Publications by authors named "Quin Lu"
Article Synopsis
- Deep learning (DL) models for automatic segmentation can save time but need extensive fine-tuning for new datasets; this study evaluates their ability to work without such adjustments.
- The researchers tested pretrained DL models on different datasets that varied by scanner type and patient characteristics, including healthy individuals and those with knee issues.
- Results showed that the DL model trained specifically on qDESS data outperformed the one trained on Osteoarthritis Initiative data in terms of segmentation accuracy and agreement with manual measurements.
Background: Conventional chest and abdominal MRI require breath-holds to reduce motion artifacts. Neonates and infants require general anesthesia with intubation to enable breath-held acquisitions.
Objective: We aimed to validate a free-breathing approach to reduce general anesthesia using a motion-insensitive radial acquisition with respiratory gating.
Magnetic resonance imaging offers unrivaled visualization of the fetal brain, forming the basis for establishing age-specific morphologic milestones. However, gauging age-appropriate neural development remains a difficult task due to the constantly changing appearance of the fetal brain, variable image quality, and frequent motion artifacts. Here we present an end-to-end, attention-guided deep learning model that predicts gestational age with R score of 0.
View Article and Find Full Text PDFBackground: Conventional pediatric volumetric MRI acquisitions of a short-axis stack typically require multiple breath-holds under anesthesia.
Objective: Here, we aimed to validate a vendor-optimized compressed-sensing approach to reduce scan time during short-axis balanced steady-state free precession (bSSFP) cine imaging.
Materials And Methods: Imaging was performed in 28 patients (16±9 years) in this study on a commercial 3-tesla (T) scanner using retrospective electrocardiogram-gated cine bSSFP.