In radiotherapy, the respiratory motion of the patient in treatment position is determined from gated cone-beam CT images. This method consists in selecting projections according to a respiratory signal for the reconstruction of a CT image of each respiratory state. This selection can be based on the amplitude or the phase of the signal. The number of selected projections also depends on the width of the gating window. The present study compares different reconstructions using a dynamic digital phantom of the thorax and a known respiratory signal. We applied both regular and irregular motions to this phantom and reconstructed the exhale state using different widths. We evaluated these reconstructions with the signal-to-noise ratio, the contrast-to-noise ratio and a blur criterion. In the case of a regular motion, there was no difference between the amplitude and the phase. The signal quality was high, even for the smallest width, and the blur increased with width. In the case of an irregular motion, the difference was noticeable. Amplitude-based reconstructions suffered from severe artifacts with the smallest width because there were respiratory cycles for which no projection was selected. This drawback is overcome by increasing the width of the gating window. Phase-based reconstructions also allowed to avoid artifacts, whatever the width. But the blur was higher, even for the smallest width applied. These results suggest that the gating process must be adjusted in order to select at least one projection per respiratory cycle. Phase gating is a robust way to achieve this goal when respiration is irregular. Amplitude gating may be more effective in terms of blur, but the width must be carefully chosen to avoid severe artifacts. Finally, we observed the potential of dynamic reconstruction by using a motion model to deform different gated CT images toward a common reference and compute the weighted mean. The resulting CT image suffered less from artifacts than each gated CT image separately even if artifacts were still visible.
Breath holding (BH) allows to immobilize organs during radiotherapy treatment of lung cancer. Deformable registration methods applied on 3D Computerized Tomography (CT) scans acquired in BH can be used to evaluate the breath holding reproducibility. Resulting 3D vector fields could then be used to adapt internal margins for each patient. In this work we compare three non-rigid registration schemes with Gaussian, linear-elastic and Nagel-Enckelmann based regularizations. As we do not dispose of gold standard, we analyze vector fields by several operators (transitivity, symmetry, volume dilatation, Jacobian). Experiments were based on clinical data sets of two patients: one with normal lung behavior and second with lung discrepancies which lead to bad BH reproducibility. Results show that none of operators allows to clearly highlight the superiority of a method, except for convergence rapidity and Jacobian.
Conformal radiotherapy is a cancer treatment technique, that targets high-energy X-rays to tumors with minimal
exposure to surrounding healthy tissues. Irradiation ballistics is calculated based on an initial 3D Computerized
Tomography (CT) scan. At every treatment session, the random positioning of the patient, compared
to the reference position defined by the initial 3D CT scan, can generate treatment inaccuracies. Positioning
errors potentially predispose to dangerous exposure to healthy tissues as well as insufficient irradiation to the
tumor. A proposed solution would be the use of portal images generated by Electronic Portal Imaging Devices
(EPID). Portal images (PI) allow a comparison with reference images retained by physicians, namely Digitally
Reconstructed Radiographs (DRRs). At present, physicians must estimate patient positional errors by visual
inspection. However, this may be inaccurate and consumes time. The automation of this task has been the
subject of many researches. Unfortunately, the intensive use of DRRs and the high computing time required
have prevented real time implementation. We are currently investigating a new method for DRR generation that
calculates intermediate DRRs by 2D deformation of previously computed DRRs. We approach this investigation
with the use of a morphing-based technique named mesh warping.