Yonsei University College of Medicine

While optically stimulated luminescence dosimeters (OSLD) offer advantages in reusability and efficiency, they overestimate the surface dose due to thick effective point of measurement. The aim of this study is to investigate the detector-specific correction...

Onboard real-time MRI imaging in MRgRT offers exceptional benefits for treating abdominothoracic cancers. However, X-ray based radiotherapy at community clinics cannot provide the same advantages. We developed a deep learning network capable of real-time 3D M...

Cone-beam CT (CBCT) acquired on the linear accelerator is being increasingly used beyond image guidance to support simulation and treatment planning workflows. This work reports the clinical implementation, challenges, and solutions associated with CBCT-based...

This study aims to develop a deep learning (DL) model capable of predicting Monte Carlo (MC)-level carbon-ion dose distributions from Pencil Beam Algorithm (PBA) calculations. The objective is to achieve the high accuracy of MC simulations while maintaining t...

Magnetic resonance (MR)–only radiotherapy planning requires accurate synthetic CT (sCT) generation from images acquired using standard clinical MRI simulation protocols. However, MRI acquisition protocols vary substantially across anatomical sites, and many e...

Extremely sparse-view CT benefits for reducing radiation dose while causing streak artifact when using the traditional filtered-back projection (FBP). We propose a new learning-based reconstruction method, named HYPER (HYbrid framework combining pre-trained s...

In carbon ion radiotherapy (CIRT), treatment-day position verification is typically performed using two-dimensional (2D) X-ray images acquired at non-orthogonal angles. While effective for bone-based alignment, this approach provides limited three-dimensional...

Patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) is essential for verifying plan deliverability, while it remains resource-intensive and inefficient. We developed a multi-task deep learning framework that jointly predicts...

To mitigate the loss of spatial information inherent to DVH-based and coarse categorical descriptors used for breast cancer–related lymphedema prediction, we present a multi-modal cross-attention predictive model with self-attention refinement using patient-s...

While Denoising Diffusion Probabilistic Models (DDPMs) have set new benchmarks for synthetic CT (sCT) image quality, their prohibitive inference times hinder integration into online adaptive radiation therapy (ART) workflows. This study introduces HQ-PatchNet...