SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data

Hu, Dianlin; Zhang, Yikun; Li, Wangyao; Zhang, Weijie; Reddy, Krishna; Ding, Qiaoqiao; Zhang, Xiaoqun; Chen, Yang; Gao, Hao

doi:10.1109/tim.2023.3318712

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…This problem resembles a computer tomography inversion problem with incomplete data (limited-angle computer tomography). This problem can be approached using deep learning [38][39][40][41]. For example, in [38], the authors used a U-net-type convolutional neural network and a filtered back-projection computer tomography algorithm to work with incomplete data.…”

Section: Validation Basismentioning

confidence: 99%

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Penenko,

Emelyanov,

Rusin

et al. 2023

Mathematics

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Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms.

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Section: Validation Basismentioning

confidence: 99%

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Penenko,

Emelyanov,

Rusin

et al. 2023

Mathematics

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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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Coarse-to-Fine Learning for Planning CT-Enhanced Limited-Angle CBCT Reconstruction

Zang,

Zhang,

et al. 2024

IEEE Trans. Instrum. Meas.

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SEA-Net: Structure-Enhanced Attention Network for Limited-Angle CBCT Reconstruction of Clinical Projection Data

Cited by 3 publications

References 43 publications

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

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

Coarse-to-Fine Learning for Planning CT-Enhanced Limited-Angle CBCT Reconstruction

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