QCT: A Measuring Tool Dedicated to the Estimation of Image Parameters for Quality Assurance/Quality Control Programs of CT Scanners

Khodajou-Chokami, Hamidreza; Hosseini, Seyed Abolfazl; Ghorbanzadeh, Mohammad; Mohammadi, Mohammad Reza

doi:10.1109/memea49120.2020.9137199

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…Computed tomography (CT) is commonly recognized as a golden standard for monitoring almost all cancers [1]. The extensive use of CT has caused concerns about the associated X-ray radiation dose, posing a lifetime cancer risk [2].…”

Section: Introductionmentioning

confidence: 99%

PARS-NET: a novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

Khodajou-Chokami

Hosseini

2022

J. Inst.

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Sparse-view computed tomography (CT) is recently proposed as a promising method to speed up data acquisition and alleviate the issue of CT high dose delivery to the patients. However, traditional reconstruction algorithms are time-consuming and suffer from image degradation when faced with sparse-view data. To address this problem, we propose a new framework based on deep learning (DL) that can quickly produce high-quality CT images from sparsely sampled projections and is able for clinical use. Our DL-based proposed model is based on the convolution, and residual neural networks in a parallel manner, named the parallel residual neural network (PARS-Net). Besides, our proposed PARS-Net model benefits from a loss based on the geodesic distance to effectively reflect image structures. Experiments have been performed on the combination of two large-scale CT datasets consisting of CT images of whole-body patients for different sparse projection views including 120, 60, and 30 views. Our experimental results show that PARS-Net is 4–5 times faster than the state-of-the-art DL-based models, with fewer memory requirements, better performance in other objective quality evaluations, and improved visual quality. Results showed that our PARS-Net model was superior to the latest methods, demonstrating the feasibility of using this model for high-quality CT image reconstruction from sparsely sampled projections.

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Section: Introductionmentioning

confidence: 99%

PARS-NET: a novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

Khodajou-Chokami

Hosseini

2022

J. Inst.

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“…Lead collimator is used to make fan-shaped beam geometry [ 1 ]. Examinations with X-ray CT is rapid, and accessible to 24 h a day, and is also utilized for approximately all patients and bodily cancers [ 2 ]. However, it delivers a relatively high dose to the patient, mostly greater than 10 mSv effective doses for each examination [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

A Monte Carlo Platform for Characterization of X-Ray Radiation Dose in CT Imaging

et al. 2021

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Background: Computed tomography (CT) is currently known as a versatile imaging tool in the clinic used for almost all types of cancers. The major issue of CT is the health risk, belonging to X-ray radiation exposure. Concerning this, Monte Carlo (MC) simulation is recognized as a key computational technique for estimating and optimizing radiation dose. CT simulation with MCNP/MCNPX MC code has an inherent problem due to the lack of a fan-beam shaped source model. This limitation increases the run time and highly decreases the number of photons passing the body or phantom. Recently, a beta version of MCNP code called MCNP-FBSM (Fan-Beam Source Model) has been developed to pave the simulation way of CT imaging procedure, removing the need of the collimator. This is a new code, which needs to be validated in all aspects. Objective: In this work, we aimed to develop and validate an efficient computational platform based on modified MCNP-FBSM for CT dosimetry purposes. Material and Methods: In this experimental study, a setup is carried out to measure CTDI 100 in air and standard dosimetry phantoms. The accuracy of the developed MC CT simulator results has been widely benchmarked through comparison with our measured data, UK’s National Health Service’s reports (known as ImPACT), manufacturer’s data, and other published results. Results: The minimum and maximum observed mean differences of our simulation results and other above-mentioned data were the 1.5%, and 9.79%, respectively. Conclusion: The developed FBSM MC computational platform is a beneficial tool for CT dosimetry.

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Personalized Computational Human Phantoms via a Hybrid Model-based Deep Learning Method

Khodajou-Chokami

Bitarafan

Dylov

et al. 2020

2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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QCT: A Measuring Tool Dedicated to the Estimation of Image Parameters for Quality Assurance/Quality Control Programs of CT Scanners

Cited by 7 publications

References 13 publications

PARS-NET: a novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

PARS-NET: a novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

A Monte Carlo Platform for Characterization of X-Ray Radiation Dose in CT Imaging

Personalized Computational Human Phantoms via a Hybrid Model-based Deep Learning Method

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