SynthesisMedical physics2022
Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.
Synthesis in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 79 citations in OpenAlex.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Prospective multi-institutional study of library-based adaptive radiotherapy for cervical cancer: Evaluation of plan-of-the-day selection and population analysis.Journal of applied clinical medical physics · 2025Trial
- Quantitative stability of CT numbers in novel cone-beam CT: An intra-institution cross-scanner and longitudinal analysis.Journal of applied clinical medical physics · 2026Article
- Article
- Knowledge-distilled diffusion models for improving cone-beam CT image quality with meta-learning under imbalanced data.Medical physics · 2026Article
- Auto-segmentation and dose accuracy evaluation of synthetic computed tomography generated from pelvis cone-beam computed tomography images using a cycle-generative adversarial network with a novel loss function.Physics and imaging in radiation oncology · 2026Article
- Experimental evaluation of an intraoperative-imaging based workflow for electron beam radiotherapy of pancreatic cancer using in situ dosimetry.Journal of applied clinical medical physics · 2026Article
- Self-learning GAN based synthetic CT generation: unlocking CBCT-based adaptive radiotherapy.Frontiers in oncology · 2026Article
- A privacy-preserving federated learning framework for generalizable CBCT to synthetic CT translation in head and neck.Frontiers in digital health · 2026Article
- Evaluation of the accuracy of cone-beam CT-based dose calculation for target volumes and organs at risk in left-sided breast cancer radiotherapy.Frontiers in oncology · 2026Article
- Restoration of missing regions in limited field of view computer tomography using an image- and sinogram-based conditional generative adversarial network model.BJR artificial intelligence · 2026Article
- A Study of Why We Need to Reassess Full Reference Image Quality Assessment with Medical Images.Journal of imaging informatics in medicine · 2025Review
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- Few-shot CBCT-based synthetic CT generation with denoising diffusion probabilistic model.Medical physics · 2025Article
- A cone-beam computed tomography based workflow for online adaptive ultra-hypofractionated radiotherapy of prostate cancer.Physics and imaging in radiation oncology · 2025Article
- Super-resolution CBCT on a new generation flat panel imager of a C-arm gantry linear accelerator.Medical physics · 2025Article
- New Approaches in Radiotherapy.Cancers · 2025Review
- Cone-beam computed tomography (CBCT) image-quality improvement using a denoising diffusion probabilistic model conditioned by pseudo-CBCT of pelvic regions.Radiological physics and technology · 2025Article
- Deep learning-based dose prediction for low-energy electron beam superficial radiotherapy.Precision radiation oncology · 2025Article
- Artificial intelligence in four-dimensional imaging for motion management in radiation therapy.Artificial intelligence review · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up-to-date patient anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings-with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state-of-the-art methods utilized in radiation oncology.
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What Socratic 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 Socratic graph.