Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …

Brain tumors are abnormal cell growths inside the skull that damage brain cells needed for
brain function. The complex structure of the human brain makes it challenging to identify and …

Operando monitoring of internal and local electrochemical processes within lithium-ion
batteries (LIBs) is crucial, necessitating a range of non-invasive, real-time imaging …

Magnetic resonance imaging (MRI) is renowned for its exceptional soft tissue contrast and
high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep …

Magnetic resonance imaging (MRI) faces ongoing challenges associated with prolonged
acquisition times and susceptibility to motion artifacts. Compressed Sensing (CS) principles …

Abnormal behavior methods have attempted to reduce execution time, computational
complexity, efficiency, robustness against pixel occlusion, and generalizability. This …

Magnetic Resonance Imaging represents an important diagnostic modality; however, its
inherently slow acquisition process poses challenges in obtaining fully sampled k-space …

To extract maximum features ResAttNet (RAN) network structure is chosen as an alternative
to the convolutional layer and it enhances image feature extraction. Additionally, a …

Respiratory motion in abdomen generates motion artifacts during Dynamic Contrast
Enhanced MRI (DCE-MRI) data acquisition and it is clinically challenging to minimize the …

Abstract Background Recent Convolutional Neural Networks (CNNs) perform low-error
reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image …