معرفی
Florian Knoll, PhD, serves as an Adjunct Assistant Professor in the Department of Radiology at NYU Grossman School of Medicine, New York University. His academic profile centers on advancing medical imaging through computational methods, with a focus on magnetic resonance imaging and artificial intelligence.
Dr. Knoll's educational background includes:
- PhD from Graz University of Technology
- Postdoctoral Training at Graz University of Technology, Institute of Medical Engineering
- Postdoctoral Training at NYU School of Medicine, Center for Biomedical Imaging, Department of Radiology
His research prominently features Magnetic Resonance Imaging, Machine Learning, and Image Reconstruction. He investigates deep learning techniques for accelerating MRI scans and enhancing image quality across clinical applications including breast, cardiac, and musculoskeletal imaging. His work bridges physics-based modeling and data-driven approaches to solve medical imaging challenges, with emphasis on clinical translation and computational efficiency.
Analysis of Dr. Knoll's recent publications (2023-2025) reveals consistent innovation in deep learning for medical image reconstruction. Key themes include development of open datasets (FastMRI Breast), toolkits for quantitative evaluation, denoising methods for low-dose imaging, and physics-integrated AI models. His work spans breast, cardiac, knee, and chest imaging applications, demonstrating strong clinical relevance and methodological rigor.
Dr. Knoll has not listed any scientific awards in his current profile.
Information regarding student advising and research grants is not provided in available materials, though his publications indicate active collaboration with clinical and engineering teams.
Dr. Knoll's research is anchored at NYU Langone Health's Center for Biomedical Imaging, where he completed postdoctoral training. He collaborates with interdisciplinary teams combining radiology, biomedical engineering, and computer science expertise to advance medical imaging technologies toward clinical implementation.
