Surrogate-driven deformable motion model for organ motion tracking in particle radiation therapy

Abstract

The aim of this study is the development and experimental testing of a tumor tracking method for particle radiation therapy, providing the daily respiratory dynamics of the patient's thoraco-abdominal anatomy as a function of an external surface surrogate combined with an a priori motion model. The proposed tracking approach is based on a patient-specific breathing motion model, estimated from the four-dimensional (4D) planning computed tomography (CT) through deformable image registration. The model is adapted to the interfraction baseline variations in the patient's anatomical configuration. The driving amplitude and phase parameters are obtained intrafractionally from a respiratory surrogate signal derived from the external surface displacement. The developed technique was assessed on a dataset of seven lung cancer patients, who underwent two repeated 4D CT scans. The first 4D CT was used to build the respiratory motion model, which was tested on the second scan. The geometric accuracy in localizing lung lesions, mediated over all breathing phases, ranged between 0.6 and 1.7 mm across all patients. Errors in tracking the surrounding organs at risk, such as lungs, trachea and esophagus, were lower than 1.3 mm on average. The median absolute variation in water equivalent path length (WEL) within the target volume did not exceed 1.9 mm-WEL for simulated particle beams. A significant improvement was achieved compared with error compensation based on standard rigid alignment. The present work can be regarded as a feasibility study for the potential extension of tumor tracking techniques in particle treatments. Differently from current tracking methods applied in conventional radiotherapy, the proposed approach allows for the dynamic localization of all anatomical structures scanned in the planning CT, thus providing complete information on density and WEL variations required for particle beam range adaptation.

Examination of a deformable motion model for respiratory movements and 4D dose calculations using different driving surrogates

“…The respiratory motion model, initially introduced by Vandemeulebroucke et al and further developed by Fassi et al , consists of a reference CT volume and a 4D transformation that maps every state of the planning 4DCT to the reference image. In this convention, x is the spatial coordinate and ϑ = [0, …, 1) is the respiratory phase.…”

“…The last method was previously described by Fassi et al and provides a three‐dimensional information consisting in the displacement of the thoraco‐abdominal surface. The patient surface was extracted from the abdominal part of the CT images as a mesh.…”

“…Motion models that are able to predict certain respiratory CT phases based on surrogate‐driven input parameters were previously investigated by McClelland et al, Fayad et al or Martin et al Another example for this type of motion model was initially introduced by Vandemeulebroucke et al and further developed by Fassi et al A feasibility study was carried out considering only a single driving surrogate as thoraco‐abdominal surface imaging and a small population of seven lung cancer patients who had two repeated time‐resolved 4DCTs at the same day or over a few days …”

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

“…Current clinical implementations of tracking in photon radiotherapy [Kilby et al, 2010, Keall et al, 2014 can not be directly used in particle therapy, since they only provide the position of single internal points. Fassi et al [Fassi et al, 2015] were able to account for inter-and intra-fractional variability of patient's anatomical configuration with a designated modeling technique [Fassi et al, 2014]. The measured median of water-equivalent path length in target was within 2 mm of a simulated one.…”

A Perceptual Depth Shape-based CRF Model for Deformable Surface Labeling

“…They rely on feature points tracked through a sequence to retrieve the deformed shape of a surface. In [5], a feature point-based deformable mesh registration method is applied to a medical image diagnostic system for noninvasive tumor tracking. A common assumption in such methods is to consider the deformations as being a linear combination of basis vectors [6] [7].…”