Real-time MRI
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Real-time MRI
Real-time magnetic resonance imaging (RT-MRI) refers to the continuous monitoring ("filming") of moving objects in real time. Because MRI is based on time-consuming scanning of k-space, real-time MRI was possible only with low image quality or low temporal resolution. Using an iterative reconstruction algorithm these limitations have recently been removed: a new method for real-time MRI achieves a temporal resolution of 20 to 30 milliseconds for images with an in-plane resolution of 1.5 to 2.0 mm.M Uecker, S Zhang, D Voit, A Karaus, KD Merboldt, J Frahm (2010a) Real-time MRI at a resolution of 20 ms. NMR Biomed 23: 986-994 Real-time MRI promises to add important information about diseases of the joints and the heart. In many cases MRI examinations may become easier and more comfortable for patients. History 1977/1978 - Raymond Damadian built the first MRI scanner and achieved the first MRI scan of a healthy human body (1977) with the intent of diagnosing cancer. Addit ...
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Real-time MRI Of A Human Heart (2-chamber View)
Real-time or real time describes various operations in computing or other processes that must guarantee response times within a specified time (deadline), usually a relatively short time. A real-time process is generally one that happens in defined time steps of maximum duration and fast enough to affect the environment in which it occurs, such as inputs to a computing system. Examples of real-time operations include: Computing * Real-time computing, hardware and software systems subject to a specified time constraint * Real-time clock, a computer clock that keeps track of the current time * Real-time Control System, a reference model architecture suitable for software-intensive, real-time computing * Real-time Programming Language, a compiled database programming language which expresses work to be done by a particular time Applications * Real-time computer graphics, sub-field of computer graphics focused on producing and analyzing images in real time ** Real-time camera system ...
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Iterative Reconstruction
Iterative reconstruction refers to iterative algorithms used to reconstruct 2D and 3D images in certain imaging techniques. For example, in computed tomography an image must be reconstructed from projections of an object. Here, iterative reconstruction techniques are usually a better, but computationally more expensive alternative to the common filtered back projection (FBP) method, which directly calculates the image in a single reconstruction step.Herman, G. T.Fundamentals of computerized tomography: Image reconstruction from projection 2nd edition, Springer, 2009 In recent research works, scientists have shown that extremely fast computations and massive parallelism is possible for iterative reconstruction, which makes iterative reconstruction practical for commercialization. Basic concepts The reconstruction of an image from the acquired data is an inverse problem. Often, it is not possible to exactly solve the inverse problem directly. In this case, a direct algorithm h ...
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Fast Fourier Transform
A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT). Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. The DFT is obtained by decomposing a sequence of values into components of different frequencies. This operation is useful in many fields, but computing it directly from the definition is often too slow to be practical. An FFT rapidly computes such transformations by factorizing the DFT matrix into a product of sparse (mostly zero) factors. As a result, it manages to reduce the complexity of computing the DFT from O\left(N^2\right), which arises if one simply applies the definition of DFT, to O(N \log N), where N is the data size. The difference in speed can be enormous, especially for long data sets where ''N'' may be in the thousands or millions. In the presence of round-off error, many FFT algorithm ...
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Inverse Fourier Transform
In mathematics, the Fourier inversion theorem says that for many types of functions it is possible to recover a function from its Fourier transform. Intuitively it may be viewed as the statement that if we know all frequency and phase information about a wave then we may reconstruct the original wave precisely. The theorem says that if we have a function f:\R \to \Complex satisfying certain conditions, and we use the convention for the Fourier transform that :(\mathcalf)(\xi):=\int_ e^ \, f(y)\,dy, then :f(x)=\int_ e^ \, (\mathcalf)(\xi)\,d\xi. In other words, the theorem says that :f(x)=\iint_ e^ \, f(y)\,dy\,d\xi. This last equation is called the Fourier integral theorem. Another way to state the theorem is that if R is the flip operator i.e. (Rf)(x) := f(-x), then :\mathcal^=\mathcalR=R\mathcal. The theorem holds if both f and its Fourier transform are absolutely integrable (in the Lebesgue sense) and f is continuous at the point x. However, even under more general ...
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Aliasing
In signal processing and related disciplines, aliasing is an effect that causes different signals to become indistinguishable (or ''aliases'' of one another) when sampled. It also often refers to the distortion or artifact that results when a signal reconstructed from samples is different from the original continuous signal. Aliasing can occur in signals sampled in time, for instance digital audio, or the stroboscopic effect, and is referred to as temporal aliasing. It can also occur in spatially sampled signals (e.g. moiré patterns in digital images); this type of aliasing is called spatial aliasing. Aliasing is generally avoided by applying low-pass filters or anti-aliasing filters (AAF) to the input signal before sampling and when converting a signal from a higher to a lower sampling rate. Suitable reconstruction filtering should then be used when restoring the sampled signal to the continuous domain or converting a signal from a lower to a higher sampling rate. For spa ...
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Steady-state Free Precession Imaging
Steady-state free precession (SSFP) imaging is a magnetic resonance imaging (MRI) sequence which uses steady states of magnetizations. In general, SSFP MRI sequences are based on a (low flip angle) gradient echo MRI sequence with a short repetition time which in its generic form has been described as the FLASH MRI technique. While spoiled gradient-echo sequences refer to a steady state of the longitudinal magnetization only, SSFP gradient-echo sequences include transverse coherences (magnetizations) from overlapping multi-order spin echoes and stimulated echoes. This is usually accomplished by refocusing the phase-encoding gradient in each repetition interval in order to keep the phase integral (or gradient moment) constant. Fully balanced SSFP MRI sequences achieve a phase of zero by refocusing all imaging gradients. Gradient moments are zero or not If, within one TR, either one of the gradient moments of magnetic gradients along three logical directions, including slice selectio ...
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Magnetic Susceptibility
In electromagnetism, the magnetic susceptibility (Latin: , "receptive"; denoted ) is a measure of how much a material will become magnetized in an applied magnetic field. It is the ratio of magnetization (magnetic moment per unit volume) to the applied magnetizing field intensity . This allows a simple classification, into two categories, of most materials' responses to an applied magnetic field: an alignment with the magnetic field, , called paramagnetism, or an alignment against the field, , called diamagnetism. Magnetic susceptibility indicates whether a material is attracted into or repelled out of a magnetic field. Paramagnetic materials align with the applied field and are attracted to regions of greater magnetic field. Diamagnetic materials are anti-aligned and are pushed away, toward regions of lower magnetic fields. On top of the applied field, the magnetization of the material adds its own magnetic field, causing the field lines to concentrate in paramagnetism, or be excl ...
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Millisecond
A millisecond (from '' milli-'' and second; symbol: ms) is a unit of time in the International System of Units (SI) equal to one thousandth (0.001 or 10−3 or 1/1000) of a second and to 1000 microseconds. A unit of 10 milliseconds may be called a centisecond, and one of 100 milliseconds a decisecond, but these names are rarely used. To help compare orders of magnitude of different times, this page lists times between 10−3 seconds and 100 seconds (1 millisecond and one second). ''See also'' times of other orders of magnitude. Examples The Apollo Guidance Computer used metric units internally, with centiseconds used for time calculation and measurement. *1 millisecond (1 ms) – cycle time for frequency 1 kHz; duration of light for typical photo flash strobe; time taken for sound wave to travel about 34 cm; repetition interval of GPS C/A PN code *1 millisecond - time taken for light to travel 204.19 km in a single mode fiber optic cable for a wavelength o ...
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Filter (signal Processing)
In signal processing, a filter is a device or process that removes some unwanted components or features from a signal. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal. Most often, this means removing some frequencies or frequency bands. However, filters do not exclusively act in the frequency domain; especially in the field of image processing many other targets for filtering exist. Correlations can be removed for certain frequency components and not for others without having to act in the frequency domain. Filters are widely used in electronics and telecommunication, in radio, television, audio recording, radar, control systems, music synthesis, image processing, and computer graphics. There are many different bases of classifying filters and these overlap in many different ways; there is no simple hierarchical classification. Filters may be: *non-linear or linear *time-variant or t ...
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Regularization (mathematics)
In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is a process that changes the result answer to be "simpler". It is often used to obtain results for ill-posed problems or to prevent overfitting. Although regularization procedures can be divided in many ways, following delineation is particularly helpful: * Explicit regularization is regularization whenever one explicitly adds a term to the optimization problem. These terms could be priors, penalties, or constraints. Explicit regularization is commonly employed with ill-posed optimization problems. The regularization term, or penalty, imposes a cost on the optimization function to make the optimal solution unique. * Implicit regularization is all other forms of regularization. This includes, for example, early stopping, using a robust loss function, and discarding outliers. Implicit regularization is essentially ubiquitous in modern machine learning appr ...
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Redundancy (information Theory)
In information theory, redundancy measures the fractional difference between the entropy of an ensemble , and its maximum possible value \log(, \mathcal_X, ). Informally, it is the amount of wasted "space" used to transmit certain data. Data compression is a way to reduce or eliminate unwanted redundancy, while forward error correction is a way of adding desired redundancy for purposes of error detection and correction when communicating over a noisy channel of limited capacity. Quantitative definition In describing the redundancy of raw data, the rate of a source of information is the average entropy per symbol. For memoryless sources, this is merely the entropy of each symbol, while, in the most general case of a stochastic process, it is :r = \lim_ \frac H(M_1, M_2, \dots M_n), in the limit, as ''n'' goes to infinity, of the joint entropy of the first ''n'' symbols divided by ''n''. It is common in information theory to speak of the "rate" or "entropy" of a language. Th ...
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Inverse Problem
An inverse problem in science is the process of calculating from a set of observations the causal factors that produced them: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. It is called an inverse problem because it starts with the effects and then calculates the causes. It is the inverse of a forward problem, which starts with the causes and then calculates the effects. Inverse problems are some of the most important mathematical problems in science and mathematics because they tell us about parameters that we cannot directly observe. They have wide application in system identification, optics, radar, acoustics, communication theory, signal processing, medical imaging, computer vision, geophysics, oceanography, astronomy, remote sensing, natural language processing, machine learning, nondestructive testing, slope stability analysis and man ...
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