Feasibility of an image planning system for kilovoltage image-guided radiation therapy.

PURPOSE Image guidance has become a standard of care for many treatment scenarios in radiation therapy. This is most typically accomplished by use of kV x-ray devices mounted onto the linear accelerator (Linac) gantry that yield planar, fluoroscopic, and cone-beam computed tomography (CBCT) images. Image acquisition parameters are chosen via preset techniques that rely on broad categorizations in patient anatomy and imaging goal. However, the optimal imaging technique results in detectability of the features of interest while exposing the patient to minimum dose. Herein, the authors present an investigation into the feasibility of developing an image planning system (IPS) for radiotherapy. METHODS In this first phase, the authors focused on developing an algorithm to predict tissue contrast produced by a common radiotherapy planar imaging chain. Input parameters include a CT dataset and simulated planar imaging technique settings that include kV and mAs. Energy-specific attenuation through each voxel of the CT dataset was calculated in the algorithm to derive a net transmitted intensity. The response of the flat panel detector was integrated into the image simulation algorithm. Verification was conducted by comparing simulated and measured images using four phantoms. Comparisons were made in both high and low contrast settings, as well as changes in the geometric appearance due to image saturation. RESULTS The authors studied a lung nodule test object to assess the planning system's ability to predict object contrast and detectability. Verification demonstrated that the slope of the pixel intensities is similar, the presence of the nodule is evident, and image saturation at high mAs values is evident in both images. The appearance of the lung nodule is a function of the image detector saturation. The authors assessed the dimensions of the lung nodule in measured and simulated images. Good quantitative agreement affirmed the algorithm's predictive capabilities. The invariance of contrast with kVp and mAs prior to saturation was predicted, as well as the gradual loss of object detectability as saturation was approached. Small changes in soft tissue density were studied using a mammography step wedge phantom. Data were acquired at beam qualities of 80 and 120 kVp and over exposure values ranging from 0.04 to 500 mAs. The data showed good agreement in terms of the absolute value of pixel intensities predicted, as well as small variations across the step wedge pattern. The saturation pixel intensity was consistent between the two beam qualities studied. Boney tissue contrast was assessed using two abdominal phantoms. Measured and calculated values agree in terms of predicting the mAs value at which detector saturation, and subsequent loss of contrast occurs. The lack of variation in contrast over mAs values lower than 10 suggests that there is wide latitude for minimizing patient dose. CONCLUSIONS The authors developed and tested an algorithm that can be used to assist in kV imaging technique selection during localization for radiotherapy. Phantom testing demonstrated the algorithm's predictive accuracy for both low and high contrast imaging scenarios. Detector saturation with subsequent loss of imaging detail, both in terms of object size and contrast were accurately predicted by the algorithm.

[1]  Jean-Philippe Pignol,et al.  Correlation of lung tumor motion with external surrogate indicators of respiration. , 2004, International journal of radiation oncology, biology, physics.

[2]  X. Tao,et al.  Textile-structured human body surface biopotential signal acquisition electrode , 2011, 2011 4th International Congress on Image and Signal Processing.

[3]  Steve B. Jiang,et al.  The management of imaging dose during image-guided radiotherapy: report of the AAPM Task Group 75. , 2007, Medical physics.

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

[5]  Steve B. Jiang,et al.  Fast Monte Carlo simulation for patient-specific CT/CBCT imaging dose calculation , 2011, Physics in medicine and biology.

[6]  A V Lakshminarayanan,et al.  Automatic exposure control for a slot scanning full field digital mammography system. , 2005, Medical physics.

[7]  K. Doi,et al.  Radiation dose in diagnostic radiology: Monte Carlo simulation studies. , 1984, Medical physics.

[8]  J J DeMarco,et al.  A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient-specific models , 2003, Physics in medicine and biology.

[9]  J M Galvin,et al.  The use of digitally reconstructed radiographs for three-dimensional treatment planning and CT-simulation. , 1995, International journal of radiation oncology, biology, physics.

[10]  F Verhaegen,et al.  SpekCalc: a program to calculate photon spectra from tungsten anode x-ray tubes , 2009, Physics in medicine and biology.

[11]  M G Stabin,et al.  Monte Carlo MCNP-4B-based absorbed dose distribution estimates for patient-specific dosimetry. , 2001, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[12]  Joao Seco,et al.  Monte Carlo modelling of a-Si EPID response: the effect of spectral variations with field size and position. , 2006, Medical physics.

[13]  R. Mohan,et al.  Monte Carlo computation of dosimetric amorphous silicon electronic portal images. , 2004, Medical physics.

[14]  Mari Varjonen,et al.  Anatomically adaptable automatic exposure control (AEC) for amorphous selenium (a-Se) full field digital mammography (FFDM) system , 2006, SPIE Medical Imaging.

[15]  Masayuki Zuguchi,et al.  Total entrance skin dose: an effective indicator of maximum radiation dose to the skin during percutaneous coronary intervention. , 2007, AJR. American journal of roentgenology.

[16]  Jianxin Li,et al.  The Monte Carlo simulation of CT based on flat panel detector , 2011, 2011 4th International Congress on Image and Signal Processing.

[17]  T. Shope,et al.  Radiation-induced skin injuries from fluoroscopy. , 1996, Radiographics : a review publication of the Radiological Society of North America, Inc.

[18]  Willi A. Kalender,et al.  Validation of a Monte Carlo tool for patient-specific dose simulations in multi-slice computed tomography , 2008, European Radiology.

[19]  E H Baldini,et al.  A technique for optimization of digitally reconstructed radiographs of the chest in virtual simulation. , 2001, International journal of radiation oncology, biology, physics.

[20]  B. McParland,et al.  Entrance skin dose estimates derived from dose-area product measurements in interventional radiological procedures. , 1998, The British journal of radiology.

[21]  T Cullip,et al.  A portable software tool for computing digitally reconstructed radiographs. , 1995, International journal of radiation oncology, biology, physics.