Minimizing dose during fluoroscopic tracking through geometric performance feedback

There is a growing concern regarding the dose delivered during x-ray fluoroscopy guided procedures, particularly in interventional cardiology and neuroradiology, and in real-time tumor tracking radiotherapy and radiosurgery. Many of these procedures involve long treatment times, and as such, there is cause for concern regarding the dose delivered and the associated radiation related risks. An insufficient dose, however, may convey less geometric information, which may lead to inaccuracy and imprecision in intervention placement. The purpose of this study is to investigate a method for achieving the required tracking uncertainty for a given interventional procedure using minimal dose.

A simple model is used to demonstrate that a relationship exists between imaging dose and tracking uncertainty. A feedback framework is introduced that exploits this relationship to modulate the tube current (and hence the dose) in order to maintain the required uncertainty for a given interventional procedure. This framework is evaluated in the context of a fiducial tracking problem associated with image-guided radiotherapy in the lung. A particle filter algorithm is used to robustly track the fiducial as it traverses through regions of high and low quantum noise. Published motion models are incorporated in a tracking test suite to evaluate the dose-localization performance trade-offs.

It is shown that using this framework, the entrance surface exposure can be reduced by up to 28.6% when feedback is employed to operate at a geometric tracking uncertainty of 0.3 mm.

The analysis reveals a potentially powerful technique for dynamic optimization of fluoroscopic imaging parameters to control the applied dose by exploiting the trade-off between tracking uncertainty and x-ray exposure per frame.