Non-local MRI upsampling

Manjón, José V.; Coupé, Pierrick; Buades, Antoni; Fonov, Vladimir S.; Collins, D. Louis; Robles, Montserrat

doi:10.1016/j.media.2010.05.010

Cited by 238 publications

(238 citation statements)

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“…Recently, PBSR methods have been introduced and validated for MRI (Manjon et al, 2010a;Manjon et al, 2010b;Rousseau, 2008;Rousseau, 2010). PBSR was first proposed for multimodal reconstruction by Rousseau in (2008).…”

Section: Patch-based Super-resolutionmentioning

confidence: 99%

“…More recently, this type of approach was extended using a model of the physical acquisition process to improve reconstruction quality (Manjon et al, 2010a). In addition, the possibility to reconstruct HR MRI using only the LR image itself has been investigated for MRI super-resolution (Manjon et al, 2010b;Rousseau, 2010). In this case, only the self-similarity prior is used to drive the reconstruction procedure.…”

Section: Patch-based Super-resolutionmentioning

confidence: 99%

“…A review of single image SR techniques for natural images can be found in (van Ouwerkerk, 2006). Recently such post-processing, single image, super-resolution methods have been proposed for MRI (Manjon et al, 2010a;Manjon et al, 2010b;Rousseau, 2008;Rousseau, 2010). The main idea is to use the image content to reconstruct information at higher-resolution.…”

Section: Introductionmentioning

confidence: 99%

“…In the following, the operator is approximated by a 3D boxcar function as done in (Bai et al, 2004;Carmi et al, 2006;Manjon et al, 2010a;Manjon et al, 2010b;Rousseau, 2010).…”

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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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Section: Patch-based Super-resolutionmentioning

confidence: 99%

Section: Patch-based Super-resolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In the following, the operator is approximated by a 3D boxcar function as done in (Bai et al, 2004;Carmi et al, 2006;Manjon et al, 2010a;Manjon et al, 2010b;Rousseau, 2010).…”

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

et al. 2013

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“…In a recent work, Buades et al [5] shows that non-local means filtering gives state-of-the-art performance in structurepreserving image denoising. The strategy has also been applied to brain image labeling [6], image registration [8], and MR image super-resolution [9]. We employ nonlocal averaging for combining all matching patches that have been determined based on the distance measure as described in Section 2.2.…”

Section: Non-local Approachmentioning

confidence: 99%

Non-local Means Resolution Enhancement of Lung 4D-CT Data

Zhang

Yap

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Image resolution in 4D-CT is a crucial bottleneck that needs to be overcome for improved dose planning in radiotherapy for lung cancer. In this paper, we propose a novel patch-based algorithm to enhance the image quality of 4D-CT data. Our premise is that anatomical information missing in one phase can be recovered from complementary information embedded in other phases. We employ a patch-based mechanism to propagate information across phases for reconstruction of intermediate slices in the axial direction, where resolution is normally the lowest. Specifically, structurally-matching and spatially-nearby patches are combined for reconstruction of each patch. For greater sensitivity to anatomical nuances, we further employ a quad-tree technique to adaptively partition each slice of the image in each phase for more fine-grained refinement. Our evaluation based on a public 4D-CT lung data indicates that our algorithm gives very promising results with significantly enhanced image structures. Introduction4D-CT is becoming increasingly popular in lung cancer treatment for providing respiratory-related information that is essential for guiding radiation therapy effectively. However, due to the risk of radiation [1], only a limited number of CT segments are usually acquired, which often results in very low resolution along the inferior-superior direction. This low-resolution (LR) data are usually plagued with visible imaging artifacts such as vessel discontinuity and partial volume effect. More importantly, insufficient resolution further distorts the shape of a tumor. This distortion might finally interfere with optimal dose planning. Super-resolution (SR) reconstruction is an effective approach for improving image resolution. Classical SR methods can be divided into two major categories: interpolation-based and model-based. The main advantage of interpolation-based methods is their simplicity. However, blurred edges and undesirable artifacts are inevitable. Currently, more attention has been directed to model-based SR approach. The general assumption of model-based SR approaches is that the LR image is a degraded version

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Automated segmentation of medial temporal lobe subregions on in vivo T1‐weighted MRI in early stages of Alzheimer's disease

Xie

Wisse

Pluta

et al. 2019

Human Brain Mapping

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Medial temporal lobe (MTL) substructures are the earliest regions affected by neurofibrillary tangle pathology—and thus are promising biomarkers for Alzheimer’s disease (AD). However, automatic segmentation of the MTL using only T1-weighted (T1w) magnetic resonance imaging (MRI) is challenging due to the large anatomical variability of the MTL cortex and the confound of the dura mater, which is commonly segmented as gray matter by state-of-the-art algorithms because they have similar intensity in T1w MRI. To address these challenges, we developed a novel atlas set, consisting of 15 cognitively normal older adults and 14 patients with mild cognitive impairment with a label explicitly assigned to the dura, that can be used by the multiatlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS-T1]) for the segmentation of MTL subregions, including anterior/posterior hippocampus, entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36, and parahippocampal cortex on T1w MRI. Cross-validation experiments indicated good segmentation accuracy of ASHS-T1 and that the dura can be reliably separated from the cortex (6.5% mislabeled as gray matter). Conversely, FreeSurfer segmented majority of the dura mater (62.4%) as gray matter and the degree of dura mislabeling decreased with increasing disease severity. To evaluate its clinical utility, we applied the pipeline to T1w images of 663 ADNI subjects and significant volume/thickness loss is observed in BA35, ERC, and posterior hippocampus in early prodromal AD and all subregions at later stages. As such, the publicly available new atlas and ASHS-T1 could have important utility in the early diagnosis and monitoring of AD and enhancing brain-behavior studies of these regions.

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Non-local MRI upsampling

Cited by 238 publications

References 24 publications

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

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

Non-local Means Resolution Enhancement of Lung 4D-CT Data

Automated segmentation of medial temporal lobe subregions on in vivo T1‐weighted MRI in early stages of Alzheimer's disease

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