All conference submissions

It was critical yet impossible to quantitatively assess the impact of patient-specific cardiorespiratory motion on dose plans for stereotactic arrhythmia radioablation (STAR) treatments. To fill the gap, a fast algorithm, SPM (sp...

To develop and characterize a flexible silica-silicone composite incorporating phosphoric acid-impregnated silica to improve phosphorus-32 immobilization and leachability control for plesiobrachytherapy applications.

Grade ≥2 radiation pneumonitis (RP) occurs in approximately 30% of patients undergoing radiotherapy for lung cancer. Therefore, a predictive model to estimate RP risk before radiotherapy is required. Radiomics and dosiomics have...

This study aims to quantitatively evaluate the three-dimensional positional fidelity of implanted fiducial markers in deep learning-based sparse-view reconstruction from on-treatment kV images acquired during prostate stereotacti...

Accurate survival prediction in head and neck squamous cell carcinoma (HNSCC) is clinically important for risk stratification but remains challenging due to small cohort sizes and the difficulty of integrating high-dimensional PE...

Accurate multi-organ auto-segmentation is essential for efficient clinical workflows. Although nnU-Net-based models like TotalSegmentator achieve strong baseline performance, residual errors can lead to boundary outliers that req...

To develop an MR organs-at-risk (OAR) auto-contouring pipeline that is robust in severely limited-data settings across contrasts, specifically for MR-Linac pancreatic cancer treatments where full-volume manual contouring can take...

Deep Learning (DL) auto-segmentation models rely on voxel-wise probability maps, often resulting in jagged boundaries or "spill-over". Traditional post-processing, such as 2D active contours, fails in low-contrast scenarios due t...

Existing deep learning-based dose prediction methods primarily learn empirical mappings between anatomy and dose, without modeling beam delivery physics. This gap may limit their robustness and accuracy, especially in heterogeneo...

This study is to report our recent comprehensive validation of an in-house preclinical alpha irradiation platform with versatile control over fluence rate, energy, temporal, and spatial irradiation patterns.

In the rapidly developing field of radiopharmaceuticals, the skeleton poses a uniquely daunting challenge to any effort to safely employ these powerful tools. The skeleton’s wide distribution throughout the body as well as its va...

Accurate assessment of early radiotherapy response in tumors provides crucial guidance for optimizing radiotherapy protocols. We developed a 3D deep learning model termed Attention Med3D based on transfer learning and attention m...

Dosimetry software programs use different computational approaches which may lead to clinically relevant differences in reported absorbed doses (AD). This study aims to evaluate the impact of software methodology on AD calculatio...

With advances in Targeted Alpha Therapy (TAT) preclinical and clinical research, the importance for accurate and traceable dosimetry with sub-organ resolution becomes critical. Current limitations in verifying imaging resolution...

Accurate PET/CT registration is essential for quantitative cardiac PET. Common approaches include manual rigid registration of CT to PET (MRR) and average CT derived from cine CT (ACT). We evaluated whether data-driven gated PET...

Despite the widespread use of Lu-177-PSMA for treatment of prostate cancer, the association between renal dose and toxicity remains unclear. This study investigates this relationship.

Direct imaging of Y‑90 bremsstrahlung with SPECT remains challenging because the continuous photon energy spectrum introduces scatter and septal penetration which reduces contrast and complicates quantitative reconstruction. Opti...

The Bragg peak of proton beam enables precise dose delivery and superior dose modulation in proton lattice radiotherapy (LRT), yet it remains susceptible to uncertainties. This study aims to investigate the impact of robust param...

The purpose of this study is to improve the performance of diffusion-based neural networks by conditioning using representations from pre-trained models. Conditioning with a 3D encoder enforces volumetric consistency across slice...

To enhance radiotherapy quality and safety through a structured, peer to peer clinical audit initiative conducted at a regional level, aiming to establish a scalable national framework. The project integrates multidisciplinary on...

Ovarian cancer, recognized as the most lethal gynecologic malignancy, is frequently detected at advanced stages and is associated with an unfavorable prognosis and heterogeneous therapeutic responses. Poly (ADP-ribose) polymerase...

Biological in vivo three-dimensional dose distribution verification, introduced as a new concept in radiotherapy, was proposed to enable evaluation of the impact of respiratory motion on dose-delivery accuracy and to allow the in...

To assess dual-layer DECT-derived SPR images for proton therapy by identifying tissue-specific deviations versus SECT, validating discrepancies using measured SPR in biological tissues, and evaluating dosimetric impact of DECT-ba...

Peer review is a critical quality assurance step in radiation therapy (RT), but not all cases require the same level of attention. We aimed to develop a machine learning (ML) tool to help prioritize breast RT plans for peer revie...