Supplementary Material (Part I) for "Learning-Based Approaches for Reconstructions with Inexact Operators in nanoCT Applications"
Creators: name: Lütjen, Tom
name: Schönfeld, Fabian
name: Oberacker, Alice
name: Leuschner, Johannes
name: Schmidt, Maximilian
name: Wald, Anne
name: Kluth, Tobias
Publisher: name: Zenodo
Year: 2023
Subjects: computed tomography
operator inexactness
nano-CT

description: Supplementary Material (Part I) for the paper Learning-Based Approaches for Reconstructions with Inexact Operators in nanoCT Applications. Source code is available or referenced at https://gitlab.informatik.uni-bremen.de/inn4ip/cond-inn4nanoct. Experiments are performed on Simulated nanoCT Datasets. In the following description, we assume the parallel-beam dataset and the fan-beam dataset to be extracted at path "parallel_beam/experiment#1/" and "fan_beam/experiment#1/", respectively. This record (together with Part II) has the following contents: Reconstructions from using two iterative methods (Dremel, RESESOP-Kaczmarz) taking into account operator inexactness. These methods are computationally expensive due to their iterative nature. Each zip file contains the reconstructions for the full dataset (training and test), unless its name ends with "_test_only.zip". See also the notes below on where to unpack the zip files. parallel_results_dremel.zip parallel_results_resesop.zip parallel_unperturbed_results_dremel_test_only.zip parallel_unperturbed_results_resesop_test_only.zip parallel_results_resesop-1.2.zip (placed in Part II) parallel_results_resesop-0.8.zip (placed in Part II) fan_results_dremel.zip fan_results_resesop.zip Trained network parameters trained-models.zip Network reconstruction images and metrics trained-models_images.zip trained-models_images_input_mixing.zip (placed in Part II) trained-models_metrics.zip <strong>Folder structure / unpacking zip files</strong> The reconstructions from the iterative methods (Dremel, RESESOP-Kaczmarz) should be placed in the respective dataset folders: Zip files starting with "parallel_" should be unpacked inside "parallel_beam_nano_ct_dataset/experiment#1/" and the zip files starting with "fan_" should be unpacked inside "fan_beam_nano_ct_dataset/experiment#1/". This will yield the following directory structure: <pre><code>parallel_beam_nano_ct_dataset/experiment#1/ ... &lt;-- original dataset folders results/ &lt;-- contains the RESESOP-Kaczmarz reconstructions results_dremel/ &lt;-- contains the Dremel reconstructions ... fan_beam_nano_ct_dataset/experiment#1/ ... &lt;-- original dataset folders results/ &lt;-- contains the RESESOP-Kaczmarz reconstructions results_dremel/ &lt;-- contains the Dremel reconstructions ... </code></pre> For the training or evaluation of the neural networks, the phantom and reconstruction data must be converted to .npy files, which can be done by executing the Python script "src/learned_reco/data.py" in https://gitlab.informatik.uni-bremen.de/inn4ip/cond-inn4nanoct. You may specify a custom --spath_out argument to the script; note that it should be separate from the dataset folders, because it will contain both parallel-beam and fan-beam reconstructions. In the following, we assume the argument --spath_out "data/". The trained neural network parameters "trained-models.zip" contains both models for parallel-beam data and models for fan-beam data, and hence should also be unpacked separately from the dataset folders, e.g. next to the "data/" folder, resulting in the following directory structure: <pre><code>data/ phantom/ recos/ trained-models/ cinn/ iunet/ unet/ </code></pre> The trained network models can then be evaluated using the test scripts, which will write the evaluation results next to the respective "checkpoints/" sub-directories under "trained-models/". Evaluation results (images and metrics) are also provided in this supplementary material: The files "trained-models_images.zip", "trained-models_images_input_mixing.zip" (placed in Part II for file size reasons), and "trained-models_metrics.zip" contain the reconstructions (and for the flow-based networks also the standard deviation images), and the PSNR and SSIM metrics of the reconstructions. These files use the same directory structure as "trained-models.zip" and may all be unpacked at the same location as "trained-models.zip" to place the results next to the checkpoints, just like evaluation using the test scripts would do.