| 1. |
- Gu, Xuan, 1988-, et al.
(författare)
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Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
- 2019
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media S.A.. - 1662-5196. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.
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| 2. |
- Krishnan, Palani Thanaraj, et al.
(författare)
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Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer
- 2024
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers. - 1662-5196. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans.Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification.Results: Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986.Conclusion: RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.
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| 3. |
- Gu, Xuan, 1988-, et al.
(författare)
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Evaluation of Six Phase Encoding Based Susceptibility Distortion Correction Methods for Diffusion MRI
- 2019
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media S.A.. - 1662-5196. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Susceptibility distortions impact diffusion MRI data analysis and is typically corrected during preprocessing. Correction strategies involve three classes of methods: registration to a structural image, the use of a fieldmap, or the use of images acquired with opposing phase encoding directions. It has been demonstrated that phase encoding based methods outperform the other two classes, but unfortunately, the choice of which phase encoding based method to use is still an open question due to the absence of any systematic comparisons.Methods: In this paper we quantitatively evaluated six popular phase encoding based methods for correcting susceptibility distortions in diffusion MRI data. We employed a framework that allows for the simulation of realistic diffusion MRI data with susceptibility distortions. We evaluated the ability for methods to correct distortions by comparing the corrected data with the ground truth. Four diffusion tensor metrics (FA, MD, eigenvalues and eigenvectors) were calculated from the corrected data and compared with the ground truth. We also validated two popular indirect metrics using both simulated data and real data. The two indirect metrics are the difference between the corrected LR and AP data, and the FA standard deviation over the corrected LR, RL, AP, and PA data.Results: We found that DR-BUDDI and TOPUP offered the most accurate and robust correction compared to the other four methods using both direct and indirect evaluation metrics. EPIC and HySCO performed well in correcting b0 images but produced poor corrections for diffusion weighted volumes, and also they produced large errors for the four diffusion tensor metrics. We also demonstrate that the indirect metric (the difference between corrected LR and AP data) gives a different ordering of correction quality than the direct metric.Conclusion: We suggest researchers to use DR-BUDDI or TOPUP for susceptibility distortion correction. The two indirect metrics (the difference between corrected LR and AP data, and the FA standard deviation) should be interpreted together as a measure of distortion correction quality. The performance ranking of the various tools inferred from direct and indirect metrics differs slightly. However, across all tools, the results of direct and indirect metrics are highly correlated indicating that the analysis of indirect metrics may provide a good proxy of the performance of a correction tool if assessment using direct metrics is not feasible.
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| 4. |
- Kunkel, Susanne, et al.
(författare)
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The NEST Dry-Run Mode : Efficient Dynamic Analysis of Neuronal Network Simulation Code
- 2017
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Media SA. - 1662-5196. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- NEST is a simulator for spiking neuronal networks that commits to a general purpose approach: It allows for high flexibility in the design of network models, and its applications range from small-scale simulations on laptops to brain-scale simulations on supercomputers. Hence, developers need to test their code for various use cases and ensure that changes to code do not impair scalability. However, running a full set of benchmarks on a supercomputer takes up precious compute-time resources and can entail long queuing times. Here, we present the NEST dry-run mode, which enables comprehensive dynamic code analysis without requiring access to high-performance computing facilities. A dry-run simulation is carried out by a single process, which performs all simulation steps except communication as if it was part of a parallel environment with many processes. We show that measurements of memory usage and runtime of neuronal network simulations closely match the corresponding dry-run data. Furthermore, we demonstrate the successful application of the dry-run mode in the areas of profiling and performance modeling.
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| 5. |
- Nazem, Ali, et al.
(författare)
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Parallel implementation of a biologically inspired model of figure-ground segregation : Application to real-time data using MUSIC
- 2011
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Ingår i: Frontiers in Neuroinformatics. - : Frontiers Research Foundation. - 1662-5196.
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Tidskriftsartikel (refereegranskat)abstract
- MUSIC, the multi-simulation coordinator, supports communication between neuronal-network simulators, or other (parallel) applications, running in a cluster super-computer. Here, we have developed a class library that interfaces between MUSIC-enabled software and applications running on computers outside of the cluster. Specifically, we have used this component to interface the cameras of a robotic head to a neuronal-network simulation running on a Blue Gene/L supercomputer. Additionally, we have developed a parallel implementation of a model for figure ground segregation based on neuronal activity in the Macaque visual cortex. The interface enables the figure ground segregation application to receive real-world images in real-time from the robot. Moreover, it enables the robot to be controlled by the neuronal network.
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