Deep learning reduced combined truncation and metal artifacts in half-detector CBCT
HD-TMAR improved metal-region PSNR from 25.5 to 35.5 dB in simulated half-detector CBCT while preserving dental anatomy.
Tag
cbct
Expiratory-phase CBCT reduced respiratory blurring during pancreatic SBRT
Repeated end-expiratory breath-hold CBCT achieved boundary sharpness statistically equivalent to daily CT in 10 pancreatic SBRT patients.
First on-treatment CBCT best predicted lung radiotherapy response in a small cohort
Adding the first treatment CBCT improved response and survival prediction, while additional scans progressively reduced classification performance.
Early CBCT shifts failed to predict dosimetric compromise in head and neck VMAT
Early three-dimensional CBCT shifts performed no better than chance for identifying patients with at least 3% PTV coverage loss.
HyperSight protocol selection substantially changed CBCT organ dose
Low Dose HyperSight protocols reduced exposure by 55%, while Slow and Large protocols increased imaging dose and accentuated secondary-risk estimates.
Deep learning reconstructed one-minute gated CBCT for pancreatic radiotherapy
Deep-learning reconstruction produced pancreatic gated cone-beam computed tomography comparable with conventional imaging while reducing estimated acquisition time and imaging dose.
Fraction 15 was optimal for one-time offline replanning in head and neck proton therapy
A single offline replan at fraction 15 captured most achievable dosimetric benefit in simulated head and neck proton therapy.
Posterior tumour drift threatens lung stereotactic ablative radiotherapy target coverage
In free-breathing lung stereotactic ablative radiotherapy, posterior drift exceeded the 4-millimetre margin in 15.7% of patients within 20 minutes.
Deep learning synthetic computed tomography supported head-and-neck adaptive radiotherapy dose recalculation
Synthetic computed tomography from cone-beam computed tomography achieved a 98.7% gamma pass rate for head-and-neck dose recalculation.
Physics-constrained network improved cone-beam computed tomography reconstruction from orthogonal pelvic radiographs
A physics-constrained network reconstructed pelvic cone-beam computed tomography from two simulated radiographs with substantially lower error than generative baselines.
Dual-scan conformal cone-beam computed tomography improves target-region image quality
Dynamic collimation improved target-region contrast and signal-to-noise ratio while preserving full-field information without increasing the total photon budget.