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2 results on '"Manuela Moor"'

1. Associations between fully-automated, 3D-based functional analysis of the left atrium and classification schemes in atrial fibrillation.

Author

Maurice Pradella, Constantin Anastasopoulos, Shan Yang, Manuela Moor, Patrick Badertscher, Julian E Gehweiler, Florian Spies, Philip Haaf, Michael Zellweger, Gregor Sommer, Bram Stieltjes, Jens Bremerich, Stefan Osswald, Michael Kühne, Christian Sticherling, and Sven Knecht

Subjects
Medicine, Science
Abstract

BackgroundAtrial fibrillation (AF) has been linked to left atrial (LA) enlargement. Whereas most studies focused on 2D-based estimation of static LA volume (LAV), we used a fully-automatic convolutional neural network (CNN) for time-resolved (CINE) volumetry of the whole LA on cardiac MRI (cMRI). Aim was to investigate associations between functional parameters from fully-automated, 3D-based analysis of the LA and current classification schemes in AF.MethodsWe retrospectively analyzed consecutive AF patients who underwent cMRI on 1.5T systems including a stack of oblique-axial CINE series covering the whole LA. The LA was automatically segmented by a validated CNN. In the resulting volume-time curves, maximum, minimum and LAV before atrial contraction were automatically identified. Active, passive and total LA emptying fractions (LAEF) were calculated and compared to clinical classifications (AF Burden score (AFBS), increased stroke risk (CHA2DS2VASc≥2), AF type (paroxysmal/persistent), EHRA score, and AF risk factors). Moreover, multivariable linear regression models (mLRM) were used to identify associations with AF risk factors.ResultsOverall, 102 patients (age 61±9 years, 17% female) were analyzed. Active LAEF (LAEF_active) decreased significantly with an increase of AFBS (minimal: 44.0%, mild: 36.2%, moderate: 31.7%, severe: 20.8%, pConclusionsFully-automatic morphometry of the whole LA derived from cMRI showed significant relationships between LAEF_active with increased stroke risk and severity of AFBS. Furthermore, higher age, higher AFBS and presence of heart failure were independent predictors of reduced LAEF_active, indicating its potential usefulness as an imaging biomarker.

Published
2022
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2. Evaluation of 3D fat-navigator based retrospective motion correction in the clinical setting of patients with brain tumors

Author

Manuela Moor, Christian Federau, Daniel Gallichan, Nicolin Hainc, Carl G Glessgen, University of Zurich, and Federau, Christian

Subjects
Adult, Male, medicine.medical_specialty, Image quality, Contrast Media, 610 Medicine & health, 2705 Cardiology and Cardiovascular Medicine, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Motion, 0302 clinical medicine, Imaging, Three-Dimensional, Meglumine, 10043 Clinic for Neuroradiology, medicine.artery, Image Interpretation, Computer-Assisted, medicine, Anterior cerebral artery, Organometallic Compounds, Humans, 2741 Radiology, Nuclear Medicine and Imaging, Radiology, Nuclear Medicine and imaging, In patient, Neuroradiology, Aged, Retrospective Studies, Aged, 80 and over, business.industry, Brain Neoplasms, Motion correction, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, 2728 Neurology (clinical), Middle cerebral artery, Female, Neurology (clinical), Radiology, Anatomic Landmarks, Cardiology and Cardiovascular Medicine, business, Artifacts, 030217 neurology & neurosurgery
Abstract

Purpose\ud \ud A 3D fat-navigator (3D FatNavs)-based retrospective motion correction is an elegant approach to correct for motion as it requires no additional hardware and can be acquired during existing ‘dead-time’ within common 3D protocols. The purpose of this study was to clinically evaluate 3D FatNavs in the work-up of brain tumors.\ud \ud \ud Methods\ud \ud An MRI-based fat-excitation motion navigator incorporated into a standard MPRAGE sequence was acquired in 40 consecutive patients with (or with suspected) brain tumors, pre and post-Gadolinium injection. Each case was categorized into key anatomical landmarks, the temporal lobes, the infra-tentorial region, the basal ganglia, the bifurcations of the middle cerebral artery, and the A2 segment of the anterior cerebral artery. First, the severity of motion in the non-corrected MPRAGE was assessed for each landmark, using a 5-point score from 0 (no artifacts) to 4 (non-diagnostic). Second, the improvement in image quality in each pair and for each landmark was assessed blindly using a 4-point score from 0 (identical) to 3 (strong correction).\ud \ud \ud Results\ud \ud The mean image improvement score throughout the datasets was 0.54. Uncorrected cases with light and no artifacts displayed scores of 0.50 and 0.13, respectively, while cases with moderate artifacts, severe artifacts, and non-diagnostic image quality revealed a mean score of 1.17, 2.25, and 1.38, respectively.\ud \ud \ud Conclusion\ud \ud Fat-navigator-based retrospective motion correction significantly improved MPRAGE image quality in restless patients during MRI acquisition. There was no loss of image quality in patients with little or no motion, and improvements were consistent in patients who moved more.

Published
2019

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