Deep learning for retrospective motion correction in MRI: a comprehensive review
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
Principles of artificial intelligence and its application in cardiovascular medicine
H Wieneke, I Voigt - Clinical Cardiology, 2024 - Wiley Online Library
Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis
and therapy in many areas of medicine. Despite this enormous progress, many physicians …
and therapy in many areas of medicine. Despite this enormous progress, many physicians …
Motion correction and its impact on quantification in dynamic total-body 18F-fluorodeoxyglucose PET
Background The total-body positron emission tomography (PET) scanner provides an
unprecedented opportunity to scan the whole body simultaneously, thanks to its long axial …
unprecedented opportunity to scan the whole body simultaneously, thanks to its long axial …
Reconstruction-driven motion estimation for motion-compensated MR CINE imaging
In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach
to address highly undersampled acquisitions by incorporating motion information between …
to address highly undersampled acquisitions by incorporating motion information between …
[HTML][HTML] Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care …
Patient care workflows are highly multimodal and intertwined: the intersection of data
outputs provided from different disciplines and in different formats remains one of the main …
outputs provided from different disciplines and in different formats remains one of the main …
Deformation-compensated learning for image reconstruction without ground truth
Deep neural networks for medical image reconstruction are traditionally trained using high-
quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has …
quality ground-truth images as training targets. Recent work on Noise2Noise (N2N) has …
Robust Fast Inter-Bin Image Registration for Undersampled Coronary MRI Based on a Learned Motion Prior
F Yang, Z Xue, H Lu, J Xu, H Chen… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Objective: To propose a 3D nonrigid registration method that accurately estimates the 3D
displacement field from artifact-corrupted Coronary Magnetic Resonance Angiography …
displacement field from artifact-corrupted Coronary Magnetic Resonance Angiography …
[HTML][HTML] Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to …
Background Cardiovascular magnetic resonance (CMR) is an important imaging modality
for the assessment of heart disease; however, limitations of CMR include long exam times …
for the assessment of heart disease; however, limitations of CMR include long exam times …
Stop moving: MR motion correction as an opportunity for artificial intelligence
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can
seriously deteriorate the image quality. Various prospective and retrospective methods have …
seriously deteriorate the image quality. Various prospective and retrospective methods have …
Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical …
Background Cardiovascular magnetic resonance (CMR) is an important imaging modality
for the assessment of heart disease; however, limitations of CMR include long exam times …
for the assessment of heart disease; however, limitations of CMR include long exam times …