Data for paper: "Data-driven image mechanics (D2IM): a deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images - Evaluation of bone mechanics"

Data used in the paper: "Data-driven image mechanics (D2IM): a deep learning approach to predict displacement and strain fields from undeformed X-ray tomography images - Evaluation of bone mechanics"by Peter Soar, Marco Palanca, Enrico Dall'Ara and Gianluca Tozzi<br>submitted to publication in Extreme Mechanical Letters, 2024<br>There are 2 parts of this directory:The main dataset 'D2IM_Data':<br>- 'input' comprises slices of XTC scans of porcine vertebra. There is a subfolder 'Removed' containing slices that were manually removed from the dataset due to not containing enough bone. The original 3D scans of the images can be found at: https://orda.shef.ac.uk/articles/dataset/Data_for_paper_MicroFE_models_of_porcine_vertebrae_with_induced_bone_focal_lesions_validation_of_predicted_displacements_with_Digital_Volume_Correlation_/16732441/1<br>- 'mask' is another input that was generated from these scan slices. <br>' 'Target' contains three subfolders of the displacements ('U','V','W'), used as the target/ground truth. These fields were obtained using the DIC/DVC software SPAM.<br>'Clinical' folder contains a few examples used for testing the sensitivity of the D2IM model to lower resolution input. Clinical images were collected at the CT Scan Department, Northern General Hospital, Sheffield.The 'Test Predictions' directory has three archives, each containing images showing the predicted displacements and displacement errors for the full set of test data. Each archive corresponds to the named displacement component u, v or w.A copy of the code for the D2IM prototype model can be found on GitHub at: https://github.com/PeterSoar/D2IM_PrototypeFor any questions the reader is encouraged to contact Dr Peter Soar ([e-mail]).