A review of machine learning applications for the proton MR spectroscopy workflow
DMJ van de Sande, JP Merkofer… - Magnetic …, 2023 - Wiley Online Library
This literature review presents a comprehensive overview of machine learning (ML)
applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS …
applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS …
Emerging trends in fast MRI using deep-learning reconstruction on undersampled k-space data: a systematic review
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 …
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
DSMENet: Detail and structure mutually enhancing network for under-sampled MRI reconstruction
Y Wang, Y Pang, C Tong - Computers in Biology and Medicine, 2023 - Elsevier
Reconstructing zero-filled MR images (ZF) from partial k-space by convolutional neural
networks (CNN) is an important way to accelerate MRI. However, due to the lack of attention …
networks (CNN) is an important way to accelerate MRI. However, due to the lack of attention …
The role of AI in prostate MRI quality and interpretation: Opportunities and challenges
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues,
particularly in the diagnosis and management of prostate cancer. With the widespread …
particularly in the diagnosis and management of prostate cancer. With the widespread …
Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives
Radiofrequency (RF) coils for magnetic resonance imaging (MRI) applications serve to
generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF …
generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF …
Suppressing image blurring of PROPELLER MRI via untrained method
Objective. Periodically rotated overlapping parallel lines with enhanced reconstruction
(PROPELLER) used in magnetic resonance imaging (MRI) is inherently insensitive to …
(PROPELLER) used in magnetic resonance imaging (MRI) is inherently insensitive to …
[PDF][PDF] Direct: Deep image reconstruction toolkit
DIRECT is a Python, end-to-end pipeline for solving inverse problems emerging in image
processing. It is built with PyTorch (Paszke et al., 2019) and stores state-of-the-art deep …
processing. It is built with PyTorch (Paszke et al., 2019) and stores state-of-the-art deep …
De-aliasing and accelerated sparse magnetic resonance image reconstruction using fully dense CNN with attention gates
When sparsely sampled data are used to accelerate magnetic resonance imaging (MRI),
conventional reconstruction approaches produce significant artifacts that obscure the …
conventional reconstruction approaches produce significant artifacts that obscure the …
Machine Learning and Deep Learning Applications in Magnetic Particle Imaging
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging
technique depicting high sensitivity and spatial resolution. It originated in the early 2000s …
technique depicting high sensitivity and spatial resolution. It originated in the early 2000s …
AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language
Introduction The complexity of Magnetic Resonance Imaging (MRI) sequences requires
expert knowledge about the underlying contrast mechanisms to select from the wide range …
expert knowledge about the underlying contrast mechanisms to select from the wide range …