All conference submissions

Deep learning-based dose prediction has emerged as a transformative approach for rapid radiotherapy planning. However, these models typically operate as "black boxes," providing only deterministic predictions without quantifying...

Radiopharmaceuticals, Theranostics, and Nuclear Medicine

Apparent diffusion coefficient (ADC) is a widely used marker of tumor cellularity in diffusion MRI, but ADC values depend on b-value selection and are prone to reduced tumor conspicuity in the presence of edema. Restriction Spect...

To compare different dual-energy cone-beam CT (DE-CBCT) configurations for the Varian HyperSight system via relative electron density (ED) and effective atomic number (EAN) quantification, as well as contrast improvement.

Manual background selection for contrast-to-noise ratio (CNR) calculations in CT image quality assessment is time-consuming, operator-dependent, and introduces >15% measurement variability that compromises reproducibility. Advanc...

Clinical Trials Specialty Program

To develop a predictive tool to assess need for treatment plan adaptation.

Therapy Physics

Professional

Deep learning (DL)-based dose prediction has become an important component of modern radiotherapy treatment planning. However, most existing approaches depend on site-specific models, necessitating separate training for each anat...

Inverse radiotherapy treatment planning involves solving a highly non-convex optimization problem where Large language model (LLM) shows promise for its strong interactive reasoning capabilities. We propose a machine-to-machine i...

To update U.S. national adult CT DRLs and ADs using 2025 data and to demonstrate a scalable, acquisition-level, size-normalized framework for generating timely and clinically relevant national benchmarks.

Spectral imaging confers several benefits such as material decomposition, increased soft tissue contrast, and metal artifact reduction. Novel methods currently under evaluation for adoption to linac onboard imaging include a kV d...

Online adaptive proton therapy is limited by the time required for target/OAR re-segmentation and dosimetric review. We developed a contourless patient-specific AI framework to automatically trigger online plan adaptation in orop...

Differentiating radiation necrosis (RN) from tumor recurrence (TR) after radiotherapy remains challenging using conventional MRI. We investigated dynamic contrast-enhanced MRI (DCE-MRI) analysis using unbalanced regularized optim...

Clinical Trials Specialty Program

IM/TH- Radiopharmaceutical Therapy

To evaluate whether a Large Language Model (LLM)–driven autonomous planning system can self-learn planning strategies from human planner logs and apply this knowledge to generate clinically compatible radiotherapy plans without m...

Diagnostic reference levels (DRLs) and achievable doses (ADs) have been established in the United States for pediatric energy-integrating detector CT (EID-CT) based on ACR Dose Index Registry data. However, these benchmarks may n...

Proton dose distributions can be very sensitive to setup and anatomical variations. Specifically, back tissue correspondence in posterior beam spine SBRT and changes in lung density or pleural effusion in lung SBRT are scenarios...

The rapid advancement of Vision-Language Models (VLMs) offers transformative potential for automated radiology; however, their ability to interpret 3D brain tumor imaging remains underexplored. This study introduces a rigorous be...

Nonstop gated CBCT (ngCBCT) was developed to overcome the limitations of current gated CBCT (gCBCT), enabling 2-8x faster acquisitions and 2.5-3.5x lower imaging dose. However, ngCBCT produces highly non-uniform and under-sampled...

Professional

Therapy Physics