A feasibility study of tumor motion monitoring for SBRT of lung cancer based on 3D point cloud detection and stacking ensemble learning.

Purpose: To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective.

Methods: A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions.

A feasibility study of dosimetry for breast cancer radiotherapy based on body surface changes.

Background: The requirement for precise and effective delivery of the actual dose to the patient grows along with the complexity of breast cancer radiotherapy. Dosimetry during treatment has become a crucial component of guaranteeing the efficacy and security.

Purpose: To propose a dosimetry method during breast cancer radiotherapy based on body surface changes.

From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.

Purpose: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviation and estimate planning delivery quality according to the VMAT plans before delivery.

Methods: A total of 744 "dynalog" files for 23 VMAT plans were extracted randomly from treatment database.

A generalized model of cardiac surface motion for evaluating left anterior descending coronary artery dose in left breast cancer radiotherapy.

Background: Retrospective studies indicate that radiation damage to left anterior descending coronary artery (LAD) may be critical for late-stage radiation-induced cardiac morbidity. Developing a method that accurately depicts LAD motion and perform dose assessment is crucial.

Purpose: To construct a generalized cardiac surface motion model for LAD dose assessment in left breast cancer radiotherapy.

A markerless beam's eye view motion monitoring algorithm based on structure conversion and demons registration in medical image with partial data.

Purpose: To propose a markerless beam's eye view (BEV) motion monitoring algorithm, which works with the inferior quality megavolt (MV) images with multi-leaf collimator (MLC) occlusion-compatible.

Methods: A thorax phantom was used to verify the accuracy of the algorithm. Lung tumor quality assurance (QA) plans were generated for the phantom, and delivered 10 times on the linear accelerator with manually treatment offsets in various directions.

A markerless beam's eye view tumor tracking algorithm based on unsupervised deformable registration learning framework of VoxelMorph in medical image with partial data.

Purpose: To propose an unsupervised deformable registration learning framework-based markerless beam's eye view (BEV) tumor tracking algorithm for the inferior quality megavolt (MV) images with occlusion and deformation.

Methods: Quality assurance (QA) plans for thorax phantom were delivered to the linear accelerator with artificially treatment offsets. Electronic portal imaging device (EPID) images (682 in total) and corresponding digitally reconstructed radiograph (DRR) were gathered as the moving and fixed image pairs, which were randomly divided into training and testing set in a ratio of 0.