Towards Computational Fluoroscopy
For a number of image-guided clinical procedures, X-ray image guidance is indispensable in providing geometric information on objects of interest. However, several complex procedures, particularly in interventional cardiology and neuroradiology, and in radiotherapy, involve high dose rates and long treatment times. There is growing concern regarding the dose delivered during these procedures as the ionizing nature of X-rays leads to immediate and long term damaging effects. Imaging modalities involve controllable acquisition variables that dictate the dose delivered to patients and also affect image quality and hence the ability to localize regions of interest in an image with a certain degree of confidence. The opportunity for feedback exists in image guidance in driving these tradeoffs. This work introduces a framework for dynamically adapting imaging parameters using feedback of geometric performance. In this framework, the operator specifies a desired targeting precision; a state estimator tracks objects of interest in a 2D X-ray fluoroscopic image sequence and produces a probability density function (pdf) on the state of the object of interest. Moments drawn from this pdf (e.g. uncertainty) are provided to a controller which then modulates the imaging parameters to achieve the required targeting precision at minimal dose. This framework aims to: (i) automatically maintain the geometric objectives of the therapy whenever possible and report to the operator when these objectives cannot be achieved, (ii) optimize the system parameters by dynamically assigning them based on feedback of performance, and (iii) reduce the level of human intervention required to carry out the therapy.