Deep learning for accelerated and robust MRI reconstruction

R Heckel, M Jacob, A Chaudhari, O Perlman… - … Resonance Materials in …, 2024 - Springer
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …

Deep learning for accelerated and robust MRI reconstruction: a review

R Heckel, M Jacob, A Chaudhari, O Perlman… - arxiv preprint arxiv …, 2024 - arxiv.org
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

B Koçak, A Ponsiglione, A Stanzione… - Diagnostic and …, 2024 - zora.uzh.ch
Although artificial intelligence (AI) methods hold promise for medical imaging-based
prediction tasks, their integration into medical practice may present a double-edged sword …

K-band: self-supervised MRI reconstruction via stochastic gradient descent over k-space subsets

F Wang, H Qi, A De Goyeneche, R Heckel… - arxiv preprint arxiv …, 2023 - arxiv.org
Although deep learning (DL) methods are powerful for solving inverse problems, their
reliance on high-quality training data is a major hurdle. This is significant in high …

fastMRI Breast: A publicly available radial k-space dataset of breast dynamic contrast-enhanced MRI

E Solomon, PM Johnson, Z Tan, R Tibrewala… - Radiology: Artificial …, 2025 - pubs.rsna.org
The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital
Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI …

Advancing medical imaging research through standardization: The path to rapid development, rigorous validation, and robust reproducibility

K Jeon, WY Park, CE Kahn Jr, P Nagy… - Investigative …, 2023 - journals.lww.com
Artificial intelligence (AI) has made significant advances in radiology. Nonetheless,
challenges in AI development, validation, and reproducibility persist, primarily due to the …

Data resources: Milestones and building blocks

CE Kahn Jr - Radiology: Artificial Intelligence, 2023 - pubs.rsna.org
research in medical imaging AI through transparent and collaborative sharing of resources
such as software, data, methodology, and education. Open science presents an opportunity …

The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC) repository: rationale and blueprint

H Baazaoui, ST Engelter, H Gensicke… - Frontiers in …, 2025 - frontiersin.org
Purpose The Multicentre Acute ischemic stroke imaGIng and Clinical data (MAGIC)
repository is a collaboration established in 2024 by seven stroke centres in Europe. MAGIC …

Artificial Intelligence in Gastrointestinal Imaging: Advances and Applications

JJR Chong, A Kirpalani, R Moreland… - Radiologic …, 2025 - radiologic.theclinics.com
The potential benefits of artificial intelligence (AI) in medical imaging include enhanced
diagnostic accuracy, increased interpretive efficiency, greater consistency among …

Navigating the Future of Prostate Cancer Care: AI-Driven Imaging and Theranostics Through the Lens of RELAINCE

AJN Wong, HS Ko, MS Hofman - Journal of Nuclear Medicine, 2024 - jnm.snmjournals.org
Centre of Excellence Preceptorship of 2024 held in Melbourne, Australia, a presentation on
artificial intelligence (AI) in prostate cancer imaging was delivered in which the …