Quantifying the image quality and dose reduction of respiratory triggered 4D cone-beam computed tomography with patient-measured breathing

Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patient's respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations. Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique. A set containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 pairs of RT 4D CBCT and conventional 4D CBCT image sets from realistic simulations of a 4D CBCT system using a Rando phantom and the digital phantom, XCAT. Each of these image sets were compared to a ground truth dataset from which a mean absolute pixel difference (MAPD) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation was counted and was assumed as a surrogate for imaging dose. Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT, the average image quality was reduced by 7.6% (Rando study) and 11.1% (XCAT study). However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). The simulation studies have demonstrated that the RT 4D CBCT method can potentially offer a 53% saving in imaging dose on average compared to conventional 4D CBCT in simulation studies using a wide range of patient-measured breathing traces with a minimal impact on image quality.

[1]  Gig S Mageras,et al.  Investigation of gated cone-beam CT to reduce respiratory motion blurring. , 2013, Medical physics.

[2]  Tinsu Pan,et al.  Target-specific optimization of four-dimensional cone beam computed tomography. , 2012, Medical physics.

[3]  Steve B. Jiang,et al.  GPU-based iterative cone-beam CT reconstruction using tight frame regularization , 2010, Physics in medicine and biology.

[4]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.

[5]  Radhe Mohan,et al.  Audio-visual biofeedback for respiratory-gated radiotherapy: impact of audio instruction and audio-visual biofeedback on respiratory-gated radiotherapy. , 2006, International journal of radiation oncology, biology, physics.

[6]  P J Keall,et al.  The application of the sinusoidal model to lung cancer patient respiratory motion. , 2005, Medical physics.

[7]  Matthias Guckenberger,et al.  Accuracy and inter-observer variability of 3D versus 4D cone-beam CT based image-guidance in SBRT for lung tumors , 2012, Radiation oncology.

[8]  J. Galvin,et al.  Comparative dose evaluations between XVI and OBI cone beam CT systems using Gafchromic XRQA2 film and nanoDot optical stimulated luminescence dosimeters. , 2013, Medical physics.

[9]  S. Leng,et al.  High temporal resolution and streak-free four-dimensional cone-beam computed tomography , 2008, Physics in medicine and biology.

[10]  L. Xing,et al.  Optimizing 4D cone-beam CT acquisition protocol for external beam radiotherapy. , 2007, International journal of radiation oncology, biology, physics.

[11]  Geoffrey D. Hugo,et al.  Advances in 4D radiation therapy for managing respiration: part II - 4D treatment planning. , 2012, Zeitschrift fur medizinische Physik.

[12]  Steve B. Jiang,et al.  GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation. , 2010, Medical physics.

[13]  P. Shrimpton,et al.  The tissue-equivalence of the Alderson Rando anthropomorphic phantom for x-rays of diagnostic qualities. , 1981, Physics in medicine and biology.

[14]  Marcus Brehm,et al.  Artifact-resistant motion estimation with a patient-specific artifact model for motion-compensated cone-beam CT. , 2013, Medical physics.

[15]  Paul J Keall,et al.  Optimizing 4D cone beam computed tomography acquisition by varying the gantry velocity and projection time interval , 2013, Physics in medicine and biology.

[16]  Simon Rit,et al.  The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK) , 2014 .

[17]  Guang-Hong Chen,et al.  Extraction of tumor motion trajectories using PICCS-4DCBCT: a validation study. , 2011, Medical physics.

[18]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[19]  D Ruan,et al.  Real-time profiling of respiratory motion: baseline drift, frequency variation and fundamental pattern change , 2009, Physics in medicine and biology.

[20]  Fang-Fang Yin,et al.  A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections. , 2013, Medical physics.

[21]  Paul J Keall,et al.  Respiratory motion guided four dimensional cone beam computed tomography: encompassing irregular breathing. , 2014, Physics in medicine and biology.

[22]  E Yorke,et al.  Phase and amplitude binning for 4D-CT imaging , 2006, Physics in medicine and biology.

[23]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[24]  Jan-Jakob Sonke,et al.  Variability of four-dimensional computed tomography patient models. , 2008, International journal of radiation oncology, biology, physics.

[25]  Ehsan Samei,et al.  Kilovoltage cone-beam CT: comparative dose and image quality evaluations in partial and full-angle scan protocols. , 2010, Medical physics.

[26]  Simon Rit,et al.  Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam CT Reconstruction of the Thorax , 2009, IEEE Transactions on Medical Imaging.

[27]  Jing Wang,et al.  High-quality four-dimensional cone-beam CT by deforming prior images. , 2013, Physics in medicine and biology.

[28]  Marcus Brehm,et al.  Motion-compensated 4D cone-beam computed tomography , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[29]  Guang-Hong Chen,et al.  Streaking artifacts reduction in four-dimensional cone-beam computed tomography. , 2008, Medical physics.

[30]  Martin F Fast,et al.  Actively triggered 4d cone-beam CT acquisition. , 2013, Medical physics.

[31]  Steve B. Jiang,et al.  Internal-external correlation investigations of respiratory induced motion of lung tumors. , 2007, Medical physics.

[32]  Tinsu Pan,et al.  Slow gantry rotation acquisition technique for on-board four-dimensional digital tomosynthesis. , 2010, Medical physics.

[33]  P. Munro,et al.  Four-dimensional cone beam CT with adaptive gantry rotation and adaptive data sampling. , 2007, Medical physics.

[34]  Hideomi Yamashita,et al.  Verification of planning target volume settings in volumetric modulated arc therapy for stereotactic body radiation therapy by using in-treatment 4-dimensional cone beam computed tomography. , 2013, International journal of radiation oncology, biology, physics.

[35]  Paul J Keall,et al.  Respiratory triggered 4D cone-beam computed tomography: a novel method to reduce imaging dose. , 2013, Medical physics.