PND-Net: Physics-Inspired Non-Local Dual-Domain Network for Metal Artifact Reduction.

Metal artifacts caused by the presence of metallic implants tremendously degrade the quality of reconstructed computed tomography (CT) images and therefore affect the clinical diagnosis or reduce the accuracy of organ delineation and dose calculation in radiotherapy. Although various deep learning methods have been proposed for metal artifact reduction (MAR), most of them aim to restore the corrupted sinogram within the metal trace, which removes beam hardening artifacts but ignores other components of metal artifacts. In this paper, based on the physical property of metal artifacts which is verified via Monte Carlo (MC) simulation, we propose a novel physics-inspired non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component and develop an image restoration network (IR-Net) to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the data fidelity of anatomical structures in the image domain and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. NSD-Net, IR-Net, and F-Net are jointly trained so that they can benefit from one another. Extensive experiments on simulation and clinical data demonstrate that our method outperforms state-of-the-art MAR methods.

Adaptive scatter kernel deconvolution modeling for cone-beam CT scatter correction via deep reinforcement learning.

BACKGROUND: Scattering photons can seriously contaminate cone-beam CT (CBCT) image quality with severe artifacts and substantial degradation of CT value accuracy, which is a major concern limiting the widespread application of CBCT in the medical field. The scatter kernel deconvolution (SKD) method commonly used in clinic requires a Monte Carlo (MC) simulation to determine numerous quality-related kernel parameters, and it cannot realize intelligent scatter kernel parameter optimization, causing limited accuracy of scatter estimation. PURPOSE: Aiming at improving the scatter estimation accuracy of the SKD algorithm, an intelligent scatter correction framework integrating the SKD with deep reinforcement learning (DRL) scheme is proposed. METHODS: Our method firstly builds a scatter kernel model to iteratively convolve with raw projections, and then the deep Q-network of the DRL scheme is introduced to intelligently interact with the scatter kernel to achieve a projection adaptive parameter optimization. The potential of the proposed framework is demonstrated on CBCT head and pelvis simulation data and experimental CBCT measurement data. Furthermore, we have implemented the U-net based scatter estimation approach for comparison. RESULTS: The simulation study demonstrates that the mean absolute percentage error (MAPE) of the proposed method is less than 9.72% and the peak signal-to-noise ratio (PSNR) is higher than 23.90 dB, while for the conventional SKD algorithm, the minimum MAPE is 17.92% and the maximum PSNR is 19.32 dB. In the measurement study, we adopt a hardware-based beam stop array algorithm to obtain the scatter-free projections as a comparison baseline, and our method can achieve superior performance with MAPE < 17.79% and PSNR > 16.34 dB. CONCLUSIONS: In this paper, we propose an intelligent scatter correction framework that integrates the physical scatter kernel model with DRL algorithm, which has the potential to improve the accuracy of the clinical scatter correction method to obtain better CBCT imaging quality.

A transmission measurement-based spectrum estimation method incorporating X-ray tube voltage fluctuation.

Background: The energy spectrum is the property of the X-ray tube that describes the energy fluence per unit interval of photon energy. The existing indirect methods for estimating the spectrum ignore the influence caused by the voltage fluctuation of the X-ray tube. Methods: In this work, we propose a method for estimating the X-ray energy spectrum more accurately by including the voltage fluctuation of the X-ray tube. It expresses the spectrum as the weighted summation of a set of model spectra within a certain voltage fluctuation range. The difference between the raw projection and the estimated projection is considered as the objective function for obtaining the corresponding weight of each model spectrum. The equilibrium optimizer (EO) algorithm is used to find the weight combination that minimizes the objective function. Finally, the estimated spectrum is obtained. We refer to the proposed method as the poly-voltage method. The method is mainly aimed at the cone-beam computed tomography (CBCT) system. Results: The model spectra mixture evaluation and projection evaluation showed that the reference spectrum can be combined by multiple model spectra. They also showed that it is appropriate to choose about 10% of the preset voltage as the voltage range of the model spectra, which can match the reference spectrum and projection quite well. The phantom evaluation showed that the beam-hardening artifact can be corrected using the estimated spectrum via the poly-voltage method, and the poly-voltage method provides not only the accurate reprojection but also an accurate spectrum. The normalized root mean square error (NRMSE) index between the spectrum generated via the poly-voltage method and the reference spectrum could be kept within 3% according to above evaluations. There existed a 1.77% percentage error between the estimated scatter of polymethyl methacrylate (PMMA) phantom using the two spectra generated via the poly-voltage method and the single-voltage method, and it could be considered for scatter simulation. Conclusions: Our proposed poly-voltage method could estimate the spectrum more accurately for both ideal and more realistic voltage spectra, and it is robust to the different modes of voltage pulse.

Multimodality MRI synchronous construction based deep learning framework for MRI-guided radiotherapy synthetic CT generation.

Synthesizing computed tomography (CT) images from magnetic resonance imaging (MRI) data can provide the necessary electron density information for accurate dose calculation in the treatment planning of MRI-guided radiation therapy (MRIgRT). Inputting multimodality MRI data can provide sufficient information for accurate CT synthesis: however, obtaining the necessary number of MRI modalities is clinically expensive and time-consuming. In this study, we propose a multimodality MRI synchronous construction based deep learning framework from a single T1-weight (T1) image for MRIgRT synthetic CT (sCT) image generation. The network is mainly based on a generative adversarial network with sequential subtasks of intermediately generating synthetic MRIs and jointly generating the sCT image from the single T1 MRI. It contains a multitask generator and a multibranch discriminator, where the generator consists of a shared encoder and a splitted multibranch decoder. Specific attention modules are designed within the generator for feasible high-dimensional feature representation and fusion. Fifty patients with nasopharyngeal carcinoma who had undergone radiotherapy and had CT and sufficient MRI modalities scanned (5550 image slices for each modality) were used in the experiment. Results showed that our proposed network outperforms state-of-the-art sCT generation methods well with the least MAE, NRMSE, and comparable PSNR and SSIM index measure. Our proposed network exhibits comparable or even superior performance than the multimodality MRI-based generation method although it only takes a single T1 MRI image as input, thereby providing a more effective and economic solution for the laborious and high-cost generation of sCT images in clinical applications.

Deep learning based de-overlapping correction of projections from a flat-panel micro array X-ray source: Simulation study.

PURPOSE: Flat-panel X-ray source is an experimental X-ray emitter with target application of static computer tomography (CT), which can save imaging space and time. However, the X-ray cone beams emitted by the densely arranged micro-ray sources are overlapped, causing serious structural overlapping and visual blur in the projection results. Traditional deoverlapping methods can hardly solve this problem well. METHOD: We converted the overlapping cone beam projections to parallel beam projections through a U-like neural network and selected structural similarity (SSIM) loss as the loss function. In this study, we converted three kinds of overlapping cone beam projections of the Shepp-Logan, line-pairs, and abdominal data with two overlapping levels to corresponding parallel beam projections. Training completed, we tested the model using the test set data that was not used at the training phase, and evaluated the difference between the test set conversion results and their corresponding parallel beams through three indicators: mean squared error (MSE), peak signal-to-noise ratio (PSNR) and SSIM. In addition, projections from head phantoms were applied for generalization test. RESULT: In the Shepp-Logan low-overlapping task, we obtained a MSE of 1.624×10-5, a PSNR of 47.892 dB, and a SSIM of 0.998 which are the best results of the six experiments. For the most challenging abdominal task, the MSE, PSNR, and SSIM are 1.563×10-3, 28.0586 dB, and 0.983, respectively. In more generalized data, the model also achieved good results. CONCLUSION: This study proves the feasibility of utilizing the end-to-end U-net for deblurring and deoverlapping in the flat-panel X-ray source domain.

A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction.

BACKGROUND: In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. PURPOSE: In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. METHODS: Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. RESULTS: Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25 s for the whole iterative scatter correction process. CONCLUSIONS: The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

Multisequence MR-generated sCT is promising for HNC MR-only RT: A comprehensive evaluation of previously developed sCT generation networks.

PURPOSE: To verify the feasibility of our in-house developed multisequence magnetic resonance (MR)-generated synthetic computed tomography (sCT) for accurate dose calculation and fractional positioning for head and neck MR-only radiation therapy (RT). METHODS: Forty-five patients with nasopharyngeal carcinoma were retrospectively studied. By applying our previously in-house developed network, a patient's sCT can rapidly be generated with respect to feeding the sole T1 image, T1C image, T1DixonC image, T2 image, and their combination (five pipelines in total). A k(5)-fold strategy was implemented during model establishment. Dose recalculation was performed for each pipeline generation to attain a dosimetric feasibility evaluation. Fractional positioning evaluation was performed by calculating the digitally reconstructed radiograph (DRR) of the sCT and planning CT and their offset to the portal image. RESULTS: The dose mean absolute error values were (0.47±0.16)%, (0.48±0.15)% (p < 0.05), (0.50±0.16)% (p < 0.05), (0.50±0.15)% (p < 0.05), and (0.45±0.16)% (p < 0.05) for the T1, T1C, T1Dixon C, T2, and 4-channel generated sCT to the prescription dose, respectively. The 4-channel-generated sCT outperforms any other single-sequence pipeline. Among the single-sequence MR imaging-generated sCTs, the T1-generated sCT shows the most accurate HU image quality and provides a reliable dose result. Quantified positioning errors with calculation of the difference to the planning CT offsets are (-0.26±0.50) mm, (-0.58±0.52) mm (p < 0.05), (-0.27±0.57) mm (p > 0.05), (-0.31±0.44) mm (p > 0.05), and (-0.19±0.37) mm (p > 0.05) at LNG and (0.34±0.53) mm, (0.48±0.56) mm (p > 0.05), (0.55±0.56) mm (p > 0.05), (0.37±0.61) mm (p > 0.05), and (0.24±0.43) mm (p > 0.05) at LAT of the anterior-posterior direction for the five pipelines. CONCLUSION: Multisequence MR-generated sCT allows for accurate dose calculation and fractional positioning for head and neck MR-only RT.

Deep learning-based 3Din vivodose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.

Objective.To develop a novel deep learning-based 3Din vivodose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).Approach.The proposed method directly back-projected 2D portal dose into 3D patient coarse dose, which bypassed the complicated patient-to-EPID scatter estimation step used in conventional methods. A pre-trained convolutional neural network (CNN) was then employed to map the coarse dose to the final accurate dose. The electron return effect caused by the magnetic field was captured with the CNN model. Patient dose and portal dose datasets were synchronously generated with Monte Carlo simulation for 96 patients (78 cases for training and validation and 18 cases for testing) treated with fixed-beam intensity-modulated radiotherapy in four different tumor sites, including the brain, nasopharynx, lung, and rectum. Beam angles from the training dataset were further rotated 2-3 times, and doses were recalculated to augment the datasets.Results.The comparison between reconstructed doses and MC ground truth doses showed mean absolute errors <0.88% for all tumor sites. The averaged 3Dγ-passing rates (3%, 2 mm) were 97.42%±2.66% (brain), 98.53%±0.95% (nasopharynx), 99.41%±0.46% (lung), and 98.63%±1.01% (rectum). The dose volume histograms and indices also showed good consistency. The average dose reconstruction time, including back projection and CNN dose mapping, was less than 3 s for each individual beam.Significance.The proposed method can be potentially used for accurate and fast 3D dosimetric verification for online adaptive radiotherapy using MR-LINACs.

Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

OBJECTIVES: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases. MATERIALS AND METHODS: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models. RESULTS: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37). CONCLUSION: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.

Multi-sequence MR image-based synthetic CT generation using a generative adversarial network for head and neck MRI-only radiotherapy.

PURPOSE: The purpose of this study is to investigate the effect of different magnetic resonance (MR) sequences on the accuracy of deep learning-based synthetic computed tomography (sCT) generation in the complex head and neck region. METHODS: Four sequences of MR images (T1, T2, T1C, and T1DixonC-water) were collected from 45 patients with nasopharyngeal carcinoma. Seven conditional generative adversarial network (cGAN) models were trained with different sequences (single channel) and different combinations (multi-channel) as inputs. To further verify the cGAN performance, we also used a U-net network as a comparison. Mean absolute error, structural similarity index, peak signal-to-noise ratio, dice similarity coefficient, and dose distribution were evaluated between the actual CTs and sCTs generated from different models. RESULTS: The results show that the cGAN model with multi-channel (i.e., T1 + T2 + T1C + T1DixonC-water) as input to predict sCT achieves higher accuracy than any single MR sequence model. The T1-weighted MR model achieves better results than T2, T1C, and T1DixonC-water models. The comparison between cGAN and U-net shows that the sCTs predicted by cGAN retains additional image details are less blurred and more similar to the actual CT. CONCLUSIONS: Conditional generative adversarial network with multiple MR sequences as model input shows the best accuracy. The T1-weighted MR images provide sufficient image information and are suitable for sCT prediction in clinical scenarios with limited acquisition sequences or limited acquisition time.

A double-helix-structured triboelectric nanogenerator enhanced with positive charge traps for self-powered temperature sensing and smart-home control systems.

Triboelectric nanogenerators (TENGs) have been in spotlight for their excellent capability to drive miniature electronics. Herein, we report a sophisticated double-helix-structured triboelectric nanogenerator (DHS-TENG) enhanced with positive charge traps for self-powered temperature sensing and smart-home control system. The DHS-TENG increases the charge density on the contact surfaces by taking advantage of the ferroelectric characteristics of polyvinylidene fluoride (PVDF). In addition, the flexible double-helix-structure endows DHS-TENG with excellent elastic property as it has no external supporting materials. The reported DHS-TENG, with the dimensions of 3 cm × 3 cm × 5 cm and a light weight of 10 g, can deliver a peak output power of 9.03 mW under a loading resistance of 4 MΩ. It also delivers an enhanced output performance of 460 V, 140 µA and 400 nC under a constant contact force of 40 N. Furthermore, the DHS-TENG is capable of powering 120 green LEDs and enabling a temperature sensor to work properly. In particular, the DHS-TENG demonstrates the capability of successful remote data transmission for application in smart-home control systems within 10 meters.

[Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning].

OBJECTIVE: To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical prediction of the plan dosimetric features. METHODS: A total of 25 fixed 13-field clinical prostate cancer intensity-modulated radiation therapy (IMRT)/stereotactic body radiation therapy (SBRT) plans were collected with a prescribed dose of 50 Gy. With the distance from each voxel to the planned target volume (PTV) boundary, the distance from each voxel to each organ-at-risk (OAR), and the volume of PTV as the geometric anatomical characteristics of the patients, the voxel deposition dose was used as the plan dosimetric feature. A neural network was used to construct the correlation model between the selected input features and output dose distribution, and the model was trained with 20 randomly selected cases and verified in 5 cases. RESULTS: The constructed model showed a small model training error, small dose differences among the verification samples, and produced accurate prediction results. In the model training, the point-to-point mean dose difference (hereinafter dose difference) of the 3D dose distribution was no greater than 0.0919∓3.6726 Gy, and the average of the relative volume values corresponding to the fixed dose sequence in the DVH (hereinafter DVH difference) did not exceed 1.7%. The dose differences among the 5 samples for validation was 0.1634∓10.5246 Gy with percent dose differences within 2.5% and DVH differences within 3%. The 3D dose distribution showed that the dose difference was small with reasonable predicted dose distribution. This model showed better performances for dose distribution prediction for bladder and rectum than for the femoral heads. CONCLUSION: We established the relationships between the geometric anatomical characteristics of the patients and the corresponding planning 3D dose distribution via feed-forward back-propagation neural network in patients receiving IMRT/SBRT for the same tumor site. The proposed model provides individualized quality standards for automatic plan quality control.