majumdar Kunal Narayan Chaudhury Rabab Ward |
| author_facet | Angshul Majumdar Kunal Narayan Chaudhury Rabab Ward |
| author_sort | Angshul Majumdar |
| collection | DOAJ |
| description | State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets—eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used—Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods—CS SENSE and l1SPIRiT and two calibration free techniques—Distributed CS and SAKE. Our method yields better reconstruction results than all of them. |
| first_indexed | 2024-04-11T14:03:34Z |
| format | Article |
| id | doaj.art-549d176edd8f4d0ba3e9da8755cf6a53 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T14:03:34Z |
| publishDate | 2013-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-549d176edd8f4d0ba3e9da8755cf6a532022-12-22T04:19:58ZengMDPI AGSensors1424-82202013-12-011312167141673510.3390/s131216714s131216714Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity ModelAngshul Majumdar0Kunal Narayan Chaudhury1Rabab Ward2Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaProgram in Applied and Computational Mathematics (PACM), Princeton University, Princeton, NJ 08544, USADepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaState-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets—eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used—Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods—CS SENSE and l1SPIRiT and two calibration free techniques—Distributed CS and SAKE. Our method yields better reconstruction results than all of them.http://www.mdpi.com/1424-8220/13/12/16714compressed sensingmagnetic resonance imagingoptimization |
| spellingShingle | Angshul Majumdar Kunal Narayan Chaudhury Rabab Ward Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model Sensors compressed sensing magnetic resonance imaging optimization |
| title | Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model |
| title_full | Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model |
| title_fullStr | Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model |
| title_full_unstemmed | Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model |
| title_short | Calibrationless Parallel Magnetic Resonance Imaging: A Joint Sparsity Model |
| title_sort | calibrationless parallel magnetic resonance imaging a joint sparsity model |
| topic | compressed sensing magnetic resonance imaging optimization |
| url | http://www.mdpi.com/1424-8220/13/12/16714 |
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