Patients with novel coronavirus infection still have many functional disorders during the recovery period. The timely intervention of rehabilitation treatment has important clinical significance in improving the patients’ functions and their ability of daily living. Based on the current evidence of evidence-based medicine and clinical practice, this paper summarizes the rehabilitation treatment and precautions of patients with simple novel coronavirus infection and different groups with previous dysfunction and novel coronavirus infection (such as neurological dysfunction, chronic pain, and bone and joint diseases) with a view to providing clinical reference for the rehabilitation treatment of patients with novel coronavirus infection during the recovery period.

With the improvement of the complexity of medical image synthesis and the demand for the accuracy of clinical radiotherapy, deep learning algorithm plays an increasingly important role in pseudo CT image synthesis and analysis. This paper classifies and analyzes the pseudo CT image synthesis technology based on deep learning method in terms of the types of image modes, and describes the ongoing application in radiotherapy.

Image-guided techniques are critical to improving the accuracy of radiotherapy for tumors. Ultrasound images have been gradually applied in the set-up verification of clinical radiotherapy and adaptive radiotherapy because of the real-time, reproducible and non-radiative characteristics. In this paper, the application of ultrasound image-guided technology in radiotherapy was classified and analyzed, and the latest research progress was introduced.

Objective An improved method for obtaining pseudo-computed tomography (CT ps) based on ultrasound deformation field.Methods The three-dimensional image data of computed tomography and ultrasound for three postoperative cervical cancer patients were selected,including the CT (CTsim) and ultrasound (USsim) images obtained during the simulated positioning stage,and the cone beam CT (CBCT) and ultrasound images obtained during the positioning verification stage of the treatment one week later.Binary masks of the OROI and OROW were created and applied in ultrasound image registration;thus,the deformation field was obtained.The deformation field was applied to CTsim images and different pseudo-CT images were obtained.Similarities between these pseudo-CT images and those of CBCT were compared,and registration accuracies between pseudo-CT images under different binary masks and CTsim were discussed.Results The averages of the correlation coefficient between pseudo-CT based on OROI,OROW,no binary mask and CBCT were 0.95,0.82 and 0.64 respectively.The average of the normalized mean square Error were 0.12,0.42 and 0.57 respectively.Conclusion The pseudo-CT based on OROI binary mask matches the best with CTsim and achieves the highest similarity with CBCT.

Endoplasmic reticulum (ER) stress is an important mediator causing inflammatory response, which additionally exerts secondary injury after intracerebral hemorrhage (ICH) to influence brain function recovery through a variety of cell signaling and response, to regulate neural cells survival or death. Therefore, further understanding about the relation between ER stress and inflammatory response as well as the signal transduction pathway can provide new therapeutic strategies and evidence for treatment of ICH. The present review summarizes the process of ER stress activation and the relation between inflammatory response and ER stress activation after ICH.

More and more patients were treated by surgery with metal implants in clinic.Metal artifacts in CT images caused by metal implants brought challenges for postoperative evaluation and diagnosis of tumor.It also led to the possibility of missed diagnosis and misdiagnosis.In recent decades,the improved methods based on filtered-backprojection and iterative reconstruction algorithms have great progresses in reducing effects of metal artifacts.The actuality of techniques for metal artifact reduction were reviewed in this article.

Objective To investigate the impact on registration accuracy with the different registration ranges of CBCT images and CT images.Methods CBCT and CT scans were performed on the of 5 patients.The registration ranges of five patients' images of abdomen,head and chest performed CBCT and CT scanning were processed with four modes.Mode 1:the registration range of CT images was larger than the registration range of CBCT images,mode 2:the registration range of CT images and CBCT images were equally,mode 3:taking a 5 cm translation of CT images range from mode 2,mode 4:The CBCT range and CT range reduced 2 cm both sides simultaneously from mode 2.Using the registration program from Insight Segmentation and Registration Toolkit (ITK) to the four modes,the Mean Square Difference (MSD) metric values of four modes after registration were compared and the relationship between mode 2 and another three modes was analyzed by paired t test.Results For the MSD metric values,mode 3 was maximum,mode 2 and 4 were minimum,and mode 1 was centered.The difference between the mode 2 and mode 4 was not statistically significant(P ＞ 0.05).The difference between the mode 2 and mode 1 was statistically significant(t =-4.586,-4.164,-5.618,P ＜ 0.05).The difference between the mode 2 and mode 3 was statistically significant(t =-6.423,-8.109,-19.601,P＜0.05).Conclusion The registration ranges of CBCT and CT images has a certain extent of influence on the accuracy of image registration.The closer the registration range of CBCT and CT is,the higher the registration accuracy is.

Radiomics refers to the comprehensive quantification of information such as tumor biological feature and heterogeneity through assessing a large number of quantitative image features from ROI of CT,MRI and PET images.With the development of data science,radiomics has been paid more and more attention.Feature extraction is an important step of Radiomics.The processes in feature extraction of radiomics were reviewed in this paper.

Objective To apply metal artifact reduction algorithm to reduce metal artifacts based on 12 bit and 16 bit CT images,and aimed to analyze the effects on CT value and dose distribution.Methods The metal implant was inserted into the phantom,and the original 12 bit CT image and original 16 bit CT image were derived from CT scanning.The images were processed using NMAR algorithm,so the corrected 12 bit image and corrected 16 bit image were obtained.A patient's CT was chosen with artificial femur,and used the NMAR algorithm to reduce metal artifacts.Furthermore,the CT values of original images and corrected images were compared and analyzed.In the planning system,dose distribution was calculated based on each image by same radiation treatment plan.The dose distribution difference of each image was compared and analyzed.Results For the 12 bit image,the CT value of metal was 3 071 HU,which was much smaller than the metal's actual CT value ll 080 HU.The metal's CT value for the 16bit image was 11 098 HU,which was very close to the actual value.The original CT images contained a lot of artifacts around the metal,resulting in a large deviation of CT values from the reference image.After NMAR correction,metal artifacts were reduced significantly,and the CT values were close to the reference images.The dose distribution of the corrected 16 bit image was closest to that of the reference image.The maximum dose deviation on the central axis was 1.8％.The difference between the 12 bit image and the reference image downstream the metal was notable,and the maximum dose deviation on the central axis was 81.6％.The X-rays passed through the artifact region in original image,the dose distribution was obviously different from the reference image,and the maximum dose deviation was 21.6％.Conclusions For the patient with metal implant,using the NAMR algorithm on the 16 bit image result in accurate CT value of CT image,so that the accurate dose distribution will be obtained.

With the development of computer techniques and medical software in image analysis and visualization,medical image registration as a key step before image processing(e.g., image fusion)is important in research and medical diagnosis. Therefore, many studies have focused on medical registration methods and algorithms. Up to now, several registration methods have been applied in clinical practice. In this paper,we make a classification and analysis of registration methods clinically applied in radiotherapy. How to improve the accuracy,efficiency,and robustness of medical image registration remains an issue to be solved in the future.