Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors

● We created a joint bilateral filter for energy-selective detectors with encouraging first results. ● The SNR was improved from 3.3 to 72.3, while a low rRMSE is preserved and only little cross-talk between the channels is introduced. Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors Michael Manhart1,2, Rebecca Fahrig3, Joachim Hornegger1,4, Arnd Doerfler2, Andreas Maier1,4 1 Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nuremberg, Germany 2 Department of Neuroradiology, Universitatsklinikum Erlangen, Germany 3 Department of Radiology, Stanford University, CA, USA 4 Erlangen Graduate School in Advanced Optical Technologies (SAOT), Friedrich-Alexander University Erlangen-Nuremberg, Germany

[1]  Michael F. Cohen,et al.  Digital photography with flash and no-flash image pairs , 2004, ACM Trans. Graph..

[2]  Gengsheng L Zeng,et al.  A filtered backprojection algorithm with ray-by-ray noise weighting. , 2013, Medical physics.

[3]  Armando Manduca,et al.  Adaptive modulation of bilateral filtering based on a practical noise model for streaking and noise reduction in multi-slice CT , 2010, Medical Imaging.

[4]  Cynthia M. McCollough,et al.  Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT. , 2009, Medical physics.

[5]  W. Kalender,et al.  Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT. , 2001, Medical physics.

[6]  Gengsheng Lawrence Zeng,et al.  Medical Image Reconstruction: A Conceptual Tutorial , 2010 .

[7]  Michael Balda,et al.  Quantitative Computed Tomography , 2020, Definitions.

[8]  T. Michel,et al.  Using the Medipix2 detector for photon counting computed tomography , 2005, IEEE Nuclear Science Symposium Conference Record, 2005.

[9]  Rainer Raupach,et al.  Adaptive iterative reconstruction , 2011, Medical Imaging.

[10]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[11]  M. Kachelriess,et al.  Dose minimization for material-selective CT with energy-selective detectors , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[12]  Jörg Weule,et al.  Non-Linear Gaussian Filters Performing Edge Preserving Diffusion , 1995, DAGM-Symposium.

[13]  Armando Manduca,et al.  Adaptive non-local means filtering based on local noise level for CT denoising , 2012, Medical Imaging.

[14]  Jean-Baptiste Thibault,et al.  A three-dimensional statistical approach to improved image quality for multislice helical CT. , 2007, Medical physics.

[15]  Rebecca Fahrig,et al.  Fast simulation of x-ray projections of spline-based surfaces using an append buffer , 2012, Physics in medicine and biology.

[16]  Christian Riess,et al.  CONRAD--a software framework for cone-beam imaging in radiology. , 2013, Medical physics.

[17]  Rebecca Fahrig,et al.  Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT. , 2011, Medical physics.