Feasibility Study of Convolutional Long Short Term Memory Network for Pulmonary Movement Prediction in CT Images

Background: During X-ray imaging, pulmonary movements can cause many image artifacts. To tackle this issue, several studies, including mathematical algorithms and 2D-3D image registration methods, have been presented. Recently, the application of deep artificial neural networks has been considered for image generation and prediction.Objective: In this study, a convolutional long short-term memory (ConvLSTM) neural network is used to predict spatiotemporal 4DCT images.Material and Methods: In this analytical analysis study, two ConvLSTM structures, consisting of stacked ConvLSTM models along with the hyperparameter optimizer algorithm and a new design of the ConvLSTM model are proposed. The hyperparameter optimizer algorithm in the conventional ConvLSTM includes the number of layers, number of filters, kernel size, epoch number, optimizer, and learning rate. The two ConvLSTM structures were also evaluated through six experiments based on Root Mean Square Error (RMSE) and structural similarity index (SSIM).Results: Comparing the two networks demonstrates that the new design of the ConvLSTM network is faster, more accurate, and more reliable in comparison to the tuned-stacked ConvLSTM model. For all patients, the estimated RMSE and SSIM were 3.17 and 0.988, respectively, and a significant improvement can be observed in comparison to the previous studies. Conclusion: Overall, the results of the new design of the ConvLSTM network show excellent performances in terms of RMSE and SSIM. Also, the generated CT images with the new design of the ConvLSTM model show a good consistency with the corresponding references regarding registration accuracy and robustness

Calculation of Inter- and Intra-Fraction Motion Errors at External Radiotherapy Using a Markerless Strategy Based on Image Registration Combined with Correlation Model

Introduction: A new method based on image registration technique and an intelligent correlation model to calculate. The present study aimed to propose inter- and intra-fraction motion errors in order to address the limitations of conventional Patient positioning methods. Material and Methods: The configuration of the markerless method was accomplished by using four-dimensional computed tomography (4DCT) datasets. Firstly, the MeVisLab software package was used to extract a three-dimensional (3D) surface model of the patient and determine the tumor location. Then, the patient-specific 3D surface model which also included the breathing phases was imported into the MATLAB software package in order to define several control points on the thorax region as virtual external markers. Finally, based on the correlation of breathing signals/patient position with breathing signals/tumor coordinate, an adaptive neuro fuzzy inference system was proposed to both verify and align the inter- and intra-fraction motion errors in radiotherapy, if needed. In order to validate the proposed method, the 4DCT data acquired from four real patients was considered. Results: Final results revealed that our hybrid configuration method was capable of aligning patient setup with lower uncertainties, compared to other available methods. In addition, the 3D root-mean-square error has been reduced from 5.26 to 1.5 mm for all patients. Conclusion: In this study, a markerless method based on the image registration technique in combination with a correlation model was proposed to address the limitations of the available methods, including dependence on operator’s attention, use of passive markers, and rigid-only constraint for patient setup

A Simulation Study on Patient Setup Errors in External Beam Radiotherapy Using an Anthropomorphic 4D Phantom

Introduction Patient set-up optimization is required in radiotherapy to fill the accuracy gap between personalized treatment planning and uncertainties in the irradiation set-up. In this study, we aimed to develop a new method based on neural network to estimate patient geometrical setup using 4-dimensional (4D) XCAT anthropomorphic phantom. Materials and Methods To access 4D modeling of motion of dynamic organs, a phantom employs non-uniform rational B-splines (NURBS)-based Cardiac-Torso method with spline-based model to generate 4D computed tomography (CT) images. First, to generate all the possible roto-translation positions, the 4D CT images were imported to Medical Image Data Examiner (AMIDE). Then, for automatic, real time verification of geometrical setup, an artificial neural network (ANN) was proposed to estimate patient displacement, using training sets. Moreover, three external motion markers were synchronized with a patient couch position as reference points. In addition, the technique was validated through simulated activities by using reference 4D CT data acquired from five patients. Results The results indicated that patient geometrical set-up is highly depended on the comprehensiveness of training set. By using ANN model, the average patient setup error in XCAT phantom was reduced from 17.26 mm to 0.50 mm. In addition, in the five real patients, these average errors were decreased from 18.26 mm to 1.48 mm various breathing phases ranging from inhalation to exhalation were taken into account for patient setup. Uncertainty error assessment and different setup errors were obtained from each respiration phase. Conclusion This study proposed a new method for alignment of patient setup error using ANN model. Additionally, our correlation model (ANN) could estimate true patient position with less error