Evaluating iterative algebraic algorithms in terms of convergence and image quality for cone beam CT

Qiu, Wei; Pengpan, Thanyawee; Smith, Nathan D.; Soleimani, Masoud

doi:10.1016/j.cmpb.2012.09.006

Cited by 9 publications

(1 citation statement)

References 22 publications

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“…As to image-based filtering, these methods usually changes the appearance of the CT image and sacrifices the low-contrast detect -ability [4]. The image reconstruction method can reconstruct the CT image from less than full-scan projection data, but the utilization of those redundant data is desirable in terms of reducing image noise [4,7]. Therefore, in the light of the advantage and limits of these methods, we had developed the new methods to reduce the loss of low-contrast detect-ability of CT image and enhance the image contrast in less radiation dose by obtaining CT projection data with existing CT system in which only K-edge filter is added and applying energy weighting methods and iterative…”

Section: Introductionmentioning

confidence: 99%

Radiation dose reduction in CBCT imaging using K-edge filtering and energy weighting

Kang

Lee

Woo

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper presents K-edge filtering and energy weighting methods which enhance the contrast with less radiation does. Usually, energy weighting methods are used with photon-counting detector based CT for each energy bin data obtained to enhance the quality of image. However, we used these methods combine with K-edge filtering in energy-integrating detector. Using K-edge filtering, different energy bin data for energy weighting methods were obtained, and then energy weighting factors were calculated to enhance the contrast of image. We report an evaluation of the contrast-to-noise ratio (CNR) of reconstructed image with and without these two methods. This evaluation was proceeded with two phantoms; one is the phantom created personally, and the other is Sendentexct IQ dental CBCT (SENDENTEXCT, EU). As for the phantom created personally, the CNR of images reconstructed with these methods were increased than CNR of standard images. It was seen that 31% to 81% in each energy weighting method for optimizing each material (cortical bone, inner bone, soft tissue, iodine (18.5 g/l), iodine (37 g/l)). In conclusion, we can enhance the contrast of CT images with less radiation dose using K-edge filtering and energy weighting method.

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Section: Introductionmentioning

confidence: 99%

Radiation dose reduction in CBCT imaging using K-edge filtering and energy weighting

Kang

Lee

Woo

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Non‐coplanar CBCT image reconstruction using a generative adversarial network for non‐coplanar radiotherapy

Wei,

Song,

Pan

et al. 2024

J Applied Clin Med Phys

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PurposeTo develop a non‐coplanar cone‐beam computed tomography (CBCT) image reconstruction method using projections within a limited angle range for non‐coplanar radiotherapy.MethodsA generative adversarial network (GAN) was utilized to reconstruct non‐coplanar CBCT images. Data from 40 patients with brain tumors and two head phantoms were used in this study. In the training stage, the generator of the GAN used coplanar CBCT and non‐coplanar projections as the input, and an encoder with a dual‐branch structure was utilized to extract features from the coplanar CBCT and non‐coplanar projections separately. Non‐coplanar CBCT images were then reconstructed using a decoder by combining the extracted features. To improve the reconstruction accuracy of the image details, the generator was adversarially trained using a patch‐based convolutional neural network as the discriminator. A newly designed joint loss was used to improve the global structure consistency rather than the conventional GAN loss. The proposed model was evaluated using data from eight patients and two phantoms at four couch angles (±45°, ±90°) that are most commonly used for brain non‐coplanar radiotherapy in our department. The reconstructed accuracy was evaluated by calculating the root mean square error (RMSE) and an overall registration error ε, computed by integrating the rigid transformation parameters.ResultsIn both patient data and phantom data studies, the qualitative and quantitative metrics results indicated that ± 45° couch angle models performed better than ±90° couch angle models and had statistical differences. In the patient data study, the mean RMSE and ε values of couch angle at 45°, −45°, 90°, and −90° were 58.5 HU and 0.42 mm, 56.8 HU and 0.41 mm, 73.6 HU and 0.48 mm, and 65.3 HU and 0.46 mm, respectively. In the phantom data study, the mean RMSE and ε values of couch angle at 45°, −45°, 90°, and −90° were 91.2 HU and 0.46 mm, 95.0 HU and 0.45 mm, 114.6 HU and 0.58 mm, and 102.9 HU and 0.52 mm, respectively.ConclusionsThe results show that the reconstructed non‐coplanar CBCT images can potentially enable intra‐treatment three‐dimensional position verification for non‐coplanar radiotherapy.

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Dose Adjustments for Accuracy: Ultralow Dose Radiation 3D CBCT for Dental and Orthodontic Application

Galang‐Boquiren

Kusnoto

Zheng

et al. 2019

Craniofacial 3D Imaging

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Evaluating iterative algebraic algorithms in terms of convergence and image quality for cone beam CT

Cited by 9 publications

References 22 publications

Radiation dose reduction in CBCT imaging using K-edge filtering and energy weighting

Radiation dose reduction in CBCT imaging using K-edge filtering and energy weighting

Non‐coplanar CBCT image reconstruction using a generative adversarial network for non‐coplanar radiotherapy

Dose Adjustments for Accuracy: Ultralow Dose Radiation 3D CBCT for Dental and Orthodontic Application

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