Deep learning–based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI
Objective To compare the image quality and diagnostic performance between standard
turbo spin-echo MRI and accelerated MRI with deep learning (DL)–based image …
turbo spin-echo MRI and accelerated MRI with deep learning (DL)–based image …
[HTML][HTML] A systematic review and identification of the challenges of deep learning techniques for undersampled magnetic resonance image reconstruction
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 …
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …
[HTML][HTML] 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 …
[HTML][HTML] Diagnostic performance of deep learning–based reconstruction algorithm in 3D MR neurography
F Ensle, M Kaniewska, A Tiessen, M Lohezic… - Skeletal Radiology, 2023 - Springer
Objective The study aims to evaluate the diagnostic performance of deep learning–based
reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and …
reconstruction method (DLRecon) in 3D MR neurography for assessment of the brachial and …
[HTML][HTML] Diagnostic image quality of a low-field (0.55 T) knee MRI protocol using deep learning image reconstruction compared with a standard (1.5 T) knee MRI …
I Lopez Schmidt, N Haag, I Shahzadi… - Journal of Clinical …, 2023 - mdpi.com
Objectives: Low-field MRI at 0.55 Tesla (T) with deep learning image reconstruction has
recently become commercially available. The objective of this study was to evaluate the …
recently become commercially available. The objective of this study was to evaluate the …
[HTML][HTML] Application of deep learning–based image reconstruction in MR imaging of the shoulder joint to improve image quality and reduce scan time
M Kaniewska, E Deininger-Czermak, JM Getzmann… - European …, 2023 - Springer
Objectives To compare the image quality and diagnostic performance of conventional
motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction …
motion-corrected periodically rotated overlapping parallel line with enhanced reconstruction …
[HTML][HTML] Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers
T Dratsch, F Siedek, C Zäske, K Sonnabend… - European Radiology …, 2023 - Springer
Background To investigate the potential of combining compressed sensing (CS) and deep
learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic …
learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic …
Deep learning reconstruction enables prospectively accelerated clinical knee MRI
Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep
learning (DL) methods have provided accelerated high-quality image reconstructions from …
learning (DL) methods have provided accelerated high-quality image reconstructions from …
Self-attention convolutional neural network for improved MR image reconstruction
MRI is an advanced imaging modality with the unfortunate disadvantage of long data
acquisition time. To accelerate MR image acquisition while maintaining high image quality …
acquisition time. To accelerate MR image acquisition while maintaining high image quality …
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Purpose To advance research in the field of machine learning for MR image reconstruction
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
with an open challenge. Methods We provided participants with a dataset of raw k‐space …