Prior‐adapted progressive time‐resolved CBCT reconstruction using a dynamic reconstruction and motion estimation method

Abstract

Background
Cone‐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 methods
DREME‐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).


Results
DREME‐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.


Conclusions
With high efficiency and accuracy, DREME‐adapt‐pro allows on‐board time‐resolved CBCT reconstruction and enhances the clinical adoption potential of the DREME framework.