MRI Simulation and Reconstruction

Matlab Framework for MRI Simulation and Reconstruction. This package is a collection of Matlab functions that provides 1) analytical and rasterized multi-channel MRI simulations of realistic phantoms and 2) a collection of basic and state-of-the-art reconstruction methods including an efficient wavelet-based non-linear one. Demonstration and testing scripts are included. A detailed documentation is provided. The analytical phantom simulation tools allow sound validations of reconstruction methods. The reconstruction framework is rather general and should be easy to adapt to any linear inverse problem. Wavelet transform and wavelet coefficients can be easily manipulated like Matlab’s matrices and vectors.

References in zbMATH (referenced in 16 articles )

Showing results 1 to 16 of 16.
Sorted by year (citations)

  1. Adcock, Ben; Antun, Vegard; Hansen, Anders C.: Uniform recovery in infinite-dimensional compressed sensing and applications to structured binary sampling (2021)
  2. Genzel, Martin; Kutyniok, Gitta; März, Maximilian: (\ell^1)-analysis minimization and generalized (co-)sparsity: when does recovery succeed? (2021)
  3. Cai, Jian-Feng; Choi, Jae Kyu; Wei, Ke: Data driven tight frame for compressed sensing MRI reconstruction via off-the-grid regularization (2020)
  4. Lang, Lukas F.; Neumayer, Sebastian; Öktem, Ozan; Schönlieb, Carola-Bibiane: Template-based image reconstruction from sparse tomographic data (2020)
  5. Lazarus, Carole; März, Maximilian; Weiss, Pierre: Correcting the side effects of ADC filtering in MR image reconstruction (2020)
  6. Adcock, Ben; Gelb, Anne; Song, Guohui; Sui, Yi: Joint sparse recovery based on variances (2019)
  7. Neumayer, Sebastian; Persch, Johannes; Steidl, Gabriele: Regularization of inverse problems via time discrete geodesics in image spaces (2019)
  8. Escande, Paul; Weiss, Pierre: Accelerating (\ell^1)-(\ell^2) deblurring using wavelet expansions of operators (2018)
  9. Wettenhovi, Ville-Veikko; Kolehmainen, Ville; Huttunen, Joanna; Kettunen, Mikko; Gröhn, Olli; Vauhkonen, Marko: State estimation with structural priors in fMRI (2018)
  10. Abergel, Rémy; Moisan, Lionel: The Shannon total variation (2017)
  11. Adcock, Ben; Hansen, Anders C.; Poon, Clarice; Roman, Bogdan: Breaking the coherence barrier: a new theory for compressed sensing (2017)
  12. Bastounis, Alexander; Hansen, Anders C.: On the absence of uniform recovery in many real-world applications of compressed sensing and the restricted isometry property and nullspace property in levels (2017)
  13. Adcock, Ben; Hansen, Anders C.: Generalized sampling and infinite-dimensional compressed sensing (2016)
  14. Boyer, Claire; Chauffert, Nicolas; Ciuciu, Philippe; Kahn, Jonas; Weiss, Pierre: On the generation of sampling schemes for magnetic resonance imaging (2016)
  15. Ongie, Greg; Jacob, Mathews: Off-the-grid recovery of piecewise constant images from few Fourier samples (2016)
  16. Compton, Ryan; Osher, Stanley; Bouchard, Louis-S.: Hybrid regularization for MRI reconstruction with static field inhomogeneity correction (2013)