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Prognostication may be difficult in comatose cardiac arrest survivors. Magnetic resonance imaging (MRI) is potentially useful in the prediction of neurological outcome, and it may detect acute ischemia at an early stage. In a pilot setting we determined the prevalence and development of cerebral ischemia using serial MRI examinations and neurological assessment. Ten witnessed out-of-hospital cardiac arrest patients were included. MRI was carried out approximately 2h after admission to the hospital, repeated after 24h of therapeutic hypothermia and 96 h after the arrest. The images were assessed for development of acute ischemic lesions. Neurophysiological and cognitive tests as well as a self-reported quality-of-life questionnaire, Short Form-36 (SF-36), were administered minimum 12 months after discharge. None of the patients had acute cerebral ischemia on MRI at admission. Three patients developed ischemic lesions after therapeutic hypothermia. There was a change in the apparent diffusion coefficient, which significantly correlated with the temperature (p
To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. 2014 Wiley Periodicals, Inc.