M. .. Material,

, Non-local spatio-temporal super-resolution method

.. .. Results,

.. .. Discussion,

-. Multi and . Super, 86 6.2.1 Non-local spatio-temporal super-resolution method, p.86

.. .. Results,

. .. Discussion, sain : coupes sagittales de a) l'image structurelle, b) la carte de CBF issue de la série pCASL acquise à basse résolution, c) la carte reconstruite par interpolation trilinéaire, d) par application de Bsplines d'ordre 3, et e) l'image obtenue à l'aide de la méthode proposée. xii 4 Sujet sain: coupes axiales d'une image structurelle (a), de la carte de CBF de basse résolution (b) associée, de celle de haute résolution (c), celle reconstruite avec SR3D (d), et des estimations de CBF obtenues suite aux applications de SR3D MULTI (e) et SR4D (f). . . . . . . . xv 5 Sujet sain: coupes axiales d'une image structurelle

, Representation of the brain lobes and some sulci and gyri

, Representation of a) the carotids and vertebral arteries, b) an axial view of arteries ramifications at the basis of the brain and c) the circle of Willis

, Image representing the head and neck most important veins, 2014.

, From left to right: MR diffusion image presenting an hyper-signal in the middle cerebral territory, associated with an hypo-perfusion on 3 axial slices of an ASL perfusion image, and to an occlusion of the middle cerebral artery on the MR angiography

, From left to right: FLAIR, post-gadolinium T 1 -weighted, and CBF images obtained with ASL of a subject diagnosed with a brain tumour. The tumour appears as hyper-perfused on the CBF maps

A. Fdg-pet and . Cbf, Left predominant bitemporal anterior hypo-perfusion can be observed on the CBF maps, in agreement with hypo-metabolisms visible on the PET scans

, Comparison of gray matter cerebral blood flow contribution maps: a) difference images obtained by subtration of the reference GM contribution map from the images obtained by applying the linear regression (Lin Reg) to the low resolution (LR) CBF image, b) the linear regression to the LR ASL and M 0 images, and c) the proposed SR method to the LR CBF image (SNR=5)

, Schematic representation of the reconstruction performed in the proposed SR4D method

, Schematic description of the SR3D MULTI method, p.90

, Simulated dataset: PSNR obtained by comparing reference CBF and ATT maps with the SR4D reconstructions obtained with different ? parameter values

, Simulated dataset: PSNR obtained by comparing reference CBF and ATT maps with the splines, SR3D, SR3D MULTI and SR4D reconstructions

, Simulated dataset: CBF maps from : a) the reference image, b) the CBF estimation from the noise corrupted downsampled series, c) the splines interpolation, d) the SR3D reconstruction, e) estimations from the SR3D MULTI and f) SR4D reconstructed ASL time series, p.93

, ATT maps from: a) the reference image, b) the ATT estimation from the noise corrupted downsampled series, c) the splines interpolation, d) the SR3D reconstruction, e) estimations from the SR3D MULTI and f) SR4D reconstructed ASL time series, p.94

, Simulated dataset: ASL signal temporal evolution of a voxel in the reference, 2 noisy low resolution and the 2 corresponding SR3D MULTI and SR4D reconstructed time series

, Healthy subjects: mean ± standard deviation (std) and percentage of brain voxels with correlations to the single compartment model superior to 0.5 for the low resolution (LR), high resolution (HR), SR3D MULTI and proposed SR4D reconstructed ASL signal time series

, Images from one healthy subject: axial slices of a structural image (a), low resolution CBF map (b), high resolution CBF map (c), SR3D CBF map reconstruction (d), and CBF estimations from the SR3D

, MULTI (e) and SR4D (f) reconstructions

, Images from one healthy subject: axial slices of a structural image (a), low resolution ATT map (b), high resolution ATT map (c), p.3

, ATT map reconstruction (d), and ATT estimations from the SR3D

, MULTI (e) and SR4D (f) reconstructions

, low resolution CBF map (b), high resolution CBF map (c), SR3D CBF map reconstruction (d), and CBF estimations from the SR3D MULTI (e) and SR4D (f) reconstructions, Healthy subject: sagittal slices of a structural image (a)

, Healthy subject: sagittal slices of a structural image (a), low resolution ATT map (b), high resolution ATT map (c), SR3D ATT map reconstruction (d), and ATT estimations from the SR3D MULTI (e) and SR4D (f) reconstructions

]. D. Alsop and J. A. Detre, Reduced Transit-Time Sensitivity in Noninvasive Magnetic Resonance Imaging of Human Cerebral Blood Flow, Journal of Cerebral Blood Flow & Metabolism, vol.16, issue.6, pp.1236-1249, 1996.

C. David, J. A. Alsop, X. Detre, M. Golay, J. Günther et al., Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia, Magnetic Resonance in Medicine, vol.73, issue.1, pp.102-116, 2015.

L. R. Jesper, S. Andersson, J. Skare, and . Ashburner, How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging, NeuroImage, vol.20, issue.2, pp.870-888, 2003.

J. Ashburner and K. J. Friston, Unified segmentation, NeuroImage, vol.26, issue.3, p.27, 2005.

I. Asllani, A. Borogovac, and T. R. Brown, Regression algorithm correcting for partial volume effects in arterial spin labeling MRI, Magnetic Resonance in Medicine, vol.60, issue.6, pp.1362-1371, 2008.

R. P. Bokkers, H. B. Van-der-worp, W. P. Mali, and J. Hendrikse, Noninvasive MR imaging of cerebral perfusion in patients with a carotid artery stenosis, Neurology, vol.73, issue.11, pp.869-875, 2009.

E. Boudes, G. Gilbert, I. R. Leppert, X. Tan, G. B. Pike et al., Measurement of brain perfusion in newborns: Pulsed arterial spin labeling (PASL) versus pseudo-continuous arterial spin labeling (pCASL), NeuroImage: Clinical, vol.6, p.53, 2014.

A. Bowring, C. Maumet, and T. Nichols, Exploring the Impact of Analysis Software on Task fMRI Results, vol.102, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01760535

R. B. Buxton, L. R. Frank, E. C. Wong, B. Siewert, S. Warach et al., A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine, vol.40, pp.383-396, 1998.

J. Carp, On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments, Frontiers in Neuroscience, vol.6, 2012.

A. Carsin-vu, I. Corouge, O. Commowick, G. Bouzillé, C. Barillot et al., Measurement of pediatric regional cerebral blood flow from 6 months to 15 years of age in a clinical population, European Journal of Radiology, vol.101, p.103, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01708945

H. Chang and J. M. Fitzpatrick, A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities, IEEE Transactions on Medical Imaging, vol.11, issue.3, p.56, 1992.

M. A. Chappell, A. R. Groves, B. J. Macintosh, M. J. Donahue, P. Jezzard et al., Partial volume correction of multiple inversion time arterial spin labeling MRI data, Magnetic Resonance in Medicine, vol.65, issue.4, pp.1173-1183, 2011.

Y. Chen, D. A. Wolk, J. S. Reddin, M. Korczykowski, P. M. Martinez et al., Voxel-level comparison of arterial spin-labeled perfusion MRI and FDG-PET in Alzheimer disease, Neurology, vol.77, issue.22, pp.1977-1985, 2011.

Y. Chen, F. Shi, A. G. Christodoulou, Z. Zhou, Y. Xie et al., Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network. CoRR, p.66, 2018.

Y. Chen, Y. Xie, Z. Zhou, F. Shi, A. G. Christodoulou et al., Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks. CoRR, p.66, 2018.

, OpenStax College. Anatomy & physiology. OpenStax CNX, 2014.

S. J. Colloby, M. J. Firbank, J. He, A. J. Thomas, A. Vasudev et al., Regional cerebral blood flow in late-life depression: arterial spin labelling magnetic resonance study, British Journal of Psychiatry, vol.200, issue.02, pp.150-155, 2012.

C. Conan, J. M. Batail, I. Corouge, J. Palaric, G. Robert et al., Apathy in depression: An arterial spin labeling study, European Psychiatry, vol.41, p.102, 2017.
URL : https://hal.archives-ouvertes.fr/inserm-01704746

P. Coupé, J. V. Manjón, and M. Chamberland, Collaborative patch-based super-resolution for diffusion-weighted images, NeuroImage, vol.83, p.66, 2013.

W. Dai, D. Garcia, C. De-bazelaire, and D. C. Alsop, Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields, Magnetic Resonance in Medicine, vol.60, issue.6, p.19, 2008.

W. Dai, A. Philip-m-robson, D. C. Shankaranarayanan, and . Alsop, Reduced resolution transit delay prescan for quantitative continuous arterial spin labeling perfusion imaging, Magnetic Resonance in Medicine, vol.67, issue.5, p.96, 2011.

W. Dai, T. Fong, R. N. Jones, E. Marcantonio, E. Schmitt et al., Effects of arterial transit delay on cerebral blood flow quantification using arterial spin labeling in an elderly cohort, Journal of Magnetic Resonance Imaging, vol.45, issue.2, p.103, 2016.

J. A. Detre, J. S. Leigh, D. S. Williams, and A. P. Koretsky, Perfusion imaging. Magnetic Resonance in Medicine, vol.23, issue.1, pp.37-45, 1992.

J. Manus, E. Donahue, . Achten, M. Petrice, F. Cogswell et al., Consensus statement on current and emerging methods for the diagnosis and evaluation of cerebrovascular disease, Journal of Cerebral Blood Flow & Metabolism, vol.38, issue.9, p.103, 2017.

B. Duhameau, J. Ferré, P. Jannin, J. Gauvrit, M. Vérin et al., , 2010.

, Chronic and treatment-resistant depression: A study using arterial spin labeling perfusion MRI at 3Tesla, Psychiatry Research: Neuroimaging, vol.182, issue.2, pp.111-116, 2010.

J. S. Duncan, Imaging in the surgical treatment of epilepsy, Nature Reviews Neurology, vol.6, issue.10, pp.537-550, 2010.

B. R-r-edelman, D. Siewert, V. Darby, A. Thangaraj, M. C-nobre et al., Qualitative mapping of cerebral blood flow and functional localization with echo-planar MR imaging and signal targeting with alternating radio frequency, Radiology, vol.192, issue.2, p.19, 1994.

K. V. Embleton, H. A. Haroon, D. M. Morris, M. A. , L. Ralph et al., Distortion correction for diffusionweighted MRI tractography and fMRI in the temporal lobes, Human Brain Mapping, vol.31, issue.10, p.56, 2010.

A. Esquevin, H. Raoult, S. Belliard, I. Corouge, E. Bannier et al., Étude de la perfusion cérébrale par Arterial Spin Labeling : principes et applications en neurosciences cliniques, vol.5, p.107, 2013.

S. M. Fallatah, F. B. Pizzini, B. Gomez-anson, J. Magerkurth, E. Vita et al., A visual quality control scale for clinical arterial spin labeling images, European Radiology Experimental, vol.2, issue.1, p.101, 2018.

A. Fazlollahi, P. Bourgeat, X. Liang, F. Meriaudeau, A. Connelly et al., Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods, NeuroImage, vol.117, p.102, 2015.

A. María, Z. Fernández-seara, J. Wang, H. Wang, M. Rao et al., Continuous arterial spin labeling perfusion measurements using single shot 3D GRASE at 3 T. Magnetic Resonance in Medicine, vol.54, p.21, 2005.

J. Ferré, J. Petr, E. Bannier, C. Barillot, and J. Gauvrit, Improving quality of arterial spin labeling MR imaging at 3 tesla with a 32-channel coil and parallel imaging, Journal of Magnetic Resonance Imaging, vol.35, issue.5, p.41, 2012.

M. Dairon, G. Garcia, D. C. Duhamel, and . Alsop, Efficiency of inversion pulses for background suppressed arterial spin labeling, Magnetic Resonance in Medicine, vol.54, issue.2, p.21, 2005.

X. Golay, T. Esben, F. Petersen, and . Hui, Pulsed star labeling of arterial regions (PULSAR): A robust regional perfusion technique for high field imaging, Magnetic Resonance in Medicine, vol.53, issue.1, p.19, 2004.

K. Gorgolewski, C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko et al., Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python, Frontiers in Neuroinformatics, vol.5, 2011.

J. Krzysztof, T. Gorgolewski, V. D. Auer, R. C. Calhoun, S. Craddock et al., The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments, Scientific Data, vol.3, p.28, 2016.

M. Grade, J. A. Hernandez-tamames, F. B. Pizzini, E. Achten, X. Golay et al., A neuroradiologist's guide to arterial spin labeling MRI in clinical practice, Neuroradiology, vol.57, issue.12, pp.1181-1202, 2015.

H. Gray, Anatomy of the human body, Lea & Febiger, vol.107, 1918.

A. Mark, P. M. Griswold, R. M. Jakob, M. Heidemann, V. Nittka et al., Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magnetic Resonance in Medicine, vol.47, pp.1202-1210, 2002.

M. Günther, M. Bock, and L. R. Schad, Arterial spin labeling in combination with a look-locker sampling strategy: Inflow turbosampling EPI-FAIR (ITS-FAIR), Magnetic Resonance in Medicine, vol.46, issue.5, p.13, 2001.

M. Günther, K. Oshio, and D. A. Feinberg, Singleshot 3D imaging techniques improve arterial spin labeling perfusion measurements, Magnetic Resonance in Medicine, vol.54, issue.2, p.21, 2005.

R. Hedouin, O. Commowick, E. Bannier, B. Scherrer, M. Taquet et al., BlockMatching Distortion Correction of Echo-Planar Images With Opposite Phase Encoding Directions, IEEE Transactions on Medical Imaging, vol.36, issue.5, pp.1106-1115, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01436561

M. J. ]-jeroen-hendrikse, D. R. Van-osch, C. J. Rutgers, L. J. Bakker, X. Kappelle et al., Internal Carotid Artery Occlusion Assessed at Pulsed Arterial Spin-labeling Perfusion MR Imaging at Multiple Delay Times, Radiology, vol.233, issue.3, pp.899-904, 2004.

T. C. Ho, J. Wu, D. D. Shin, T. T. Liu, S. F. Tapert et al.,

Y. , Altered Cerebral Perfusion in Executive, Affective, and Motor Networks During Adolescent Depression, Journal of the American Academy of Child & Adolescent Psychiatry, vol.52, issue.10, pp.1076-1091, 2013.

P. Homan, M. Kindler, D. Hauf, T. Hubl, and . Dierks, Cerebral blood flow identifies responders to transcranial magnetic stimulation in auditory verbal hallucinations, Translational Psychiatry, vol.2, issue.11, pp.189-189, 2012.

X. Hong, X. V. To, I. Teh, J. , R. Soh et al., Evaluation of EPI distortion correction methods for quantitative MRI of the brain at high magnetic field, Magnetic Resonance Imaging, vol.33, issue.9, p.56, 2015.

S. Jain, D. M. Sima, F. Sanaei-nezhad, G. Hangel, W. Bogner et al., Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis, Frontiers in Neuroscience, vol.11, 2017.

P. Jezzard and R. S. Balaban, Correction for geometric distortion in echo planar images from B0 field variations, Magnetic Resonance in Medicine, vol.34, issue.1, p.56, 1995.

Y. Jia, A. Gholipour, Z. He, and S. K. Warfield, A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans, IEEE Transactions on Medical Imaging, vol.36, issue.5, p.66, 2017.

M. E. Johnston, K. Lu, J. A. Maldjian, and Y. Jung, Multi-TI Arterial Spin Labeling MRI with Variable TR and Bolus Duration for Cerebral Blood Flow and Arterial Transit Time Mapping, IEEE Transactions on Medical Imaging, vol.34, issue.6, p.31, 2015.

S. Seymour, C. F. Kety, and . Schmidt, THE NITROUS OXIDE METHOD FOR THE QUANTITATIVE DETERMINATION OF CERE-BRAL BLOOD FLOW IN MAN: THEORY, PROCEDURE AND NORMAL VALUES 1, Journal of Clinical Investigation, vol.27, issue.4, pp.476-483, 1948.

S. Kim, Quantification of relative cerebral blood flow change by flow-sensitive alternating inversion recovery (FAIR) technique: Application to functional mapping, Magnetic Resonance in Medicine, vol.34, issue.3, pp.293-301, 1995.

T. Kim, W. Shin, T. Zhao, E. B. Beall, M. J. Lowe et al., Whole brain perfusion measurements using arterial spin labeling with multiband acquisition, Magnetic Resonance in Medicine, vol.70, issue.6, p.23, 2013.

H. Kimura, H. Kado, Y. Koshimoto, T. Tsuchida, Y. Yonekura et al., Multislice continuous arterial spin-labeled perfusion MRI in patients with chronic occlusive cerebrovascular disease: A correlative study with CO2 PET validation, Journal of Magnetic Resonance Imaging, vol.22, issue.2, pp.189-198, 2005.

J. Kindler, K. Jann, P. Homan, M. Hauf, S. Walther et al., Static and Dynamic Characteristics of Cerebral Blood Flow During the Resting State in Schizophrenia, Schizophrenia Bulletin, vol.41, issue.1, pp.163-170, 2013.

V. G. Kiselev, On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI, Magnetic Resonance in Medicine, vol.46, issue.6, pp.1113-1122, 2001.

L. Knutsson, D. Van-westen, E. T. Petersen, K. M. Bloch, S. Holtås et al., Absolute quantification of cerebral blood flow: correlation between dynamic susceptibility contrast MRI and model-free arterial spin labeling, Magnetic Resonance Imaging, vol.28, issue.1, p.72, 2010.

N. A. Lassen, A. R. Andersen, L. Friberg, and O. B. , Paulson. The Retention of [99mTc]-d, l-HM-PAO in the Human Brain after Intracarotid Bolus Injection: A Kinetic Analysis, Journal of Cerebral Blood Flow & Metabolism, vol.8, issue.1_suppl, p.11, 1988.

X. Li, D. Wang, E. J. Auerbach, S. Moeller, K. Ugurbil et al., Theoretical and experimental evaluation of multi-band EPI for high-resolution whole brain pCASL Imaging, NeuroImage, vol.106, p.23, 2015.

P. Liu, J. Uh, and H. Lu, Determination of spin compartment in arterial spin labeling MRI, Magnetic Resonance in Medicine, vol.65, issue.1, p.13, 2010.

W. Luh, E. C. Wong, P. A. Bandettini, and J. S. Hyde, QUIPSS II with thin-slice TI1 periodic saturation: A method for improving accuracy of quantitative perfusion imaging using pulsed arterial spin labeling, Magnetic Resonance in Medicine, vol.41, issue.6, p.19, 1999.

H. Ma, Z. Wang, K. Xu, Z. Shao, C. Yang et al., Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusionweighted imaging value in diagnosing glioma grade prior to surgery. Experimental and Therapeutic Medicine, vol.13, p.72, 2017.

J. Bradley, N. Macintosh, M. A. Filippini, M. W. Chappell, C. E. Woolrich et al., Assessment of arterial arrival times derived from multiple inversion time pulsed arterial spin labeling MRI, Magnetic Resonance in Medicine, vol.63, issue.3, p.13, 2010.

V. I. Madai, S. Z. Martin, F. C. Samsonhimmelstjerna, C. X. Herzig, M. A. Mutke et al., Correction for Susceptibility Distortions Increases the Performance of Arterial Spin Labeling in Patients with Cerebrovascular Disease, Journal of Neuroimaging, vol.26, issue.4, pp.436-444, 2016.

K. F. Henry, Q. Mak, Z. Chan, E. T. Zhang, D. Petersen et al., Quantitative Assessment of Cerebral Hemodynamic Parameters by QUASAR Arterial Spin Labeling in Alzheimerâs Disease and Cognitively Normal Elderly Adults at 3-Tesla, Journal of Alzheimerâs Disease, vol.31, issue.1, pp.33-44, 2012.

N. Maleki, W. Dai, and D. C. Alsop, Optimization of background suppression for arterial spin labeling perfusion imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, vol.25, issue.2, p.21, 2011.

V. José, P. Manjón, A. Coupé, D. Buades, L. Collins et al., MRI Superresolution Using Self-Similarity and Image Priors, International Journal of Biomedical Imaging, vol.2010, p.66, 2010.

P. José, T. Marques, G. Kober, and . Krueger, Wietske van der Zwaag, Pierre-François Van de Moortele and Rolf Gruetter. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field, NeuroImage, vol.49, issue.2, pp.1271-1281, 2010.

C. Maumet, P. Maurel, J. Ferrã, and C. Barillot, Robust estimation of the cerebral blood flow in arterial spin labelling, Magnetic Resonance Imaging, vol.32, issue.5, p.66, 2014.
URL : https://hal.archives-ouvertes.fr/inserm-00942814

C. Meurée, P. Maurel, J. Ferré, and C. Barillot, Patch-based super-resolution of arterial spin labeling magnetic resonance images, NeuroImage, vol.189, pp.85-94, 2019.

P. S. Morgan, R. W. Bowtell, D. J. Mcintyre, and B. S. Worthington, Correction of spatial distortion in EPI due to inhomogeneous static magnetic fields using the reversed gradient method, Journal of Magnetic Resonance Imaging, vol.19, issue.4, p.56, 2004.

P. John, J. R. Mugler, and . Brookeman, Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magnetic Resonance in Medicine, vol.15, pp.152-157, 1990.

E. S. Musiek, Y. Chen, M. Korczykowski, B. Saboury, P. M. Martinez et al., Per Julin

J. A. Detre, Direct comparison of fluorodeoxyglucose positron emission tomography and arterial spin labeling magnetic resonance imaging in Alzheimers disease, Alzheimers & Dementia, vol.8, issue.1, pp.51-59, 2012.

J. M. Henri, M. J. Mutsaerts, F. O. Van-osch, D. J. Zelaya, W. Wang et al., Multi-vendor reliability of arterial spin labeling perfusion MRI using a near-identical sequence: Implications for multi-center studies, NeuroImage, vol.113, p.41, 2015.

W. Thomas, . Okell, A. Michael, . Chappell, E. Michael et al., Cerebral Blood Flow Quantification Using Vessel-Encoded Arterial Spin Labeling, Journal of Cerebral Blood Flow & Metabolism, vol.33, issue.11, pp.1716-1724, 2013.

L. Østergaard, R. M. Weisskoff, D. A. Chesler, C. Gyldensted, and B. R. Rosen, High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis, Magnetic Resonance in Medicine, vol.36, issue.5, pp.715-725, 1996.

D. Owen, A. Melbourne, Z. Eaton-rosen, D. L. Thomas, N. Marlow et al., Deep Convolutional Filtering for Spatio-Temporal Denoising and Artifact Removal in Arterial Spin Labelling MRI, Medical Image Computing and Computer Assisted Intervention -MICCAI 2018, p.66, 2018.

L. M. Parkes and P. S. Tofts, Improved accuracy of human cerebral blood perfusion measurements using arterial spin labeling: Accounting for capillary water permeability, Magnetic Resonance in Medicine, vol.48, issue.1, p.26, 2002.

L. M. Parkes, W. Rashid, T. Declan, P. S. Chard, and . Tofts, Normal cerebral perfusion measurements using arterial spin labeling: Reproducibility, stability, and age and gender effects, Magnetic Resonance in Medicine, vol.51, issue.4, pp.736-743, 2004.

J. Petr, J. Ferre, J. Gauvrit, and C. Barillot, Denoising arterial spin labeling MRI using tissue partial volume, Proc.SPIE, vol.7623, p.79, 2010.
URL : https://hal.archives-ouvertes.fr/inserm-00601147

J. Petr, J. Ferre, J. Gauvrit, and C. Barillot, Improving arterial spin labeling data by temporal filtering, Proc.SPIE, vol.7623, p.66, 2010.

C. Pham, F. Ronan, and F. Rousseau, Multi-scale brain MRI super-resolution using deep 3D convolutional networks, p.66, 2017.

M. E. Phelps, S. C. Huang, E. J. Hoffman, C. Selin, L. Sokoloff et al., Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-D-glucose: Validation of method, Annals of Neurology, vol.6, issue.5, p.11, 1979.

J. L. Prince, Medical imaging signals and systems, vol.12, 2005.

M. Proisy, B. Bruneau, C. Rozel, C. Tréguier, K. Chouklati et al., Arterial spin labeling in clinical pediatric imaging, Diagnostic and Interventional Imaging, vol.97, pp.151-158, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01361497

M. Protter, M. Elad, H. Takeda, and P. Milanfar, Generalizing the Nonlocal-Means to Super-Resolution Reconstruction, IEEE Transactions on Image Processing, vol.18, issue.1, pp.36-51, 2009.

P. Klaas, M. Pruessmann, M. B. Weiger, P. Scheidegger, and . Boesiger, SENSE: Sensitivity encoding for fast MRI, Magnetic Resonance in Medicine, vol.42, issue.5, p.23, 1999.

F. Rousseau, O. A. Glenn, B. Iordanova, C. Rodriguez-carranza, D. B. Vigneron et al., Registration-Based Approach for Reconstruction of HighResolution In Utero Fetal MR Brain Images, Academic Radiology, vol.13, issue.9, p.66, 2006.

F. Rousseau, K. Kim, C. Studholme, M. Koob, and J. L. Dietemann, On super-resolution for fetal brain MRI, Med Image Comput Comput Assist Interv, vol.13, p.66, 2010.

, A non-local approach for image superresolution using intermodality priors, Medical Image Analysis, vol.14, issue.4, p.66, 2010.

A. Rueda, N. Malpica, and E. Romero, Single-image super-resolution of brain MR images using overcomplete dictionaries, Medical Image Analysis, vol.17, issue.1, p.66, 2013.

L. Ruthotto, . Kugel, . Olesch, J. Fischer, M. Modersitzki et al., Diffeomorphic susceptibility artifact correction of diffusionweighted magnetic resonance images, Physics in Medicine and Biology, vol.57, issue.18, pp.5715-5731, 2012.

L. Ruthotto, S. Mohammadi, and C. Heck, Hyperelastic Susceptibility Artifact Correction of DTI in SPM, Bildverarbeitung für die Medizin, p.344, 2013.

H. Springer-berlin, , vol.56, 2013.

B. Scherrer, A. Gholipour, and S. K. Warfield, Superresolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions, Medical Image Analysis, vol.16, issue.7, p.66, 2012.

F. Shi, J. Cheng, and L. Wang, Pew-Thian Yap and Dinggang Shen. LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations, IEEE Transactions on Medical Imaging, vol.34, issue.12, p.66, 2015.

M. Stephen, M. Smith, M. W. Jenkinson, C. F. Woolrich, T. E. Beckmann et al., Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, vol.23, p.57, 2004.

O. Speck, L. Chang, N. , M. Desilva, and T. Ernst, Perfusion MRI of the human brain with dynamic susceptibility contrast: Gradient-echo versus spin-echo techniques, Journal of Magnetic Resonance Imaging, vol.12, issue.3, p.12, 2000.

, Statistical parametric mapping: The analysis of functional brain images, vol.27, 2006.

G. Van-steenkiste, D. H. Poot, B. Jeurissen, A. J. Den-dekker, F. Vanhevel et al., Super-resolution T1 estimation: Quantitative high resolution T1 mapping from a set of low resolution T1-weighted images with different slice orientations, Magnetic Resonance in Medicine, vol.77, issue.5, p.66, 2016.

D. Tortora, P. A. Mattei, R. Navarra, V. Panara, R. Salomone et al., Detre and Massimo Caulo. Prematurity and brain perfusion: Arterial spin labeling MRI, NeuroImage: Clinical, vol.15, p.53, 2017.

J. Vardal, A. Raimo, C. Salo, A. M. Larsson, D. Dale et al., Correction of B0-Distortions in Echo-Planar-Imaging-Based Perfusion-Weighted MRI, Journal of Magnetic Resonance Imaging, vol.39, issue.3, p.56, 2013.

M. Vidorreta, Z. Wang, I. Rodríguez, M. A. Pastor, J. A. Detre et al., Fernández-Seara. Comparison of 2D and 3D single-shot ASL perfusion fMRI sequences, NeuroImage, vol.66, p.23, 2013.

U. Henning, R. Voss, A. M. Watts, D. Ulu?, and . Ballon, Fiber tracking in the cervical spine and inferior brain regions with reversed gradient diffusion tensor imaging, Magnetic Resonance Imaging, vol.24, issue.3, p.56, 2006.

J. Wang, D. C. Alsop, H. K. Song, J. A. Maldjian, K. Tang et al., Arterial transit time imaging with flow encoding arterial spin tagging (FEAST), Magnetic Resonance in Medicine, vol.50, issue.3, p.23, 2003.

Z. Wang, J. Wang, and J. A. Detre, Improved data reconstruction method for GRAPPA, Magnetic Resonance in Medicine, vol.54, issue.3, pp.738-742, 2005.

C. Warmuth, M. Günther, and C. Zimmer, Quantification of Blood Flow in Brain Tumors: Comparison of Arterial Spin Labeling and Dynamic Susceptibility-weighted Contrast-enhanced MR Imaging, Radiology, vol.228, issue.2, pp.523-532, 2003.

M. A. Weber, S. Zoubaa, M. Schlieter, E. Juttler, H. B. Huttner et al., Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors, Neurology, vol.66, issue.12, pp.1899-1906, 2006.

N. Weiduschat and M. J. Dubin, Prefrontal cortical blood flow predicts response of depression to rTMS, Journal of Affective Disorders, vol.150, issue.2, pp.699-702, 2013.

A. Jack, . Wells, M. Mark-f-lythgoe, . Choy, G. David et al.,

, Arterial Spin Labelling Signal in MRI Using a Multiecho Acquisition Approach, Journal of Cerebral Blood Flow & Metabolism, vol.29, issue.11, p.13, 2009.

D. S. Williams, J. A. Detre, J. S. Leigh, and A. P. Koretsky, Magnetic resonance imaging of perfusion using spin inversion of arterial water, Proceedings of the National Academy of Sciences, vol.89, issue.1, p.16, 1992.

M. Wintermark, M. Sesay, E. Barbier, K. Borbe-ly, W. P. Dillon et al., Comparative Overview of Brain Perfusion Imaging Techniques. Stroke, vol.36, issue.9, 2005.

P. Robert-christian-wolf, F. Arthur-thomann, and . Sambataro, Nenad Vasic, Markus Schmid and Nadine Donata Wolf. Orbitofrontal cortex and impulsivity in borderline personality disorder: an MRI study of baseline brain perfusion, European Archives of Psychiatry and Clinical Neuroscience, vol.262, issue.8, pp.677-685, 2012.

E. C. Wong, R. B. Buxton, and L. R. Frank, Implementation of quantitative perfusion imaging techniques for functional brain mapping using pulsed arterial spin labeling, NMR in Biomedicine, vol.10, issue.4-5, p.19, 1997.

E. C. Wong, R. B. Buxton, and L. R. Frank, Quantitative imaging of perfusion using a single subtraction (QUIPSS and QUIPSS II), Magnetic Resonance in Medicine, vol.39, issue.5, p.19, 1998.

E. C. Wong, Vessel-encoded arterial spin-labeling using pseudocontinuous tagging, Magnetic Resonance in Medicine, vol.58, issue.6, pp.1086-1091, 2007.

W. Wu, M. Fernández-seara, J. A. Detre, F. W. Wehrli, and J. Wang, A theoretical and experimental investigation of the tagging efficiency of pseudocontinuous arterial spin labeling. Magnetic Resonance in Medicine, vol.58, p.71, 2007.

T. Yoshiura, A. Hiwatashi, T. Noguchi, K. Yamashita, Y. Ohyagi et al., Arterial spin labelling at 3-T MR imaging for detection of individuals with Alzheimer's disease, European Radiology, vol.19, issue.12, pp.2819-2825, 2009.

C. Tae-jin-yun, M. H. Sohn, B. Han, H. Yoon, J. E. Kang et al., Effect of carotid artery stenting on cerebral blood flow: evaluation of hemodynamic changes using arterial spin labeling, Neuroradiology, vol.55, issue.3, pp.271-281, 2012.

Y. Moss, M. Zhao, A. R. Mezue, T. W. Segerdahl, I. Okell et al., A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI, NeuroImage, vol.162, pp.384-397, 2017.

C. Meurée, P. Maurel, J. Ferré, and C. Barillot, Patchbased super-resolution of arterial spin labeling magnetic resonance images, NeuroImage, vol.189, pp.85-94, 2019.

C. Meurée, P. Maurel, É. Bannier, and C. Barillot, Patch-based super-resolution for arterial spin labeling MRI, International Society for Magnetic Resonance in Medicine 25th Annual Meeting & Exhibition (ISMRM, 2017.

C. Meurée, É. Bannier, I. Tropres, J. Warnking, J. Ferré et al., United Kingdom Software Integration of a super-resolution module into the Siemens Healthineers postprocessing syngo, European cooperation in science and technology workshop, 2015.