Patient-specific scatter correction in clinical cone beam computed tomography imaging made possible by the combination of Monte Carlo simulations and a ray tracing algorithm

Abstract Purpose. Cone beam computed tomography (CBCT) image quality is limited by scattered photons. Monte Carlo (MC) simulations provide the ability of predicting the patient-specific scatter contamination in clinical CBCT imaging. Lengthy simulations prevent MC-based scatter correction from being...

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Veröffentlicht in:Acta oncologica Jg. 52; H. 7; S. 1477 - 1483
Hauptverfasser: Thing, Rune S., Bernchou, Uffe, Mainegra-Hing, Ernesto, Brink, Carsten
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Informa Healthcare 01.10.2013
Schlagworte:
Algorithms
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - radiotherapy
calibration
computed tomography scanner
Computer Simulation
Cone-Beam Computed Tomography
Humans
Image Processing, Computer-Assisted
image quality
imaging system
Monte Carlo Method
Pelvic Neoplasms - diagnostic imaging
Pelvic Neoplasms - radiotherapy
phantom
Phantoms, Imaging
radiation dose distribution
radiation scattering
Radiographic Image Enhancement
Radiotherapy, Image-Guided
Scattering, Radiation
simulation
Thoracic Neoplasms - diagnostic imaging
Thoracic Neoplasms - radiotherapy
ISSN:0284-186X, 1651-226X, 1651-226X
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Zusammenfassung:Abstract Purpose. Cone beam computed tomography (CBCT) image quality is limited by scattered photons. Monte Carlo (MC) simulations provide the ability of predicting the patient-specific scatter contamination in clinical CBCT imaging. Lengthy simulations prevent MC-based scatter correction from being fully implemented in a clinical setting. This study investigates the combination of using fast MC simulations to predict scatter distributions with a ray tracing algorithm to allow calibration between simulated and clinical CBCT images. Material and methods. An EGSnrc-based user code (egs_cbct), was used to perform MC simulations of an Elekta XVI CBCT imaging system. A 60keV x-ray source was used, and air kerma scored at the detector plane. Several variance reduction techniques (VRTs) were used to increase the scatter calculation efficiency. Three patient phantoms based on CT scans were simulated, namely a brain, a thorax and a pelvis scan. A ray tracing algorithm was used to calculate the detector signal due to primary photons. A total of 288 projections were simulated, one for each thread on the computer cluster used for the investigation. Results. Scatter distributions for the brain, thorax and pelvis scan were simulated within 2% statistical uncertainty in two hours per scan. Within the same time, the ray tracing algorithm provided the primary signal for each of the projections. Thus, all the data needed for MC-based scatter correction in clinical CBCT imaging was obtained within two hours per patient, using a full simulation of the clinical CBCT geometry. Conclusions. This study shows that use of MC-based scatter corrections in CBCT imaging has a great potential to improve CBCT image quality. By use of powerful VRTs to predict scatter distributions and a ray tracing algorithm to calculate the primary signal, it is possible to obtain the necessary data for patient specific MC scatter correction within two hours per patient.
Bibliographie:ObjectType-Article-1
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ISSN:0284-186X
1651-226X
1651-226X
DOI:10.3109/0284186X.2013.813641