Figure 2 shows coronal and sagittal slices of a 3-D CBCT scan, as well as for the peak-exhale the 4-D CBCT scan of a lung cancer patient. The 4-D scans was reconstructed using about 75 projection images per breathing phase, on average corresponding to a 2.7° gantry angle increment per projection. Visual examination shows that the blurring of the moving objects is reduced considerably in the 4-D data such that the shape of the moving structures can be identified more easily and accurately. Furthermore, the 4-D data set provides information on the trajectory of these structures, absent in the 3-D data. Note, however, that 4-D CBCT imaging, similar to 4-D CT imaging, relies on a regular breathing pattern, while irregular breathing induces artefacts in 4-D reconstructions.

Breathing Signal Extraction

Any type of respiratory-correlated imaging needs a breathing signal to correlate the acquired data with the respiratory motion. Classically, a variety of external respiratory monitoring systems are used for this purpose, such as infrared reflecting makers, spirometry or stretching belts. A CBCT scanner acquires a series of 2-D projection data, showing the internal anatomy, including the position of moving structures, as a function of time. The breathing signal can be automatically obtained by tracking the position of the diaphragm from this series of projection data,9 eliminating the need for an additional respiratory monitor system. In short, this method enhances diaphragm-like features in the individual X-ray images, projects these features on the cranio-caudal axis, combines all successive 1-D projections to a 2-D image (see Figure 3) and extracts from this image the region with the most temporal variation. Finally, each 1-D signal is aligned to the next, resulting in a sequence of displacements which represents the respiratory signal. This method relies on the fact that frame-by-frame changes in the cranio-caudal direction due to respiratory motion are considerably faster than changes due to gantry rotation.

Assessment of Geometrical Uncertainties

Given the ability to acquire a 4-D patient model just prior to treatment on the treatment machine, deviations of the target relative to the planning position can be easily assessed. Figure 4 shows an example of a 4-D CBCT scan showing the peak-exhale, mid-inhale and peak-inhale phase. Clearly, the time-averaged mean position in the CBCT scan does not correspond with position of the overlaid gross tumour volume (GTV) contour. The impact of uncertainties in the position of the mean position or baseline tend to dominate the uncertainties of the intra-fractional breathing motion. Therefore, the author and colleagues developed an infrastructure to measure and correct the baseline variations of the target over the course of treatment. In order to register the CBCT scan, the planning CT, the position of the isocentre and the delineated structures are imported as a reference through Digital Imaging and Communications in Medicine (DICOM)-RT. By registering a region of interest (expansion of the delineated GTV) defined in the planning CT with each phase of the 4-D-CBCT (about 30s execution time), the 3-D trajectory information of the target is obtained. Given the tumour position in each breathing phase, the peak-to-peak amplitude and mean tumour position are validated.

Corrections to accommodate for geometrical errors are currently limited to a single shift of the treatment couch, re-aligning the patient and/or the target relative to the treatment beam. Therefore, two correction protocols have been developed to correct for deviations of the mean tumour position relative to the planned position first an offline shrinking action level (SAL) correction protocol for conventional fractionation schemes limiting corrections of the target relative to the bony anatomy to the cranio-caudal direction; and second an online correction protocol for hypo-fractionated RT (3 x 20Gy) correcting the cranio-caudal and anteriorposterior deviations with an action level of 1mm.

Note that any correction to account for organ motion needs to be validated to check its impact on the dose constraints of the organs at risk.

Conclusions and Discussion

4-D CT permits generation of temporally varying (‘4-D’) model of respiratory motion in patients from a single session of scanning. Even a 4-D CT scan, however, is just a snap shot movie loop of the patients respiratory cycle and needs to be validated over the course of treatment delivery. 4-D CBCT allows the validation of this model with the patient in the treatment position immediately before treatment. Correction strategies to account for discrepancies found between treatment and planning are currently limited to simple couch shifts. Future developments are focused on the development of more advanced correction strategies taking dosimetric consideration into account.