Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.

Purpose: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance.

Methods And Materials: Patients with NPC scheduled for MSI RT workflow were prospectively enrolled. This workflow integrates RT procedures from computed tomography (CT) scan to beam delivery, all performed with the patient on the treatment couch.

Prior Knowledge-Guided U-Net for Automatic Clinical Target Volume Segmentation in Postmastectomy Radiation Therapy of Breast Cancer.

Purpose: This study aimed to design and evaluate a prior-knowledge-guided U-Net (PK-UNet) for automatic clinical target volume (CTV) segmentation in postmastectomy radiation therapy for breast cancer.

Methods And Materials: A total of 102 computed tomography (CT) scans from breast cancer patients who underwent postmastectomy were retrospectively collected. Of these, 80 scans were used for training with 5-fold cross-validation, and 22 scans for independent testing.

Symmetry engineering in 2D bioelectronics facilitating augmented biosensing interfaces.

Symmetry lies at the heart of two-dimensional (2D) bioelectronics, determining material properties at the fundamental level. Breaking the symmetry allows emergent functionalities and effects. However, symmetry modulation in 2D bioelectronics and the resultant applications have been largely overlooked.

Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

Objective: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.

Methods: A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set.

Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.

Background And Purpose: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.

Materials And Methods: Conventional T2-weighted MR and CT images were acquired from 90 rectal cancer patients at Peking University People's Hospital and 19 patients in public datasets. This study proposed a new model combining contrastive learning loss and consistency regularization loss to enhance the generalization of model for multi-center pelvic MRI-to-CT synthesis.

Semi-supervised auto-segmentation method for pelvic organ-at-risk in magnetic resonance images based on deep-learning.

Background And Purpose: In radiotherapy, magnetic resonance (MR) imaging has higher contrast for soft tissues compared to computed tomography (CT) scanning and does not emit radiation. However, manual annotation of the deep learning-based automatic organ-at-risk (OAR) delineation algorithms is expensive, making the collection of large-high-quality annotated datasets a challenge. Therefore, we proposed the low-cost semi-supervised OAR segmentation method using small pelvic MR image annotations.

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors.

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world's first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose.

[Sparse-view CT image restoration multiscale wavelet residual network].

Objective: Sparse-view CT has the advantages of accelerated data collection and reduced radiation dose, but data missing arising from the data collection process causes serious streaking artifact and noise in the images reconstructed using the traditional filtering back projection algorithm (FBP). To solve this problem, we propose a multi-scale wavelet residual network (MWResNet) to restore sparse-view CT images.

Methods: The MWResNet was based on the combination of deep learning and traditional model in MWCNN, and the wavelet network was combined with the residual block to enhance the network's ability to embed image features and speed up network training.

[Sparse-view helical CT reconstruction based on tensor total generalized variation minimization].

Objective: We propose a sparse-view helical CT iterative reconstruction algorithm based on projection of convex set tensor total generalized variation minimization (TTGV-POCS) to reduce the X-ray dose of helical CT scanning.

Methods: The three-dimensional volume data of helical CT reconstruction was viewed as the third-order tensor. The tensor generalized total variation (TTGV) was used to describe the structural sparsity of the three-dimensional image.

[Key technologies in digital breast tomosynthesis system:theory, design, and optimization].

Objective: To develop a digital breast tomosynthesis (DBT) imaging system with optimizes imaging chain.

Methods: Based on 3D tomography and DBT imaging scanning, we analyzed the methods for projection data correction, geometric correction, projection enhancement, filter modulation, and image reconstruction, and established a hardware testing platform. In the experiment, the standard ACR phantom and high-resolution phantom were used to evaluate the system stability and noise level.

Non-rigid image registration with dynamic Gaussian component density and space curvature preservation.

Image registration plays an important role in military and civilian applications, such as natural disaster damage assessment, environmental monitoring, ground change detection and military damage assessment, etc. This work presents a new feature-based non-rigid image registration method. The main contributions of this work are: (i) a dynamic Gaussian component density is designed to better exploit available potential image information and provide sufficient inlier pairs for image transformation; (ii) a spatial structure preservation, which consists of an image transformation space curvature preservation and a local spatial structure constrain, is proposed to constrain the image transforming cost as well as the local structure of feature points during feature point set registration.

[Design and optimization of a cone-beam CT system for extremity imaging].

Objective: To establish a cone beam computed tomography (ECBCT) system for high-resolution imaging of the extremities.

Methods: Based on three-dimensional X-Ray CT imaging and high-resolution flat plate detector technique, we constructed a physical model and a geometric model for ECBCT imaging, optimized the geometric calibration and image reconstruction methods, and established the scanner system. In the experiments, the pencil vase phantom, image quality (IQ) phantom and a swine feet were scanned using this imaging system to evaluate its effectiveness and stability.