Adaptive spatio-temporal denoising of fluoroscopic X-ray sequences

Abstract Lowering the cumulative radiation dose to a patient undergoing fluoroscopic examination requires efficient denoising algorithms. We propose a method, which extensively utilizes temporal dimension in order to maximize denoising efficiency. A set of subsequent images is processed and two estimates of denoised images are calculated. One is based on a special implementation of an adaptive edge preserving wavelet transform, while the other is based on the statistical method intersection of confidence intervals (ICI) rule. Wavelet transform is thought to produce high quality denoised images and ICI estimate can be used to further improve denoising performance about object edges. The estimates are fused to produce the final denoised image. We show that the proposed method performs very well and do not suffer from blurring in clinically important parts of images. As a result, its application could allow for significant lowering of the fluoroscope single frame dose.

[1]  J R Williams,et al.  The interdependence of staff and patient doses in interventional radiology. , 1997, The British journal of radiology.

[2]  David L. Wilson,et al.  X-ray fluoroscopy spatio-temporal filtering with object detection , 1995, IEEE Trans. Medical Imaging.

[3]  Lena Costaridou,et al.  Intelligent Processing of Medical Images in the Wavelet Domain , 2007, Emerging Artificial Intelligence Applications in Computer Engineering.

[4]  Paul W. Fieguth,et al.  Wavelet Video Denoising with Regularized Multiresolution Motion Estimation , 2006, EURASIP J. Adv. Signal Process..

[5]  K N Jabri,et al.  Detection improvement in spatially filtered x-ray fluoroscopy image sequences. , 1999, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  Patrick Bouthemy,et al.  Multiresolution Parametric Estimation of Transparent Motions and Denoising of Fluoroscopic Images , 2005, MICCAI.

[7]  Patrick Bouthemy,et al.  Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences—Application to Noise Reduction in X-Ray Image Sequences , 2009, EURASIP J. Adv. Signal Process..

[8]  Rolf-Rainer Grigat,et al.  Real-Time Denoising of Medical X-Ray Image Sequences: Three Entirely Different Approaches , 2006, ICIAR.

[9]  E Vaño,et al.  Occupational radiation doses in interventional cardiology: a 15-year follow-up. , 2006, The British journal of radiology.

[10]  Vladimir Katkovnik,et al.  A new method for varying adaptive bandwidth selection , 1999, IEEE Trans. Signal Process..

[11]  L. Berlin,et al.  Radiation-induced skin injuries and fluoroscopy. , 2001, AJR. American journal of roentgenology.

[12]  Karen O. Egiazarian,et al.  An Approach to Adaptive Enhancement of Diagnostic X-Ray Images , 2003, EURASIP J. Adv. Signal Process..

[13]  Jean-Michel Morel,et al.  Denoising image sequences does not require motion estimation , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[14]  Aleksandra Pizurica,et al.  Wavelet-Domain Video Denoising Based on Reliability Measures , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Luigi Paura,et al.  Noise reduction in fluoroscopic image sequences for joint kinematics analysis , 2010 .

[16]  Brendan J. Frey,et al.  Video Epitomes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Andrew F. Laine,et al.  Adaptive spatial-temporal filtering applied to x-ray fluoroscopy angiography , 2005, SPIE Medical Imaging.

[18]  Patrick Bouthemy,et al.  Space-Time Adaptation for Patch-Based Image Sequence Restoration , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jelena Kovacevic,et al.  Wavelet families of increasing order in arbitrary dimensions , 2000, IEEE Trans. Image Process..

[20]  A. Pižurica,et al.  Combined wavelet domain and temporal video denoising , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[21]  Régis Vaillant,et al.  A device enhancing and denoising algorithm for X-ray cardiac fluoroscopy , 2008, 2008 19th International Conference on Pattern Recognition.

[22]  Lei Zhu,et al.  Noise reduction in low-dose x-ray fluoroscopy for image-guided radiation therapy. , 2009, International journal of radiation oncology, biology, physics.

[23]  Karen O. Egiazarian,et al.  Video denoising by sparse 3D transform-domain collaborative filtering , 2007, 2007 15th European Signal Processing Conference.

[24]  Damir Sersic,et al.  Edge-preserving adaptive wavelet denoising using ICI rule , 2008 .

[25]  R M Harrison,et al.  A comparison of two methods for measuring the signal to noise ratio on MR images , 1999, Physics in medicine and biology.

[26]  Jaakko Astola,et al.  Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule , 2002, Journal of Mathematical Imaging and Vision.

[27]  Yuan F. Zheng,et al.  Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising , 2006, IEEE Transactions on Circuits and Systems for Video Technology.