Auswahl der wissenschaftlichen Literatur zum Thema „Compressed-Sensing fMRI“
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Zeitschriftenartikel zum Thema "Compressed-Sensing fMRI"
Han, Paul Kyu, Sung-Hong Park, Seong-Gi Kim und Jong Chul Ye. „Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T“. BioMed Research International 2015 (2015): 1–24. http://dx.doi.org/10.1155/2015/131926.
Der volle Inhalt der QuelleZong, Xiaopeng, Juyoung Lee, Alexander John Poplawsky, Seong-Gi Kim und Jong Chul Ye. „Compressed sensing fMRI using gradient-recalled echo and EPI sequences“. NeuroImage 92 (Mai 2014): 312–21. http://dx.doi.org/10.1016/j.neuroimage.2014.01.045.
Der volle Inhalt der QuelleHolland, D. J., C. Liu, X. Song, E. L. Mazerolle, M. T. Stevens, A. J. Sederman, L. F. Gladden, R. C. N. D'Arcy, C. V. Bowen und S. D. Beyea. „Compressed sensing reconstruction improves sensitivity of variable density spiral fMRI“. Magnetic Resonance in Medicine 70, Nr. 6 (06.02.2013): 1634–43. http://dx.doi.org/10.1002/mrm.24621.
Der volle Inhalt der QuelleJeromin, Oliver, Marios S. Pattichis und Vince D. Calhoun. „Optimal compressed sensing reconstructions of fMRI using 2D deterministic and stochastic sampling geometries“. BioMedical Engineering OnLine 11, Nr. 1 (2012): 25. http://dx.doi.org/10.1186/1475-925x-11-25.
Der volle Inhalt der QuelleChavarrías, C., J. F. P. J. Abascal, P. Montesinos und M. Desco. „Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior-based algorithm (PICCS)“. Medical Physics 42, Nr. 7 (09.06.2015): 3814–21. http://dx.doi.org/10.1118/1.4921365.
Der volle Inhalt der QuelleAmor, Zaineb, Philippe Ciuciu, Chaithya G. R., Guillaume Daval-Frérot, Franck Mauconduit, Bertrand Thirion und Alexandre Vignaud. „Non-Cartesian 3D-SPARKLING vs Cartesian 3D-EPI encoding schemes for functional Magnetic Resonance Imaging at 7 Tesla“. PLOS ONE 19, Nr. 5 (13.05.2024): e0299925. http://dx.doi.org/10.1371/journal.pone.0299925.
Der volle Inhalt der QuelleChavarrías, C., J. F. P. J. Abascal, P. Montesinos und M. Desco. „Erratum: “Exploitation of temporal redundancy in compressed sensing reconstruction of fMRI studies with a prior-based algorithm (PICCS)” [Med. Phys. 42 , 3814-3821 (2015)]“. Medical Physics 42, Nr. 8 (31.07.2015): 4997. http://dx.doi.org/10.1118/1.4926781.
Der volle Inhalt der QuelleTesfamicael, Solomon. „Clustered Compressed Sensing in fMRI Data Analysis Using a Bayesian Framework“. International Journal of Information and Electronics Engineering 4, Nr. 2 (2014). http://dx.doi.org/10.7763/ijiee.2014.v4.412.
Der volle Inhalt der QuelleWang, Keyan, Wenbo Zhang, Shuman Li, Xiaoming Bi, Michaela Schmidt, Jing An, Jie Zheng und Jingliang Cheng. „Prognosis in patients with coronary heart disease and breath-holding limitations: a free-breathing cardiac magnetic resonance protocol at 3.0 T“. BMC Cardiovascular Disorders 21, Nr. 1 (Dezember 2021). http://dx.doi.org/10.1186/s12872-021-02402-x.
Der volle Inhalt der QuelleDissertationen zum Thema "Compressed-Sensing fMRI"
Amor, Zaineb. „Non-Cartesian Sparkling encoding for High spatio-temporal resolution functional Magnetic Resonance Imaging (fMRI) at 7 Tesla and beyond“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST032.
Der volle Inhalt der QuelleFunctional MRI (fMRI) is currently one of the most commonly used functional neuroimaging techniques to probe brain activity non-invasively through the blood oxygen level-dependent (BOLD) contrast that reflects neurovascular coupling. It offers an interesting trade-off between spatial and temporal resolution in order to study the whole brain as an aggregation of intrinsic functional systems. The quest for higher spatial and/or temporal resolution in fMRI while preserving a sufficient temporal signal-to-noise ratio~(tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs. 3D acquisition strategies, parallel imaging and/or compressed sensing~(CS) accelerations and simultaneous multi-slice acquisitions to cite a few. In this work, we focus on the use of CS in fMRI; more specifically, we consider Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING) encoding scheme.The main focus and goal of this thesis involves the evaluation of 3D-SPARKLING as a viable acquisition scheme for high-resolution whole-brain fMRI. In this regard, we initially compared its capabilities with state-of-the-art 3D-EPI. After observing higher sensitivity to static and dynamic magnetic field imperfections in 3D-SPARKLING data, we established an experimental protocol to correct them. Finally, we studied the capabilities and limitations of employing a sliding-window reconstruction in combination with the SPARKLING encoding scheme to enhance temporal resolution during image reconstruction in fMRI retrospectively. A simulation study where the ground truth is controlled was conducted and demonstrated the possibility of detecting high-frequency oscillations in the BOLD signal and separating physiological noise from neural activity
Buchteile zum Thema "Compressed-Sensing fMRI"
Chavarrias, C., J. F. P. J. Abascal, P. Montesinos und M. Desco. „How Does Compressed Sensing Affect Activation Maps in Rat fMRI?“ In IFMBE Proceedings, 202–5. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00846-2_50.
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