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  • 1
    Keywords: brain ; Germany ; MODEL ; MODELS ; PERFUSION ; imaging ; SYSTEM ; CONTRAST ; MAGNETIC-RESONANCE ; magnetic resonance imaging ; NERVOUS-SYSTEM ; CEREBRAL-BLOOD-FLOW ; CENTRAL-NERVOUS-SYSTEM ; CONTRAST-ENHANCED MRI ; functional MRI ; ENHANCEMENT ; INVERSION ; ARTERIAL ; contrast-enhanced ; functional imaging ; ARTERIAL WATER ; ASL ; BALLOON MODEL ; BOLD-fMRI ; BRAIN PERFUSION ; dynamic contrast enhanced-MRI ; dynamic susceptibility contrast-MRI ; FMRI ; HIGH-SPATIAL-RESOLUTION ; OXYGEN-CONSUMPTION ; QUIPSS II
    Abstract: This review presents the basic principles of functional imaging of the central nervous system utilizing magnetic resonance imaging. The focus is set on visualization of different functional aspects of the brain and related pathologies. Additionally, clinical cases are presented to illustrate the applications of functional imaging techniques in the clinical setting. The relevant physics and physiology of contrast-enhanced and non-contrast-enhanced methods are discussed. The two main functional MR techniques requiring contrast-enhancement are dynamic T1 - and T2(*)-MRI to image perfusion. Based on different pharmacokinetic models of contrast enhancement diagnostic applications for neurology and radio-oncology are discussed. The functional non-contrast enhanced imaging techniques are based on "blood oxygenation level dependent (BOLD)-fMRI and arterial spin labeling (ASL) technique. They have gained clinical impact particularly in the fields of psychiatry and neurosurgery
    Type of Publication: Journal article published
    PubMed ID: 15871087
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  • 2
    Keywords: MODEL ; MODELS ; PROSTATE ; THERAPY ; imaging ; VOLUME ; DRUG ; ACCURACY ; TIME ; MRI ; treatment ; MAGNETIC-RESONANCE ; magnetic resonance imaging ; TRIAL ; BENIGN ; nuclear medicine ; LOCATION ; monitoring ; EXTENSION ; HYPERPLASIA ; BENIGN PROSTATIC HYPERPLASIA ; ACCURATE ; COIL ; prostate volume ; THERMOTHERAPY ; TRANSRECTAL ULTRASOUND
    Abstract: Objectives: We sought to evaluate the capabilities of different magnetic resonance imaging (MRI)-based methodologies for measuring prostate volume. Materials and Methods: Twenty-four male beagles with benign prostatic hyperplasia were enrolled in a drug trial and imaged at 5 time points. A total of 120 prostate volumes were determined by MRI-based semiautomated segmentation. For planimetric assessment, 8 diameter locations were determined in the axial and coronal plane of the MRI slice with maximum extension of the prostate. Thirteen calculation models based on these diameters were determined by comparison to the reference volume and evaluated during treatment. Results: The segmented MRI prostate volume significantly correlated with post necropsy Volume. The best diameter-based model also worked very well for monitoring prostate volume of dogs under treatment. Conclusions: MRI-based segmentation is highly accurate in assessing prostate volume. Diameter-based measurements are closely correlated to the segmented prostate volume and are feasible to monitor therapy
    Type of Publication: Journal article published
    PubMed ID: 15770143
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  • 3
    Keywords: SPECTRA ; CANCER ; tumor ; COMBINATION ; Germany ; CLASSIFICATION ; imaging ; QUANTIFICATION ; TISSUE ; PATTERNS ; COMPONENT ; BRAIN-TUMORS ; pattern recognition ; methods ; MRSI ; SIGNALS ; GENERALIZED LINEAR-REGRESSION ; magnetic resonance spectroscopic imaging ; SHORT ECHO TIME
    Abstract: Despite its diagnostic value and technological availability, H-1 NMR spectroscopic imaging (MRSI) has not found its way into clinical routine yet. Prerequisite for the clinical application is an automated and reliable method for the diagnostic evaluation of MRS images. In the present paper, different approaches to the estimation of tumor probability from MRSI in the prostate are assessed. Two approaches to feature extraction are compared: quantification (VARPRO, AMARES, QUEST) and sub-space methods on spectral patterns (principal components, independent components, nonnegative matrix factorization, partial least squares). Linear as well as nonlinear classifiers (support vector machines, Gaussian processes, random forests) are applied and discussed. Quantification-based approaches are much more sensitive to the choice and parameterization of the quantification algorithm than to the choice of the classifier. Furthermore, linear methods based on magnitude spectra easily achieve equal performance and also allow for biochemical interpretation in combination with subspace methods. Nonlinear methods operating directly on magnitude spectra achieve the best results but are less transparent than the linear methods
    Type of Publication: Journal article published
    PubMed ID: 17191229
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