ArticleIEEE transactions on medical imaging2018
Intelligent Parameter Tuning in Optimization-Based Iterative CT Reconstruction via Deep Reinforcement Learning.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed.
- The role of diffusion-weighted MRI in biological image-guided radiation therapy: a roadmap.Physics in medicine and biology · 2026Review
- Prior-adapted progressive time-resolved CBCT reconstruction using a dynamic reconstruction and motion estimation method.Medical physics · 2025Article
- Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction.Sensors (Basel, Switzerland) · 2023Article
- Simultaneous spatial and temporal regularization in low-dose dynamic contrast-enhanced CT cerebral perfusion studies.Journal of applied clinical medical physics · 2023Article
- Deep-Learning-Based Ultrasound Sound-Speed Tomography Reconstruction with Tikhonov Pseudo-Inverse Priori.Ultrasound in medicine & biology · 2022Article
- Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency.Physics in medicine and biology · 2022Review
- Convex optimization algorithms in medical image reconstruction-in the age of AI.Physics in medicine and biology · 2022Review
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Patient-specific hyperparameter learning for optimization-based CT image reconstruction.Physics in medicine and biology · 2021Article
- A prior image constraint robust principal component analysis reconstruction method for sparse segmental multi-energy computed tomography.Quantitative imaging in medicine and surgery · 2021Article
- Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation.Sensors (Basel, Switzerland) · 2021Article
- SPECTnet: a deep learning neural network for SPECT image reconstruction.Annals of translational medicine · 2021Article
- Improving efficiency of training a virtual treatment planner network via knowledge-guided deep reinforcement learning for intelligent automatic treatment planning of radiotherapy.Medical physics · 2021Article
- Artificial intelligence and machine learning for medical imaging: A technology review.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography.Sensors (Basel, Switzerland) · 2021Article
- Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging.Journal of medical systems · 2021Article
- Review and Prospect: Artificial Intelligence in Advanced Medical Imaging.Frontiers in radiology · 2021Review
- Microwave Imaging by Deep Learning Network: Feasibility and Training Method.IEEE transactions on antennas and propagation · 2020Article
- Operating a treatment planning system using a deep-reinforcement learning-based virtual treatment planner for prostate cancer intensity-modulated radiation therapy treatment planning.Medical physics · 2020Article
- Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state-of-art applications.Medical physics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
A number of image-processing problems can be formulated as optimization problems. The objective function typically contains several terms specifically designed for different purposes. Parameters in front of these terms are used to control the relative importance among them. It is of critical importance to adjust these parameters, as quality of the solution depends on their values. Tuning parameters are a relatively straight forward task for a human, as one can intuitively determine the direction of parameter adjustment based on the solution quality. Yet manual parameter tuning is not only tedious in many cases, but also becomes impractical when a number of parameters exist in a problem. Aiming at solving this problem, this paper proposes an approach that employs deep reinforcement learning to train a system that can automatically adjust parameters in a human-like manner. We demonstrate our idea in an example problem of optimization-based iterative computed tomography (CT) reconstruction with a pixel-wise total-variation regularization term. We set up a parameter-tuning policy network (PTPN), which maps a CT image patch to an output that specifies the direction and amplitude by which the parameter at the patch center is adjusted. We train the PTPN via an end-to-end reinforcement learning procedure. We demonstrate that under the guidance of the trained PTPN, reconstructed CT images attain quality similar or better than those reconstructed with manually tuned parameters.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.