Purpose: To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. Materials and Methods: A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder–decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. Results: The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. Conclusions: Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.

Background and Objective: As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Methods: Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included “radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network”. Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. Key Content and Findings: In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Conclusions: Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

Objective: To investigation noise annoyance among the exposed anesthesiologists, and to analyze the influencing factors of noise annoyance. Methods: From January to May 2017, cluster sampling was used to select 62 anesthesiologists from Peking University Third Hospital as the survey subjects. The Likert Scale (five-level) and the Visual Analog Scale (0-10 points) were used to conduct noise annoyance and noise sensitivity survey. Linear regression was used for multi-factor analysis to explore the influencing factors of noise annoyance. Results: The age of the research subjects was (32.3±6.1) years old, the working experience was 4.9 (2.0, 8.9) years, and the daily working hours were (9.8±2.8) h. The noise level in the operating room on the measurement day was (64.4±1.7) dB (A) . In the Likert Scale and the Visual Analog Scale, 88.7% (55/62) and 95.2% (59/62) anesthesiologists believed that operating room noise had adverse reactions, 6.5% (4/62) and 14.5% (9/62) anesthesiologists measured a high degree of annoyance. The results of linear regression analysis showed that noise sensitivity was an independent factor influencing the anesthesiologists' noise annoyance in the Likert Scale and the Visual Analog Scale (r=0.524, 0.700, P<0.05) . Conclusion: Noise in the operating room can cause anesthesiologists to feel annoyed and is a hidden danger to the quality of medical services, which should be paid attention to by the academic and management departments.

Objective. This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART). Approach. The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection. Main results. Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose, V ₂₀, and V ₅ were significantly reduced by 1.13 Gy (p < .001), 2.01% (p < .001), and 6.66% (p < .001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p < .01), fV ₂₀ by 0.90% (p = 0.099), and fV ₅ by 5.07% (p < .01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection. Significance. AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans.

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.

Deep learning algorithms can synthesize pulmonary functional images from CT images. However, previous studies have only been able to predict either ventilation or perfusion from CT, limiting the holistic evaluation of lung function. This study aimed to develop a deep learning-based framework for simultaneously generating lung perfusion and ventilation images from three-dimensional CT. A total of 98 cases who underwent single-photon emission CT perfusion images (SPECT PI) with ^99mTc-labeled macroaggregated albumin, ventilation images (VI) with ^99mTc-Technegas, and three-dimensional CT images were collected. The three-dimensional CT and SPECT images were registered and cropped to include only the lung parenchyma. A dual-decoder residual attention network (DDRAN) was constructed to generate both PI and VI simultaneously from three-dimensional CT images. For comparative assessment, we additionally employed a conventional single-decoder residual attention network (RAN) to individually generate PI and VI. The structural similarity index (SSIM) and Spearman's rank correlation coefficient (Rs) were utilized to assess voxel-wise agreement. Additionally, the Dice similarity coefficient (DSC) was applied to evaluate function-wise concordance. We used the Wilcoxon signed-rank test to statistically evaluate the differences between the images synthesized by DDRAN and RAN. Beyond image-similarity metrics, we evaluated overall model performance using threshold-based classification. Lastly, a two-part reader study was conducted: (I) qualitative image acceptability for clinical review, and (II) illustrative diagnostic interpretation based on synthesized image pairs alone. Overall, DDRAN and RAN achieved comparable performance. The average SSIM values of the DDRAN/RAN model were 0.871/0.866 (p < 0.05) for PI and 0.830/0.825 (p < 0.05) for VI, and the Rs values were 0.836/0.819 and 0.732/0.731, respectively. The DDRAN/RAN model achieved average DSC values of 0.795/0.796 for PI and 0.708/0.718 for VI in low-function regions, and 0.857/0.849 for PI and 0.793/0.793 for VI in high-function regions. In the two-part reader study, the synthesized perfusion and ventilation images received almost acceptable scores across all experience levels and demonstrated diagnostic potential. We have developed a dual-decoder residual attention network that enables the simultaneous synthesis of lung perfusion and ventilation images from three-dimensional CT. Preliminary results indicate moderate-to-high structural-wise and functional-wise concordances, and our proposed model demonstrates comparable accuracy when benchmarked against single-decoder models. The synthesized perfusion and ventilation images can potentially be used for precise diagnosis and guiding functional lung avoidance radiotherapy.