Literature DB >> 31313397

Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.

Akshay S Chaudhari1, Kathryn J Stevens1,2, Jeff P Wood3, Amit K Chakraborty1, Eric K Gibbons4, Zhongnan Fang5, Arjun D Desai1, Jin Hyung Lee6,7,8, Garry E Gold1,2,7, Brian A Hargreaves1,7,9.   

Abstract

BACKGROUND: Super-resolution is an emerging method for enhancing MRI resolution; however, its impact on image quality is still unknown.
PURPOSE: To evaluate MRI super-resolution using quantitative and qualitative metrics of cartilage morphometry, osteophyte detection, and global image blurring. STUDY TYPE: Retrospective. POPULATION: In all, 176 MRI studies of subjects at varying stages of osteoarthritis. FIELD STRENGTH/SEQUENCE: Original-resolution 3D double-echo steady-state (DESS) and DESS with 3× thicker slices retrospectively enhanced using super-resolution and tricubic interpolation (TCI) at 3T. ASSESSMENT: A quantitative comparison of femoral cartilage morphometry was performed for the original-resolution DESS, the super-resolution, and the TCI scans in 17 subjects. A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans. STATISTICAL TESTS: Mann-Whitney U-tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super-resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super-resolution and TCI images, with the original-resolution as a reference.
RESULTS: DC for the original-resolution (90.2 ± 1.7%) and super-resolution (89.6 ± 2.0%) were significantly higher (P < 0.001) than TCI (86.3 ± 5.6%). Segmentation overlap of super-resolution with the original-resolution (DC = 97.6 ± 0.7%) was significantly higher (P < 0.0001) than TCI overlap (DC = 95.0 ± 1.1%). Cartilage image quality for sharpness and contrast levels, and the through-plane quantitative blur factor for super-resolution images, was significantly (P < 0.001) better than TCI. Super-resolution osteophyte detection sensitivity of 80% (76-82%), specificity of 93% (92-94%), and DOR of 32 (22-46) was significantly higher (P < 0.001) than TCI sensitivity of 73% (69-76%), specificity of 90% (89-91%), and DOR of 17 (13-22). DATA
CONCLUSION: Super-resolution appears to consistently outperform naïve interpolation and may improve image quality without biasing quantitative biomarkers. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:768-779.
© 2019 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  artificial intelligence; cartilage segmentation; image acceleration; machine learning interpretability; osteoarthritis biomarkers; super-resolution

Mesh:

Substances:

Year:  2019        PMID: 31313397      PMCID: PMC6962563          DOI: 10.1002/jmri.26872

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


  27 in total

Review 1.  SMASH, SENSE, PILS, GRAPPA: how to choose the optimal method.

Authors:  Martin Blaimer; Felix Breuer; Matthias Mueller; Robin M Heidemann; Mark A Griswold; Peter M Jakob
Journal:  Top Magn Reson Imaging       Date:  2004-08

2.  Knee osteoarthritis has doubled in prevalence since the mid-20th century.

Authors:  Ian J Wallace; Steven Worthington; David T Felson; Robert D Jurmain; Kimberly T Wren; Heli Maijanen; Robert J Woods; Daniel E Lieberman
Journal:  Proc Natl Acad Sci U S A       Date:  2017-08-14       Impact factor: 11.205

3.  Knee cartilage defects: association with early radiographic osteoarthritis, decreased cartilage volume, increased joint surface area and type II collagen breakdown.

Authors:  Changhai Ding; Patrick Garnero; Flavia Cicuttini; Fiona Scott; Helen Cooley; Graeme Jones
Journal:  Osteoarthritis Cartilage       Date:  2005-03       Impact factor: 6.576

4.  Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score).

Authors:  D J Hunter; A Guermazi; G H Lo; A J Grainger; P G Conaghan; R M Boudreau; F W Roemer
Journal:  Osteoarthritis Cartilage       Date:  2011-05-23       Impact factor: 6.576

5.  Five-minute knee MRI for simultaneous morphometry and T2 relaxometry of cartilage and meniscus and for semiquantitative radiological assessment using double-echo in steady-state at 3T.

Authors:  Akshay S Chaudhari; Marianne S Black; Susanne Eijgenraam; Wolfgang Wirth; Susanne Maschek; Bragi Sveinsson; Felix Eckstein; Edwin H G Oei; Garry E Gold; Brian A Hargreaves
Journal:  J Magn Reson Imaging       Date:  2017-11-01       Impact factor: 4.813

6.  Defining radiographic incidence and progression of knee osteoarthritis: suggested modifications of the Kellgren and Lawrence scale.

Authors:  David T Felson; Jingbo Niu; Ali Guermazi; Burton Sack; Piran Aliabadi
Journal:  Ann Rheum Dis       Date:  2011-09-08       Impact factor: 19.103

Review 7.  Epidemiology of osteoarthritis.

Authors:  Yuqing Zhang; Joanne M Jordan
Journal:  Clin Geriatr Med       Date:  2010-08       Impact factor: 3.076

Review 8.  The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee.

Authors:  C G Peterfy; E Schneider; M Nevitt
Journal:  Osteoarthritis Cartilage       Date:  2008-09-10       Impact factor: 6.576

9.  Super-resolution musculoskeletal MRI using deep learning.

Authors:  Akshay S Chaudhari; Zhongnan Fang; Feliks Kogan; Jeff Wood; Kathryn J Stevens; Eric K Gibbons; Jin Hyung Lee; Garry E Gold; Brian A Hargreaves
Journal:  Magn Reson Med       Date:  2018-03-26       Impact factor: 4.668

10.  Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010.

Authors:  Theo Vos; Abraham D Flaxman; Mohsen Naghavi; Rafael Lozano; Catherine Michaud; Majid Ezzati; Kenji Shibuya; Joshua A Salomon; Safa Abdalla; Victor Aboyans; Jerry Abraham; Ilana Ackerman; Rakesh Aggarwal; Stephanie Y Ahn; Mohammed K Ali; Miriam Alvarado; H Ross Anderson; Laurie M Anderson; Kathryn G Andrews; Charles Atkinson; Larry M Baddour; Adil N Bahalim; Suzanne Barker-Collo; Lope H Barrero; David H Bartels; Maria-Gloria Basáñez; Amanda Baxter; Michelle L Bell; Emelia J Benjamin; Derrick Bennett; Eduardo Bernabé; Kavi Bhalla; Bishal Bhandari; Boris Bikbov; Aref Bin Abdulhak; Gretchen Birbeck; James A Black; Hannah Blencowe; Jed D Blore; Fiona Blyth; Ian Bolliger; Audrey Bonaventure; Soufiane Boufous; Rupert Bourne; Michel Boussinesq; Tasanee Braithwaite; Carol Brayne; Lisa Bridgett; Simon Brooker; Peter Brooks; Traolach S Brugha; Claire Bryan-Hancock; Chiara Bucello; Rachelle Buchbinder; Geoffrey Buckle; Christine M Budke; Michael Burch; Peter Burney; Roy Burstein; Bianca Calabria; Benjamin Campbell; Charles E Canter; Hélène Carabin; Jonathan Carapetis; Loreto Carmona; Claudia Cella; Fiona Charlson; Honglei Chen; Andrew Tai-Ann Cheng; David Chou; Sumeet S Chugh; Luc E Coffeng; Steven D Colan; Samantha Colquhoun; K Ellicott Colson; John Condon; Myles D Connor; Leslie T Cooper; Matthew Corriere; Monica Cortinovis; Karen Courville de Vaccaro; William Couser; Benjamin C Cowie; Michael H Criqui; Marita Cross; Kaustubh C Dabhadkar; Manu Dahiya; Nabila Dahodwala; James Damsere-Derry; Goodarz Danaei; Adrian Davis; Diego De Leo; Louisa Degenhardt; Robert Dellavalle; Allyne Delossantos; Julie Denenberg; Sarah Derrett; Don C Des Jarlais; Samath D Dharmaratne; Mukesh Dherani; Cesar Diaz-Torne; Helen Dolk; E Ray Dorsey; Tim Driscoll; Herbert Duber; Beth Ebel; Karen Edmond; Alexis Elbaz; Suad Eltahir Ali; Holly Erskine; Patricia J Erwin; Patricia Espindola; Stalin E Ewoigbokhan; Farshad Farzadfar; Valery Feigin; David T Felson; Alize Ferrari; Cleusa P Ferri; Eric M Fèvre; Mariel M Finucane; Seth Flaxman; Louise Flood; Kyle Foreman; Mohammad H Forouzanfar; Francis Gerry R Fowkes; Richard Franklin; Marlene Fransen; Michael K Freeman; Belinda J Gabbe; Sherine E Gabriel; Emmanuela Gakidou; Hammad A Ganatra; Bianca Garcia; Flavio Gaspari; Richard F Gillum; Gerhard Gmel; Richard Gosselin; Rebecca Grainger; Justina Groeger; Francis Guillemin; David Gunnell; Ramyani Gupta; Juanita Haagsma; Holly Hagan; Yara A Halasa; Wayne Hall; Diana Haring; Josep Maria Haro; James E Harrison; Rasmus Havmoeller; Roderick J Hay; Hideki Higashi; Catherine Hill; Bruno Hoen; Howard Hoffman; Peter J Hotez; Damian Hoy; John J Huang; Sydney E Ibeanusi; Kathryn H Jacobsen; Spencer L James; Deborah Jarvis; Rashmi Jasrasaria; Sudha Jayaraman; Nicole Johns; Jost B Jonas; Ganesan Karthikeyan; Nicholas Kassebaum; Norito Kawakami; Andre Keren; Jon-Paul Khoo; Charles H King; Lisa Marie Knowlton; Olive Kobusingye; Adofo Koranteng; Rita Krishnamurthi; Ratilal Lalloo; Laura L Laslett; Tim Lathlean; Janet L Leasher; Yong Yi Lee; James Leigh; Stephen S Lim; Elizabeth Limb; John Kent Lin; Michael Lipnick; Steven E Lipshultz; Wei Liu; Maria Loane; Summer Lockett Ohno; Ronan Lyons; Jixiang Ma; Jacqueline Mabweijano; Michael F MacIntyre; Reza Malekzadeh; Leslie Mallinger; Sivabalan Manivannan; Wagner Marcenes; Lyn March; David J Margolis; Guy B Marks; Robin Marks; Akira Matsumori; Richard Matzopoulos; Bongani M Mayosi; John H McAnulty; Mary M McDermott; Neil McGill; John McGrath; Maria Elena Medina-Mora; Michele Meltzer; George A Mensah; Tony R Merriman; Ana-Claire Meyer; Valeria Miglioli; Matthew Miller; Ted R Miller; Philip B Mitchell; Ana Olga Mocumbi; Terrie E Moffitt; Ali A Mokdad; Lorenzo Monasta; Marcella Montico; Maziar Moradi-Lakeh; Andrew Moran; Lidia Morawska; Rintaro Mori; Michele E Murdoch; Michael K Mwaniki; Kovin Naidoo; M Nathan Nair; Luigi Naldi; K M Venkat Narayan; Paul K Nelson; Robert G Nelson; Michael C Nevitt; Charles R Newton; Sandra Nolte; Paul Norman; Rosana Norman; Martin O'Donnell; Simon O'Hanlon; Casey Olives; Saad B Omer; Katrina Ortblad; Richard Osborne; Doruk Ozgediz; Andrew Page; Bishnu Pahari; Jeyaraj Durai Pandian; Andrea Panozo Rivero; Scott B Patten; Neil Pearce; Rogelio Perez Padilla; Fernando Perez-Ruiz; Norberto Perico; Konrad Pesudovs; David Phillips; Michael R Phillips; Kelsey Pierce; Sébastien Pion; Guilherme V Polanczyk; Suzanne Polinder; C Arden Pope; Svetlana Popova; Esteban Porrini; Farshad Pourmalek; Martin Prince; Rachel L Pullan; Kapa D Ramaiah; Dharani Ranganathan; Homie Razavi; Mathilda Regan; Jürgen T Rehm; David B Rein; Guiseppe Remuzzi; Kathryn Richardson; Frederick P Rivara; Thomas Roberts; Carolyn Robinson; Felipe Rodriguez De Leòn; Luca Ronfani; Robin Room; Lisa C Rosenfeld; Lesley Rushton; Ralph L Sacco; Sukanta Saha; Uchechukwu Sampson; Lidia Sanchez-Riera; Ella Sanman; David C Schwebel; James Graham Scott; Maria Segui-Gomez; Saeid Shahraz; Donald S Shepard; Hwashin Shin; Rupak Shivakoti; David Singh; Gitanjali M Singh; Jasvinder A Singh; Jessica Singleton; David A Sleet; Karen Sliwa; Emma Smith; Jennifer L Smith; Nicolas J C Stapelberg; Andrew Steer; Timothy Steiner; Wilma A Stolk; Lars Jacob Stovner; Christopher Sudfeld; Sana Syed; Giorgio Tamburlini; Mohammad Tavakkoli; Hugh R Taylor; Jennifer A Taylor; William J Taylor; Bernadette Thomas; W Murray Thomson; George D Thurston; Imad M Tleyjeh; Marcello Tonelli; Jeffrey A Towbin; Thomas Truelsen; Miltiadis K Tsilimbaris; Clotilde Ubeda; Eduardo A Undurraga; Marieke J van der Werf; Jim van Os; Monica S Vavilala; N Venketasubramanian; Mengru Wang; Wenzhi Wang; Kerrianne Watt; David J Weatherall; Martin A Weinstock; Robert Weintraub; Marc G Weisskopf; Myrna M Weissman; Richard A White; Harvey Whiteford; Steven T Wiersma; James D Wilkinson; Hywel C Williams; Sean R M Williams; Emma Witt; Frederick Wolfe; Anthony D Woolf; Sarah Wulf; Pon-Hsiu Yeh; Anita K M Zaidi; Zhi-Jie Zheng; David Zonies; Alan D Lopez; Christopher J L Murray; Mohammad A AlMazroa; Ziad A Memish
Journal:  Lancet       Date:  2012-12-15       Impact factor: 79.321

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  10 in total

Review 1.  Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Authors:  Dana J Lin; Patricia M Johnson; Florian Knoll; Yvonne W Lui
Journal:  J Magn Reson Imaging       Date:  2020-02-12       Impact factor: 4.813

Review 2.  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

3.  Clinical evaluation of fully automated thigh muscle and adipose tissue segmentation using a U-Net deep learning architecture in context of osteoarthritic knee pain.

Authors:  Jana Kemnitz; Christian F Baumgartner; Felix Eckstein; Akshay Chaudhari; Anja Ruhdorfer; Wolfgang Wirth; Sebastian K Eder; Ender Konukoglu
Journal:  MAGMA       Date:  2019-12-23       Impact factor: 2.310

4.  Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution.

Authors:  Qiyuan Tian; Berkin Bilgic; Qiuyun Fan; Chanon Ngamsombat; Natalia Zaretskaya; Nina E Fultz; Ned A Ohringer; Akshay S Chaudhari; Yuxin Hu; Thomas Witzel; Kawin Setsompop; Jonathan R Polimeni; Susie Y Huang
Journal:  Cereb Cortex       Date:  2021-01-01       Impact factor: 5.357

5.  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

6.  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

7.  Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI.

Authors:  Seonyeong Park; H Michael Gach; Siyong Kim; Suk Jin Lee; Yuichi Motai
Journal:  IEEE J Transl Eng Health Med       Date:  2021-04-28

8.  Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning.

Authors:  Jonas Bianchi; Antônio Carlos de Oliveira Ruellas; João Roberto Gonçalves; Beatriz Paniagua; Juan Carlos Prieto; Martin Styner; Tengfei Li; Hongtu Zhu; James Sugai; William Giannobile; Erika Benavides; Fabiana Soki; Marilia Yatabe; Lawrence Ashman; David Walker; Reza Soroushmehr; Kayvan Najarian; Lucia Helena Soares Cevidanes
Journal:  Sci Rep       Date:  2020-05-15       Impact factor: 4.379

9.  Deep-learning-based image quality enhancement of compressed sensing magnetic resonance imaging of vessel wall: comparison of self-supervised and unsupervised approaches.

Authors:  Da-In Eun; Ryoungwoo Jang; Woo Seok Ha; Hyunna Lee; Seung Chai Jung; Namkug Kim
Journal:  Sci Rep       Date:  2020-08-18       Impact factor: 4.379

Review 10.  Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis.

Authors:  Akshay S Chaudhari; Feliks Kogan; Valentina Pedoia; Sharmila Majumdar; Garry E Gold; Brian A Hargreaves
Journal:  J Magn Reson Imaging       Date:  2019-11-21       Impact factor: 4.813

  10 in total

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