Kinect-Based Correction of Overexposure Artifacts in Knee Imaging with C-Arm CT Systems

Objective. To demonstrate a novel approach of compensating overexposure artifacts in CT scans of the knees without attaching any supporting appliances to the patient. C-Arm CT systems offer the opportunity to perform weight-bearing knee scans on standing patients to diagnose diseases like osteoarthritis. However, one serious issue is overexposure of the detector in regions close to the patella, which can not be tackled with common techniques. Methods. A Kinect camera is used to algorithmically remove overexposure artifacts close to the knee surface. Overexposed near-surface knee regions are corrected by extrapolating the absorption values from more reliable projection data. To achieve this, we develop a cross-calibration procedure to transform surface points from the Kinect to CT voxel coordinates. Results. Artifacts at both knee phantoms are reduced significantly in the reconstructed data and a major part of the truncated regions is restored. Conclusion. The results emphasize the feasibility of the proposed approach. The accuracy of the cross-calibration procedure can be increased to further improve correction results. Significance. The correction method can be extended to a multi-Kinect setup for use in real-world scenarios. Using depth cameras does not require prior scans and offers the possibility of a temporally synchronized correction of overexposure artifacts. To achieve this, we develop a cross-calibration procedure to transform surface points from the Kinect to CT voxel coordinates.

[1]  M. Glas,et al.  Principles of Computerized Tomographic Imaging , 2000 .

[2]  Joachim Hornegger,et al.  Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering , 2013, Medical Imaging.

[3]  Andreas Maier,et al.  Analog non-linear transformation-based tone mapping for image enhancement in C-arm CT , 2016, 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).

[4]  Martin Spahn,et al.  X-ray detectors in medical imaging , 2013 .

[5]  Daniel Kolditz,et al.  Volume-of-interest (VOI) imaging in C-arm flat-detector CT for high image quality at reduced dose. , 2010, Medical physics.

[6]  D L Parker,et al.  Optimal short scan convolution reconstruction for fanbeam CT. , 1982, Medical physics.

[7]  D. Parker Optimal short scan convolution reconstruction for fan beam CT , 1982 .

[8]  Rebecca Fahrig,et al.  Effective one step-iterative fiducial marker-based compensation for involuntary motion in weight-bearing C-arm cone-beam CT scanning of knees , 2014, Medical Imaging.

[9]  S. Mukherji,et al.  Conebeam CT of the Head and Neck, Part 1: Physical Principles , 2009, American Journal of Neuroradiology.

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

[11]  P. Gevenois,et al.  Dose reduction in multidetector CT using attenuation-based online tube current modulation. , 2003, AJR. American journal of roentgenology.

[12]  Thomas Toth,et al.  The influence of patient centering on CT dose and image noise. , 2007, Medical physics.

[13]  A. Maier,et al.  Efficient 2D filtering for cone-beam VOI reconstruction , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[14]  Andreas Keil,et al.  Analysis of vertical and horizontal circular C-arm trajectories , 2011, Medical Imaging.

[15]  Xiaochuan Pan,et al.  Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography , 2014, Journal of medical imaging.

[16]  Rebecca Fahrig,et al.  Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. Part I. Numerical model-based optimization. , 2013, Medical physics.

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

[18]  Andreas Maier,et al.  Optimization-based Extrapolation for Truncation Correction , 2012 .

[19]  J. Hsieh,et al.  A novel reconstruction algorithm to extend the CT scan field-of-view. , 2004, Medical physics.

[20]  Kourosh Khoshelham,et al.  Accuracy analysis of kinect depth data , 2012 .

[21]  Jian Sun,et al.  Guided Image Filtering , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  J. Hornegger,et al.  Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA) , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[23]  K. Perez Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 2014 .

[24]  Daniel Kolditz,et al.  Volume-of-interest (VOI) imaging in C-arm flat-detector CT for high image quality at reduced dose. , 2010, Medical physics.

[25]  Andreas Maier,et al.  Region-of-interest reconstruction on medical C-arms with the ATRACT algorithm , 2012, Medical Imaging.

[26]  M. Kalra,et al.  Techniques and applications of automatic tube current modulation for CT. , 2004, Radiology.

[27]  Michael Knaup,et al.  Digitization and visibility issues in flat detector CT: A simulation study , 2012, 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC).

[28]  Sven Haase,et al.  RITK: The Range Imaging Toolkit - A Framework for 3-D Range Image Stream Processing , 2011, VMV.

[29]  Benno Heigl,et al.  Improving 3D image quality of x-ray C-arm imaging systems by using properly designed pose determination systems for calibrating the projection geometry , 2003, SPIE Medical Imaging.

[30]  Andreas K. Maier,et al.  Over-Exposure Correction in CT Using Optimization-Based Multiple Cylinder Fitting , 2015, Bildverarbeitung für die Medizin.

[31]  Rebecca Fahrig,et al.  Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment. , 2014, Medical physics.