Prostate MRI Super-Resolution using Discrete Residual Diffusion Model
Bibliographic record
Abstract
Prostate cancer (PCa) is one of the most common malignant tumors. High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to create a higher-resolution MRI. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems, called Prostate MRI Super-Resolution using Discrete Residual Diffusion Model (DR-DM). First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables. The backward process optimizes a variant of the variational lower bound, training diffusion models effectively address the mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging, thereby improving the fidelity of the reconstructed images. Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves image quality, resulting in greater clarity and improved diagnostic accuracy for patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".