Recent Advances in X-ray CT Image Reconstruction Technique

Mahmoudi, Golshan; Ghadiri, Hossein

doi:10.18502/fbt.v6i3.1693

Cited by 2 publications

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“…Several image reconstruction methods were applicable in UCT systems [28,29]; the FBP technique used the Ram-Lack filter for image reconstruction [24]. The TOF images would be calculated by reconstructing the TOF sinogram.…”

Section: Image Reconstructionmentioning

confidence: 99%

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

Solgi

2022

J Biomed Phys Eng

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Background: In body tissues, tumors generally have different speeds of sound (SOS) than normal tissues. In this respect, ultrasound computed tomography (UCT) can generate a cross-sectional SOS map as an innovative ultrasound imaging method. This technique can produce images with a resolution of millimeters and a high signal-to-noise ratio.Objective: This study aimed to improve UCT image quality without increasing breast cancer screening and diagnosis time. Material and Methods:In this analytical study, a ring-shaped UCT breast imaging system was simulated using the K-wave toolbox of MATLAB. The system has a 20 cm diameter and 256 ultrasonic piezoelectrics placed in the ring's circumference. Different beamforming techniques imaged two designed phantoms (i.e., resolution and contrast), and the resolution and contrast to noise ratio (CNR) were calculated. Results:The results of resolution phantom imaging without any beamforming showed that only bars with the value of 0.125 and 0.167 lp/mm were distinguishable, and the 0.1 bars were not recognizable in the imaging. In addition, increasing the number of transmitters led to no noticeable change in resolution for 0.125 and 0.167 lp/mm bars. In all beamforming techniques for imaging the contrast phantom, the CNR parameter up to an object with a diameter of 8 mm increases with increasing diameter without any change. Conclusion:The beamforming technique using three simultaneous transmitters improved the resolution by about 1 mm compared to the normal strategy. In addition to highcontrast images, beamforming with 9 simultaneous transmitters led to a preferable technique.

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Section: Image Reconstructionmentioning

confidence: 99%

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

Solgi

2022

J Biomed Phys Eng

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“…Though IR techniques produce superior results to the FBP, they are computationally expensive. Additionally, when encountering undersampled data in SVCTR, they cannot maintain image quality [11]. To address this issue, the compressed sensing (CS) theory has demonstrated great performance in reconstructing CT images from sparsely sampled measurements [12].…”

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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Recent Advances in X-ray CT Image Reconstruction Technique

Abstract: This article is an Editorial and does not include an Abstract.

Cited by 2 publications

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Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

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

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