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J Med Phys. 2019 Apr-Jun;44(2):106-112. doi: 10.4103/jmp.JMP_48_18.

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

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1
Department of Medical Physics, University of the Free State, Bloemfontein, South Africa.

Abstract

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.

KEYWORDS:

Image weighting; Monte Carlo; multi-energy computed tomography

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