3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

Abstract

3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we develop and perform initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and use these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparing to ground truth digital and physical phantom images. The performance of 4DCBCT- and 4DCT- based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms, and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.

Development of a four‐axis moving phantom for patient‐specific QA of surrogate signal‐based tracking IMRT

“…7,8 Of these, recent interest has focused on the DTT technique, which is able to dynamically reposition the radiation beam or the robotic couch in accordance with the position of the target. [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] DTT decreases internal uncertainties due to target motion, without the need for a prolonged treatment time or the burden of shallow breathing or breath-holding for patients. There are two approaches to achieving DTT: direct and indirect tracking methods.…”

Evaluating the four-dimensional cone beam computed tomography with varying gantry rotation speed

“…Recently, the time synchronized CBCT (4DCBCT) method has been introduced to manage respiration-induced target motion during treatment verification imaging. [22][23][24][25][26][27][28][29][30] In 4DCBCT imaging, two-dimensional (2D) projections acquired at different times and positions are sorted into several groups, according to their respiratory phases, and each phase group is then reconstructed independently to obtain a volumetric image corresponding to that specific phase. The respiratory wave required for retrospective 4DCBCT reconstructions is derived using either 2D projections of CBCT itself or an external surrogate system.…”

Automatic assessment of average diaphragm motion trajectory from 4DCT images through machine learning

“…High correlations (0.94–0.98 and 0.98 ± 0.02) have been reported between the diaphragm and tumor motion in lung (Cervino et al 2009) and liver patients (Yang et al 2014). Reports have shown that diaphragm motion can be used as a surrogate for tumor motion without implanted fiducials (Li et al 2009c, Lin et al 2009, Dhou et al 2015). In cine megavoltage electronic portal imaging during beam-on time, initial study has shown the feasibility of extracting volumetric treatment images based on 4DCT-based motion modeling (Mishra et al 2014).…”

“…In cone-beam CT (CBCT) imaging, projection images can be utilized by combining deformable image registration and principal component analysis (PCA) to estimate the tumor position with the diaphragm as the major anatomic landmark (Zhang et al 2007, Li et al 2010a, 2010b, Li et al 2011). In other CBCT studies, an automatic method was developed to detect the diaphragm motion (Siochi 2009, Chen and Siochi 2010, Dhou et al 2015). In 4DCT reconstruction, the diaphragm can be used as an internal surrogate for respiratory binning.…”

“…One widely-applied methodology is the PCA to encode the respiratory motion, where the space spanned by the leading eigenvectors is employed to represent the original data. In respiratory motion studies, PCA has been utilized to simplify the problem set in various approaches (Yan et al 2013, Zhang et al 2013, Mishra et al 2014, Wilms et al 2014, Dhou et al 2015). Another methodology is manifold learning: projecting a manifold in higher dimensional space to a lower dimensional space while preserving the local neighborhood (Wachinger et al 2012, Usman et al 2013).…”