[Biomarkers of atherothrombotic and cardioembolic subtypes of acute ischemic stroke].

Objective: To evaluate the concentrations of CC-chemokines and stable metabolites of nitric oxide (NO) and endothelin-1 (ET-1) in patients with atherothrombotic (AT) and cardioembolic (CE) subtypes of ischemic stroke (IS) in the acute period.

Material And Methods: Sixty patients diagnosed with IS in the carotid basin were examined. Group 1 included 30 patients with AT, group 2 - 30 patients with CE subtype of IS.

Optimal Setup and Parameters of Diffusion-Weighted Magnetic Resonance Imaging for Translational Evaluation of a Tumor Progression Model for Soft Tissue Sarcomas.

Purpose: Defining a microscopic tumor infiltration boundary is critical to the success of radiation therapy. Currently, radiation oncologists use margins to geometrically expand the visible tumor for radiation treatment planning in soft tissue sarcomas (STS). Image-based models of tumor progression would be critical to personalize the treatment radiation field to the pattern of sarcoma spread.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Anatomy-based diffusion-weighted MRI quality metric: a proof-of-concept for deriving accurate muscle fiber orientation.

Diffusion-weighted MRI (DW-MRI) is used to quantitatively characterize the microscopic structure of muscle through anisotropic water diffusion in soft tissue. Applications such as tumor propagation modeling require precise detection of muscle fiber orientation. That is, the direction along the fibers that coincides with the direction of the principal eigenvector of the diffusion tensor reconstructed from DW-MRI data.

Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI.

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST.