نتایج جستجو برای: Synthetic MR

تعداد نتایج: 205485  

Introduction: Radiation therapy planning (RTP) is one of the clinical applications in which both CT scan and MRI are used. MR and CT images are applied to determine the target volume and calculation of dose distribution, respectively. In addition, using two imaging modalities increases the department workload and cost. In this study, an algorithm was presented to create synthet...

Introduction: Nowadays, magnetic resonance imaging (MRI) in combination with computed-tomography (CT) is increasingly being used in radiation therapy planning. MR and CT images are applied to determine the target volume and calculate dose distribution, respectively. Since the use of these two imaging modalities causes registration uncertainty and increases department w...

Journal: :American Journal of Neuroradiology 2019

Journal: :AJNR. American journal of neuroradiology 2017
A Hagiwara M Hori K Yokoyama M Y Takemura C Andica T Tabata K Kamagata M Suzuki K K Kumamaru M Nakazawa N Takano H Kawasaki N Hamasaki A Kunimatsu S Aoki

BACKGROUND AND PURPOSE Synthetic MR imaging enables the creation of various contrast-weighted images including double inversion recovery and phase-sensitive inversion recovery from a single MR imaging quantification scan. Here, we assessed whether synthetic MR imaging is suitable for detecting MS plaques. MATERIALS AND METHODS Quantitative and conventional MR imaging data on 12 patients with ...

Journal: :AJNR. American journal of neuroradiology 2016
T Granberg M Uppman F Hashim C Cananau L E Nordin S Shams J Berglund Y Forslin P Aspelin S Fredrikson M Kristoffersen-Wiberg

BACKGROUND AND PURPOSE Quantitative MR imaging techniques are gaining interest as methods of reducing acquisition times while additionally providing robust measurements. This study aimed to implement a synthetic MR imaging method on a new scanner type and to compare its diagnostic accuracy and volumetry with conventional MR imaging in patients with MS and controls. MATERIALS AND METHODS Twent...

Ali Asghar Javidparvar, Fahimeh Hajipour, Haizheng Zhong, Jialun Tang, Khosro Khajeh, Mohammad Ali Amoozegar, Sedigheh Asad,

Background: Azo dyes are the most widely used synthetic colorants in the textile, food, pharmaceutical, cosmetic, and other industries, accounting for nearly 70% of all dyestuffs consumed. Recently, much research attention has been paid to efficient monitoring of these hazardous chemicals and their related metabolites because of their potentially harmful effect on environmental issues. In contr...

Journal: :AJNR. American journal of neuroradiology 2017
L N Tanenbaum A J Tsiouris A N Johnson T P Naidich M C DeLano E R Melhem P Quarterman S X Parameswaran A Shankaranarayanan M Goyen A S Field

BACKGROUND AND PURPOSE Synthetic MR imaging enables reconstruction of various image contrasts from 1 scan, reducing scan times and potentially providing novel information. This study is the first large, prospective comparison of synthetic-versus-conventional MR imaging for routine neuroimaging. MATERIALS AND METHODS A prospective multireader, multicase noninferiority trial of 1526 images read...

Journal: :Acta radiologica 2012
I Blystad J B M Warntjes O Smedby A-M Landtblom P Lundberg E-M Larsson

BACKGROUND Conventional magnetic resonance imaging (MRI) has relatively long scan times for routine examinations, and the signal intensity of the images is related to the specific MR scanner settings. Due to scanner imperfections and automatic optimizations, it is impossible to compare images in terms of absolute image intensity. Synthetic MRI, a method to generate conventional images based on ...

2006
E. A. Zanaty

Classical and clustering techniques for image segmentation are important tools in medical sciences. Classical techniques include histogram, region growing, watershed, and contour. The more recent clustering techniques include standard fuzzy c-means clustering, kernelized c-means, spatial constrained fuzzy c-means, and k-means clustering. These methods are applied on different images, synthetic ...

Journal: :International journal of radiation research 2022

Deep learning-based synthetic CT generation from MR images: comparison of generative adversarial and residual neural networks

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