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Gall, Didier Le

doi:10.1145/103085.103090

Cited by 1,731 publications

(417 citation statements)

References 3 publications

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“…For our Cartesian k-t FOCUSS implementation (12), we introduced two efficient prediction methods: twodimensional reduced-encoding imaging by generalizedseries reconstruction (12,27) and ME/MC (15). However, the application of reduced-encoding imaging by generalized-series reconstruction to radial trajectories is not well established due to the difficulty involved with generalized series representation on non-Cartesian grids.…”

Section: Suitability Of Me/mc In Dynamic Mrimentioning

confidence: 99%

Radial k‐t FOCUSS for high‐resolution cardiac cine MRI

Jung

Park

Yoo

et al. 2009

Magnetic Resonance in Med

111

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A compressed sensing dynamic MR technique called k-t FOCUSS (k-t FOCal UnderdeterminedSystem Solver) has been recently proposed. It outperforms the conventional k-t BLAST/SENSE (Broad-use Linear Acquisition Speed-up Technique/SENSitivity Encoding) technique by exploiting the sparsity of x-f signals. This paper applies this idea to radial trajectories for high-resolution cardiac cine imaging. Radial trajectories are more suitable for high-resolution dynamic MRI than Cartesian trajectories since there is smaller tradeoff between spatial resolution and number of views if streaking artifacts due to limited views can be resolved. As shown for Cartesian trajectories, k-t FOCUSS algorithm efficiently removes artifacts while preserving high temporal resolution. k-t FOCUSS algorithm applied to radial trajectories is expected to enhance dynamic MRI quality. Rather than using an explicit gridding method, which transforms radial k-space sampling data to Cartesian grid prior to applying k-t FOCUSS algorithms, we use implicit gridding during FOCUSS iterations to prevent k-space sampling errors from being propagated. In addition, motion estimation and motion compensation after the first FOCUSS iteration were used to further sparsify the residual image. By applying an additional k-t FOCUSS step to the residual image, Cardiac cine MRI requires high-speed data acquisition to maximize spatiotemporal resolution while reducing breath-hold times for patients. For studies of left ventricular functions, an in-plane spatial resolution of 2-2.5 mm is usually used. Higher resolutions up to 1-2 mm are often required to study finer structures, such as cardiac valves and patent foramen ovale (1). Typical imaging field of view (FOV) for cardiac MR studies is about 250×250 mm 2 . Therefore, to cover the entire volume of a heart, standard matrix sizes of 100×256 and higher resolution matrix sizes of 192×256 are used to meet resolution requirements (1).

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Section: Suitability Of Me/mc In Dynamic Mrimentioning

confidence: 99%

Radial k‐t FOCUSS for high‐resolution cardiac cine MRI

Jung

Park

Yoo

et al. 2009

Magnetic Resonance in Med

111

View full text Add to dashboard Cite

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“…JPEG and MPEG [6] are developed for compressing still images and 2D video data with controllable size and distortion tradeoff. Embedded coding algorithms such as embedded zero tree wavelet (EZW) [21] and set partitioning in hierarchical trees (SPIHT) [19] are useful for progressive transmission and multimedia applications.…”

Section: Compressionmentioning

confidence: 99%

Volumetric video compression for interactive playback

Sohn

Bajaj

Siddavanahalli

2004

Computer Vision and Image Understanding

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We develop a volumetric video system which supports interactive browsing of compressed timevarying volumetric features (significant isosurfaces and interval volumes). Since the size of even one volumetric frame in a time-varying 3D data set is very large, transmission and on-line reconstruction are the main bottlenecks for interactive remote visualization of time-varying volume and surface data. We describe a compression scheme for encoding time-varying volumetric features in a unified way, which allows for on-line reconstruction and rendering. To increase the run-time decompression speed and compression ratio, we decompose the volume into small blocks and encode only the significant blocks that contribute to the isosurfaces and interval volumes. The results show that our compression scheme achieves high compression ratio with fast reconstruction, which is effective for client-side rendering of time-varying volumetric features.

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“…This could be applied in interpolative coding, as in MPEG [41]. If we consider only a first-order neighborhood system (i.e., with neighborhoods η g (t −1 ) = {t}, η g (t) = {t −1 , t +1 }, η g (t +1 ) = {t}), then there are two two-element cliques, {t −1 , t} and {t, t +1 }.…”

Section: Structural Model and Matching Errormentioning

confidence: 99%

“…Thus, the knowledge of motion helps in removing significant interimage redundancy, as is the case in predictive or hybrid (predictive/transform or predictive/subband) coding compensated for motion. In fact, these techniques include most algorithms currently used in videoconferencing [41], [42] and proposed for High Definition Television (HDTV) [21], [60].…”

mentioning

confidence: 99%

Estimation of 2-D Motion Fields from Image Sequences with Application to Motion-Compensated Processing

Dubois¹,

Konrad²

1993

Motion Analysis and Image Sequence Processing

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In this chapter we are concerned with the estimation of 2-D motion from time-varying images and with the application of the computed motion to image sequence processing. Our goal for motion estimation is to propose a general formulation that incorporates object acceleration, nonlinear motion trajectories, occlusion effects and multichannel (vector) observations. To achieve this objective we use Gibbs-Markov models linked together by the Maximum A Posteriori Probability criterion which results in minimization of a multiple-term cost function. The specific applications of motion-compensated processing of image sequences are prediction, noise reduction and spatiotemporal interpolation.Estimation of motion from dynamic images is a very difficult task due to its ill-posedness [4]. Despite this difficulty, however, many approaches to the problem have been proposed in the last dozen years [27], [24], [40]. This activity can certainly be attributed in large measure to the importance of motion in the processing and coding of image sequences. Below we explain why motion is important in these tasks.Efficient encoding of time-varying images is essential to provide economical use of network or storage facilities in the provision of video services. Image sequences can be compressed by independent coding of each frame (intraframe coding) or by straightforward extension of spatial coding techniques to three dimensions (e.g., 3-D transform coding). However, such approaches ignore the fact that the majority of new information (innovations) in a time-varying image is carried by the motion. The correlation of image intensity or color is very high along the direction of motion. Thus, the knowledge of motion helps in removing significant interimage redundancy, as is the case in predictive or hybrid (predictive/transform or predictive/subband) coding compensated for motion. In fact, these techniques include most algorithms currently used in videoconferencing [41], [42] and proposed for High Definition Television (HDTV) [21], [60].Sampling structure conversion and noise reduction are two examples of image sequence processing that can benefit greatly from the knowledge of motion. In the case of conversion, two scenarios are possible. In one situation a missing image must be recovered. Again, due to the high correlation along motion trajectories, motion-compensated interpolation is the most effective tool [53], [15]. In the other

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Mpeg

Cited by 1,731 publications

References 3 publications

Radial k‐t FOCUSS for high‐resolution cardiac cine MRI

Radial k‐t FOCUSS for high‐resolution cardiac cine MRI

Volumetric video compression for interactive playback

Estimation of 2-D Motion Fields from Image Sequences with Application to Motion-Compensated Processing

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