A Review of Low-Dose, Limited-Angle, and Sparse-View CT Reconstruction Models Based on Deep Learning
Bibliographic record
Abstract
Computed tomography (CT), as a noninvasive method first invented in the early 1970s, has become a crucial technique in medical imaging with a variety of applications including detections of cancer, cardiovascular, neurological disorders, and so on. However, conventional CT scans are inherited with the issue of a high radiation dose, which may raise health concerns against radiologists and patients. Thus, CT radiation dose is intentionally decreased in exchange for human well-being. However, a lower radiation dose causes the problem of producing a CT image with degraded quality compared with a normal dose. Furthermore, quick and small-scale CT scans are often required when young patients or dynamic organs are being inspected. Therefore, CT scans may fail to capture full-angular coverage or acquire a sufficient number of scans for the patients, thereby having limited-angle and sparse-view of CT images, which makes it hard for them to reflect the accurate features of the target object. To fix these issues while not raising the time and amount of dose of CT scans, low-dose computed tomography (LDCT), limited-angle computed tomography (LACT), and sparse-view computed tomography have emerged as promising solutions for CT image reconstruction. However, traditional approaches in implementing LDCT, LACT, and sparse-view CT may struggle with insufficient data or secondary artifacts. Therefore, it is a foreseeable trend for LDCT, LACT, and sparse-view CT to combine with deep learning so that less data is required to reconstruct CT images with fewer secondary artifacts and improved qualities. For instance, DDPNet model with global residual learning + UNet-structured network and DuDoUFNet with blocked residual learning + UNet-structured network for LDCT; DIOR with residual block + UNet-structured network and DOLCE with denoising diffusion probabilistic model (DDPM) for LACT; PINER with black-box deep model + physical-consistency optimization and DreamNet with residual block + UNet-structured network for sparse-view CT. This paper aims to introduce state-of-the-art deep models in LDCT, LACT, and sparse-view CT based on workflow and data analysis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".