Artificial intelligence (AI) as an emerging technology is gaining momentum in medical
imaging. Recently, deep learning-based AI techniques have been actively investigated in …

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

Purpose To introduce a dual‐domain reconstruction network with V‐Net and K‐Net for
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …

Image reconstruction from undersampled k-space data plays an important role in
accelerating the acquisition of MR data, and a lot of deep learning-based methods have …

Magnetic resonance imaging (MRI) has become one of the most powerful imaging
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …

Deep learning MRI reconstruction methods are often based on Convolutional neural
network (CNN) models; however, they are limited in capturing global correlations among …

Abstract Magnetic Resonance Imaging (MRI) has been widely applied in medical clinical
diagnosis. Generally, obtaining a high spatial resolution MR image takes up to tens of …

Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume
under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the …

Accurate navigation and targeting are critical for neurological interventions including biopsy
and deep brain stimulation. Real-time image guidance further improves surgical planning …