Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose patients to additional radiation doses, increasing the risk of secondary malignant tumors. Low-dose CBCT image reconstruction technology, which employs advanced algorithms to reduce radiation dose while enhancing image quality, has emerged as a focal point of recent research. This review systematically examined deep learning-based methods for low-dose CBCT reconstruction. It compared different network architectures in terms of noise reduction, artifact removal, detail preservation, and computational efficiency, covering three approaches: image-domain, projection-domain, and dual-domain techniques. The review also explored how emerging technologies like multimodal fusion and self-supervised learning could enhance these methods. By summarizing the strengths and weaknesses of current approaches, this work provides insights to optimize low-dose CBCT algorithms and support their clinical adoption.
Cone-Beam Computed Tomography/methods* ; Deep Learning ; Humans ; Algorithms ; Image Processing, Computer-Assisted/methods* ; Radiation Dosage ; Artifacts

OBJECTIVE: To study effective methods for reducing lung V₅, V₁₀, and mean lung dose (MLD) in the design of volumetric modulated arc therapy for central lung cancer by using different arc configurations and dose-limiting blocks designs. METHODS: Five groups of plans were designed for the enrolled patients. Group A used a full-arc field. Group B used a partial-arc field. Groups C, D, and E used full-arc fields with vertical-length, semi-ring, and triangular dose-limiting blocks added respectively. The dosimetric similarities of target areas and the dosimetric differences in lung V₅, V₁₀, V₂₀, and MLD among the groups were compared. RESULTS: Compared with group A, groups B, C, D, and E had decreased homogeneity and conformity of the target area, but significantly lower V₅ and V₁₀ of the whole lung. The MLD of groups C, D, and E was lower than that of group A. CONCLUSION Using a full-arc field combined with dose-limiting blocks can effectively reduce lung V₅, V₁₀, MLD, and monitor units (MU).
Lung Neoplasms/radiotherapy* ; Humans ; Radiotherapy, Intensity-Modulated/methods* ; Radiotherapy Planning, Computer-Assisted/methods* ; Radiotherapy Dosage ; Lung/radiation effects*

OBJECTIVES: We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion. METHODS: The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data. RESULTS: In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively. CONCLUSIONS The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.
Tomography, X-Ray Computed/methods* ; Algorithms ; Phantoms, Imaging ; Anisotropy ; Image Processing, Computer-Assisted/methods* ; Humans ; Radiation Dosage

OBJECTIVES: We propose a low-dose CT image restoration method based on central guidance and alternating optimization (FedGP). METHODS: The FedGP framework revolutionizes the traditional federated learning model by adopting a structure without a fixed central server, where each institution alternatively serves as the central server. This method uses an institution-modulated CT image restoration network as the core of client-side local training. Through a federated learning approach of central guidance and alternating optimization, the central server leverages local labeled data to guide client-side network training to enhance the generalization capability of the CT imaging model across multiple institutions. RESULTS: In the low-dose and sparse-view CT image restoration tasks, the FedGP method showed significant advantages in both visual and quantitative evaluation and achieved the highest PSNR (40.25 and 38.84), the highest SSIM (0.95 and 0.92), and the lowest RMSE (2.39 and 2.56). Ablation study of FedGP demonstrated that compared with FedGP(w/o GP) without central guidance, the FedGP method better adapted to data heterogeneity across institutions, thus ensuring robustness and generalization capability of the model in different imaging conditions. CONCLUSIONS FedGP provides a more flexible FL framework to solve the problem of CT imaging heterogeneity and well adapts to multi-institutional data characteristics to improve generalization ability of the model under diverse imaging geometric configurations.
Tomography, X-Ray Computed/methods* ; Humans ; Radiation Dosage ; Image Processing, Computer-Assisted/methods* ; Algorithms

Objective To explore the application value of multi-model adaptive statistical iterative reconstruction-Veo (ASiR-V) in ultra-low dose chest CT examination of children in the plateau area. Methods The children who underwent chest CT examination in Xizang Autonomous Region People's Hospital were enrolled in this study and assigned into two groups according to the scanning conditions.Group A underwent scanning at a tube voltage of 100 kV and ASiR-V 50% reconstruction,and group B underwent scanning at a tube voltage of 80 kV and ASiR-V 0 (Group B1) and ASiR-V 50% (Group B2) reconstruction.The image quality of each group was evaluated objectively and subjectively.The radiation dose and image quality were compared between groups. Results Groups A and B showed the volume CT dose indexes of (2.33±0.62) mGy and (0.86±0.01) mGy and the dose length products of (65.01±25.12) mGy·cm and (23.55±3.38) mGy·cm,respectively,which presented differences between groups (both P<0.001).The image noise in the bilateral upper and middle lung areas in group B2 was lower than that in group B1 but higher than that in group A (all P<0.001).There was no significant difference in image quality score of the lung window among groups (all P>0.05).Groups A,B1,and B2 had no significant differences in ascending aorta (P=0.538) or liver CT value (P=0.175) in the mediastinal window.The signal-to-noise ratios and contrast-to-noise ratios of ascending aorta and liver in group B2 were higher than those in group B1 (all P<0.001) and lower than those in group A (all P<0.05).The image quality score of the mediastinal window followed a descending order of group A>group B2>group B1 (all P<0.001)。Conclusion ASiR-V combined with low tube voltage can effectively reduce the radiation dose and guarantee the image quality of chest CT of children in the plateau area.
Humans ; Radiation Dosage ; Tomography, X-Ray Computed/methods* ; Child ; Male ; Female ; Child, Preschool ; Radiography, Thoracic/methods* ; Infant ; Models, Statistical ; Image Processing, Computer-Assisted/methods*

The Monte Carlo N-Particle (MCNP) is often used to calculate the radiation dose during computed tomography (CT) scans. However, the physical calculation process of the model is complicated, the input file structure of the program is complex, and the three-dimensional (3D) display of the geometric model is not supported, so that the researchers cannot establish an accurate CT radiation system model, which affects the accuracy of the dose calculation results. Aiming at these two problems, this study designed a software that visualized CT modeling and automatically generated input files. In terms of model calculation, the theoretical basis was based on the integration of CT modeling improvement schemes of major researchers. For 3D model visualization, LabVIEW was used as the new development platform, constructive solid geometry (CSG) was used as the algorithm principle, and the introduction of editing of MCNP input files was used to visualize CT geometry modeling. Compared with a CT model established by a recent study, the root mean square error between the results simulated by this visual CT modeling software and the actual measurement was smaller. In conclusion, the proposed CT visualization modeling software can not only help researchers to obtain an accurate CT radiation system model, but also provide a new research idea for the geometric modeling visualization method of MCNP.
Radiation Dosage ; Software Design ; Tomography, X-Ray Computed/methods* ; Software ; Algorithms ; Phantoms, Imaging ; Monte Carlo Method

Objective To evaluate the impact of deep learning reconstruction algorithm on the image quality of head and neck CT angiography (CTA) at 100 kVp. Methods CT scanning was performed at 100 kVp for the 37 patients who underwent head and neck CTA in PUMC Hospital from March to April in 2021.Four sets of images were reconstructed by three-dimensional adaptive iterative dose reduction (AIDR 3D) and advanced intelligent Clear-IQ engine (AiCE) (low,medium,and high intensity algorithms),respectively.The average CT value,standard deviation (SD),signal-to-noise ratio (SNR),and contrast-to-noise ratio (CNR) of the region of interest in the transverse section image were calculated.Furthermore,the four sets of sagittal maximum intensity projection images of the anterior cerebral artery were scored (1 point:poor,5 points:excellent). Results The SNR and CNR showed differences in the images reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D (all P<0.01).The quality scores of the image reconstructed by AiCE (low,medium,and high intensity) and AIDR 3D were 4.78±0.41,4.92±0.27,4.97±0.16,and 3.92±0.27,respectively,which showed statistically significant differences (all P<0.001). Conclusion AiCE outperformed AIDR 3D in reconstructing the images of head and neck CTA at 100 kVp,being capable of improving image quality and applicable in clinical examinations.
Humans ; Computed Tomography Angiography/methods* ; Radiation Dosage ; Deep Learning ; Radiographic Image Interpretation, Computer-Assisted/methods* ; Signal-To-Noise Ratio ; Algorithms

During interventional procedures,subjects are exposed to direct and scattered X-rays.Establishing diagnostic reference levels is an ideal way to optimize the radiation dose and reduce radiation hazard.In recent years,diagnostic reference levels in interventional radiology have been established in different countries.However,because of the too many indicators for characterizing the radiation dose,the indicators used to establish diagnostic reference levels vary in different countries.The research achievements in this field remain to be reviewed.We carried out a retrospective analysis of the definition,establishment method,application,and main factors influencing the dose difference of the diagnostic reference level,aiming to provide a basis for establishing the diagnostic reference level for interventional procedures in China.
Humans ; Diagnostic Reference Levels ; Radiology, Interventional/methods* ; Radiation Dosage ; Retrospective Studies ; Radiography

Objective: To compare the incidence of radiation-related toxicities between conventional and hypofractionated intensity-modulated radiation therapy (IMRT) for limited-stage small cell lung cancer (SCLC), and to explore the risk factors of hypofractionated radiotherapy-induced toxicities. Methods: Data were retrospectively collected from consecutive limited-stage SCLC patients treated with definitive concurrent chemoradiotherapy in Cancer Hospital of Chinese Academy of Medical Sciences from March 2016 to April 2022. The enrolled patients were divided into two groups according to radiation fractionated regimens. Common Terminology Criteria for Adverse Events (CTCAE, version 5.0) was used to evaluate the grade of radiation esophagus injuries and lung injuries. Logistic regression analyses were used to identify factors associated with radiation-related toxicities in the hypofractionated radiotherapy group. Results: Among 211 enrolled patients, 108 cases underwent conventional IMRT and 103 patients received hypofractionated IMRT. The cumulative incidences of acute esophagitis grade ≥2 [38.9% (42/108) vs 35.0% (36/103), P=0.895] and grade ≥ 3 [1.9% (2/108) vs 5.8% (6/103), P=0.132] were similar between conventional and hypofractionated IMRT group. Late esophagus injuries grade ≥2 occurred in one patient in either group. No differences in the cumulative incidence of acute pneumonitis grade ≥2[12.0% (13/108) vs 5.8% (6/103), P=0.172] and late lung injuries grade ≥2[5.6% (6/108) vs 10.7% (11/103), P=0.277] were observed. There was no grade ≥3 lung injuries occurred in either group. Using multiple regression analysis, mean esophageal dose ≥13 Gy (OR=3.33, 95% CI: 1.23-9.01, P=0.018) and the overlapping volume between planning target volume (PTV) and esophageal ≥8 cm(3)(OR=3.99, 95% CI: 1.24-12.79, P=0.020) were identified as the independent risk factors associated with acute esophagitis grade ≥2 in the hypofractionated radiotherapy group. Acute pneumonitis grade ≥2 was correlated with presence of chronic obstructive pulmonary disease (COPD, P=0.025). Late lung injuries grade ≥2 was correlated with tumor location(P=0.036). Conclusions: Hypofractionated IMRT are tolerated with manageable toxicities for limited-stage SCLC patients treated with IMRT. Mean esophageal dose and the overlapping volume between PTV and esophageal are independently predictive factors of acute esophagitis grade ≥2, and COPD and tumor location are valuable factors of lung injuries for limited-stage SCLC patients receiving hyofractionated radiotherapy. Prospective studies are needed to confirm these results.
Humans ; Small Cell Lung Carcinoma/pathology* ; Lung Neoplasms/pathology* ; Radiotherapy, Intensity-Modulated/methods* ; Retrospective Studies ; Lung Injury ; Radiotherapy Dosage ; Radiation Injuries/epidemiology* ; Esophagitis/epidemiology* ; Risk Factors ; Pulmonary Disease, Chronic Obstructive/complications*

Performing external beam radiotherapy alone without chemotherapy or brachytherapy for locally advanced cervical cancer with multiple lymph node metastases is challenging. The purpose of this case report is to present the efficacy of high‑dose adaptive extended‑field intensity‑modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB) in treating locally advanced cervical cancer with multiple lymph node metastases. A 67‑year‑old woman with locally advanced squamous cell carcinoma of the uterine cervix was treated by external beam radiotherapy alone due to the refusal of chemotherapy and intracavitary brachytherapy. In order to maximize the efficacy of treatment, extended‑field radiotherapy that includes the paraaortic lymph nodes as well as an adaptive IMRT‑SIB plan, was applied. The treatment was successful, resulting in complete tumor disappearance without severe adverse events. In conclusion, high‑dose adaptive IMRT‑SIB may be an alternative treatment option for locally advanced cervical cancer with multiple lymph node metastases.
Radiation Dosage ; Uterine Cervical Neoplasms