EGS_cbct: Simulation of a fan beam CT and RMI phantom for measured HU verification

[An Easy-to-use Method for CT Image Simulation with Parameter Optimization Using a Water Phantom]

PURPOSE

We developed an easy-to-use method to generate computed tomography (CT) images that simulate the images obtained when using an actual scanner.

METHODS

The developed method generates images by simulating the data acquisition and image reconstruction processes of a scanner from a linear attenuation coefficient map of an object numerically generated. This approach is similar to general image simulation methods. However, we introduced adjustable parameters for the CT data acquisition process, for example, parameters related to X-ray attenuation in the anode of the X-ray tube and the bowtie filter. These parameters were optimized in advance by minimizing the difference between the simulated and measured images of a water phantom. To verify the validity of the developed method, a simulated image was generated for a torso phantom and then compared with the measured image of the phantom obtained using the scanner.

RESULTS

The simulated and measured images of the torso phantom were in good agreement. The spatial resolution and noise characteristics of these two images were also comparable, further indicating the accuracy of the developed method.

CONCLUSION

In the existing methods, various information/data related to an actual scanner, including difficult-to-acquire ones, were essential for image simulation. In the developed method, instead of determining the difficult-to-acquire information/data, we introduced adjustable parameters. Therefore, the developed method was easier to use than the existing methods.

Set-up error validation with EPID images: Measurements vs Egs_cbct simulation

Aim

In this study, the egs_cbct code's ability to replicate an electronic portal imaging device (EPID) is explored.

Background

We have investigated head and neck (H&N) setup verification on an Elekta Precise linear accelerator. It is equipped with an electronic portal imaging device (EPID) that can capture a set of projection images over different gantry angles.

Methods and materials

Cone-beam computed tomography (CBCT) images were reconstructed from projection images of two different setup scenarios. Projections of an Anthropomorphic Rando head phantom were also simulated by using the egs_cbct Monte Carlo code for comparison with the measured projections.Afterwards, CBCT images were reconstructed from this data. Image quality was evaluated against a metric defined as the image acquisition interval (IAI). It determines the number of projection images to be used for CBCT image reconstruction.

Results

From this results it was established that phantom shifts could be determined within 2 mm and rotations within one degree accuracy using only 20 projection images (IAI = 10 degrees). Similar results were obtained with the simulated data.

Conclusion

In this study it is demonstrated that a head and neck setup can be verified using substantially fewer projection images. Bony landmarks and air cavities could still be observed in the reconstructed Rando head phantom. The egs_cbct code can be used as a tool to investigate setup errors without tedious measurements with an EPID system.

Multi-Energy Computed Tomography Breast Imaging with Monte Carlo Simulations: Contrast-to-Noise-Based Image Weighting

Context:

Photon-counting detectors and breast computed tomography imaging have been an active area of research. With these detectors, photons are assigned an equal weight and weighting schemes can be enabled. More weight can be assigned to lower energies, resulting in an increase in the contrast-to-noise ratio (CNR).

Aims:

The aim of this study is to develop and evaluate an energy weighting imaging technique to improve the CNR of simulated breast phantoms and to improve tumour detection.

Materials and Methods:

Breast phantoms consisting of adipose, glandular, malignant tissues and iodine contrast were constructed with BreastSimulator software. The phantoms were used in egs_cbct simulations for energies ranging between 20 and 65 keV from which multiple images were reconstructed. A new CNR-based image weighting method was proposed based on the CNR values obtained from the images. This method improves on previous methods and can be applied to complicated phantoms since no structural information is needed.

Results:

An increase in the CNR can be seen for lower energies. A sharp increase in the CNR is seen just above the K-edge for the phantoms with the iodine contrast. The CNR-based image weighting leads to a 68.47% (1.68-fold) increase in the CNR for the malignant tissue without iodine. For the malignant tissue with iodine contrast, the increase in the CNR was 96.14% (1.96-fold).

Conclusions:

The new proposed CNR-based image weighting scheme is easy to implement and can be used for complicated phantoms with varying structures. A large increase in the CNR is seen with or without the use of iodine contrast.