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MRI Artifact
An MRI artifact is a visual artifact (an anomaly seen during visual representation) in magnetic resonance imaging (MRI). It is a feature appearing in an image that is not present in the original object. Many different artifacts can occur during MRI, some affecting the diagnostic quality, while others may be confused with pathology. Artifacts can be classified as patient-related, signal processing-dependent and hardware (machine)-related. Patient-related MR artifacts Motion artifacts A motion artifact is one of the most common artifacts in MR imaging. Motion can cause either ghost images or diffuse image noise in the phase-encoding direction. The reason for mainly affecting data sampling in the phase-encoding direction is the significant difference in the time of acquisition in the frequency- and phase-encoding directions. Frequency-encoding sampling in all the rows of the matrix (128, 256 or 512) takes place during a single echo (milliseconds). Phase-encoded sampling takes severa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Visual Artifact
Visual artifacts (also artefacts) are artifact (error), anomalies apparent during visual representation as in digital graphics and other forms of imagery, especially photography and microscopy. In digital graphics * Image quality#Image quality factors, Image quality factors, different types of visual artifacts * Compression artifacts * Digital artifacts, visual artifacts resulting from digital image processing * Image noise, Noise * Screen-door effect, also known as fixed-pattern noise (FPN), a visual artifact of digital projection technology *Ghosting (television) *Screen burn-in * Distortion * Silk screen effect * Rainbow effect * Screen tearing * Moiré pattern * Color banding In video entertainment Many people who use their computers as a hobby experience artifacting due to a hardware or software malfunction. The cases can differ but the usual causes are: * Temperature issues, such as failure of cooling fan. * Unsuited video card (graphics card) drivers. * Drivers that have ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-space (magnetic Resonance Imaging)
In magnetic resonance imaging (MRI), ''k''-space is the 2D or 3D Fourier transform of the image measured. It was introduced in 1979 by Likes and in 1983 by Ljunggren and Twieg. In MRI physics, complex values are sampled in ''k''-space during an MR measurement in a premeditated scheme controlled by a ''pulse sequence'', i.e. an accurately timed sequence of radiofrequency and gradient pulses. In practice, ''k''-space often refers to the ''temporary image space'', usually a matrix, in which data from digitized MR signals are stored during data acquisition. When ''k''-space is full (at the end of the scan) the data are mathematically processed to produce a final image. Thus ''k''-space holds ''raw'' data before ''reconstruction''. The concept of ''k''-space is situated in the spatial frequency domain. Thus if we define k_\mathrm and k_\mathrm such that :k_\mathrm=\bar G_\mathrmm\Delta t and :k_\mathrm=\bar n\Delta G_\mathrm \tau where FE refers to ''frequency encoding'', PE to '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Batch Normalization
Batch normalization (also known as batch norm) is a method used to make training of artificial neural networks faster and more stable through normalization of the layers' inputs by re-centering and re-scaling. It was proposed by Sergey Ioffe and Christian Szegedy in 2015. While the effect of batch normalization is evident, the reasons behind its effectiveness remain under discussion. It was believed that it can mitigate the problem of ''internal covariate shift'', where parameter initialization and changes in the distribution of the inputs of each layer affect the learning rate of the network. Recently, some scholars have argued that batch normalization does not reduce internal covariate shift, but rather smooths the objective function, which in turn improves the performance. However, at initialization, batch normalization in fact induces severe gradient explosion in deep networks, which is only alleviated by skip connections in residual networks. Others maintain that batch nor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Neural Network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neuro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
K-space (magnetic Resonance Imaging)
In magnetic resonance imaging (MRI), ''k''-space is the 2D or 3D Fourier transform of the image measured. It was introduced in 1979 by Likes and in 1983 by Ljunggren and Twieg. In MRI physics, complex values are sampled in ''k''-space during an MR measurement in a premeditated scheme controlled by a ''pulse sequence'', i.e. an accurately timed sequence of radiofrequency and gradient pulses. In practice, ''k''-space often refers to the ''temporary image space'', usually a matrix, in which data from digitized MR signals are stored during data acquisition. When ''k''-space is full (at the end of the scan) the data are mathematically processed to produce a final image. Thus ''k''-space holds ''raw'' data before ''reconstruction''. The concept of ''k''-space is situated in the spatial frequency domain. Thus if we define k_\mathrm and k_\mathrm such that :k_\mathrm=\bar G_\mathrmm\Delta t and :k_\mathrm=\bar n\Delta G_\mathrm \tau where FE refers to ''frequency encoding'', PE to '' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convolutional Neural Network
In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series. CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neuro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body. MRI does not involve X-rays or the use of ionizing radiation, which distinguishes it from CT and PET scans. MRI is a medical application of nuclear magnetic resonance (NMR) which can also be used for imaging in other NMR applications, such as NMR spectroscopy. MRI is widely used in hospitals and clinics for medical diagnosis, staging and follow-up of disease. Compared to CT, MRI provides better contrast in images of soft-tissues, e.g. in the brain or abdomen. However, it may be perceived as less comfortable by patients, due to the usually longer and louder measurements with the subject in a long, confining tube, though "Open" MRI designs mostly relieve this. Additionally, implants and oth ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Network
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MRI With Slice-to-slice Interference
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes inside the body. MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body. MRI does not involve X-rays or the use of ionizing radiation, which distinguishes it from computed tomography (CT) and positron emission tomography (PET) scans. MRI is a medical application of nuclear magnetic resonance (NMR) which can also be used for imaging in other NMR applications, such as NMR spectroscopy. MRI is widely used in hospitals and clinics for medical diagnosis, staging and follow-up of disease. Compared to CT, MRI provides better contrast in images of soft tissues, e.g. in the brain or abdomen. However, it may be perceived as less comfortable by patients, due to the usually longer and louder measurements with the subject in a long, confining tube, although "open" MRI d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
MRI With Surface Coil Artifact
Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes inside the body. Physics of magnetic resonance imaging#MRI scanner, MRI scanners use strong magnetic fields, magnetic field gradients, and radio waves to generate images of the organs in the body. MRI does not involve X-rays or the use of ionizing radiation, which distinguishes it from CT scan, computed tomography (CT) and positron emission tomography (PET) scans. MRI is a nuclear magnetic resonance#Medicine, medical application of nuclear magnetic resonance (NMR) which can also be used for imaging in other nuclear magnetic resonance#Applications, NMR applications, such as nuclear magnetic resonance spectroscopy, NMR spectroscopy. MRI is widely used in hospitals and clinics for medical diagnosis, cancer staging, staging and follow-up of disease. Compared to CT, MRI provides better contrast in images of soft tissues, e.g. in the mag ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Flip Angle
The flip angle is the rotation of the net magnetization vector by a radiofrequency pulse relative to the main magnetic field A magnetic field is a vector field that describes the magnetic influence on moving electric charges, electric currents, and magnetic materials. A moving charge in a magnetic field experiences a force perpendicular to its own velocity and to .... To improve the signal when Magnetic Resonance imaging, the flip angle needs to be chosen using the Ernst angle. Magnetic resonance imaging {{electromagnetism-stub ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
