Automated Detection of Brain Metastases on T1-Weighted MRI Using a Convolutional Neural Network: Impact of Volume Aware Loss and Sampling Strategy.

Background: Detection of brain metastases (BM) and segmentation for treatment planning could be optimized with machine learning methods. Convolutional neural networks (CNNs) are promising, but their trade-offs between sensitivity and precision frequently lead to missing small lesions.

Hypothesis: Combining volume aware (VA) loss function and sampling strategy could improve BM detection sensitivity.

Inflammation and Hypervascularization in a Large Animal Model of Knee Osteoarthritis: Imaging with Pathohistologic Correlation.

Purpose: To evaluate if synovial inflammation and hypervascularization are present in a dog model of knee osteoarthritis and can be detected on conventional magnetic resonance imaging (MRI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), contrast-enhanced magnetic resonance imaging (CE-MRI), and quantitative digital subtraction angiography (Q-DSA) imaging.

Materials And Methods: Six dogs underwent MRI and angiography of both knees before and 12 weeks after right knee anterior cruciate ligament injury. Synovial vascularity was evaluated on CE-MRI, DCE-MRI, and Q-DSA by 2 independent observers.