Analysis and comparison of motion‐correction techniques in diffusion‐weighted imaging

Trouard, Theodore P.; Sabharwal, Yashvinder; Altbach, María I.; Gmitro, Arthur F.

doi:10.1002/jmri.1880060614

Cited by 72 publications

(88 citation statements)

References 15 publications

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“…The effect of rigid body motion during the application of diffusion-sensitizing gradients has been studied by other authors (4,5), but we will rederive their results briefly using the present notation. Rigid-body motion can be decomposed into rotational and translational components: v͑r͒ ϭ ⍀ rot ϫ r ϩ v trans , [2] where ⍀ rot is the angular velocity and r is the position of the tissue element.…”

Section: Theorymentioning

confidence: 99%

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Partial k‐space reconstruction in single‐shot diffusion‐weighted echo‐planar imaging

Storey

Frigo²,

Hinks³

et al. 2007

Magnetic Resonance in Med

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Partial k-space sampling is frequently used in single-shot diffusion-weighted echo-planar imaging (DW-EPI) to reduce the TE and thereby improve the SNR. However, it increases the sensitivity of the technique to bulk rotational motion, which introduces a phase gradient across the tissue that shifts the echo in k-space. If the echo is displaced into the high spatial frequencies, conventional homodyne reconstruction fails, causing intensity oscillations across the image. Zero-padding, on the other hand, compromises the image resolution and may cause truncation artifacts. We present an adaptive version of the homodyne algorithm that detects the location of the echo in k-space and adjusts the center and width of the homodyne filters accordingly. The adaptive algorithm produces artifactfree images when the echo is shifted into the high positive k-space range, and reduces to the standard homodyne algorithm in the absence of bulk motion. Magn Reson Med 57: 614 -619, 2007.

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Section: Theorymentioning

confidence: 99%

“…Rigid-body rotational motion during the application of the diffusion-sensitizing gradients introduces a linear phase shift across the tissue, which displaces the echo in k-space (4,5). If the rotational speed is sufficiently high, the DC component of the signal may be displaced outside the k-space sampling range, causing dramatic signal loss (6).…”

mentioning

confidence: 99%

Partial k‐space reconstruction in single‐shot diffusion‐weighted echo‐planar imaging

Storey

Frigo²,

Hinks³

et al. 2007

Magnetic Resonance in Med

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show abstract

“…Robustness against susceptibility variations can be provided by spin echo acquisition schemes and motion tolerance can be achieved using radial (or polar) trajectories in Fourier space [31]. This means that k-space is covered by a radial pattern of straight trajectories with equal angular spacing and with each passing through its center, k = 0.…”

Section: Introductionmentioning

confidence: 99%

“…These projections can be transformed into the final image using standard algorithms for filtered backprojection (FBP). To improve the robustness of this method, the phase information of the (complex) projections can be discarded before applying the FBP algorithm [31].…”

Section: Introductionmentioning

confidence: 99%

Diffusion-weighted imaging of the spine using radialk-space trajectories

Dietrich

Herlihy

Dannels³

et al. 2001

MAGMA

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“…For instance, in radial trajectory, the low-frequency region of the k-space centre is inherently oversampled, which provides superior performance in motion artefact reduction in dynamic MRI [128,129]. In addition, radially sampled k-space may provide improved temporal resolution throughout the field of view (FOV), which can be used to reduce the total scan time, as demonstrated in angularly under-sampled projection-reconstruction (PR) for 3D cardiac MR (CMR) [130,131].…”

Section: Introductionmentioning

confidence: 99%

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

Yang¹

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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...

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Analysis and comparison of motion‐correction techniques in diffusion‐weighted imaging

Cited by 72 publications

References 15 publications

Partial k‐space reconstruction in single‐shot diffusion‐weighted echo‐planar imaging

Partial k‐space reconstruction in single‐shot diffusion‐weighted echo‐planar imaging

Diffusion-weighted imaging of the spine using radialk-space trajectories

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

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