Principles of Magnetic Resonance Imaging

Liang, Zhi‐Pei; Lauterbur, Paul C.

doi:10.1109/9780470545652

Cited by 514 publications

(640 citation statements)

References 0 publications

Supporting

Mentioning

624

Contrasting

Unclassified

Order By: Relevance

“…Finally, the radio-frequency (RF) system uses a transmitter coil to generate an RF magnetic field to excite the tissue and a receiver coil is used to convert the processing NMR signals into a recordable electrical signal. So the main magnet, a set of gradient coils and one or more RF coils combine to form the basic components of an MRI system [17]. All of these operations are in the RF range, thus avoiding the use of ionizing radiation and its associated potential harmful effects.…”

Section: Thesis Outlinementioning

confidence: 99%

“…All of these operations are in the RF range, thus avoiding the use of ionizing radiation and its associated potential harmful effects. In contrast to other tomographic imaging devices, MRI can generate 2D sectional images at any orientation, 3D volumetric images, or even 4D images representing spatial-spectral distributions without any additional mechanical adjustments [17].…”

Section: Thesis Outlinementioning

confidence: 99%

“…As long as the theorem is followed, the data can fields. During the frequency encoding, the gradient amplitude is constant and all the points on one row along the direction can be obtained rapidly within one repetition time (TR) using either a spin or gradient echo [17]. As the echo signal is recorded in quadrature, each point in the k-space contains real and imaginary components.…”

Section: Compressed Sensing Mrimentioning

confidence: 99%

“…However, the phase encoding sampling period is generally equal to or longer than the interval of most physiologic motions, therefore, the majority of motion artefacts propagate in the phase encoding direction. There are two types of common artefacts due to motion: blurring and ghost (or misregistration) [17]. The blurring artefact (see In addition to the motion artefacts, the low imaging speed of MRI further limits it application in dynamic or rapid imaging.…”

Section: Compressed Sensing Mrimentioning

confidence: 99%

“…In general, these techniques can be separated into accelerated fully-sampled acquisition and acceleration by under-sampled acquisition, which will be reviewed in the following section. [18] and conceptually, FSE is an extension of the conventional spin-echo (CSE) [17]. Instead of encoding only one line of the k-space per repetition time using CSE, FSE/TSE techniques change the hardware setup so that multiple lines of k-space can be encoded per repetition time.…”

Section: Compressed Sensing Mrimentioning

confidence: 99%

See 4 more Smart Citations

Improving the image quality in compressed sensing MRI by the exploitation of data properties

Yang¹

View full text Add to dashboard Cite

Minimizing scan time without compromising image quality has been the main thrust of magnetic resonance imaging (MRI) since the establishment of the field. Tremendous effort has been put into MRI systems in pursuit of faster MR imaging techniques, which can substantially facilitate clinical diagnoses and improve the patient experience. Traditional MRI accelerated approaches mainly focused on hardware improvements to improve the speed of scanning. These methods are still governed by Nyquist-Shannon sampling rate. Hence, when the acceleration rate is pushed beyond the theoretical limit, aliasing artefacts are introduced which degrade the reconstructed image quality and are difficult to eliminate via current methods. Recently, it has been found that a newly developed signal processing theory, compressed sensing (CS), can be used to create high-resolution signal data sets from sparse samples, despite violating the classical Nyquist-Shannon sampling criteria. The application of CS to MRI opens up an appealing avenue to offer potentially significant scan time reductions. CS reconstructs MR images from incomplete k-space measurements. Taking advantage of the fact that MR images have sparse representations under certain mathematical transformations, CS overcomes the distortions resulting from the under-sampled k-space data. Significant progress has been made in the development of CS MRI. However, there are two major remaining challenges to its full adoption in clinical applications: (1) the small amount of under-sampled data (for example, less than 1/8 of the whole k-space data) will inherently introduce artefacts in the reconstructed image and compromise diagnosis accuracy for the reconstructed images; (2) owing to hardware limits, it is difficult to realise two-dimension random under-sampling, which is favoured by CS operation.Practically, the random phase-encode under-sampling pattern has been widely implemented.However, it introduces coherent aliasing artefacts that are hard to eliminate using the CS theory.In this thesis, several techniques are proposed that can improve the quality of images reconstructed by CS MRI. These techniques have the potential to facilitate faithful reconstruction of CS MRI in clinical applications. A novel two-stage reconstruction scheme is introduced in Chapter 3. In this method, the under-sampled k-space data is segmented into low-frequency and high-frequency parts.Then, in stage one, using dense measurements, the low-frequency region of k-space data is faithfully reconstructed. The fully reconstituted low-frequency k-space data from the first stage is then combined with the under-sampled high-frequency k-space data to complete the second stage reconstruction of the whole image. With this two-stage approach, each reconstruction inherently incorporates a lower data under-sampling rate than conventional approaches. Because the restricted isometric property is easier to satisfy than conventional CS method, the reconstruction consequently ii produces lower residual errors in each step...

show abstract

Section: Thesis Outlinementioning

confidence: 99%

Section: Thesis Outlinementioning

confidence: 99%

Section: Compressed Sensing Mrimentioning

confidence: 99%

Section: Compressed Sensing Mrimentioning

confidence: 99%

Section: Compressed Sensing Mrimentioning

confidence: 99%

See 3 more Smart Citations