Super‐resolution in magnetic resonance imaging: A review

Reeth, Eric van; Tham, Ivan; Tan, Cher Heng; Poh, Chueh Loo

doi:10.1002/cmr.a.21249

Cited by 214 publications

(172 citation statements)

References 115 publications

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“…7 x 0 .7mm3 using zero-padding. However zero-padding is similar to performing interpolation in image space and is known to introduce blur and diminish contrast (Van Reeth et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Collaborative patch-based super-resolution for diffusion-weighted images

et al. 2013

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To cite this version:Pierrick Coupé, José V Manjón, Maxime Chamberland, Maxime Descoteaux, Bassem Hiba. Collaborative patch-based super-resolution for diffusion-weighted images.. NeuroImage, Elsevier, 2013 AbstractIn this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non diffusion image (b0) t o c o n s t r a i n t h e r e c o n s t r u c t i o n o f DW i m a g e s .An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms o f improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images a r e p r e s e n t e d a t 0 . 6 x 0 . 6 x 0 . 6 m m 3 a n d 0.4x0.4x0.4mm 3 using our gold standard b a s e d o n the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach t o a c c u r a t e l y analyze white matter brain architecture.

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“…7 x 0 .7mm3 using zero-padding. However zero-padding is similar to performing interpolation in image space and is known to introduce blur and diminish contrast (Van Reeth et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…This strategy was then extended to arbitrary orientations (Poot et al, 2010) and applied to DW images . Reviews and comparisons of some of these methods can be found in (Plenge et al, 2012;Van Reeth et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Collaborative patch-based super-resolution for diffusion-weighted images

et al. 2013

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“…Improving the system machinery by, for example, increasing the number of coil receiver channels or the magnetic field strength can increase signal strength. 60 Longer acquisition times also increase resolution, although this may be hampered by the susceptibility of patients to claustrophobia, or the length of time tolerated by an animal in the machine. 61 Should cost, time available for image acquisition, or movement of a subject be prohibitive, imageprocessing techniques can be applied postscanning.…”

Section: Magnetic Resonance Imaging: Imaging With Magnetismmentioning

confidence: 99%

Cell Tracking Technologies for Acute Ischemic Brain Injury

Gavins

Smith

2015

J Cereb Blood Flow Metab

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Stem cell therapy has showed considerable potential in the treatment of stroke over the last decade. In order that these therapies may be optimized, the relative benefits of growth factor release, immunomodulation, and direct tissue replacement by therapeutic stem cells are widely under investigation. Fundamental to the progress of this research are effective imaging techniques that enable cell tracking in vivo. Direct analysis of the benefit of cell therapy includes the study of cell migration, localization, division and/or differentiation, and survival. This review explores the various imaging tools currently used in clinics and laboratories, addressing image resolution, long-term cell monitoring, imaging agents/isotopes, as well as safety and costs associated with each technique. Finally, burgeoning tracking techniques are discussed, with emphasis on multimodal imaging.

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“…While superresolution algorithms have been applied in uni-modal settings [22], they have not been widely explored for multimodal neuroimaging. We fill this gap by introducing BRAINZOOM, a novel method for super-resolution reconstruction of brain activity by combining different modalities of brain imaging.…”

Section: Introductionmentioning

confidence: 99%

BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals

Huang

Stretcu

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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How close can we zoom in to observe brain activity? Our understanding is limited by the resolution of imaging modalities that exhibit good spatial but poor temporal resolution, or vice-versa. In this paper, we propose BRAINZOOM, an efficient imaging algorithm that crossleverages multi-modal brain signals. BRAINZOOM (a) constructs high resolution brain images from multi-modal signals, (b) is scalable, and (c) is flexible in that it can easily incorporate various priors on the brain activities, such as sparsity, low rank, or smoothness. We carefully formulate the problem to tackle nonlinearity in the measurements (via variable splitting) and auto-scale between different modal signals, and judiciously design an inexact alternating optimization-based algorithmic framework to handle the problem with provable convergence guarantees. Our experiments using a popular realistic brain signal simulator to generate fMRI and MEG demonstrate that high spatio-temporal resolution brain imaging is possible from these two modalities. The experiments also suggest that smoothness seems to be the best prior, among several we tried.

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Super‐resolution in magnetic resonance imaging: A review

Cited by 214 publications

References 115 publications

Collaborative patch-based super-resolution for diffusion-weighted images

Collaborative patch-based super-resolution for diffusion-weighted images

Cell Tracking Technologies for Acute Ischemic Brain Injury

BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals

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