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Resolution: Medical MRI resolution is typically about 1 mm; the desired resolution of MRM is 100 μm or smaller to 10 μm, comparable with histology. Specimen size: Medical MRI machines are designed so that a patient may fit inside. MRM chambers are usually small, typically less than 1 cm 3 for the imaging of rats, mice and rodents. BrukerBio ...
MRI has the advantages of having very high spatial resolution and is very adept at morphological imaging and functional imaging. MRI does have several disadvantages though. First, MRI has a sensitivity of around 10 −3 mol/L to 10 −5 mol/L, which, compared to other types of imaging, can be very limiting.
The first MR images of a human brain were obtained in 1978 by two groups of researchers at EMI Laboratories led by Ian Robert Young and Hugh Clow. [1] In 1986, Charles L. Dumoulin and Howard R. Hart at General Electric developed MR angiography, [2] and Denis Le Bihan obtained the first images and later patented diffusion MRI. [3]
MRI, in general, has better spatial resolution than EEG and MEG, but not as good a resolution as invasive procedures such as single-unit electrodes. While typical resolutions are in the millimeter range, ultra-high-resolution MRI or MR spectroscopy works at a resolution of tens of micrometers.
In some radar and sonar imaging applications (e.g. magnetic resonance imaging (MRI), high-resolution computed tomography), subspace decomposition-based methods (e.g. MUSIC [1]) and compressed sensing-based algorithms (e.g., SAMV [2]) are employed to achieve SR over standard periodogram algorithm.
A Vastly undersampled Isotropic Projection Reconstruction (VIPR) is a radially acquired MRI sequence which results in high-resolution MRA with significantly reduced scan times, and without the need for breath-holding.
This process as a whole significantly accelerates the MRI process. Image segmentation or identification of lesions can be achieved through machine learning. In deep learning, with a convolutional neural network, the mapping function can be specified by the network. ML and DL improve image resolution as well as imaging speed. [37]
Conventional MRI sequences are adapted for cardiac imaging by using ECG gating and high temporal resolution protocols. The development of cardiac MRI is an active field of research and continues to see a rapid expansion of new and emerging techniques. [2]