Search results for " mr"
showing 10 items of 495 documents
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Bias artifact suppression on MR volumes.
2007
RF-Inhomogeneity correction is a relevant research topic in the field of Magnetic Resonance Imaging (MRI). A volume corrupted by this artifact exhibits nonuni- form illumination both inside a single slice and between adjacent ones. In this work a bias correction technique is presented, which suppresses this artifact on MR vol- umes scanned from different body parts without any a-priori hypothesis on the artifact model. Theoretical foundations of the method are reported together with experimental results and a comparison is presented with both the 2D version of the algorithm and other techniques that are widely used in MRI literature.
Fully automatic multispectral MR image segmentation of prostate gland based on the fuzzy C-means clustering algorithm
2017
Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is…
Discovering Aberrant Patterns of Human Connectome in Alzheimer's Disease via Subgraph Mining
2012
Alzheimer's disease (AD) is the most common cause of age-related dementia, which prominently affects the human connectome. Diffusion weighted imaging (DWI) provides a promising way to explore the organization of white matter fiber tracts in the human brain in a non-invasive way. However, the immense amount of data from millions of voxels of a raw diffusion map prevent an easy way to utilizable knowledge. In this paper, we focus on the question how we can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: …
Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project
2021
ABSTRACTDiffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project - a large collaborative open-source project which …
Combined approach to atrial and ventricular function for assessment of diastole through MRI: Hypertrophic Cardiomyopathy (HCM) vs Healthy Controls (H…
2013
Purpose Methods and Materials Results Conclusion References Personal Information
CT AND MRI OF THYROGLOSSAL DUCT CYST
2015
Aims and objectives Methods and materials Results Conclusion Personal information References
Anatomic variants of the biliary tree at MRCP: still too rarely reported!
2015
Aims and objectives Methods and materials Results Conclusion Personal information References
Eine Kombination niedrig und hochauflösender dynamischer T1-gewichteter Sequenzen zur besseren Beurteilung der Morphologie Kontrastmittel aufnehmende…
2002
Purpose: Presentation of a new protocol for simultaneous acquisition of both low and high resolution T 1 -weighted images of breast lesions for dynamic contrast-enhanced MR mammography. Demonstration of possible diagnostic improvement with representative measurements in patients with suspected breast cancer by adding morphologic parameters from high resolution sequences to the analysis of the signal-time curve. Materials and Methods: Dynamic MR imaging was performed with a 1.5 T system (Magnetom SONATA, Siemens Medical Systems, Germany) and the manufacturer's double-breast coil. Coronal T 1 -weighted 3D FLASH sequences (spatial resolution 1.25 ×1.25 mm 2 ; slice thickness 1.7 mm) were acqui…
Graph cut-based method for segmenting the left ventricle from MRI or echocardiographic images
2017
International audience; In this paper, we present a fast and interactive graph cut method for 3D segmentation of the endocardial wall of the left ventricle (LV) adapted to work on two of the most widely used modalities: magnetic resonance imaging (MRI) and echocardiography. Our method accounts for the fundamentally different nature of both modalities: 3D echocardiographic images have a low contrast, a poor signal-to-noise ratio and frequent signal drop, while MR images are more detailed but also cluttered and contain highly anisotropic voxels. The main characteristic of our method is to work in a 3D Bezier coordinate system instead of the original Euclidean space. This comes with several ad…