Improved MR image reconstruction from sparsely sampled scans based on neural networks

Pattern Recognition Letters

Volumn 22, Issue 1, 2001, Pages 35-46

  Reczko, M ^a   Karras, D A ^b   Mertzios, B G ^a   Graveron Demilly, D ^d   Van Ormondt, D ^c  

^a DEMOCRITUS UNIVERSITY OF THRACE   (Greece)

^b UNIVERSITY OF PIRAEUS   (Greece)

^c DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d NONE

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RECONSTRUCTION; INTERPOLATION; MAGNETIC RESONANCE IMAGING; MATHEMATICAL MODELS; NEURAL NETWORKS;

SCANNING TRAJECTORIES;

LEARNING SYSTEMS;

EID: 0035194926     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(00)00097-0     Document Type: Article

References (10)

1. Barone, P., Sebastiani, G., 1992. A new method of magnetic resonance image reconstruction with short acquisition time and truncation artifact reduction. IEEE Trans. Med. Imaging 1, 250.
2. Basic Principles of MR Imaging, 1995. Philips Medical Systems, Best, The Netherlands.
3. Dologlou, I., Wajer, F.T.A.W., Fuderer, M., van Ormondt, D., 1996. Spiral MRI scan-time reduction through non-uniform angular distribution of interleaves and multichannel SVD interpolation. In: Proceedings of ISMRM Fourth Meeting, New York, p. 1489.
4. Fuderer, M., 1989. Ringing artefact reduction by an efficient likelihood improvement method. Proc. SPIE 1137, 84-90.
5. Herbert, T., Leahy, R., 1989. A generalized EM algorithm for 3-D Bayesian reconstruction from Poisson data using Gibbs priors. IEEE Trans. Med. Imaging 8, 194-202.
6. Lettington, A.H., Quong, Q.H., 1995. Image restoration using a lorentzian probability model. J. Modern Optics 42, 1367-1376.
7. 2 calculations in MRI: linear versus nonlinear methods. J. Imag. Sci. & Technol. 38, 154-157.
8. Servoss, T.G., Hornak, J.P., 2000. Spatial-spatia-spectral imaging using a clinical MRI. Experimental NMR Conference, Asilomar, CA, April.
9. Smith, M.R., Nichols, S.T., Henkelman, R.M., Wood, M.L., 1986. Application of autoregressive modelling in magnetic resonance imaging to remove noise and truncation artifacts. Magn. Reson. Imaging 4, 257.
10. Stijnman, G.H.L.A., Graveron-Demilly, D., Wajer, F.T.A.W., van Ormondt, D., 1997. MR image estimation from sparsely sampled radial scans. Proc. ProRISC/IEEE Benelux workshop on circuits, systems and signal processing, Mierlo, The Netherlands, pp. 603-611.