@InProceedings{8553476,
  author = {H. Cherkaoui and L. E. Gueddari and C. Lazarus and A. Grigis and F. Poupon and A. Vignaud and S. Farrens and J. -. Starck and P. Ciuciu},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Analysis vs Synthesis-based Regularization for Combined Compressed Sensing and Parallel MRI Reconstruction at 7 Tesla},
  year = {2018},
  pages = {36-40},
  abstract = {Compressed Sensing (CS) has allowed a significant reduction of acquisition times in MRI, especially in the high spatial resolution (e.g., 400 μm) context. Nonlinear CS reconstruction usually relies on analysis (e.g., Total Variation) or synthesis (e.g., wavelet) based priors and ℓ1 regularization to promote sparsity in the transform domain. Here, we compare the performance of several orthogonal wavelet transforms with those of tight frames for MR image reconstruction in the CS setting combined with parallel imaging (multiple receiver coil). We show that overcomplete dictionaries such as the fast curvelet transform provide improved image quality as compared to orthogonal transforms. For doing so, we rely on an analysis-based formulation where the underlying ℓ1 regularized criterion is minimized using a primal dual splitting method (e.g., Condat-Vũ algorithm). Validation is performed on ex-vivo baboon brain T2* MRI data collected at 7 Tesla and restrospectively under-sampled using non-Cartesian schemes (radial and Sparkling). We show that multiscale analysis priors based on tight frames instead of orthogonal transforms achieve better image quality (pSNR, SSIM) in particular at low signal-to-noise ratio.},
  keywords = {biomedical MRI;compressed sensing;curvelet transforms;image reconstruction;image resolution;image sampling;medical image processing;wavelet transforms;spatial resolution;image quality;fast curvelet transform;ℓ1 regularized criterion;ex-vivo baboon brain T2* MRI data;nonCartesian scheme;signal-to-noise ratio;multiscale analysis priors;MRI data;Condat-Vũ algorithm;primal dual splitting method;analysis-based formulation;overcomplete dictionaries;multiple receiver coil;parallel imaging;CS setting;MR image reconstruction;tight frames;orthogonal wavelet transforms;transform domain;Total Variation;nonlinear CS reconstruction;acquisition times;parallel MRI reconstruction;combined compressed Sensing;synthesis-based regularization;magnetic flux density 7 tesla;Magnetic resonance imaging;Image reconstruction;Image quality;Wavelet transforms;Signal processing algorithms;Signal to noise ratio},
  doi = {10.23919/EUSIPCO.2018.8553476},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570435807.pdf},
}

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E.","Lazarus, C.","Grigis, A.","Poupon, F.","Vignaud, A.","Farrens, S.","Starck, J. -.","Ciuciu, P."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["H."],"propositions":[],"lastnames":["Cherkaoui"],"suffixes":[]},{"firstnames":["L.","E."],"propositions":[],"lastnames":["Gueddari"],"suffixes":[]},{"firstnames":["C."],"propositions":[],"lastnames":["Lazarus"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Grigis"],"suffixes":[]},{"firstnames":["F."],"propositions":[],"lastnames":["Poupon"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Vignaud"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Farrens"],"suffixes":[]},{"firstnames":["J.","-."],"propositions":[],"lastnames":["Starck"],"suffixes":[]},{"firstnames":["P."],"propositions":[],"lastnames":["Ciuciu"],"suffixes":[]}],"booktitle":"2018 26th European Signal Processing Conference (EUSIPCO)","title":"Analysis vs Synthesis-based Regularization for Combined Compressed Sensing and Parallel MRI Reconstruction at 7 Tesla","year":"2018","pages":"36-40","abstract":"Compressed Sensing (CS) has allowed a significant reduction of acquisition times in MRI, especially in the high spatial resolution (e.g., 400 μm) context. Nonlinear CS reconstruction usually relies on analysis (e.g., Total Variation) or synthesis (e.g., wavelet) based priors and ℓ1 regularization to promote sparsity in the transform domain. Here, we compare the performance of several orthogonal wavelet transforms with those of tight frames for MR image reconstruction in the CS setting combined with parallel imaging (multiple receiver coil). We show that overcomplete dictionaries such as the fast curvelet transform provide improved image quality as compared to orthogonal transforms. For doing so, we rely on an analysis-based formulation where the underlying ℓ1 regularized criterion is minimized using a primal dual splitting method (e.g., Condat-Vũ algorithm). Validation is performed on ex-vivo baboon brain T2* MRI data collected at 7 Tesla and restrospectively under-sampled using non-Cartesian schemes (radial and Sparkling). We show that multiscale analysis priors based on tight frames instead of orthogonal transforms achieve better image quality (pSNR, SSIM) in particular at low signal-to-noise ratio.","keywords":"biomedical MRI;compressed sensing;curvelet transforms;image reconstruction;image resolution;image sampling;medical image processing;wavelet transforms;spatial resolution;image quality;fast curvelet transform;ℓ1 regularized criterion;ex-vivo baboon brain T2* MRI data;nonCartesian scheme;signal-to-noise ratio;multiscale analysis priors;MRI data;Condat-Vũ algorithm;primal dual splitting method;analysis-based formulation;overcomplete dictionaries;multiple receiver coil;parallel imaging;CS setting;MR image reconstruction;tight frames;orthogonal wavelet transforms;transform domain;Total Variation;nonlinear CS reconstruction;acquisition times;parallel MRI reconstruction;combined compressed Sensing;synthesis-based regularization;magnetic flux density 7 tesla;Magnetic resonance imaging;Image reconstruction;Image quality;Wavelet transforms;Signal processing algorithms;Signal to noise ratio","doi":"10.23919/EUSIPCO.2018.8553476","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570435807.pdf","bibtex":"@InProceedings{8553476,\n author = {H. Cherkaoui and L. E. Gueddari and C. Lazarus and A. Grigis and F. Poupon and A. Vignaud and S. Farrens and J. -. Starck and P. Ciuciu},\n booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},\n title = {Analysis vs Synthesis-based Regularization for Combined Compressed Sensing and Parallel MRI Reconstruction at 7 Tesla},\n year = {2018},\n pages = {36-40},\n abstract = {Compressed Sensing (CS) has allowed a significant reduction of acquisition times in MRI, especially in the high spatial resolution (e.g., 400 μm) context. Nonlinear CS reconstruction usually relies on analysis (e.g., Total Variation) or synthesis (e.g., wavelet) based priors and ℓ1 regularization to promote sparsity in the transform domain. Here, we compare the performance of several orthogonal wavelet transforms with those of tight frames for MR image reconstruction in the CS setting combined with parallel imaging (multiple receiver coil). We show that overcomplete dictionaries such as the fast curvelet transform provide improved image quality as compared to orthogonal transforms. For doing so, we rely on an analysis-based formulation where the underlying ℓ1 regularized criterion is minimized using a primal dual splitting method (e.g., Condat-Vũ algorithm). Validation is performed on ex-vivo baboon brain T2* MRI data collected at 7 Tesla and restrospectively under-sampled using non-Cartesian schemes (radial and Sparkling). 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