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7 results for “Parameter reconstruction”
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p>
SiEUGreen - Data for 'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'
<p>Dataset used in the scientific publication <a href="https://zenodo.org/record/4671852">'Deep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parameters'</a>. The data includes chemical contents of tomatoes that was measured, images and scripts used in the paper. The scripts here aim to predict tomato quality parameters, sugar content, acidity, sugar acid ratio and lycopene, of automatically segmented tomato through hyperspectral image reconstruction from single RGB image. The same data can also be found at the <a href="https://github.com/ZJiangsan/TomatoQualityPredictionOnAutomaticallySegmentedTomato">Github repository</a>. The data collection and scientific paper was produced by SiEUGreen partners at Norwegian Institute of Bioeconomy Research (NIBIO).</p>
Research data and example scripts for the paper "Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction"
<p><strong>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</strong></p> <p>This publication contains the research data and example scripts for the paper “Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction” [1]. The research data is found in the directory <code>research_data</code>, the example scripts are found in the directory <code>example_scripts</code>.</p> <p>The research data contains all necessary information to be able to reconstruct the figures and values given in the paper, as well as all result figures shown. Where possible, the directories contain the necessary scripts to recreate the results themselves, up to stochastic variations.</p> <p>The example scripts are intended to show how one can (i), perform a least-square type optimization of a model function (here we focus our efforts on the analytical model functions MGH17 and Gauss3, as described in the paper) using various methods (BTVO, LM, BO, L-BFGS-B, NM, including using derivative information when applicable), and (ii), perform Markov chain Monte Carlo (MCMC) sampling around the found maximum likelihood estimate (MLE) to estimate the uncertainties of the MLE parameter (both using a surrogate model of the actual model function, as well as using the actual model function directly).</p> <p> </p> <p><strong>Research data</strong></p> <p>Contained are directories for the experimental problem GIXRF, and the two analytical model functions MGH17 and Gauss3. What follows is a listing of directories and the contents:</p> <ul> <li><code>gauss3_optimization</code>: Optimization logs for the Gauss3 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_optimization</code>: Optimization logs for the MGH17 model function for BTVO, LM, BO, L-BFGS-B, NM (with derivatives when applicable), .npy files used for generating the plots, a <code>benchmark.py</code> file used for the generation of the data, as well as the plots shown in the paper.</li> <li><code>mgh17_mcmc_analytical</code>: Scripts for the creation of the plots (does not use an optimization log), as well as plots shown in the paper. This uses the model function directly to perform the MCMC sampling.</li> <li><code>mgh17_mcmc_surrogate</code>: Optimization log of the MGH17 function used for the creation of the MCMC plots, scripts for the creation of the plots (use the optimization log), as well as plots shown in the paper. This uses a surrogate model to perform the MCMC sampling.</li> <li><code>gixrf_optimization</code>: <code>benchmark.py</code> file to perform the optimization, the optimization logs for the various methods (BTVO, LM, BO, L-BFGS-B, NM), .npy files and scripts used for the creation of the plots, and the plots shown in the paper.</li> <li><code>gixrf_mcmc_supplement</code>: optimization log used for the creation of the plot, pickle file used for the creation of the plot, script to create the MCMC plot.</li> <li><code>gixrf_optimum_difference_supplement</code>: optimization logs of BTVO optimization of the GIXRF problem, scripts to create the difference/error plots shown for the GIXRF problem in the supplement, and the plots themselves.</li> </ul> <p><strong>Employed software for creating the research data</strong></p> <p>The software used in the creation is:</p> <ul> <li>JCMsuite Analysis and Optimization toolkit, development version, commit d55e99b (the closest commercial release is found in JCMsuite version 5.0.2)</li> <li>A list of Python packages installed (excerpt from <code>conda list</code>, name and version) <ul> <li>corner 2.1.0</li> <li>emcee 3.0.2</li> <li>jax 0.2.22</li> <li>jaxlib 0.1.72</li> <li>matplotlib 3.2.1</li> <li>numba 0.40.1</li> <li>numpy 1.18.1</li> <li>pandas 0.24.1</li> <li>python 3.7.11</li> <li>scikit-optimize 0.7.4</li> <li>scipy 1.7.1</li> <li>tikzplotlib 0.9.9</li> </ul> </li> <li>JCMsuite 4.6.3 for the evaluation of the experimental model</li> </ul> <p> </p> <p><strong>Example scripts</strong></p> <p>This directory contains a few sample files that show how parameter reconstructions can be performed using the JCMsuite analysis and optimization toolbox, with a particular focus on the Bayesian target-vector optimization method shown in the paper.</p> <p>It also contains example files that show how an uncertainty quantification can be performed using MCMC, both directly using a model function, as well as using a surrogate model of the model function.</p> <p>What follows is a listing of the contents of the directory:</p> <ul> <li><code>mcmc_mgh17_analytical.py</code>: performs a MCMC analysis of the MGH17 model function directly, without constructing a surrogate model. Uses <code>emcee</code>.</li> <li><code>mcmc_mgh17_surrogate.py</code>: performs a MCMC analysis of the MGH17 model function by constructing a surrogate model of the model function. Uses the JCMsuite analysis and optimization toolbox.</li> <li><code>opt_gauss3.py</code>: performs a parameter reconstruction of the Gauss3 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>opt_mgh17.py</code>: performs a parameter reconstruction of the MGH17 model function using various methods (BTVO, LM, BO, L-BFGS-B, NM, with derivatives when applicable).</li> <li><code>util/model_functions.py</code>: contains the MGH17 and Gauss3 model functions, their (automatic) derivatives, and objective functions used in the optimizations.</li> </ul> <p><strong>Requirements to execute the example scripts</strong></p> <p>These scripts have been developed and tested under Linux, Debian 10. We have tried to make sure that they would also work in a Windows environment, but can unfortunately give no guarantees for that.</p> <p>We mainly use Python to run the reconstructions. To execute the files, a few Python packages have to be installed. In addition to the usual scientific Python stack (NumPy, SciPy, matplotlib, pandas, etc.), the packages <code>jax</code> and <code>jaxlib</code> (for automatic differentiation of Python/NumPy functions), <code>emcee</code> and <code>corner</code> (for MCMC sampling and subsequent plotting of the results) have to be installed.</p> <p>This can be achieved for example using pip, e.g.</p> <pre><code>pip install -r requirements.txt</code></pre> <p>Additionally, JCMsuite has to be installed. For this you can visit [2] and download a free trial version.</p> <p>On Linux, the installation has to be added to the PATH, e.g. by adding the following to your <code>.bashrc</code> file:</p> <pre><code>export JCMROOT=/FULL/PATH/TO/BASE/DIRECTORY export PATH=$JCMROOT/bin:$PATH export PYTHONPATH=$JCMROOT/ThirdPartySupport/Python:$PYTHONPATH</code></pre> <p> </p> <p><strong>Bibliography</strong></p> <p>[1] <span>M. Plock</span>, <span> K. Andrle</span>, <span> S. Burger</span>, <span> P.-I. Schneider</span>, <span>Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction</span>. <em>Adv. Theory Simul.</em> <strong><span>5</span></strong>, 2200112 (2022).</p> <p>[2] https://jcmwave.com/</p>
Association Between Neuromuscular Parameters and Functional Assessment After ACL Reconstruction
ClinicalTrials.gov study NCT06524869. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Evaluating The Prebent Titanium Mesh for Zygomatico-Orbital Reconstruction in Twelve Patients Measuring Multiple Ophthalmologic Parameters, Aesthetic Results, Orbital Volume, Area of Bone Defect, Layo
ClinicalTrials.gov study NCT07103187. IPD Sharing: YES. Countries: 1. Publications: 0.
Psychological and Physiological Parameters for Return-to-sport After ACL Reconstruction
ClinicalTrials.gov study NCT07259083. IPD Sharing: NO. Countries: 1. Publications: 0.
Non-parametric reconstruction of the cosmological jerk parameter
<p>This .zip file contains a compilation of the jerk parameter data set generated on performing the non-parametric reconstruction, using the Gaussian Process method.</p> <p>The details can be found in the README file.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.