Literature DB >> 31755191

Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis.

Akshay S Chaudhari1, Feliks Kogan1, Valentina Pedoia2,3, Sharmila Majumdar2,3, Garry E Gold1,4,5, Brian A Hargreaves1,5,6.   

Abstract

Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3.
© 2019 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  compositional imaging; deep learning; morphological imaging; quantitative MRI; rapid MRI; segmentation

Mesh:

Year:  2019        PMID: 31755191      PMCID: PMC7925938          DOI: 10.1002/jmri.26991

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  131 in total

1.  Estimation of heat transfer and temperature rise in partial-body regions during MR procedures: an analytical approach with respect to safety considerations.

Authors:  Gunnar Brix; Martin Seebass; Gesine Hellwig; Jürgen Griebel
Journal:  Magn Reson Imaging       Date:  2002-01       Impact factor: 2.546

2.  Generalized autocalibrating partially parallel acquisitions (GRAPPA).

Authors:  Mark A Griswold; Peter M Jakob; Robin M Heidemann; Mathias Nittka; Vladimir Jellus; Jianmin Wang; Berthold Kiefer; Axel Haase
Journal:  Magn Reson Med       Date:  2002-06       Impact factor: 4.668

3.  Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

Authors:  Fang Liu; Zhaoye Zhou; Hyungseok Jang; Alexey Samsonov; Gengyan Zhao; Richard Kijowski
Journal:  Magn Reson Med       Date:  2017-07-21       Impact factor: 4.668

4.  Wave-CAIPI for highly accelerated 3D imaging.

Authors:  Berkin Bilgic; Borjan A Gagoski; Stephen F Cauley; Audrey P Fan; Jonathan R Polimeni; P Ellen Grant; Lawrence L Wald; Kawin Setsompop
Journal:  Magn Reson Med       Date:  2014-07-01       Impact factor: 4.668

5.  MR imaging of articular cartilage using driven equilibrium.

Authors:  B A Hargreaves; G E Gold; P K Lang; S M Conolly; J M Pauly; G Bergman; J Vandevenne; D G Nishimura
Journal:  Magn Reson Med       Date:  1999-10       Impact factor: 4.668

6.  Fast SSFP gradient echo sequence for simultaneous acquisitions of FID and echo signals.

Authors:  S Y Lee; Z H Cho
Journal:  Magn Reson Med       Date:  1988-10       Impact factor: 4.668

7.  Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.

Authors:  Enhao Gong; John M Pauly; Max Wintermark; Greg Zaharchuk
Journal:  J Magn Reson Imaging       Date:  2018-02-13       Impact factor: 4.813

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

Authors:  Kerstin Hammernik; Teresa Klatzer; Erich Kobler; Michael P Recht; Daniel K Sodickson; Thomas Pock; Florian Knoll
Journal:  Magn Reson Med       Date:  2017-11-08       Impact factor: 4.668

9.  Three-Dimensional CAIPIRINHA SPACE TSE for 5-Minute High-Resolution MRI of the Knee.

Authors:  Jan Fritz; Benjamin Fritz; Gaurav G Thawait; Heiko Meyer; Wesley D Gilson; Esther Raithel
Journal:  Invest Radiol       Date:  2016-10       Impact factor: 6.016

10.  Vastly undersampled isotropic projection steady-state free precession imaging of the knee: diagnostic performance compared with conventional MR.

Authors:  Richard Kijowski; Donna G Blankenbaker; Jessica L Klaers; Kazuhiko Shinki; Arthur A De Smet; Walter F Block
Journal:  Radiology       Date:  2009-02-12       Impact factor: 11.105

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  10 in total

1.  Layer-specific analysis of femorotibial cartilage t2 relaxation time based on registration of segmented double echo steady state (dess) to multi-echo-spin-echo (mese) images.

Authors:  David Fürst; Wolfang Wirth; Akshay Chaudhari; Felix Eckstein
Journal:  MAGMA       Date:  2020-05-26       Impact factor: 2.310

Review 2.  Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging.

Authors:  Francesco Calivà; Nikan K Namiri; Maureen Dubreuil; Valentina Pedoia; Eugene Ozhinsky; Sharmila Majumdar
Journal:  Nat Rev Rheumatol       Date:  2021-11-30       Impact factor: 20.543

3.  Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network.

Authors:  Bragi Sveinsson; Akshay S Chaudhari; Bo Zhu; Neha Koonjoo; Martin Torriani; Garry E Gold; Matthew S Rosen
Journal:  Radiol Artif Intell       Date:  2021-05-26

4.  Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

Authors:  Kevin A Thomas; Dominik Krzemiński; Łukasz Kidziński; Rohan Paul; Elka B Rubin; Eni Halilaj; Marianne S Black; Akshay Chaudhari; Garry E Gold; Scott L Delp
Journal:  Cartilage       Date:  2021-09-08       Impact factor: 3.117

5.  Ultrashort echo time Cones double echo steady state (UTE-Cones-DESS) for rapid morphological imaging of short T2 tissues.

Authors:  Hyungseok Jang; Yajun Ma; Michael Carl; Saeed Jerban; Eric Y Chang; Jiang Du
Journal:  Magn Reson Med       Date:  2021-03-23       Impact factor: 3.737

Review 6.  Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

Authors:  Akshay S Chaudhari; Christopher M Sandino; Elizabeth K Cole; David B Larson; Garry E Gold; Shreyas S Vasanawala; Matthew P Lungren; Brian A Hargreaves; Curtis P Langlotz
Journal:  J Magn Reson Imaging       Date:  2020-08-24       Impact factor: 5.119

7.  Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis.

Authors:  Yong Hu; Jie Tang; Shenghao Zhao; Ye Li
Journal:  Comput Math Methods Med       Date:  2022-04-29       Impact factor: 2.809

8.  The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset.

Authors:  Arjun D Desai; Francesco Caliva; Claudia Iriondo; Aliasghar Mortazi; Sachin Jambawalikar; Ulas Bagci; Mathias Perslev; Christian Igel; Erik B Dam; Sibaji Gaj; Mingrui Yang; Xiaojuan Li; Cem M Deniz; Vladimir Juras; Ravinder Regatte; Garry E Gold; Brian A Hargreaves; Valentina Pedoia; Akshay S Chaudhari
Journal:  Radiol Artif Intell       Date:  2021-02-10

9.  Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement.

Authors:  Akshay S Chaudhari; Murray J Grissom; Zhongnan Fang; Bragi Sveinsson; Jin Hyung Lee; Garry E Gold; Brian A Hargreaves; Kathryn J Stevens
Journal:  AJR Am J Roentgenol       Date:  2020-08-05       Impact factor: 3.959

Review 10.  Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review.

Authors:  Chen Yu; Bing Zhao; Yan Li; Hengchang Zang; Lian Li
Journal:  Int J Mol Sci       Date:  2021-05-15       Impact factor: 5.923

  10 in total

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