Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT

Authors


Abstract

Purpose:

In image-guided radiation therapy (IGRT) valuable information for patient positioning, dose verification, and adaptive treatment planning is provided by an additional kV imaging unit. However, due to the limited gantry rotation speed during treatment the typical acquisition time is quite long. Tomographic images of the thorax suffer from motion blurring or, if a gated 4D reconstruction is performed, from significant streak artifacts. Our purpose is to provide a method that reliably estimates respiratory motion in presence of severe artifacts. The estimated motion vector fields are then used for motion-compensated image reconstruction to provide high quality respiratory-correlated 4D volumes for on-board cone-beam CT (CBCT) scans.

Methods:

The proposed motion estimation method consists of a model that explicitly addresses image artifacts because in presence of severe artifacts state-of-the-art registration methods tend to register artifacts rather than anatomy. Our artifact model, e.g., generates streak artifacts very similar to those included in the gated 4D CBCT images, but it does not include respiratory motion. In combination with a registration strategy, the model gives an error estimate that is used to compensate the corresponding errors of the motion vector fields that are estimated from the gated 4D CBCT images. The algorithm is tested in combination with a cyclic registration approach using temporal constraints and with a standard 3D–3D registration approach. A qualitative and quantitative evaluation of the motion-compensated results was performed using simulated rawdata created on basis of clinical CT data. Further evaluation includes patient data which were scanned with an on-board CBCT system.

Results:

The model-based motion estimation method is nearly insensitive to image artifacts of gated 4D reconstructions as they are caused by angular undersampling. The motion is accurately estimated and our motion-compensated image reconstruction algorithm can correct for it. Motion artifacts of 3D standard reconstruction are significantly reduced, while almost no new artifacts are introduced.

Conclusions:

Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.

Ancillary