Energy-guided diffusion model for CBCT-to-CT synthesis.

Cone Beam Computed Tomography (CBCT) plays a crucial role in Image-Guided Radiation Therapy (IGRT), providing essential assurance of accuracy in radiation treatment by monitoring changes in anatomical structures during the treatment process. However, CBCT images often face interference from scatter noise and artifacts, posing a significant challenge when relying solely on CBCT for precise dose calculation and accurate tissue localization. There is an urgent need to enhance the quality of CBCT images, enabling a more practical application in IGRT. This study introduces EGDiff, a novel framework based on the diffusion model, designed to address the challenges posed by scatter noise and artifacts in CBCT images. In our approach, we employ a forward diffusion process by adding Gaussian noise to CT images, followed by a reverse denoising process using ResUNet with an attention mechanism to predict noise intensity, ultimately synthesizing CBCT-to-CT images. Additionally, we design an energy-guided function to retain domain-independent features and discard domain-specific features during the denoising process, enhancing the effectiveness of CBCT-CT generation. We conduct numerous experiments on the thorax dataset and pancreas dataset. The results demonstrate that EGDiff performs better on the thoracic tumor dataset with SSIM of 0.850, MAE of 26.87 HU, PSNR of 19.83 dB, and NCC of 0.874. EGDiff outperforms SoTA CBCT-to-CT synthesis methods on the pancreas dataset with SSIM of 0.754, MAE of 32.19 HU, PSNR of 19.35 dB, and NCC of 0.846. By improving the accuracy and reliability of CBCT images, EGDiff can enhance the precision of radiation therapy, minimize radiation exposure to healthy tissues, and ultimately contribute to more effective and personalized cancer treatment strategies.

A SwinTransformer-Based Segmentation Framework With Self-Supervised Strategy for Post-Operative Prostate Cancer Radiotherapy.

Radical prostatectomy (prostate removal) is a standard treatment for clinically localized prostate cancer and is often followed by postoperative radiotherapy. Postoperative radiotherapy requires accurate delineation of the clinical target volume (CTV) and lymph node drainage area (LNA) on computed tomography (CT) images. However, the CTV contour cannot be determined by the simple prostate expansion after resection of the prostate in the CT image. Constrained by this factor, the manual delineation process in postoperative radiotherapy is more time-consuming and challenging than in radical radiotherapy. In addition, CTV and LNA have no boundaries that can be distinguished by pixel values in CT images, and existing automatic segmentation models cannot get satisfactory results. Radiation oncologists generally determine CTV and LNA profiles according to clinical consensus and guidelines regarding surrounding organs at risk (OARs). In this work, we design a cascade segmentation block to explicitly establish correlations between CTV, LNA, and OARs, leveraging OARs features to guide CTV and LNA segmentation. Furthermore, inspired by the success of the self-attention mechanism and self-supervised learning, we adopt SwinTransformer as our backbone and propose a pure SwinTransformer-based segmentation network with self-supervised learning strategies. We performed extensive quantitative and qualitative evaluations of the proposed method. Compared to other competitive segmentation models, our model shows higher dice scores with minor standard deviations, and the detailed visualization results are more consistent with the ground truth. We believe this work can provide a feasible solution to this problem, making the postoperative radiotherapy process more efficient.

Objective research on tongue manifestation of patients with eczema.

BACKGROUND: Tongue observation often depends on subjective judgment, it is necessary to establish an objective and quantifiable standard for tongue observation. OBJECTIVE: To discuss the features of tongue manifestation of patients who suffered from eczema with different types and to reveal the clinical significance of the tongue images. METHODS: Two hundred patients with eczema were recruited and divided into three groups according to the diagnostic criteria. Acute group had 47 patients, subacute group had 82 patients, and chronic group had 71 patients. The computerized tongue image digital analysis device was used to detect tongue parameters. The L*a*b* color model was applied to classify tongue parameters quantitatively. RESULTS: For parameters such as tongue color, tongue shape, color of tongue coating, and thickness or thinness of tongue coating, there was a significant difference among acute group, subacute group and chronic group (P< 0.05). For Lab values of both tongue and tongue coating, there was statistical significance among the above types of eczema (P< 0.05). CONCLUSIONS: Tongue images can reflect some features of eczema, and different types of eczema may be related to the changes of tongue images. The computerized tongue image digital analysis device can reflect the tongue characteristics of patients with eczema objectively.