MRgRT real-time target localization using foundation models for contour point tracking and promptable mask refinement.

. This study aimed to evaluate two real-time target tracking approaches for magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) based on foundation artificial intelligence models..

Patient-Specific Deep Learning Tracking Framework for Real-Time 2D Target Localization in Magnetic Resonance Imaging-Guided Radiation Therapy.

Purpose: We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on a pair of deep learning (DL) models relying on patient-specific (PS) training.

Methods And Materials: The chosen DL models are: (1) an image registration transformer and (2) an auto-segmentation convolutional neural network (CNN). We collected over 1,400,000 cine MRI frames from 219 patients treated on a 0.

Effect of mesenchymal stem cells on the host response in severe community-acquired pneumonia.

Mesenchymal stem cells (MSC) have immune regulatory properties that may ameliorate pathophysiological processes in sepsis. We determined the effect of allogeneic adipose-derived MSCs (Cx611) on the host response during sepsis due to community-acquired bacterial pneumonia (CABP) by measuring 29 plasma biomarkers and blood transcriptomes at six time points in 82 patients randomised to two intravenous infusions of Cx611 or placebo. Cx611 treatment enhanced several endothelial cell and procoagulant response plasma biomarkers, and led to increased expression of pathways related to innate immunity, haemostasis and apoptosis.

Impact of bias field correction on 0.35 T pelvic MR images: evaluation on generative adversarial network-based OARs' auto-segmentation and visual grading assessment.

Purpose: Magnetic resonance imaging (MRI)-guided radiotherapy enables adaptive treatment plans based on daily anatomical changes and accurate organ visualization. However, the bias field artifact can compromise image quality, affecting diagnostic accuracy and quantitative analyses. This study aims to assess the impact of bias field correction on 0.

Comparison of deep learning networks for fully automated head and neck tumor delineation on multi-centric PET/CT images.

Objectives: Deep learning-based auto-segmentation of head and neck cancer (HNC) tumors is expected to have better reproducibility than manual delineation. Positron emission tomography (PET) and computed tomography (CT) are commonly used in tumor segmentation. However, current methods still face challenges in handling whole-body scans where a manual selection of a bounding box may be required.