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Purpose To investigate the predictive value of 18F-FDG PET/CT radiomic features before treatment for progression free survival(PFS)and overall survival(OS)in diffuse large B-cell lymphoma(DLBCL).Materials and Methods A total of 135 patients with pathologically proven DLBCL in Chinese PLA General Hospital from January 2016 to December 2018 and 18F-FDG PET/CT imaging prior to treatment were retrospectively collected.Patients were randomly divided into training set and test set using 8∶2,and then the training set was divided into training set and verification set using 8∶2 to construct the model.Semi-automatically delineated patients'lymphoma lesions as regions of interest and extracted the features.Univariate COX and least absolute shrinkage and selection operator regression were used to screen the features,and the non-zero radiomic features were obtained and the weight coefficients were used to calculate the Radscore value of each patient,and the predictive value of Radscore on PFS and OS was analyzed.Three models were established using traditional prognostic indicators(metabolic parameters and clinical factors),Radscore and combination.C-index,time-dependent area under the curve and decision curve were used to evaluate the model prediction efficiency.Finally,based on the optimal model,a column diagram was drawn,and the calibration curve was used to verify the efficiency of the column diagram.Results The combined model predicted PFS and OS at 3 and 5 years better than the traditional prognostic index model and Radscore model(Z=0.962 1-2.253 9,all P<0.05).Decision curve showed that the combined model achieved the greatest clinical net benefit.The calibration curve showed that the predicted values of the nomogram were in good agreement with the observed values.Conclusion Radscore is an independent prognostic factor for survival in DLBCL patients.The combined model has great application value in guiding the formulation of clinical individualized treatment plan.
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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.
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Objective: 3D segmentation is an important part of medical image analysis and visualization. It also continues to be large challenge in the medical image segmentation. While level sets have demonstrated a great potential for 3D medical image segmentation, these algorithms have a large computational burden thus are not suitable for real time processing requirement. To solve this problem, we propose a parallel accerelated method based on CUDA. Methods: We implement C-V level set algorithm in the CUDA environment which is the NVIDIA's GPGPU model.The segmentation speed can greatly improved by using independence of image pixel and concurrence of partial differential equation .The paper shows the flow chart of the parallel computing and gives the detailed introduction of the C-V level set algorithm which is implemented in the CUDA environment. Results: Realizing the C-V level set parallel accerelated algorithm. This method has faster segmentation speed while preserving the qualitative results, Conclusions: This method is viable and makes the fast 3D medical image segmentation come hue.
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Objective To develop a virtual endoscopy system which can be integrated into PACS.Methods Key techniques on virtual endoscopy were researched and we implemented a virtual endoscopy system with the help of the Visualization Toolkit VTK.Results The Virtual endoscopy system was integrated into PACS and the post-processing function of PACS was advanced.Conclusion As a novel medical image post-processing technology,virtual endoscopy provides a completely non-invaded inspection,so it has broad application prospects in the computer-aided medical teaching,surgical navigation,surgical planning and clinical diagnosis.