Academic literature on the topic 'Compressed-Sensing fMRI'

Conventional functional magnetic resonance imaging (fMRI) technique known as gradient-recalled echo (GRE) echo-planar imaging (EPI) is sensitive to image distortion and degradation caused by local magnetic field inhomogeneity at high magnetic fields. Non-EPI sequences such as spoiled gradient echo and balanced steady-state free precession (bSSFP) have been proposed as an alternative high-resolution fMRI technique; however, the temporal resolution of these sequences is lower than the typically used GRE-EPI fMRI. One potential approach to improve the temporal resolution is to use compressed sensing (CS). In this study, we tested the feasibility ofk-tFOCUSS—one of the high performance CS algorithms for dynamic MRI—for non-EPI fMRI at 9.4T using the model of rat somatosensory stimulation. To optimize the performance of CS reconstruction, different sampling patterns andk-tFOCUSS variations were investigated. Experimental results show that an optimizedk-tFOCUSS algorithm with acceleration by a factor of 4 works well for non-EPI fMRI at high field under various statistical criteria, which confirms that a combination of CS and a non-EPI sequence may be a good solution for high-resolution fMRI at high fields.

The quest for higher spatial and/or temporal resolution in functional MRI (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 paper, we investigate the use of a finely tuned version of 3D-SPARKLING. This is a non-Cartesian CS-based acquisition technique for high spatial resolution whole-brain fMRI. We compare it to state-of-the-art Cartesian 3D-EPI during both a retinotopic mapping paradigm and resting-state acquisitions at 1mm3 (isotropic spatial resolution). This study involves six healthy volunteers and both acquisition sequences were run on each individual in a randomly-balanced order across subjects. The performances of both acquisition techniques are compared to each other in regards to tSNR, sensitivity to the BOLD effect and spatial specificity. Our findings reveal that 3D-SPARKLING has a higher tSNR than 3D-EPI, an improved sensitivity to detect the BOLD contrast in the gray matter, and an improved spatial specificity. Compared to 3D-EPI, 3D-SPARKLING yields, on average, 7% more activated voxels in the gray matter relative to the total number of activated voxels.

Abstract Background and purpose Conventional cardiac magnetic resonance (CCMR) imaging is usually performed with breath-holding (BH), which is adverse in patients with BH limitations. We explored the ability of a free-breathing CMR (fCMR) protocol to prognosticate in patients with coronary heart diseases (CHD) and limited BH ability. Methods Sixty-seven patients with CHD and limited BH abilities were prospectively enrolled in this study. All patients underwent comprehensive fCMR imaging at 3.0 T. The fCMR protocols included compressed sensing (CS) single-shot cine acceleration imaging, and motion-corrected (MOCO), single-shot late gadolinium enhancement (LGE) imaging. Image quality (IQ) of the cine and LGE images was evaluated based on the 5-point Likert scale. The value of fMRI in providing a prognosis in patients with CHD was assessed. Statistical methods included the T test, Mann–Whitney test, Kappa test, Kaplan–Meier curve, Log-rank test, Cox proportional hazard regression analysis, and receiver operating characteristic curves. Results All IQ scores of the short axis CS-cine and both the short and long axes MOCO LGE images were ≥ 3 points. Over a median follow-up of 31 months (range 3.8–38.2), 25 major adverse cardiovascular events (MACE) occurred. In the univariate analysis, infarction size (IS), left ventricular ejection fraction (LVEF), 3D-Global peak longitudinal strain (3D-GPLS), heart failure classification were significantly associated with MACE. When the significantly univariate MACE predictors, added to the multivariate analysis, which showed IS (HR 1.02; 95% CI 1.00–1.05; p = 0.048) and heart failure with preserved EF (HR 0.20; 95% CI 0.04–0.98; p = 0.048) correlated positively with MACE. The optimal cutoff value for LVEF, 3D-GPLS, and IS in predicting MACE was 34.2%, − 5.7%, and 26.1% respectively, with a sensitivity of 90.5%, 64%, and 96.0% and specificity of 72%, 95.2%, and 85.7% respectively. Conclusions The fCMR protocol can be used to make prognostic assessments in patients with CHD and BH limitations by calculating IS and LVEF.

L'IRM fonctionnelle (IRMf) est actuellement l'une des techniques de neuroimagerie fonctionnelle les plus utilisées pour sonder l'activité cérébrale de manière non invasive grâce au contraste dépendant du niveau d'oxygène dans le sang (BOLD) qui reflète le couplage neurovasculaire. Elle offre un compromis intéressant entre la résolution spatiale et temporelle afin d'étudier le cerveau entier en tant qu'agrégation de systèmes fonctionnels intrinsèques. La recherche d'une résolution spatiale et/ou temporelle plus élevée en IRMf tout en préservant un rapport signal/bruit temporel suffisant~(tSNR) a généré une quantité considérable de contributions méthodologiques au cours de la dernière décennie, allant des methodes d'encodage cartésiennes ou non cartésiennes, des stratégies d'acquisition 2D ou 3D, de l'imagerie parallèle et/ou de échantillonnage compressif (CS) et des acquisitions multibande, pour n'en citer que quelques-unes. Dans ce travail, nous nous concentrons sur l'utilisation du CS dans l'IRMf, plus spécifiquement, nous considérons le schéma d'encodage SPARKLING.L'objectif principal de cette thèse est d'évaluer 3D-SPARKLING en tant que schéma d'acquisition viable pour l'IRMf à haute résolution et pour cerveau entier.À cet égard, nous avons d'abord comparé ses performances avec l'état de l'art en matière: 3D-EPI. Après avoir observé une plus grande sensibilité aux imperfections statiques et dynamiques du champ magnétique dans les données 3D-SPARKLING, nous avons établi un protocole expérimental pour les corriger. Enfin, nous avons étudié les possibilités et les limites de l'utilisation d'une reconstruction par fenêtre glissante en combinaison avec le schéma d'encodage SPARKLING pour améliorer rétrospectivement la résolution temporelle pendant la reconstruction des images en IRMf. Une étude de simulation dans laquelle la vérité terrain est contrôlée a été menée et a démontré la possibilité de détecter les oscillations à haute fréquence dans le signal BOLD et de séparer le bruit physiologique de l'activité neuronale
Functional 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