Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2018-06-15 Cooperative journals: 《南方医科大学学报》
Abstract: Objective To propose arectal toxicity prediction method based on deformable surface dose accumulation. Methods The clinical data were collected retrospectively from 42patients receiving radiotherapy for cervical cancer. With the first fraction as the reference, the other fractions of rectum surface were registered to the reference fraction to obtain the deformation vector fields (DVFs), which were used to deform and sum the fractional rectal doses to yield the cumulative rectal dose. The cumulative rectal dose was flattened via 3D-2D mapping to generate a 2D rectum surface dose map. Two dosimetric features, namely DVPs and DGPs were extracted. Logistic regression embedded with sequential forward feature selection was used as the prediction model. The predictive performance was evaluated in terms of the accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). Results Significant improvements for rectum surface DIR were achieved. The best predictive results were achieved by using both DVPs and DGPs as the features with a sensitivity of 79.5%, a specificity of 81.3% and an AUC of 0.88. Conclusion The proposed method is feasible for predicting clinical rectal toxicity in patients undergoing radiotherapy for cervical cancer.
Subjects: Medicine, Pharmacy >> Preclinical Medicine submitted time 2017-12-07 Cooperative journals: 《南方医科大学学报》
Abstract: Objective To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging. Methods Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators. Results The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal. Conclusion Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.