Beihang University

In proton therapy with pencil beam scanning (PBS), repainting mitigates respiratory motion effects but increases treatment time, burdening patients who must remain on the couch for extended periods. To address this challenge, we propose an advanced planning s...

To develop cross-attention-based multi-modal deep learning models and to preliminarily validate their performance for predicting local recurrence (LR) in patients with non-small cell lung cancer (NSCLC).

This study proposes a novel elemental composition modeling framework for constructing high-fidelity anthropomorphic phantoms, addressing the critical bottleneck of clinical data scarcity in medical imaging research.

Dual-energy CT (DECT) enables material differentiation by exploiting the energy-dependent attenuation characteristics of tissues, which is particularly beneficial for carbon ion therapy. This study systematically evaluated a recently proposed machine-learning...

To develop risk-stratification and prognosis-prediction models for limited-stage small cell lung cancer (LS-SCLC) patients, suggesting potential candidates that may benefit from the new treatment protocol using high-dose hyperfractionated simultaneous integra...

Dose-mimicking aims to generate treatment plans based on a reference dose distribution. The importance of dose-mimicking has grown substantially due to the increasing demand for rapid plan adaptation in adaptive radiotherapy—particularly in proton therapy, wh...

Intensity-modulated proton therapy (IMPT) with pencil beam scanning (PBS) delivers sequential proton spots as the fundamental delivery unit. By tuning each spot’s spatial position, energy level, and fluence intensity, it enables layer-by-layer tumor target do...

CBCT is routinely acquired prior to proton therapy for patient setup. However, the limited image quality of CBCT compromises the dose calculation accuracy and limits its use for treatment plan adjustments. This study aims to develop a high-performance CBCT-to...

Monte Carlo (MC) simulation is the gold standard for dose calculation in radiation therapy, as it can accurately reproduce the interaction processes between photons and human tissues in intensity-modulated radiation therapy (IMRT) and generate high-precision...

The escalating global burden of cancer necessitates reliable and broadly generalizable automated treatment planning (ATP) systems. Current data-driven ATP approaches often exhibit insufficient generalization when applied to diverse clinical prescriptions. To...

During radiotherapy, anatomical changes may impact the suitability of the initial treatment plan, necessitating the application of adaptive radiation therapy (ART). The MVCT serves as the patient positioning image, enabling dose reconstruction. However, its l...

High-risk locally advanced cervical cancer (HR-LACC) remains challenging due to its heterogeneous prognosis and high recurrence rates, underscoring the need for precise and personalized treatment strategies. This study aims to develop and validate an interpre...

To develop a deep learning framework that simultaneously synthesizes lung perfusion and ventilation images from three-dimensional (3D) CT and to evaluate its potential clinical utility.