4 results on '"Triay Bagur, Alexandre"'
Search Results
2. Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.
- Author
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Triay Bagur, Alexandre, Aljabar, Paul, Ridgway, Gerard R., Brady, Michael, and Bulte, Daniel P.
- Subjects
TYPE 2 diabetes ,PANCREAS ,PANCREATIC tumors ,PANCREATIC diseases ,MAGNETIC resonance imaging ,BODY mass index - Abstract
Background: Quantitative imaging studies of the pancreas have often targeted the three main anatomical segments, head, body, and tail, using manual region of interest strategies to assess geographic heterogeneity. Existing automated analyses have implemented whole‐organ segmentation, providing overall quantification but failing to address spatial heterogeneity. Purpose: To develop and validate an automated method for pancreas segmentation into head, body, and tail subregions in abdominal MRI. Study Type: Retrospective. Subjects: One hundred and fifty nominally healthy subjects from UK Biobank (100 subjects for method development and 50 subjects for validation). A separate 390 UK Biobank triples of subjects including type 2 diabetes mellitus (T2DM) subjects and matched nondiabetics. Field strength/Sequence: A 1.5 T, three‐dimensional two‐point Dixon sequence (for segmentation and volume assessment) and a two‐dimensional axial multiecho gradient‐recalled echo sequence. Assessment: Pancreas segments were annotated by four raters on the validation cohort. Intrarater agreement and interrater agreement were reported using Dice overlap (Dice similarity coefficient [DSC]). A segmentation method based on template registration was developed and evaluated against annotations. Results on regional pancreatic fat assessment are also presented, by intersecting the three‐dimensional parts segmentation with one available proton density fat fraction (PDFF) image. Statistical Test: Wilcoxon signed rank test and Mann–Whitney U‐test for comparisons. DSC and volume differences for evaluation. A P value < 0.05 was considered statistically significant. Results: Good intrarater (DSC mean, head: 0.982, body: 0.940, tail: 0.961) agreement and interrater (DSC mean, head: 0.968, body: 0.905, tail: 0.943) agreement were observed. No differences (DSC, head: P = 0.4358, body: P = 0.0992, tail: P = 0.1080) were observed between the manual annotations and our method's segmentations (DSC mean, head: 0.965, body: 0.893, tail: 0.934). Pancreatic body PDFF was different between T2DM and nondiabetics matched by body mass index. Data Conclusion: The developed segmentation's performance was no different from manual annotations. Application on type 2 diabetes subjects showed potential for assessing pancreatic disease heterogeneity. Level of Evidence: 4 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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3. Intracranial EEG analysis during spatial memory tasks: the role of high frequency oscillations
- Author
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Triay Bagur, Alexandre
- Subjects
Electroencefalografia ,Memòria - Abstract
Treball de fi de grau en Biomèdica Tutor: Adrià Tauste Historically, declarative memory has been indirectly studied using spatial navigation experiments in rodents. Theories of hippocampal function expanded with the discovery of neural ensembles (place cells, grid cells) able to depict current spatial representations of the physical world. Place cell coupling to theta rhythm, or theta phase precession, first related neural firing to brain oscillations. Replay of past place cell sequences during short brain oscillations (sharp waves) established a basis for memory encoding after learning, but the involvement of grid cells in replay is unknown. We hereby present evidence that replay of rodent grid cells during the sharp-wave ripple complex in awake spatial navigation tasks is fairly low, contrarily to replay in place cells. Overcoming the technical limitations faced in this study will help to unveil part of the hippocampal contribution to memory processes. We also present a pilot study with an epilepsy patient, one of the first attempting detection of high frequency oscillations during cognitive processes. Future more task-design controlled research might help to identify behavioral correlates of high frequency oscillations and might possibly unveil their role in cognitive function and memory processes.
- Published
- 2017
4. Magnitude‐intrinsic water–fat ambiguity can be resolved with multipeak fat modeling and a multipoint search method.
- Author
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Triay Bagur, Alexandre, Hutton, Chloe, Irving, Benjamin, Gyngell, Michael L., Robson, Matthew D., and Brady, Michael
- Abstract
Purpose: To develop a postprocessing algorithm for multiecho chemical‐shift encoded water–fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0‐100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state‐of‐the‐art complex‐based methods, and to evaluate its robustness to artefacts in abdominal PDFF maps. Methods: We introduce MAGO (MAGnitude‐Only), a magnitude‐based reconstruction that embodies multipeak liver fat spectral modeling and multipoint optimization, and which is compatible with asymmetric echo acquisitions. MAGO is assessed first for accuracy and reproducibility on publicly available phantom data. Then, MAGO is applied to N = 178 UK Biobank cases, in which its liver PDFF measures are compared using Bland‐Altman analysis with those from a version of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) algorithm, LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK). Finally, MAGO is tested on a succession of high field challenging cases for which LMS IDEAL generated artefacts in the PDFF maps. Results: Phantom data showed accurate, reproducible MAGO PDFF values across manufacturers, field strengths, and acquisition protocols. Moreover, we report excellent agreement between MAGO and LMS IDEAL for 6‐echo, 1.5 tesla human acquisitions (bias = −0.02% PDFF, 95% confidence interval = ±0.13% PDFF). When tested on 12‐echo, 3 tesla cases from different manufacturers, MAGO was shown to be more robust to artefacts compared to LMS IDEAL. Conclusion: MAGO resolves the water–fat ambiguity over the entire fat fraction dynamic range without compromising accuracy, therefore enabling robust PDFF estimation where phase data is inaccessible or unreliable and complex‐based and hybrid methods fail. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
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