PyTomography: A GPU-accelerated, Opensource Python Library For Image Reconstruction
Bibliographic record
Abstract
Fast fully-quantitative tomographic image generation opens up new possibilities in research and practice. For different tasks, numerous images may have to be generated, such as for large-scale studies, for dynamic imaging, and/or to select best reconstructions amongst many candidate techniques. As an example, optimal image reconstructions in theranostic imaging as applied to radiopharmaceutical therapies for improved assessments and personalizations remains an important frontier. We introduce PyTomography, a medical imaging tomographic reconstruction framework that generates quantitative Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) images. It is developed in Python and uses the GPUaccelerated functionality of PyTorch to efficiently perform the mathematical operations required for image reconstruction. While the software is straightforward and permits reconstruction from a variety of simulated and real data sources, such as the DICOM format, it also provides the necessary infrastructure and flexibility for the development of novel reconstruction techniques and algorithms. Development in an open-source software framework can enhance reproducibility across vendors and research groups, and may expedite corresponding implementation in a clinical setting. Our research validates PyTomography against vendor-specific reconstructions, explores the incorporation of novel prior functions in SPECT reconstruction, and tests the use of comprehensive resolution modeling in PET imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".