Abstract
We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing.
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Keywords
- Compress Sensing
- Dynamic Magnetic Resonance Imaging
- Manifold Learning
- Magnetic Resonance Imaging Acquisition
- Complex Optimisation Problem
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References
Axel, L., Sodickson, D.K.: The need for speed: Accelerating CMR imaging assessment of cardiac function. JACC Cardiovasc. Imaging 7(9), 893–895 (2014)
Baraniuk, R.G., Wakin, M.B.: Random projections of smooth manifolds. Foundations of Computational Mathematics 9, 51–77 (2009)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Bhatia, K.K., Rao, A., Price, A.N., Wolz, R., Hajnal, J., Rueckert, D.: Hierarchical manifold learning for regional image analysis. IEEE T. Med. Im. 33, 444–461 (2014)
Caballero, J., Price, A.N., Rueckert, D., Hajnal, J.: Dictionary learning and time sparsity for dynamic mr data reconstruction. IEEE T. Med. Im. 33, 979–994 (2014)
Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. International Journal of Computer Vision 90, 255–266 (2010)
Lustig, M., Donoho, D., Pauly, J.M.: Sparse mri: The application of compressed sensing for rapid MR imaging. Medical Image Analysis 58(6), 1182–1195 (2007)
Madore, B., Hoge, W., Chao, T.: Retrospectively gated cardiac cine imaging with temporal and spatial acceleration. Mag. Resonance in Medicine 29, 457–469 (2011)
Otazo, R., Candes, E., Sodickson, D.K.: Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magnetic Resonance in Medicine 73(3), 1125–1136 (2015)
Poddar, S., Lingala, S.G., Jacob, M.: Real-time cardiac MRI using manifold sensing. In: International Society of Magnetic Resonance in Medicine, p. 5309 (2014)
Tsao, J., Boesiger, P., Pruessmann, K.P.: k-t blast and k-t sense: Dynamic mri with high frame rate exploiting spatiotemporal correlations. MRM 50, 1031–1042 (2003)
Usman, M., Vaillant, G., Schaefter, T., Prieto, C.: Compressive manifold learning: estimating one-dimensional respiratory motion directly from undersampled k-space data. Magnetic Resonance in Medicine 72, 1130–1140 (2014)
Wachinger, C., Yigitsoy, M., Navab, N.: Manifold learning for image-based breathing gating with application to 4D ultrasound. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 26–33. Springer, Heidelberg (2010)
Zhao, B., Haldar, J.P., Christodoulou, A.G., Liang, Z.P.: Image reconstruction from highly undersampled (k-t)-space data with joint partial separability and sparsity constraints. IEEE T. Med. Im. 31, 1809–1820 (2012)
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Bhatia, K.K., Caballero, J., Price, A.N., Sun, Y., Hajnal, J.V., Rueckert, D. (2015). Fast Reconstruction of Accelerated Dynamic MRI Using Manifold Kernel Regression. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_61
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DOI: https://doi.org/10.1007/978-3-319-24574-4_61
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