Application of image similarity measure based on structure information and intuitionistic fuzzy set in radiotherapy setup verification / 中华放射肿瘤学杂志

Objective:To propose a method of image similarity measurement based on structure information and intuitionistic fuzzy set and measure the similarity between CT image and CBCT image of radiotherapy plan positioning, aiming to objectively measure the setup errors.Methods:A total of four pre-registration images of a nasopharyngeal carcinoma patient on the cross-sectional and sagittal planes and a pelvic tumor patient on the cross-sectional and coronal planes were randomly selected. Five methods were used to quantify the setup errors, including correlation coefficient, mean square error, image joint entropy, mutual information and similarity measure method.Results:All five methods could describe the deviation to a certain extent. Compared with other methods, the similarity measure method showed a stronger upward trend with the increase of errors. After normalization, the results of five types of error increase on the cross-sectional plane of the nasopharyngeal carcinoma patient were 0.553, 0.683, 1.055, 1.995, 5.151, and 1.171, 1.618, 1.962, 1.790, 3.572 on the sagittal plane, respectively. The results of other methods were between 0 and 2 after normalization, and the results of different errors of the same method slightly changed. In addition, the method was more sensitive to the soft tissue errors.Conclusions:The image similarity measurement method based on structure information and intuitionistic fuzzy set is more consistent with human eye perception than the existing evaluation methods. The errors between bone markers and soft tissues can be objectively quantified to certain extent. The soft tissue deviation reflected by the setup errors is of significance for individualized precision radiotherapy.

Research progress and challenges of deep learning in medical image registration / 生物医学工程学杂志

With the development of image-guided surgery and radiotherapy, the demand for medical image registration is stronger and the challenge is greater. In recent years, deep learning, especially deep convolution neural networks, has made excellent achievements in medical image processing, and its research in registration has developed rapidly. In this paper, the research progress of medical image registration based on deep learning at home and abroad is reviewed according to the category of technical methods, which include similarity measurement with an iterative optimization strategy, direct estimation of transform parameters, etc. Then, the challenge of deep learning in medical image registration is analyzed, and the possible solutions and open research are proposed.

Personalized biomechanical modeling of prostate deformation based on elastography for MRI three-dimensional transrectal ultrasound(TRUS)image registration / 中华超声影像学杂志

Objective To evaluate the clinical significance of personalized biomechanical modeling of prostate deformation based on ultrasound elastography for magnetic resonance imaging(MRI)-transrectal ultrasound(TRUS)image registration.Methods A total number of 5 patients and 1 commercial prostate phantom were imaged via transrectal ultrasound elastography,3D-TRUS and MRI from June 2016 to December 2016.A personalized biomechanical model via the patient-specific ultrasound elastography was made for the deformable registration of prostate MRI and 3D-TRUS images.The registration accuracy was evaluated by the target registration error(TRE)and also the t-test was conducted to validate the statistical significance of our results.Results All the 5 sets of patient data as well as the phantom data were successfully registered.The TRE value of the phantom data was 1.65 mm.The mean TRE value of 5 patients was 1.31 mm,compared with the 2.52 mm TRE value of the registration method without patient-specific biomechanical properties via elastography,was approximately 48% lower(P <0.05).Conclusions Personalized biomechanical modeling of prostate deformation based on ultrasound elastography for MRI-TRUS image registration possesses important clinical significance and is a promising way to provide more quality guidance and improve the accuracy of prostate biopsy.

Classification of medical image registration and its application in radiotherapy / 中华放射肿瘤学杂志

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.

Review of multi-modality medical image registration / 国际生物医学工程杂志

Multimodality medical image registration has become increasingly popular in the field of image processing,and it has significant meaning for the clinical diagnosis and treatment.In this article,the process of image registration is described,and a full review and comparison of some typical medical image registration methods are given according to the features of the images including internal and external characteristics.

2D, 3D Rigid Registration Algorithm Base on CUDA / 中国医学物理学杂志

Objective: Real time medical image registration technique is one of the key techniques in image based surgery navi-gation system. While in medical image analysis, image registration is usually a very time-cousuming operation, and this is not conducive to clinical real-time requirements. This paper studies and realizes the acceleration of the process of image registra-tion. Methods: In order to improve the regisWation rate, in this paper, we propose a new technology which is based on CUDA (Compute Unified Device Architecture) programming model to accelerate the process of registration in hardware, using paral-lel methods to achieve pixel coordinate transformation, linear interpolation, and calculate the corresponding pixel gray value residuals. Results: The registration is up to the sub-pixel level and the GPU-based registration is dozens or even hundreds of times faster than CPU-based registration. Conclusions: This method greatly enhances the speed of rigid registration without changing the alignment accuracy.

Progress on Medical Image Registration and its Application / 中国医学物理学杂志

Objective: To summarize the major progress in medical image registration in recent years. Furthermore, based on the recent advances in this field, this paper can provide a reference in following domains: three-dimensional medical image reconstruction, medical image visualization, quantitative analysis. Methods: Firstly, referring to a myriad of latest papers on medical image registration. Secondly, analyzing traits and exiting problems of techniques which presented in those papers. Finally, putting forward some efficient methods for solving these problems. Results: This paper compares the characteristics of some typical algorithms and its application and looks forward to the future research work. Conclusion: Using optimization strategy to improve the quality of image registration and studying on non-rigid image registration are the directions for future research in medical image registration field.

Development of medical image registration / 国际生物医学工程杂志

Medical image registration plays an important role in the research of medical image processing field. It is widely used in the areas of clinical diagnoses, treatment, quality assurance and evaluation of curative effect. This paper gives an overview on three medical image registration methods Correlation method, mutual information method, and wavelet transform method. Features of these method and their applications are reviewed.