By Raymond Y. Kwong
Cardiovascular Magnetic Resonance Imaging (CMR) is a swiftly evolving software for cardiovascular prognosis, and is turning into more and more vital in guiding cardiovascular interventions. Cardiovascular Magnetic Resonance Imaging provides a state of the art compilation of professional contributions to the sector, each one interpreting basic and pathologic anatomy of the cardiovascular method as assessed through magnetic resonance imaging. practical thoughts akin to myocardial perfusion imaging and review of move pace are emphasised, in addition to the interesting parts of artherosclerosis plaque imaging and special magnetic resonance imaging. Cardiovascular Magnetic Resonance Imaging represents a multi-disciplinary method of the sphere, with contributions from specialists in cardiology, radiology, physics, engineering, body structure and biochemistry and gives new instructions in noninvasive imaging.
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Additional resources for Cardiovascular Magnetic Resonance Imaging
However, the picture becomes more Basic MRI Physics 21 Fig. 20. (A) The sum of all sinusoidal waveforms originating from spins along various x-axis positions (Fig. 19F) contains the spatial information within it. (B) The Fourier transformation extracts the spectral information from the detected signal. Note that the x-axis represents frequencies in Hz. (C) Frequencies can be converted into position along the x-axis because the encoding strength of the readout gradient is known. difficult to visualize if we take into account that we actually observe the sum of all these spinning vectors, that is, the sum of all the color sinusoids seen in Fig.
19 and 20), the Fourier transformation can be used along the y-direction to unscramble these sinusoids and yield information regarding where magnetization vectors are located along the y-axis. This second Fourier transformation along the phase-encoding direction will provide us with the image of the slice of bread we placed in the magnet. Basic MRI Physics 31 It is interesting to note that each of the four experiments provides us with a phaseencoded gradient echo, that is, one row of data. , stacked into multiple rows).
In other words, we could not separate along the two different axes the effect of frequency encoding. This makes sense because magnetization at x = 3 and at y = 3 will both precess at 300 Hz according to our previous example. Once added, there is no way to differentiate the two. Phase encoding is introduced to overcome this problem. Phase encoding is based on the principle that information about the signal’s point of origin on the y-axis can be represented by the phase (also known as the angle, measured in degrees) of the magnetization vector.