X-ray fluoroscopy spatio-temporal filtering with object detection

Abstract

One potential way to reduce patient and staff X-ray fluoroscopy dose is to reduce the quantum exposure to the detector and compensate the additional noise with digital filtering. A new filtering method, spatio-temporal filtering with object detection, is described that reduces noise while minimizing motion and spatial blur. As compared to some conventional motion-detection filtering schemes, this object-detection method incorporates additional a priori knowledge of image content; i.e. much of the motion occurs in isolated long thin objects (catheters, guide wires, etc.). We create object-likelihood images and use these to control spatial and recursive temporal filtering such as to reduce blurring the objects of interest. We use automatically computed receiver operating characteristic (ROC) curves to optimize the object-likelihood enhancement method and determine that oriented matched filter kernels with 4 orientations are appropriate. The matched filter kernels are simple projected cylinders. We demonstrate the method on several representative X-ray fluoroscopy sequences to which noise is added to simulate very low dose acquisitions. With processing, we find that noise variance is significantly reduced with slightly less noise reduction near moving objects. We estimate an effective exposure reduction greater than 80%