VOXSI: A voxelized single- and dual-energy CT scenario generator for quantitative imaging

Heyden, Brent van der; Schyns, Lotte E J R; Podesta, Mark; Vaniqui, Ana; Almeida, Isabel P.; Landry, Guillaume

doi:10.1016/j.phro.2018.05.004

Cited by 12 publications

(5 citation statements)

References 47 publications

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“…ImaSim analytically simulates fan and cone beam CT through raytracing using vectorized phantoms. 17 VOXSI simulates kV fan beam CT using analytical raytracing with voxelized phantoms 18 and shows agreement with experimental images in terms of image contrast. DukeSim simulates kV fan beam CT with voxelized phantoms through a combination of analytical raytracing and GPU MC and demonstrates agreement between experimental and simulated images in terms of image contrast, noise magnitude, noise texture, and spatial resolution.…”

Section: Introductionmentioning

confidence: 56%

fastCAT: Fast cone beam CT (CBCT) simulation

O’Connell

Bazalova‐Carter

2021

Medical Physics

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Purpose To develop fastCAT, a fast cone‐beam computed tomography (CBCT) simulator. fastCAT uses pre‐calculated Monte Carlo (MC) CBCT phantom‐specific scatter and detector response functions to reduce simulation time for megavoltage (MV) and kilovoltage (kV) CBCT imaging. Methods Pre‐calculated x‐ray beam energy spectra, detector optical spread functions and energy deposition, and phantom scatter kernels are combined with GPU raytracing to produce CBCT volumes. MV x‐ray beam spectra are simulated with EGSnrc for 2.5‐ and 6 MeV electron beams incident on a variety of target materials and kV x‐ray beam spectra are calculated analytically for an x‐ray tube with a tungsten anode. Detectors were modeled in Geant4 extended by Topas and included optical transport in the scintillators. Two MV detectors were modeled—a standard Varian AS1200 GOS detector and a novel CWO high detective quantum efficiency detector. A kV CsI detector was also modeled. Energy‐dependent scatter kernels were created in Topas for two 16 cm diameter phantoms: A Catphan 515 contrast phantom and an anthropomorphic head phantom. The Catphan phantom contained inserts of 1–5 mm in diameter of six different tissue types: brain, deflated lung, compact and cortical bone, adipose, and B‐100. Results fastCAT simulations retain high fidelity to measurements and MC simulations: MTF curves were within 3.5% and 1.2% of measured values for the CWO and GOS detectors, respectively. HU values and CNR in a fastCAT Catphan 515 simulation were seen to be within 95% confidence intervals of an equivalent MC simulation for all of the tissues with root mean squared errors less than 16 HU and 1.6 in HU values and CNR comparisons, respectively. The anthropomorphic head phantom CWO detector CBCT image resulted in much higher tissue contrast and lower noise compared to the GOS detector CBCT image. A fastCAT simulation of the Catphan 515 module with an image size of 1024 × 1024 × 10 voxels took 61 s on a GPU while the equivalent Topas MC was estimated to take more than 0.3 CPU years. Conclusions We present an open source fast CBCT simulation with high fidelity to MC simulations. The fastCAT python package can be found at https://github.com/jerichooconnell/fastCAT.git.

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Section: Introductionmentioning

confidence: 56%

fastCAT: Fast cone beam CT (CBCT) simulation

O’Connell

Bazalova‐Carter

2021

Medical Physics

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“…This HU-MD conversion is already performed routinely for accurate dose calculations for kV X-rays, and using it for automatic contouring therefore does not add additional workload. Moreover, if a measured curve for a specific imaging energy is not available, one can be calculated from the knowledge of the X-ray spectrum (which can be derived from the imaging energy and filtration used (Poludniowski et al 2009, van der Heyden et al 2018) and the material characteristics of the phantom. Although a measured calibration curve is more accurate, a calculated curve may be sufficient for the purpose of automatic contouring.…”

Section: Discussionmentioning

confidence: 99%

Automatic contouring of normal tissues with deep learning for preclinical radiation studies

et al. 2022

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Objective: Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (μCBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies. Approach: A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of the μCBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD95p), and center of mass displacement (∆CoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed – 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout (MCD) uncertainty was performed for delineation of the heart. Main results: The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD < 0.25 mm, HD95p < 1 mm and ∆CoM < 0.5 mm. The qualitative assessment showed very few contours needed manual adaptations (40 kV: 20/155 contours, 80 kV: 3/155 contours). The uncertainty of the DL model is small (within 2%). Significance: A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low.

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“…The standard cone-beam CT (CBCT) scanning protocol at our institution uses a gantry speed of 0.5 rpm, which yields a CT scan beam-on time of 120 s. In this period, 60 to 300 breathing cycles would occur. To analyse the effect of the long scan period on the tumour shape and the CT blurring, the software VOXelised CT SImulator (VOXSI) 19 was used. Further information on this method is in Supplementary Material.…”

Section: Ct Blurringmentioning

confidence: 99%

On the determination of planning target margins due to motion for mice lung tumours using a four-dimensional MOBY phantom

Vaniqui

Heyden

Almeida

et al. 2019

BJR

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This work aims to analyse the effect of respiratory motion on optimal irradiation margins for murine lung tumour models. Methods: Four-dimensional mathematical phantoms with different lung tumour locations affected by respiratory motion were created. Two extreme breathing curves were adopted and divided into time-points. Each time-point was loaded in a treatment planning system and Monte Carlo (MC) dose calculations were performed for a 360° arc plan. A time-resolved dose was derived, considering the gantry rotation and the breathing motion. Radiotherapy metrics were derived to assess the final treatment plans. An interpolation function was investigated to reduce calculation cost. Results: The effect of respiratory motion on the treatment plan quality is strongly dependent on the breathing pattern and the tumour position. Tumours located closer to the diaphragm required a compromise between tumour conformity and healthy tissue damage. A recipe, which considers collimator size, was proposed to derive tumour margins and spare the organs at risk (OARs) by respecting constraints on user-defined metrics. Conclusion: It is recommended to add a target margin, especially on sites where movement is substantial. A simple recipe to derive tumour margins was developed. Advances in knowledge: This work is a first step towards a standard planning target volume concept in pre-clinical radiotherapy.

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VOXSI: A voxelized single- and dual-energy CT scenario generator for quantitative imaging

Cited by 12 publications

References 47 publications

fastCAT: Fast cone beam CT (CBCT) simulation

fastCAT: Fast cone beam CT (CBCT) simulation

Automatic contouring of normal tissues with deep learning for preclinical radiation studies

On the determination of planning target margins due to motion for mice lung tumours using a four-dimensional MOBY phantom

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