On Scale Space Radon Transform, Properties and Application in CT Image Reconstruction
Bibliographic record
Abstract
In this paper, with the aim to improve the reconstructed image quality in Computed Tomography (CT) using Filtered Backprojection (FBP), we propose to model the X-ray beam with the Scale Space Radon Transform (SSRT) instead of the Radon Transform (RT), where the assumptions made on the physical dimensions of the CT system elements, such as the X-ray focal spot, the detector cell and the image voxels, reflect better the reality.After depicting the basic properties of SSRT and its inversion, the FBP algorithm is used to reconstruct the image from the SSRT sinogram, where the RT spectrum used in FBP is replaced by SSRT and the Gaussian kernel, expressed in their frequency domain.PSNR and SSIM, as quality measures, are used to compare RT and SSRT-based image reconstruction on Shepp-Logan head and anthropomorphic abdominal phantoms.While SSRT-FBP and RT-FBP have almost the same runtime, the experiments show that SSRT-FBP is better when the number of projections is reduced and CT data is corrupted by Poisson-Gaussian noise.The experiments show the outstanding performance of SSRT-based image reconstruction method, making it more appropriate for applications requiring low-dose radiation, such as medical X-ray CT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".