aprict: v0.0.40
Authors/Creators
Description
Changes from v0.0.33:
- Add project logo.
- Tweaks to documentation; introduction of docs.aprict.au website.
- Simplify README.md; add trove classifiers to pyproject.toml to improve PyPI metadata.
- Small improvements to generation of OME-Zarr files and their viewing in neuroglancer.
- Modest improvement to GPU utilisation for FBP script, for 2D recons with fan-beam.
- Bugfix program crash when filter chunk size was too large.
See the README.md and for a full description of this software package. Here is a sample:
Description
aprict is a Python module for simulating transmission experiments in X-ray Computed Tomography, and for performing reconstructions from real and simulated data. In addition to the Python API, there is a commandline interface for some common operations.
Disclaimer
This software is provided with no warranty or guarantee of correctness. This is my research code sandbox, subject to breaking changes on a whim. This is not mature software. So, if you like it and wish to use it, then it is best to download a fixed version and use that. Updates are very likely to break things.
Hardware Requirements
This software requires at least one relatively recent CUDA-capable GPU. An RTX 3090 is recent enough. The relevant drivers will also need to be installed. You may need up-to-date drivers, depending on when the code was last updated.
It is strongly recommended to use high-speed mass storage, such as an NVMe drive. The software has not been tested on slower mass storage, and is not expected to perform well without it.
This software has been tested with the following GPUs:
- NVIDIA RTX 3090
- NVIDIA RTX PRO 6000 Blackwell
Features
-
Simulation of projection data for almost any flat-panel geometry, with almost any FORBILD phantom. Also supports voxelised volumes. The projection code computes exact line integrals (including through voxelised volumes composed of voxels of uniform attenuation coefficient), and can accept arbtirary phantoms defined in
*.phafiles using the FORBILD format. The accuracy can be further increased by averaging multiple line integrals for each pixel. The source and detector can even be placed inside the object, and projection will still be accurately computed from the source point to the detector pixels. -
Several built-in phantoms included for the user's convenience.
-
GBC and FBP reconstruction scripts supplied. These work efficiently with projection datasets of any size, e.g. a 200 GiB+ set is OK. (Requires high-speed mass storage for rapid reading/writing, e.g. an NVMe drive.)
-
Analytic reconstruction from:
- Fan-beam data with circular scan
- Cone-beam data with cylindrical scan
Files
murdock-grewar/aprict-v0.0.40.zip
Additional details
Related works
- Is supplement to
- Software: https://github.com/murdock-grewar/aprict/tree/v0.0.40 (URL)
Software
- Repository URL
- https://github.com/murdock-grewar/aprict
- Programming language
- Python , Cuda
- Development Status
- Wip