PC-POCS Sampler to reconstruct a sparse-view computer tomography image with both prior and measurements
Quan, Y.; Xue, Y.; Zhu, H.
Show abstract
Reconstructing medical images from partial measurements is a key inverse problem in computed tomography (CT), essential for reducing radiation exposure while maintaining diagnostic quality. Conventional supervised learning approaches depend on paired datasets generated under fixed acquisition settings, limiting their adaptability to unseen measurement processes. To overcome this, we propose a fully unsupervised framework based on score-based generative models. Our method learns the prior distribution of high-quality medical images and employs a physics-informed sampling strategy to reconstruct images consistent with both the learned prior and observed measurements. Experiments on multiple CT reconstruction tasks show that our approach achieves comparable or superior performance to existing methods while generalizing robustly across varying measurement conditions. The code is available.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling 94%
- Necessity and Impact of Specialization of Large Foundation Model for Medical Segmentation Tasks 93%
- SCU-Net: A deep learning method for segmentation and quantification of breast arterial calcifications on mammograms 92%
Similar papers in this journal
Similar papers in this journal
- The Effect of Image Resolution on Automated Classification of Chest X-rays 94%
- Fiberscopic Pattern Removal for Optimal Coverage in 3D Bladder Reconstructions of Fiberscope Cystoscopy Videos 93%
- A 3D CNN Classification Model for Accurate Diagnosis of Coronavirus Disease 2019 using Computed Tomography Images 92%
Similar papers in this journal
- Probabilistic Brain MR Image Transformation Using Generative Models 97%
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 95%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.