Positioning a patient accurately in treatment devices is crucial for radiological treatment, especially if accuracy vantages
of particle beam treatment are exploited. To avoid sub-millimeter misalignments, X-ray images acquired from within the
device are compared to a CT to compute respective alignment corrections. Unfortunately, deviations of the underlying
geometry model for the imaging system degrade the achievable accuracy. We propose an automatic calibration routine,
which bases on the geometry of a phantom and its automatic detection in digital radiographs acquired for various
geometric device settings during the calibration. The results from the registration of the phantom's X-ray projections and
its known geometry are used to update the model of the respective beamlines, which is used to compute the patient
alignment correction. The geometric calibration of a beamline takes all nine relevant degrees of freedom into account,
including detector translations in three directions, detector tilt by three axes and three possible translations for the X-ray
tube. Introducing a stochastic model for the calibration we are able to predict the patient alignment deviations resulting
from inaccuracies inherent to the phantom design and the calibration. Comparisons of the alignment results for a
treatment device without calibrated imaging systems and a calibrated device show that an accurate calibration can
enhance alignment accuracy.
To align patients in radiation devices in six degrees of freedom (DoF), image-guided approaches perform the task of correction computation for the patient position. Digital radiography (DR) images are compared to projections of a CT series to estimate misalignments. A problem is that digital reconstructed radiographs (DRRs) have to be created from the CT to be registered with the DRs. Depending on the X-ray tube energy, detector sensitivity and body part involved, DRRs and DRs may look very different and often cannot be registered. We present a method that reconstructs multi-spectral DRRs for different X-ray settings, which can be registered to real X-ray images. As short rendering times are crucial, multiple spectra of a DRR are generated in one ray-tracing process. We register our multi-spectral DRR with the DR and add a further DoF to find a best match not only for the translations and in-plane rotation, but also the best fitting spectral planes. The results are used to identify patient misalignments and show that higher reliability can be achieved compared to conventional approaches. Misalignments can be identified even if ineligible X-ray settings have been used. As our approach allows application of lower X-ray energies for DR creation, an additional benefit is the reduction of the delivered dose.
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