التفاصيل البيبلوغرافية
| العنوان: |
Dataset supporting: Distribution-informed and wavelength-flexible data-driven photoacoustic oximetry |
| المؤلفون: |
Gröhl, Janek, Yeung, Kylie, Gu, Kevin, Else, Thomas R, Golinska, Monika, Bunce, Ellie V, Hacker, Lina, Bohndiek, Sarah E |
| بيانات النشر: |
Department of Physics |
| سنة النشر: |
2024 |
| المجموعة: |
Apollo - University of Cambridge Repository |
| مصطلحات موضوعية: |
deep learning, image processing, oximetry, quantitative imaging, simulation |
| الوصف: |
This data set comprises simulated photoacoustic imaging data of various synthetic geometries (training_processed) and experimentally acquired data of two blood flow phantoms, of mice, and of human forearms (test_final). A complete description of these datasets is provided in the associated paper, which is available open access and on the preprint server arXiv. Furthermore, the data contain pre-trained models on each of the training datasets with various sparsity levels (models_LSTM) and a few examples of the raw simulation outputs produced by the SIMPA toolkit before preprocessing (sample_images). Unfortunately, it is not feasible to share all raw data due to its file size (>1.5 TB), but all results presented in this work are reproducible from the provided processed data. |
| نوع الوثيقة: |
dataset |
| وصف الملف: |
For full information and instructions for usage, please see the readme.txt file. All codes for data handling, training of the networks, application to the test data, and figure reproduction are available under the MIT license on the lab GitHub page: https://www.github.com/BohndiekLab/LearnedSpectralUnmixing; text/plain; application/msword; application/zip |
| اللغة: |
English |
| Relation: |
https://doi.org/10.48550/arXiv.2403.14863; https://doi.org/10.1117/1.JBO.29.S3.S33303; https://www.repository.cam.ac.uk/handle/1810/368711; https://www.repository.cam.ac.uk/handle/1810/364239; https://doi.org/10.17863/CAM.105987 |
| DOI: |
10.17863/CAM.105987 |
| الاتاحة: |
https://www.repository.cam.ac.uk/handle/1810/364239
https://doi.org/10.17863/CAM.105987 |
| Rights: |
Attribution 4.0 International (CC BY 4.0) ; https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.8A6EE497 |
| قاعدة البيانات: |
BASE |