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D-Index & Metrics

Computer Science

D-Index
36
Citations
8117
World Ranking
11044
National Ranking
550

Overview

Florian Knoll is affiliated with the University of Erlangen-Nuremberg in Germany. Their research primarily focuses on advanced techniques in magnetic resonance imaging (MRI), combining expertise in medicine and biomedical engineering with an emphasis on machine learning applications within medical imaging.

Their scholarly contributions include recent papers such as:

  • fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning, 2020, Radiology Artificial Intelligence
  • Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues, 2020, IEEE Signal Processing Magazine
  • Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction, 2021, IEEE Transactions on Medical Imaging
  • Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians, 2020, Journal of Magnetic Resonance Imaging
  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge, 2020, Magnetic Resonance in Medicine

Frequent coauthors in Florian Knoll's research include:

  • Patricia M. Johnson
  • Zhengguo Tan
  • Zhengnan Huang
  • Kerstin Hammernik
  • Frederik B. Laun

They publish regularly in prominent venues such as:

  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (22 publications)
  • arXiv (Cornell University) (10 publications)
  • Zenodo (CERN European Organization for Nuclear Research) (6 publications)
  • Magnetic Resonance in Medicine (5 publications)
  • IEEE Signal Processing Magazine (4 publications)

Their main field of study is medicine, with a particular specialization in radiology, nuclear medicine, and imaging, accounting for the majority of their research output.

Additional subfields include:

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Atomic and Molecular Physics, and Optics
  • Pulmonary and Respiratory Medicine

The main topics covered in their work focus strongly on advanced MRI techniques and applications, medical imaging techniques, and radiomics with machine learning approaches in medical imaging. Other notable thematic areas include MRI in cancer diagnosis, advanced X-ray and CT imaging, neuroimaging techniques, and cardiac imaging and diagnostics.

Overall, Florian Knoll's research integrates machine learning methods with MRI technology to address challenges in medical imaging reconstruction, contributing to datasets, survey papers, and challenge benchmarks in the field.

Best Publications

  • Learning a variational network for reconstruction of accelerated MRI data.

    Kerstin Hammernik;Teresa Klatzer;Erich Kobler;Michael P. Recht

  • fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

    Jure Zbontar;Florian Knoll;Anuroop Sriram;Matthew J. Muckley

  • Second order total generalized variation (TGV) for MRI

    Florian Knoll;Kristian Bredies;Thomas Pock;Rudolf Stollberger

  • fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.

    Florian Knoll;Jure Zbontar;Anuroop Sriram;Matthew J Muckley

  • Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues

    Florian Knoll;Kerstin Hammernik;Chi Zhang;Steen Moeller

  • Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

    Matthew J. Muckley;Bruno Riemenschneider;Alireza Radmanesh;Sunwoo Kim

  • Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

    Dana J. Lin;Patricia M. Johnson;Florian Knoll;Yvonne W. Lui

  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

    Florian Knoll;Tullie Murrell;Anuroop Sriram;Nafissa Yakubova

  • Assessment of the generalization of learned image reconstruction and the potential for transfer learning

    Florian Knoll;Kerstin Hammernik;Kerstin Hammernik;Erich Kobler;Thomas Pock;Thomas Pock

  • Multiparametric imaging with heterogeneous radiofrequency fields.

    Martijn A. Cloos;Florian Knoll;Tiejun Zhao;Tiejun Zhao;Kai T. Block

  • End-to-End Variational Networks for Accelerated MRI Reconstruction

    Anuroop Sriram;Jure Zbontar;Tullie Murrell;Aaron Defazio

  • Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting

    Jakob Assländer;Martijn A. Cloos;Florian Knoll;Daniel K. Sodickson

  • Gibbs ringing in diffusion MRI

    Jelle Veraart;Els Fieremans;Ileana O. Jelescu;Florian Knoll

  • Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

    Florian Knoll;Tullie Murrell;Anuroop Sriram;Nafissa Yakubova

  • Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions

    Soterios Gyftopoulos;Dana Lin;Florian Knoll;Ankur M. Doshi

  • Total Generalized Variation in Diffusion Tensor Imaging

    Tuomo Valkonen;Kristian Bredies;Florian Knoll

  • Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.

    Michael P. Recht;Jure Zbontar;Daniel K. Sodickson;Florian Knoll

  • Parallel Imaging with Nonlinear Reconstruction using Variational Penalties

    Florian Knoll;Christian Clason;Kristian Bredies;Martin Uecker

  • Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer

    Florian Knoll;Martin Holler;Thomas Koesters;Ricardo Otazo

  • Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI.

    Unknown

  • Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging

    Unknown

  • Adapted Random Sampling Patterns for Accelerated MRI

    Florian Knoll;Christian Clason;Clemens Diwoky;Rudolf Stollberger

  • Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting

    Jakob Assländer;Martijn A Cloos;Florian Knoll;Daniel K Sodickson

  • Assessment of the generalization of learned image reconstruction and the potential for transfer learning

    Florian Knoll;Kerstin Hammernik;Thomas Pock;Daniel K Sodickson

Frequent Co-Authors

Daniel K. Sodickson
Daniel K. Sodickson New York University
Thomas Pock
Thomas Pock Graz University of Technology
Ricardo Otazo
Ricardo Otazo Memorial Sloan Kettering Cancer Center
C. Lawrence Zitnick
C. Lawrence Zitnick Facebook (United States)
Michael Rabbat
Michael Rabbat Facebook (United States)
Michael Hintermüller
Michael Hintermüller Weierstrass Institute for Applied Analysis and Stochastics
Jean-Luc Starck
Jean-Luc Starck University of Paris-Saclay
Song-Hai Shi
Song-Hai Shi Tsinghua University
Maxim Zaitsev
Maxim Zaitsev University of Freiburg
Pascal Vincent
Pascal Vincent Facebook (United States)

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