Purpose - This paper presents a novel 3D multimodal registration strategy to fuse 3D real-time echocardiography images with cardiac cine MRI images. This alignment is performed in a saliency space, which is designed to maximize similarity between the two imaging modalities. This fusion improves the quality of the available information. Methods - Th...
Published in Machine Vision and Applications
The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-base...
Published in Brain imaging and behavior
Spatial registration is an essential step in the analysis of fMRI data because it enables between-subject analyses of brain activity, measured either during task performance or in the resting state. In this study, we investigated how anatomical registration with MRTOOL affects the reliability of task-related fMRI activity. We used as a benchmark th...
Published in Magnetic resonance imaging
Recent methodological innovations in deep learning and associated advancements in computational hardware have significantly impacted the various core subfields of quantitative medical image analysis. The generalizability, computational efficiency and open-source availability of deep learning algorithms and related software, particularly those utili...
Published in Heritage Science
A seventeenth-century canvas painting is usually comprised of varnish and (translucent) paint layers on a substrate. A viewer’s perception of a work of art can be affected by changes in and damages to these layers. Crack formation in the multi-layered stratigraphy of the painting is visible in the surface topology. Furthermore, the impact of mechan...
Published in NeuroImage
The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion f...
Published in Magma (New York, N.Y.)
Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acq...
Image registration is a vital tool in medical image analysis with a large number of applications assisting the medical experts. Currently, conventional image registration approach with predefined dissimilarity metric and iterative optimization, is widely used. In this thesis, we proposed a method to solve medical image registration problem using a ...
In this paper, an unsupervised cycle image registration convolutional neural network named CIRNet is developed for 3D medical image registration. Different from most deep learning based registration methods that require known spatial transforms, our proposed method is trained in an unsu-pervised way and predicts the dense displacement vector field....
