Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of
radiation to tumors while sparing healthy tissues over multiple days. Computed tomography …

The ability to translate medical images across different modalities is crucial for synthesizing
missing data and aiding in clinical diagnosis. However, existing learning-based techniques …

Medical datasets are vital for advancing Artificial Intelligence (AI) in healthcare. Yet biases in
these datasets on which deep-learning models are trained can compromise reliability. This …

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods
and, as such, has a high affinity for the automation potential afforded by modern artificial …

Background Although cone beam computed tomography (CBCT) has lower resolution
compared to planning CTs (pCT), its lower dose, higher high‐contrast resolution, and …

The integration of artificial intelligence (AI) techniques into radiation therapy (RT) represents
a unique opportunity to significantly enhance accuracy, efficiency and outcomes of radiation …

Background and purpose The emergence of synthetic CT (sCT) in MR-guided radiotherapy
(MRgRT) represents a significant advancement, supporting MR-only workflows and online …

Background and purpose Deep learning techniques excel in MR-based CT synthesis, but
missing uncertainty prediction limits its clinical use in proton therapy. We developed an …

Image registration, a critical process in medical imaging, involves aligning different sets of
medical imaging data into a single unified coordinate system. Deep learning networks, such …

Many clinical works require medical inter-modality imaging results since the supplementary
imaging information from different modalities can be combined to provide better decision …