ArticleMedical physics2025
Prior-adapted progressive time-resolved CBCT reconstruction using a dynamic reconstruction and motion estimation method.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- S2V-DREME: a time-resolved slice-to-volume MR image reconstruction framework with dynamic reconstruction and motion estimation.Physics in medicine and biology · 2026Article
- Time-resolved PET imaging using a joint dynamic reconstruction and motion estimation framework (DREME-PET).Medical physics · 2026Article
- Nonstop gated cone-beam computed tomography for respiratory gating pancreatic cancer radiotherapy using dual-domain deep learning reconstruction.Physics and imaging in radiation oncology · 2026Article
- 'See' through the surface: surface-derived three-dimensional AI-driven real-time imaging solution for intra-treatment image guidance.Machine Learning. Health · 2026Article
- Article
- Time-resolved dynamic CBCT reconstruction using prior-model-free spatiotemporal Gaussian representation (PMF-STGR).Physics in medicine and biology · 2025Article
- Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
Abstract
backgroundCone-beam CT (CBCT) captures on-board volumetric anatomy for image guidance and treatment adaptation in radiotherapy. To compensate for respiration-induced anatomical motion, time-resolved CBCT is highly desired to capture the spatiotemporal anatomical variations but faces challenges in accuracy and efficiency due to substantial optimization needed in image reconstruction and motion modeling. PURPOSE: We proposed a fast time-resolved CBCT reconstruction framework, based on a dynamic reconstruction and motion estimation method with new reconstructions initialized and conditioned on prior reconstructions in an adaptive fashion (DREME-adapt). MATERIALS AND
methodsDREME-adapt reconstructs a time-resolved CBCT sequence from a fractional standard CBCT scan while simultaneously generating a machine learning-based motion model that allows single-projection-driven intra-treatment CBCT estimation and motion tracking. Via DREME-adapt, a virtual fraction is generated from a pre-treatment 4D-CT set of each patient for a clean, "cold-start" reconstruction. For subsequent fractions of the same patient, DREME-adapt uses pre-derived motion models and reference CBCTs as initializations to drive a "warm-start" reconstruction, based on a lower-cost refining strategy. Three strategies: DREME-cs which drops the "warm-start" component, DREME-adapt-vfx which uses a fixed initialization (virtual fraction's reconstruction results), and DREME-adapt-pro which initialize reconstructions through a progressive daisy chain scheme (virtual fraction for fraction 1, fraction 1 for fraction 2, and so on), were evaluated on a digital phantom study (7 motion/anatomical scenarios) and a patient study (seven patients).
resultsDREME-adapt allows fast and accurate time-resolved CBCT reconstruction. For the XCAT simulation study, DREME-adapt-pro achieves image reconstruction relative error of 0.14 ± 0.01 and tumor center-of-mass tracking error of 0.92 ± 0.62 mm (mean ± s.d.), compared to 0.15 ± 0.01 and 1.06 ± 0.73 mm for DREME-adapt-vfx, and 0.18 ± 0.01 and 1.96 ± 1.35 mm for DREME-cs. For the real-time motion inference test dataset of the patient study, DREME-adapt-pro localizes moving lung landmarks to a mean ± s.d. error of 2.21 ± 1.79 mm. In comparison, the corresponding values for DREME-adapt-vfx and DREME-cs were 2.53 ± 1.93 mm and 3.22 ± 2.88 mm, respectively. The DREME-adapt-pro training takes 11 min, only 15% of the original DREME algorithm.
conclusionsWith high efficiency and accuracy, DREME-adapt-pro allows on-board time-resolved CBCT reconstruction and enhances the clinical adoption potential of the DREME framework.
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.