Impact of Rician Adapted Non-Local Means Filtering on HARDI

Descoteaux, Maxime; Wiest-Daesslé, Nicolas; Prima, Sylvain; Barillot, Christian; Deriche, Rachid

doi:10.1007/978-3-540-85990-1_15

Cited by 80 publications

(74 citation statements)

References 19 publications

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“…This fact enables the extraction of many realizations of a given process from the image itself. This self-similarity property has been recently verified for anatomical MRI Manjon et al, 2008) and DWI (Descoteaux et al, 2008;Wiest-Daessle et al, 2008) for denoising tasks. Such redundancy-based or self-similarity-based priors on image content can be also applied to many other purposes such as super-resolution.…”

Section: Patch-based Super-resolutionmentioning

confidence: 63%

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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Section: Patch-based Super-resolutionmentioning

confidence: 63%

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

et al. 2013

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“…These techniques were then extended to take into account the Rician noise nature of the DWI signal (Coupé et al, 2010;Descoteaux et al, 2008;Aja-Fernàndez et al, 2008;Tristán-Vega and Aja-Fernández, 2010;Aja-Fernández et al, 2011;Brion et al, 2011) and, recently, the non-central Chi-squared distribution in the case where parallel imaging is used Brion et al, 2011). However, only the technique of (Tristán-Vega and Aja-Fernández, 2010) performs denoising across the DWI channels, i.e.…”

Section: Theorymentioning

confidence: 99%

“…For the rest of this paper, acquisition of 258 directions with simple mirror symmetry will be abbreviated HALF, as opposed to the FULL acquisition using 515 measurements. Different symmetry completion procedure are also included for comparison, either using Gaussian smoothing with σ = 0.35 (optimal σ in our experiments which corresponds to 2.5 mm FWHM) and state-of-the-art non-local (NL) means denoising (Descoteaux et al, 2008;Coupé et al, 2010) (11x11x11 search volume and 26 neighbors local neighborhood). The denoising techniques are common and there are publicly available implementations that one can run on the raw diffusion data 10 .…”

Section: Pittsburgh Brain Competition 2009 Datasetsmentioning

confidence: 99%

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Gramfort¹,

Poupon²,

Descoteaux³

2014

Medical Image Analysis

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To cite this version:Alexandre Gramfort, Cyril Poupon, Maxime Descoteaux. Denoising and fast diffusion imaging with physically constrained sparse dictionary learning. Medical Image Analysis, Elsevier, 2013, 18 (1) Abstract Diffusion-weighted imaging (DWI) allows imaging the geometry of water diffusion in biological tissues. However, DW images are noisy at high b-values and acquisitions are slow when using a large number of measurements, such as in Diffusion Spectrum Imaging (DSI). This work aims to denoise DWI and reduce the number of required measurements, while maintaining data quality. To capture the structure of DWI data, we use sparse dictionary learning constrained by the physical properties of the signal: symmetry and positivity. The method learns a dictionary of diffusion profiles on all the DW images at the same time and then scales to full brain data. Its performance is investigated with simulations and two real DSI datasets. We obtain better signal estimates from noisy measurements than by applying mirror symmetry through the q-space origin, Gaussian denoising or state-ofthe-art non-local means denoising. Using a high-resolution dictionary learnt on another subject, we show that we can reduce the number of images acquired while still generating high resolution DSI data. Using dictionary learning, one can denoise DW images effectively and perform faster acquisitions. Higher b-value acquisitions and DSI techniques are possible with approximately 40 measurements. This opens important perspectives for the connectomics community using DSI.

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“…Images were acquired using single-shot spin-echo echo-planar imaging sequence (15 gradient (Descoteaux, Wiest-Daesslé, Prima, Barillot, & Deriche, 2008;Wiest-Daesslé, Prima, Coupé, Morrissey, & Barillot, 2008). Data were preprocessed also for eddy currents and subject motion, using affine registration to a non-weighted diffusion reference image (b =0).…”

Section: Assessmentsmentioning

confidence: 99%

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

Broce¹

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The present study characterized two fiber pathways important for language, the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) and the frontal aslant tract (FAT), and related these tracts to speech, language, and literacy skill in children five to eight years old. We used Diffusion Tensor Imaging (DTI) to characterize the fiber pathways and administered several language assessments. The FAT was identified for the first time in children. Results showed no age-related change in integrity of the FAT, but did show age-related change in the left (but not right) SLF/AF. Moreover, only the integrity of the right FAT was related to phonology but not audiovisual speech perception, articulation, language, or literacy. Both the left and right SLF/AF related to language measures, specifically receptive and expressive language, and language content.These findings are important for understanding the neurobiology of language in the developing brain, and can be incorporated within contemporary dorsal-ventral-motor models for language.

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Impact of Rician Adapted Non-Local Means Filtering on HARDI

Cited by 80 publications

References 19 publications

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

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

Denoising and fast diffusion imaging with physically constrained sparse dictionary learning

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

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