Background Achieving high-spatial-resolution pituitary MRI is challenging because of the trade-off between image noise and spatial resolution. Deep learning-based MRI reconstruction enables image denoising with sharp edges and reduced artifacts, whic...
Interdisciplinary sciences, computational life sciences
Nov 2, 2020
White matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual...
In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical tre...
Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, and it provides insights into the underlying vascular pathology (CAA vs. hypertensive angiopathy) to ...
OBJECTIVES: Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI ...
PURPOSE: To develop a fast, quantitative 3D magnetization transfer contrast (MTC) framework based on an unsupervised learning scheme, which will provide baseline reference signals for CEST and nuclear Overhauser enhancement imaging.
Computer methods and programs in biomedicine
Oct 31, 2020
BACKGROUND: Brain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient. This...
The international journal of cardiovascular imaging
Oct 29, 2020
The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early ...
Multiple sclerosis (Houndmills, Basingstoke, England)
Oct 28, 2020
BACKGROUND: Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial in...
Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs ar...
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