1
|
Bajaj S, Chhabra A, Taneja AK. 3D isotropic MRI of ankle: review of literature with comparison to 2D MRI. Skeletal Radiol 2024; 53:825-846. [PMID: 37978990 DOI: 10.1007/s00256-023-04513-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/01/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
The ankle joint has complex anatomy with different tissue structures and is commonly involved in traumatic injuries. Magnetic resonance imaging (MRI) is the primary imaging modality used to assess the soft tissue structures around the ankle joint including the ligaments, tendons, and articular cartilage. Two-dimensional (2D) fast spin echo/turbo spin echo (FSE/TSE) sequences are routinely used for ankle joint imaging. While the 2D sequences provide a good signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) with high spatial resolution, there are some limitations to their use owing to the thick slices, interslice gaps leading to partial volume effects, limited fluid contrast, and the need to acquire separate images in different orthogonal planes. The 3D MR imaging can overcome these limitations and recent advances have led to technical improvements that enable its widespread clinical use in acceptable time periods. The volume imaging renders the advantage of reconstructing into thin continuous slices with isotropic voxels enabling multiplanar reconstructions that helps in visualizing complex anatomy of the structure of interest throughout their course with improved sharpness, definition of anatomic variants, and fluid conspicuity of lesions and injuries. Recent advances have also reduced the acquisition time of the 3D datasets making it more efficient than 2D sequences. This article reviews the recent technical developments in the domain 3D MRI, compares imaging with 3D versus 2D sequences, and demonstrates the use-case scenarios with interesting cases, and benefits of 3D MRI in evaluating various ankle joint components and their lesions.
Collapse
Affiliation(s)
- Suryansh Bajaj
- Department of Radiology, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Avneesh Chhabra
- Musculoskeletal Radiology Division, Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA
- Department of Orthopaedic Surgery, UT Southwestern Medical Center, Dallas, TX, USA
- Johns Hopkins University, Baltimore, MD, USA
- Walton Center of Neurosciences, Liverpool, UK
- University of Dallas, Richardson, TX, USA
| | - Atul Kumar Taneja
- Musculoskeletal Radiology Division, Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
| |
Collapse
|
2
|
Fritz B. [Imaging of the anterior cruciate ligament and anterolateral rotational instability of the knee joint]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:261-270. [PMID: 38441595 DOI: 10.1007/s00117-024-01278-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/13/2024] [Indexed: 03/28/2024]
Abstract
The anterior cruciate ligament (ACL) is essential for the stability of the knee joint and ACL tears are one of the most common sports injuries with a high incidence, especially in sports that require rotational movements and abrupt changes in direction. Injuries of the ACL are rarely isolated and are often accompanied by meniscal and other internal knee injuries, which increase the risk of osteoarthritis. The spectrum of ACL injuries includes strains, partial tears and complete tears. Magnetic resonance imaging (MRI) plays a pivotal role in the diagnostics as it can accurately depict not only the ACL but also accompanying injuries. Proton density and T2-weighted sequences are particularly suitable for evaluating the ACL, which is usually well visible and assessable in all planes. In addition to depicting fiber disruption as a direct sign and central diagnostic indicator of an ACL tear, there are numerous other direct and indirect signs of an ACL injury in MRI. These include abnormal fiber orientations, signal increases and an anterior subluxation of the tibia relative to the femur. The bone marrow edema patterns often associated with ACL tears are indicative of the underlying injury mechanism. The treatment of ACL tears can be conservative or surgical depending on various factors, such as the patient's activity level and the presence of accompanying injuries. The precise and comprehensive description of ACL injuries by radiology is crucial for optimal treatment planning. Anterolateral rotational instability (ALRI) of the knee joint characterizes a condition of excessive lateral and rotational mobility of the tibia in relation to the femur in the anterolateral knee region. This instability is primarily caused by a rupture of the ACL, with the anterolateral ligament (ALL) that was rediscovered about 10 years ago, also being attributed a role in stabilizing the knee. Although ALRI is primarily diagnosed through clinical examinations, MRI is indispensable for detecting injuries to the ACL, ALL, and other internal knee structures, which is essential for developing an optimal treatment strategy.
Collapse
Affiliation(s)
- Benjamin Fritz
- Abteilung für Radiologie, Universitätsklinik Balgrist, Forchstr. 340, 8008, Zürich, Schweiz.
- Medizinische Fakultät, Universität Zürich, Zürich, Schweiz.
| |
Collapse
|
3
|
Chung CB, Pathria MN, Resnick D. MRI in MSK: is it the ultimate examination? Skeletal Radiol 2024:10.1007/s00256-024-04601-x. [PMID: 38277028 DOI: 10.1007/s00256-024-04601-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Christine B Chung
- Department of Radiology, University of California, San Diego, CA, USA.
- Department of Radiology, Veterans Affairs Medical Center, San Diego, CA, USA.
| | - Mini N Pathria
- Department of Radiology, University of California, San Diego, CA, USA
| | - Donald Resnick
- Department of Radiology, University of California, San Diego, CA, USA
| |
Collapse
|
4
|
Guermazi A, Omoumi P, Tordjman M, Fritz J, Kijowski R, Regnard NE, Carrino J, Kahn CE, Knoll F, Rueckert D, Roemer FW, Hayashi D. How AI May Transform Musculoskeletal Imaging. Radiology 2024; 310:e230764. [PMID: 38165245 PMCID: PMC10831478 DOI: 10.1148/radiol.230764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/18/2023] [Accepted: 07/11/2023] [Indexed: 01/03/2024]
Abstract
While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.
Collapse
Affiliation(s)
- Ali Guermazi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Patrick Omoumi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Mickael Tordjman
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Jan Fritz
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Richard Kijowski
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Nor-Eddine Regnard
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - John Carrino
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Charles E. Kahn
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Florian Knoll
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daniel Rueckert
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Frank W. Roemer
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| | - Daichi Hayashi
- From the Department of Radiology, Boston University School of
Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston
Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.);
Department of Radiology, Lausanne University Hospital and University of
Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu
Hospital and University Paris Cité, Paris, France (M.T.); Department of
Radiology, New York University Grossman School of Medicine, New York, NY (J.F.,
R.K.); Gleamer, Paris, France (N.E.R.); Réseau d’Imagerie Sud
Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.);
Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department
of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell
Medicine, New York, NY (J.C.); Department of Radiology and Institute for
Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.);
Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and
Radiology (F.W.R.), Universitätsklinikum Erlangen &
Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen,
Germany (F.K.); School of Medicine & Computation, Information and
Technology Klinikum rechts der Isar, Technical University Munich,
München, Germany (D.R.); Department of Computing, Imperial College
London, London, England (D.R.); and Department of Radiology, Tufts Medical
Center, Tufts University School of Medicine, Boston, Mass (D.H.)
| |
Collapse
|
5
|
Fritz B, de Cesar Netto C, Fritz J. Multiaxial 3D MRI of the Ankle: Advanced High-Resolution Visualization of Ligaments, Tendons, and Articular Cartilage. Foot Ankle Clin 2023; 28:529-550. [PMID: 37536817 DOI: 10.1016/j.fcl.2023.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
MRI is a valuable tool for diagnosing a broad spectrum of acute and chronic ankle disorders, including ligament tears, tendinopathy, and osteochondral lesions. Traditional two-dimensional (2D) MRI provides a high image signal and contrast of anatomic structures for accurately characterizing articular cartilage, bone marrow, synovium, ligaments, tendons, and nerves. However, 2D MRI limitations are thick slices and fixed slice orientations. In clinical practice, 2D MRI is limited to 2 to 3 mm slice thickness, which can cause blurred contours of oblique structures due to volume averaging effects within the image slice. In addition, image plane orientations are fixated and cannot be changed after the scan, resulting in 2D MRI lacking multiplanar and multiaxial reformation abilities for individualized image plane orientations along oblique and curved anatomic structures, such as ankle ligaments and tendons. In contrast, three-dimensional (3D) MRI is a newer, clinically available MRI technique capable of acquiring high-resolution ankle MRI data sets with isotropic voxel size. The inherently high spatial resolution of 3D MRI permits up to five times thinner (0.5 mm) image slices. In addition, 3D MRI can be acquired image voxel with the same edge length in all three space dimensions (isotropism), permitting unrestricted multiplanar and multiaxial image reformation and postprocessing after the MRI scan. Clinical 3D MRI of the ankle with 0.5 to 0.7 mm isotropic voxel size resolves the smallest anatomic ankle structures and abnormalities of ligament and tendon fibers, osteochondral lesions, and nerves. After acquiring the images, operators can align image planes individually along any anatomic structure of interest, such as ligaments and tendons segments. In addition, curved multiplanar image reformations can unfold the entire course of multiaxially curved structures, such as perimalleolar tendons, into one image plane. We recommend adding 3D MRI pulse sequences to traditional 2D MRI protocols to visualize small and curved ankle structures to better advantage. This article provides an overview of the clinical application of 3D MRI of the ankle, compares diagnostic performances of 2D and 3D MRI for diagnosing ankle abnormalities, and illustrates clinical 3D ankle MRI applications.
Collapse
Affiliation(s)
- Benjamin Fritz
- Department of Radiology, Balgrist University Hospital, Forchstrasse 340, Zurich 8008, Switzerland; Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Cesar de Cesar Netto
- Department of Orthopaedics and Rehabilitation, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
| | - Jan Fritz
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Avenue, New York, NY 10016, USA.
| |
Collapse
|
6
|
Sneag DB, Abel F, Potter HG, Fritz J, Koff MF, Chung CB, Pedoia V, Tan ET. MRI Advancements in Musculoskeletal Clinical and Research Practice. Radiology 2023; 308:e230531. [PMID: 37581501 PMCID: PMC10477516 DOI: 10.1148/radiol.230531] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/01/2023] [Accepted: 06/07/2023] [Indexed: 08/16/2023]
Abstract
Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.
Collapse
Affiliation(s)
- Darryl B. Sneag
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Frederik Abel
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Hollis G. Potter
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Jan Fritz
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Matthew F. Koff
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Christine B. Chung
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Valentina Pedoia
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| | - Ek T. Tan
- From the Department of Radiology and Imaging, Hospital for Special
Surgery, 535 E 70th St, New York, NY 10021 (D.B.S., F.A., H.G.P., M.F.K.,
E.T.T.); Department of Radiology, New York University Grossman School of
Medicine, New York, NY (J.F.); Department of Radiology, University of California
San Diego, La Jolla, Calif (C.B.C.); Radiology Service, Veterans Affairs San
Diego Healthcare System, La Jolla, Calif (C.B.C.); and Department of Radiology
and Biomedical Imaging, University of California San Francisco, San Francisco,
Calif (V.P.)
| |
Collapse
|
7
|
Park EH, Fritz J. The role of imaging in osteoarthritis. Best Pract Res Clin Rheumatol 2023; 37:101866. [PMID: 37659890 DOI: 10.1016/j.berh.2023.101866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 09/04/2023]
Abstract
Osteoarthritis is a complex whole-organ disorder that involves molecular, anatomic, and physiologic derangement. Advances in imaging techniques have expanded the role of imaging in evaluating osteoarthritis and functional changes. Radiography, magnetic resonance imaging, computed tomography (CT), and ultrasonography are commonly used imaging modalities, each with advantages and limitations in evaluating osteoarthritis. Radiography comprehensively analyses alignment and osseous features, while MRI provides detailed information about cartilage damage, bone marrow edema, synovitis, and soft tissue abnormalities. Compositional imaging derives quantitative data for detecting cartilage and tendon degeneration before structural damage occurs. Ultrasonography permits real-time scanning and dynamic joint evaluation, whereas CT is useful for assessing final osseous detail. Imaging plays an essential role in the diagnosis, management, and research of osteoarthritis. The use of imaging can help differentiate osteoarthritis from other diseases with similar symptoms, and recent advances in deep learning have made the acquisition, management, and interpretation of imaging data more efficient and accurate. Imaging is useful in monitoring and predicting the prognosis of osteoarthritis, expanding our understanding of its pathophysiology. Ultimately, this enables early detection and personalized medicine for patients with osteoarthritis. This article reviews the current state of imaging in osteoarthritis, focusing on the strengths and limitations of various imaging modalities, and introduces advanced techniques, including deep learning, applied in clinical practice.
Collapse
Affiliation(s)
- Eun Hae Park
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, USA; Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Jan Fritz
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, USA.
| |
Collapse
|
8
|
Abstract
Acute knee injury ranges among the most common joint injuries in professional and recreational athletes. Radiographs can detect joint effusion, fractures, deformities, and malalignment; however, MR imaging is most accurate for radiographically occult fractures, chondral injury, and soft tissue injuries. Using a structured checklist approach for systematic MR imaging evaluation and reporting, this article reviews the MR imaging appearances of the spectrum of traumatic knee injuries, including osteochondral injuries, cruciate ligament tears, meniscus tears and ramp lesions, anterolateral complex and collateral ligament injuries, patellofemoral translation, extensor mechanism tears, and nerve and vascular injuries.
Collapse
|
9
|
Burke CJ, Fritz J, Samim M. Musculoskeletal Soft-tissue Masses. Magn Reson Imaging Clin N Am 2023; 31:285-308. [PMID: 37019551 DOI: 10.1016/j.mric.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Evaluation of soft-tissue masses has become a common clinical practice indication for imaging with both ultrasound and MR imaging. We illustrate the ultrasonography and MR imaging appearances of soft-tissue masses based on the various categories, updates, and reclassifications of the 2020 World Health Organization classification.
Collapse
Affiliation(s)
- Christopher J Burke
- NYU Langone Orthopedic Hospital, 301 East 17th Street, New York, NY 10003, USA.
| | - Jan Fritz
- NYU Langone Orthopedic Hospital, 301 East 17th Street, New York, NY 10003, USA
| | - Mohammad Samim
- NYU Langone Orthopedic Hospital, 301 East 17th Street, New York, NY 10003, USA
| |
Collapse
|
10
|
Modern Low-Field MRI of the Musculoskeletal System: Practice Considerations, Opportunities, and Challenges. Invest Radiol 2023; 58:76-87. [PMID: 36165841 DOI: 10.1097/rli.0000000000000912] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) provides essential information for diagnosing and treating musculoskeletal disorders. Although most musculoskeletal MRI examinations are performed at 1.5 and 3.0 T, modern low-field MRI systems offer new opportunities for affordable MRI worldwide. In 2021, a 0.55 T modern low-field, whole-body MRI system with an 80-cm-wide bore was introduced for clinical use in the United States and Europe. Compared with current higher-field-strength MRI systems, the 0.55 T MRI system has a lower total ownership cost, including purchase price, installation, and maintenance. Although signal-to-noise ratios scale with field strength, modern signal transmission and receiver chains improve signal yield compared with older low-field magnetic resonance scanner generations. Advanced radiofrequency coils permit short echo spacing and overall compacter echo trains than previously possible. Deep learning-based advanced image reconstruction algorithms provide substantial improvements in perceived signal-to-noise ratios, contrast, and spatial resolution. Musculoskeletal tissue contrast evolutions behave differently at 0.55 T, which requires careful consideration when designing pulse sequences. Similar to other field strengths, parallel imaging and simultaneous multislice acquisition techniques are vital for efficient musculoskeletal MRI acquisitions. Pliable receiver coils with a more cost-effective design offer a path to more affordable surface coils and improve image quality. Whereas fat suppression is inherently more challenging at lower field strengths, chemical shift selective fat suppression is reliable and homogeneous with modern low-field MRI technology. Dixon-based gradient echo pulse sequences provide efficient and reliable multicontrast options, including postcontrast MRI. Metal artifact reduction MRI benefits substantially from the lower field strength, including slice encoding for metal artifact correction for effective metal artifact reduction of high-susceptibility metallic implants. Wide-bore scanner designs offer exciting opportunities for interventional MRI. This review provides an overview of the economical aspects, signal and image quality considerations, technological components and coils, musculoskeletal tissue relaxation times, and image contrast of modern low-field MRI and discusses the mainstream and new applications, challenges, and opportunities of musculoskeletal MRI.
Collapse
|
11
|
Abstract
This article provides a focused overview of emerging technology in musculoskeletal MRI and CT. These technological advances have primarily focused on decreasing examination times, obtaining higher quality images, providing more convenient and economical imaging alternatives, and improving patient safety through lower radiation doses. New MRI acceleration methods using deep learning and novel reconstruction algorithms can reduce scanning times while maintaining high image quality. New synthetic techniques are now available that provide multiple tissue contrasts from a limited amount of MRI and CT data. Modern low-field-strength MRI scanners can provide a more convenient and economical imaging alternative in clinical practice, while clinical 7.0-T scanners have the potential to maximize image quality. Three-dimensional MRI curved planar reformation and cinematic rendering can provide improved methods for image representation. Photon-counting detector CT can provide lower radiation doses, higher spatial resolution, greater tissue contrast, and reduced noise in comparison with currently used energy-integrating detector CT scanners. Technological advances have also been made in challenging areas of musculoskeletal imaging, including MR neurography, imaging around metal, and dual-energy CT. While the preliminary results of these emerging technologies have been encouraging, whether they result in higher diagnostic performance requires further investigation.
Collapse
Affiliation(s)
- Richard Kijowski
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016
| | - Jan Fritz
- From the Department of Radiology, New York University Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016
| |
Collapse
|
12
|
Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches. Invest Radiol 2023; 58:3-13. [PMID: 36070548 DOI: 10.1097/rli.0000000000000907] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
Collapse
|
13
|
Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach. Invest Radiol 2023; 58:28-42. [PMID: 36355637 DOI: 10.1097/rli.0000000000000928] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.
Collapse
|
14
|
MRI evaluation of soft tissue tumors: comparison of a fast, isotropic, 3D T2-weighted fat-saturated sequence with a conventional 2D T2-weighted fat-saturated sequence for tumor characteristics, resolution, and acquisition time. Eur Radiol 2022; 32:8670-8680. [PMID: 35751699 DOI: 10.1007/s00330-022-08937-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/24/2022] [Accepted: 05/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To test whether a 4-fold accelerated 3D T2-weighted (T2) CAIPIRINHA SPACE TSE sequence with isotropic voxel size is equivalent to conventional 2DT2 TSE for the evaluation of intrinsic and perilesional soft tissue tumors (STT) characteristics. METHODS For 108 patients with histologically-proven STTs, MRI, including 3DT2 (CAIPIRINHA SPACE TSE) and 2DT2 (TSE) sequences, was performed. Two radiologists evaluated each sequence for quality (diagnostic, non-diagnostic), tumor characteristics (heterogeneity, signal intensity, margin), and the presence or absence of cortical involvement, marrow edema, and perilesional edema (PLE); tumor size and PLE extent were measured. Signal-to-noise (SNR) and contrast-to-noise (CNR) ratios and acquisition times for 2DT2 in two planes and 3DT2 sequences were reported. Descriptive statistics and inter-method agreement were reported. RESULTS Image quality was diagnostic for all sequences (100% [108/108]). No difference was observed between 3DT2 and 2DT2 tumor characteristics (p < 0.05). There was no difference in mean tumor size (3DT2: 2.9 ± 2.5 cm, 2DT2: 2.8 ± 2.6 cm, p = 0.4) or PLE extent (3DT2:0.5 ± 1.2 cm, 2DT2:0.5 ± 1.0 cm, p = 0.9) between the sequences. There was no difference in the SNR of tumors, marrow, and fat between the sequences, whereas the SNR of muscle was higher (p < 0.05) on 3DT2 than 2DT2. CNR measures on 3DT2 were similar to 2DT2 (p > 0.1). The average acquisition time was shorter for 3DT2 compared with 2DT2 (343 ± 127 s vs 475 ± 162 s, respectively). CONCLUSION Isotropic 3DT2 MRI offers higher spatial resolution, faster acquisition times, and equivalent assessments of STT characteristics compared to conventional 2DT2 MRI in two planes. 3DT2 is interchangeable with a 2DT2 sequence in tumor protocols. KEY POINTS • Isotropic 3DT2 CAIPIRINHA SPACE TSE offers higher spatial resolution than 2DT2 TSE and is equivalent to 2DT2 TSE for assessments of soft tissue tumor intrinsic and perilesional characteristics. • Multiplanar reformats of 3DT2 CAIPIRINHA SPACE TSE can substitute for 2DT2 TSE acquired in multiple planes, thereby reducing the acquisition time of MRI tumor protocols. • 3DT2 CAIPIRINHA SPACE TSE and 2DT2 TSE had similar CNR of tissues.
Collapse
|
15
|
3D CAIPIRINHA SPACE versus standard 2D TSE for routine knee MRI: a large-scale interchangeability study. Eur Radiol 2022; 32:6456-6467. [PMID: 35353196 DOI: 10.1007/s00330-022-08715-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/03/2022] [Accepted: 03/05/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To perform a large-scale interchangeability study comparing 3D controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) TSE with standard 2D TSE for knee MRI. METHODS In this prospective study, 250 patients underwent 3 T knee MRI, including a multicontrast 3D CAIPIRINHA SPACE TSE (9:26 min) and a standard 2D TSE protocol (12:14 min). Thirty-three (13%) patients had previous anterior cruciate ligament and/or meniscus surgery. Two radiologists assessed MRIs for image quality and identified pathologies of menisci, ligaments, and cartilage by using a 4-point Likert scale according to the level of diagnostic confidence. Interchangeability of the protocols was tested under the same-reader scenario using a bootstrap percentile confidence interval. Interreader reliability and intermethod concordance were also evaluated. RESULTS Despite higher image quality and diagnostic confidence for standard 2D TSE compared to 3D CAIPIRINHA SPACE TSE, the protocols were found interchangeable for diagnosing knee abnormalities, except for patellar (6.8% difference; 95% CI: 4.0, 9.6) and trochlear (3.6% difference; 95% CI: 0.8, 6.6) cartilage defects. The interreader reliability was substantial to almost perfect for 2D and 3D MRI (range κ, 0.785-1 and κ, 0.725-0.964, respectively). Intermethod concordance was almost perfect for all diagnoses (range κ, 0.817-0.986). CONCLUSION Multicontrast 3D CAIPIRINHA SPACE TSE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. Despite the radiologist's preference for 2D TSE imaging, a pursuit towards time-saving 3D TSE knee MRI is justified for routine practice. KEY POINTS • Multicontrast 3D CAIPIRINHA SPACE and standard 2D TSE protocols perform interchangeably for diagnosing knee abnormalities, except for patellofemoral cartilage defects. • Radiologists are more confident in diagnosing knee abnormalities on 2D TSE than on 3D CAIPIRINHA SPACE TSE MRI. • Despite the radiologist's preference for 2D TSE, a pursuit towards accelerated 3D TSE knee MRI is justified for routine practice.
Collapse
|
16
|
Liu L, Wu G. Three-dimensional SPACE MR with CAIPIRINHA fourfold acceleration for assessing long head of biceps tendon. Acta Radiol 2021:2841851211055324. [PMID: 34854744 DOI: 10.1177/02841851211055324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Data regarding controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) T2-weighted sampling perfection with application optimized contrast evolution (SPACE) with fourfold acceleration factor for assessing long head of biceps tendon (LHBT) disorder is lacking. PURPOSE To investigate the feasibility of 3D CAIPIRINHA SPACE with fourfold acceleration in assessing LHBT disorder. MATERIAL AND METHODS A total of 42 consecutive patients underwent shoulder magnetic resonance (MR) examinations including CAIPIRINHA SPACE with fourfold acceleration, and non-CAIPIRINHA SPACE with twofold acceleration, and 2D fast spin echo (FSE). A subjective score of depiction of LHBT was given to 3D sequence according to a 4-point scale (0-3, "poor" to "excellent"). The Wilcoxon signed rank test was used to compare depiction scores between 3D sequences. Three statuses of LHBT were defined in the study: normal, tendonitis, and tear. McNemar's test was used compare diagnostic accuracy. RESULTS LHBT was better depicted with CAIPIRINHA SPACE versus non-CAIPIRINHA SPACE (2.1 ± 0.4 vs. 1.5 ± 0.4; P < 0.001). Inter-modality agreement between CAIPIRINHA SPACE and 2D FSE was almost perfect (kappa = 0.884 ± 0.064). The sensitivity and specificity in detecting LHBT disorder were 95% (20/21) and 95% (20/21), respectively, for CAIPIRINHA SPACE, and 71% (15/21) and 76% (16/21), respectively, for non-CAIPIRINHA SPACE (P = 0.039). CONCLUSION Fourfold acceleration CAIPIRINHA is feasible in reducing the acquisition time of SPACE MR in the shoulder. 3D CAIPIRINHA SPACE with fourfold acceleration is highly accurate in detecting LHBT disorder.
Collapse
Affiliation(s)
- Liangjin Liu
- Department of Radiology, Hubei No.3 People’s Hospital of Jianghan University, Wuhan, China
| | - Gang Wu
- Department of Radiology, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, PR China
| |
Collapse
|
17
|
Khodarahmi I, Fritz J. The Value of 3 Tesla Field Strength for Musculoskeletal Magnetic Resonance Imaging. Invest Radiol 2021; 56:749-763. [PMID: 34190717 DOI: 10.1097/rli.0000000000000801] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
ABSTRACT Musculoskeletal magnetic resonance imaging (MRI) is a careful negotiation between spatial, temporal, and contrast resolution, which builds the foundation for diagnostic performance and value. Many aspects of musculoskeletal MRI can improve the image quality and increase the acquisition speed; however, 3.0-T field strength has the highest impact within the current diagnostic range. In addition to the favorable attributes of 3.0-T field strength translating into high temporal, spatial, and contrast resolution, many 3.0-T MRI systems yield additional gains through high-performance gradients systems and radiofrequency pulse transmission technology, advanced multichannel receiver technology, and high-end surface coils. Compared with 1.5 T, 3.0-T MRI systems yield approximately 2-fold higher signal-to-noise ratios, enabling 4 times faster data acquisition or double the matrix size. Clinically, 3.0-T field strength translates into markedly higher scan efficiency, better image quality, more accurate visualization of small anatomic structures and abnormalities, and the ability to offer high-end applications, such as quantitative MRI and magnetic resonance neurography. Challenges of 3.0-T MRI include higher magnetic susceptibility, chemical shift, dielectric effects, and higher radiofrequency energy deposition, which can be managed successfully. The higher total cost of ownership of 3.0-T MRI systems can be offset by shorter musculoskeletal MRI examinations, higher-quality examinations, and utilization of advanced MRI techniques, which then can achieve higher gains and value than lower field systems. We provide a practice-focused review of the value of 3.0-T field strength for musculoskeletal MRI, practical solutions to challenges, and illustrations of a wide spectrum of gainful clinical applications.
Collapse
Affiliation(s)
- Iman Khodarahmi
- From the Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | | |
Collapse
|
18
|
Dalili D, Fritz J, Isaac A. 3D MRI of the Hand and Wrist: Technical Considerations and Clinical Applications. Semin Musculoskelet Radiol 2021; 25:501-513. [PMID: 34547815 DOI: 10.1055/s-0041-1731652] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
In the last few years, major developments have been observed in the field of magnetic resonance imaging (MRI). Advances in both scanner hardware and software technologies have witnessed great leaps, enhancing the diagnostic quality and, therefore, the value of MRI. In musculoskeletal radiology, three-dimensional (3D) MRI has become an integral component of the diagnostic pathway at our institutions. This technique is particularly relevant in patients with hand and wrist symptoms, due to the intricate nature of the anatomical structures and the wide range of differential diagnoses for most presentations. We review the benefits of 3D MRI of the hand and wrist, commonly used pulse sequences, clinical applications, limitations, and future directions. We offer guidance for enhancing the image quality and tips for image interpretation of 3D MRI of the hand and wrist.
Collapse
Affiliation(s)
- Danoob Dalili
- Epsom and St Helier University Hospitals, London, United Kingdom
| | - Jan Fritz
- NYU Grossman School of Medicine, New York University, New York, New York
| | - Amanda Isaac
- Guy's and St. Thomas' Hospitals NHS Foundation Trust, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London (KCL), London, United Kingdom
| |
Collapse
|
19
|
Ezzati F, Chalian M, Pezeshk P. 3D MRI of the Rheumatic Diseases. Semin Musculoskelet Radiol 2021; 25:425-432. [PMID: 34547808 DOI: 10.1055/s-0041-1731058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Magnetic resonance imaging (MRI) is commonly used to evaluate musculoskeletal pathologies due to its high spatial resolution and excellent tissue contrast. The diagnosis of rheumatic diseases can often be challenging. Investigation with conventional two-dimensional MRI is helpful for diagnosis and monitoring treatment. In the past few years, three-dimensional (3D) MRI has been more commonly used to assess joint pathologies including inflammatory and rheumatic diseases. This review discusses the techniques and protocols of 3D MRI and its diagnostic yield in the assessment of rheumatic diseases, along with different examples.
Collapse
Affiliation(s)
- Fatemeh Ezzati
- Division of Rheumatic and Autoimmune Diseases, Department of Medicine, UT Southwestern Medical Center, Dallas, Texas
| | - Majid Chalian
- Division of Musculoskeletal Radiology, Department of Radiology, University of Washington, Seattle, Washington
| | - Parham Pezeshk
- Division of Musculoskeletal Radiology, Department of Radiology, UT Southwestern Medical Center, Dallas, Texas
| |
Collapse
|
20
|
Del Grande F, Hinterholzer N, Nanz D. 3D MRI: Technical Considerations and Practical Integration. Semin Musculoskelet Radiol 2021; 25:381-387. [PMID: 34547803 DOI: 10.1055/s-0041-1731059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
One of the main advantages of three-dimensional (3D) magnetic resonance imaging (MRI) is the possibility of isotropic voxels and reconstructed planar cuts through the volumetric data set in any orientation with multiplanar reformation software through real-time evaluation. For example, reformats by the radiologist during reporting allows exploitation of the full potential of isotropic 3D volumetric acquisition or through standardized retrospective reformats of thicker predefined slices of an isotropic volumetric data set by technologists. The main challenges for integrating 3D fast spin echo (FSE) and turbo spin-echo (TSE) MRI in clinical practice are a long acquisition time and some artifacts, whereas for integrating 3D gradient-recalled echo protocols, the main challenges are lower signal-to-noise ratios (SNRs) and the inability to produce intermediate, and T2-weighted contrast. The implementation of bidirectional parallel imaging acquisition and random undersampling acceleration strategies of 3D TSE pulse sequences substantially shortens the examination time with only minor SNR reductions. This article provides an overview of general technical considerations of 3D FSE and TSE sequences in musculoskeletal MRI. It also describes how these sequences achieve efficient data acquisition and reviews the main advantages and challenges for their introduction to clinical practice.
Collapse
Affiliation(s)
- Filippo Del Grande
- Clinica di Radiologia EOC, Istituto di Imaging della Svizzera Italiana (IIMSI), Lugano, Svizzera
| | - Natalie Hinterholzer
- SCMI, Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Zürich, Switzerland
| | - Daniel Nanz
- SCMI, Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Zürich, Switzerland.,University of Zürich, Zürich, Switzerland
| |
Collapse
|
21
|
Abstract
Osteoarthritis, characterized by the breakdown of articular cartilage and other joint structures, is one of the most prevalent and disabling chronic diseases in the United States. Magnetic resonance imaging is a commonly used imaging modality to evaluate patients with joint pain. Both two-dimensional fast spin-echo sequences (2D-FSE) and three-dimensional (3D) sequences are used in clinical practice to evaluate articular cartilage. The 3D sequences have many advantages compared with 2D-FSE sequences, such as their high in-plane spatial resolution, thin continuous slices that reduce the effects of partial volume averaging, and ability to create multiplanar reformat images following a single acquisition. This article reviews the different 3D imaging techniques available for evaluating cartilage morphology, illustrates the strengths and weaknesses of 3D approaches compared with 2D-FSE approaches for cartilage imaging, and summarizes the diagnostic performance of 2D-FSE and 3D sequences for detecting cartilage lesions within the knee and hip joints.
Collapse
Affiliation(s)
- Richard Kijowski
- Department of Radiology, New York University Grossman School of Medicine, New York, New York
| |
Collapse
|
22
|
Fritz B, Fritz J, Sutter R. 3D MRI of the Ankle: A Concise State-of-the-Art Review. Semin Musculoskelet Radiol 2021; 25:514-526. [PMID: 34547816 DOI: 10.1055/s-0041-1731332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Magnetic resonance imaging (MRI) is a powerful imaging modality for visualizing a wide range of ankle disorders that affect ligaments, tendons, and articular cartilage. Standard two-dimensional (2D) fast spin-echo (FSE) and turbo spin-echo (TSE) pulse sequences offer high signal-to-noise and contrast-to-noise ratios, but slice thickness limitations create partial volume effects. Modern three-dimensional (3D) FSE/TSE pulse sequences with isotropic voxel dimensions can achieve higher spatial resolution and similar contrast resolutions in ≤ 5 minutes of acquisition time. Advanced acceleration schemes have reduced the blurring effects of 3D FSE/TSE pulse sequences by affording shorter echo train lengths. The ability for thin-slice partitions and multiplanar reformation capabilities eliminate relevant partial volume effects and render modern 3D FSE/TSE pulse sequences excellently suited for MRI visualization of several oblique and curved structures around the ankle. Clinical efficiency gains can be achieved by replacing two or three 2D FSE/TSE sequences within an ankle protocol with a single isotropic 3D FSE/TSE pulse sequence. In this article, we review technical pulse sequence properties for 3D MRI of the ankle, discuss practical considerations for clinical implementation and achieving the highest image quality, compare diagnostic performance metrics of 2D and 3D MRI for major ankle structures, and illustrate a broad spectrum of ankle abnormalities.
Collapse
Affiliation(s)
- Benjamin Fritz
- Department of Radiology, University Hospital Balgrist, Zurich, Switzerland.,Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Jan Fritz
- New York University Grossman School of Medicine, New York University, New York, New York
| | - Reto Sutter
- Department of Radiology, University Hospital Balgrist, Zurich, Switzerland.,Faculty of Medicine, University of Zurich, Zurich, Switzerland
| |
Collapse
|
23
|
Abstract
Magnetic resonance imaging provides a comprehensive evaluation of the shoulder including the rotator cuff muscles and tendons, glenoid labrum, long head biceps tendon, and glenohumeral and acromioclavicular joint articulations. Most institutions use two-dimensional sequences acquired in all three imaging planes to accurately evaluate the many important structures of the shoulder. Recently, the addition of three-dimensional (3D) acquisitions with 3D reconstructions has become clinically feasible and helped improve our understanding of several important pathologic conditions, allowing us to provide added value for referring clinicians. This article briefly describes techniques used in 3D imaging of the shoulder and discusses applications of these techniques including measuring glenoid bone loss in anterior glenohumeral instability. We also review the literature on routine 3D imaging for the evaluation of common shoulder abnormalities as 3D imaging will likely become more common as imaging software continues to improve.
Collapse
Affiliation(s)
- Steven P Daniels
- Department of Radiology, New York University Grossman School of Medicine, New York University, New York, New York
| | - Soterios Gyftopoulos
- Department of Radiology, New York University Grossman School of Medicine, New York University, New York, New York
| |
Collapse
|
24
|
Pasoglou V, Van Nieuwenhove S, Peeters F, Duchêne G, Kirchgesner T, Lecouvet FE. 3D Whole-Body MRI of the Musculoskeletal System. Semin Musculoskelet Radiol 2021; 25:441-454. [PMID: 34547810 DOI: 10.1055/s-0041-1730401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
With its outstanding soft tissue contrast, spatial resolution, and multiplanar capacities, magnetic resonance imaging (MRI) has become a widely used technique. Whole-body MRI (WB-MRI) has been introduced among diagnostic methods for the staging and follow-up assessment in oncologic patients, and international guidelines recommend its use. In nononcologic applications, WB-MRI is as a promising imaging tool in inflammatory diseases, such as seronegative arthritis and inflammatory myopathies. Technological advances have facilitated the introduction of three-dimensional (3D) almost isotropic sequences in MRI examinations covering the whole body. The possibility to reformat 3D images in any plane with equal or almost equal resolution offers comprehensive understanding of the anatomy, easier disease detection and characterization, and finally contributes to correct treatment planning. This article illustrates the basic principles, advantages, and limitations of the 3D approach in WB-MRI examinations and provides a short review of the literature.
Collapse
Affiliation(s)
- Vassiliki Pasoglou
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Sandy Van Nieuwenhove
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Frank Peeters
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Gaetan Duchêne
- MR applications, General Electric Healthcare, Diegem, Belgium
| | - Thomas Kirchgesner
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Frederic E Lecouvet
- Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| |
Collapse
|
25
|
Abstract
High-resolution isotropic volumetric three-dimensional (3D) magnetic resonance neurography (MRN) techniques enable multiplanar depiction of peripheral nerves. In addition, 3D MRN provides anatomical and functional tissue characterization of different disease conditions affecting the peripheral nerves. In this review article, we summarize clinically relevant technical considerations of 3D MRN image acquisition and review clinical applications of 3D MRN to assess peripheral nerve diseases, such as entrapments, trauma, inflammatory or infectious neuropathies, and neoplasms.
Collapse
Affiliation(s)
- Omid Khalilzadeh
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Laura M Fayad
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Shivani Ahlawat
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, Maryland
| |
Collapse
|
26
|
Fritz J. Musculoskeletal 3D MRI: A Decade of Developments and Innovations Coming to Fruition. Semin Musculoskelet Radiol 2021; 25:379-380. [PMID: 34547802 DOI: 10.1055/s-0041-1733946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Jan Fritz
- Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, New York, New York
| |
Collapse
|
27
|
Comparison of CAIPIRINHA-accelerated 3D fat-saturated-SPACE MRI with 2D MRI sequences for the assessment of shoulder pathology. Eur Radiol 2021; 32:593-601. [PMID: 34258637 DOI: 10.1007/s00330-021-08183-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 06/20/2021] [Accepted: 06/29/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVES To compare the performance of 6-min MRI with a fat-saturated 3D-controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) Sampling perfection with application-optimized contrast using different flip angle evolution (SPACE) TSE protocol with 10-min 2D TSE MRI protocol for assessment of abnormalities of the shoulder. METHODS Forty-nine subjects underwent both 3D fat-saturated-CAIPIRINHA SPACE and 2D TSE sequences of the shoulder on a 3.0-T system. Following randomization and anonymization, two musculoskeletal radiologists evaluated the 2D and 3D images independently for image quality and diagnostic capability. Descriptive statistics, inter-observer, and inter-method concordance were investigated. p values < 0.05 were considered significant. RESULTS For image quality assessment, 2D images were similar to 3D CAIPIRINHA SPACE images (p = 0.05). 3D had lower noise standard deviation (SD) and higher fluid CNR than 2D images (p = 0.00). For diagnostic capability assessment, using 2D TSE as a standard of reference, sensitivity, specificity, and accuracy of 3D SPACE were, respectively, 94.81%, 94.12%, and 94.39% for tendon abnormalities; 97.06%, 80.00%, and 91.84% for acromioclavicular joint abnormalities; 88.89%, 100.00%, and 93.89% for adjacent bone alterations; and 97.30%, 100%, and 97.96% for joint fluid/effusion assessment. The inter-method concordance was moderate to almost perfect. The inter-observer-concordance of the shoulder assessment was also moderate to almost perfect, with SSP lesions demonstrating the greatest concordance. CONCLUSIONS The performance of 6-min 3D fat-saturated-CAIPIRINHA SPACE MRI for shoulder MRI is similar to that of 10-min 2D TSE MRI. 3D fat-saturated-CAIPIRINHA SPACE MRI can be utilized to reduce scan time without degradation in image quality. KEY POINTS • CAIPIRINHA acceleration 3D fat-saturated-MRI of the shoulder is achievable in 6 min with high spatial resolution. • 3D fat-saturated CAIPIRINHA MRI is similar to 2D MRI in the shoulder assessment. • 3D CAIPIRINHA MRI images enable rapid diagnosis of shoulder abnormalities without image quality degradation.
Collapse
|
28
|
Chaudhari AS, Grissom MJ, Fang Z, Sveinsson B, Lee JH, Gold GE, Hargreaves BA, Stevens KJ. Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement. AJR Am J Roentgenol 2021; 216:1614-1625. [PMID: 32755384 PMCID: PMC8862596 DOI: 10.2214/ajr.20.24172] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different (p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS (p = .81); and 46% and 39% for qDESS with T2 mapping (p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS (p = .02), and 58% and 40% for qDESS with T2 mapping (p < .001). CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.
Collapse
Affiliation(s)
- Akshay S Chaudhari
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
| | | | | | - Bragi Sveinsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA
- Department of Radiology, Harvard Medical School, Boston, MA
| | - Jin Hyung Lee
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Neurosurgery, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Garry E Gold
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
| | - Brian A Hargreaves
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Bioengineering, Stanford University, Stanford, CA
- Department of Electrical Engineering, Stanford University, Stanford, CA
| | - Kathryn J Stevens
- Department of Radiology, Lucas Center for Imaging, Stanford University, 1201 Welch Rd, PS 055B, Stanford, CA 94305
- Department of Orthopaedic Surgery, Stanford University, Redwood City, CA
| |
Collapse
|
29
|
York V, Sultan N, Thapa M, Chaturvedi A. Musculoskeletal MRI in Infants: Technical Considerations, Pitfalls and Optimization Strategies. Semin Roentgenol 2021; 56:277-287. [PMID: 34281680 DOI: 10.1053/j.ro.2021.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Vincent York
- Department of Radiology, Rochester General Hospital, Rochester, NY.
| | - Nadia Sultan
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY
| | - Mahesh Thapa
- Department of Radiology, University of Washington, Seattle, WA
| | - Apeksha Chaturvedi
- Department of Imaging Sciences, University of Rochester Medical Center, Rochester, NY
| |
Collapse
|
30
|
Van Dyck P, Smekens C, Vanhevel F, De Smet E, Roelant E, Sijbers J, Jeurissen B. Super-Resolution Magnetic Resonance Imaging of the Knee Using 2-Dimensional Turbo Spin Echo Imaging. Invest Radiol 2021; 55:481-493. [PMID: 32404629 DOI: 10.1097/rli.0000000000000676] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
OBJECTIVES The purpose of this study was to assess the technical feasibility of 3-dimensional (3D) super-resolution reconstruction (SRR) of 2D turbo spin echo (TSE) knee magnetic resonance imaging (MRI) and to compare its image quality with conventional 3D TSE sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) MRI. MATERIALS AND METHODS Super-resolution reconstruction 2D TSE MRI and 3D TSE SPACE images were acquired from a phantom and from the knee of 22 subjects (8 healthy volunteers and 14 patients) using a clinical 3-T scanner. For SRR, 7 anisotropic 2D TSE stacks (voxel size, 0.5 × 0.5 × 2.0 mm; scan time per stack, 1 minute 55 seconds; total scan time, 13 minutes 25 seconds) were acquired with the slice stack rotated around the phase-encoding axis. Super-resolution reconstruction was performed at an isotropic high-resolution grid with a voxel size of 0.5 × 0.5 × 0.5 mm. Direct isotropic 3D image acquisition was performed with the conventional SPACE sequence (voxel size, 0.5 × 0.5 × 0.5 mm; scan time, 12 minutes 42 seconds). For quantitative evaluation, perceptual blur metrics and edge response functions were obtained in the phantom image, and signal-to-noise and contrast-to-noise ratios were measured in the images from the healthy volunteers. Images were qualitatively evaluated by 2 independent radiologists in terms of overall image quality, edge blurring, anatomic visibility, and diagnostic confidence to assess normal and abnormal knee structures. Nonparametric statistical analysis was performed, and significance was defined for P values less than 0.05. RESULTS In the phantom, perceptual blur metrics and edge response functions demonstrated a clear improvement in spatial resolution for SRR compared with conventional 3D SPACE. In healthy subjects, signal-to-noise and contrast-to-noise ratios in clinically relevant structures were not significantly different between SRR and 3D SPACE. Super-resolution reconstruction provided better overall image quality and less edge blurring than conventional 3D SPACE, yet the perceived image contrast was better for 3D SPACE. Super-resolution reconstruction received significantly better visibility scores for the menisci, whereas the visibility of cartilage was significantly higher for 3D SPACE. Ligaments had high visibility on both SRR and 3D SPACE images. The diagnostic confidence for assessing menisci was significantly higher for SRR than for conventional 3D SPACE, whereas there were no significant differences between SRR and 3D SPACE for cartilage and ligaments. The interreader agreement for assessing menisci was substantial with 3D SPACE and almost perfect with SRR, and the agreement for assessing cartilage was almost perfect with 3D SPACE and moderate with SRR. CONCLUSIONS We demonstrate the technical feasibility of SRR for high-resolution isotropic knee MRI. Our SRR results show superior image quality in terms of edge blurring, but lower image contrast and fluid brightness when compared with conventional 3D SPACE acquisitions. Further contrast optimization and shortening of the acquisition time with state-of-the-art acceleration techniques are necessary for future clinical validation of SRR knee MRI.
Collapse
Affiliation(s)
- Pieter Van Dyck
- From the Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem
| | | | - Floris Vanhevel
- From the Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem
| | - Eline De Smet
- From the Department of Radiology, Antwerp University Hospital and University of Antwerp, Edegem
| | - Ella Roelant
- Clinical Trial Center (CTC), CRC Antwerp, Antwerp University Hospital and University of Antwerp, Edegem
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Wilrijk, Belgium
| | - Ben Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Wilrijk, Belgium
| |
Collapse
|
31
|
Del Grande F, Rashidi A, Luna R, Delcogliano M, Stern SE, Dalili D, Fritz J. Five-Minute Five-Sequence Knee MRI Using Combined Simultaneous Multislice and Parallel Imaging Acceleration: Comparison with 10-Minute Parallel Imaging Knee MRI. Radiology 2021; 299:635-646. [PMID: 33825510 DOI: 10.1148/radiol.2021203655] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Rapid knee MRI using combined simultaneous multislice (SMS) technique and parallel imaging (PI) acceleration can add value through reduced acquisition time but requires validation of clinical efficacy. Purpose To evaluate the performance of clinical fourfold SMS-PI-accelerated, 5-minute, five-sequence, multicontrast knee MRI protocols compared with standard twofold PI-accelerated, 10-minute knee MRI protocols. Materials and Methods Adults with painful knee conditions were prospectively enrolled from April 2018 to October 2019. Participants underwent fourfold SMS-PI-accelerated, 5-minute, turbo spin-echo (TSE) knee MRI and standard-of-care twofold PI-accelerated, 10-minute, TSE knee MRI at either 1.5 T or 3.0 T. Three radiologists independently evaluated the knee MRI studies for meniscal, tendinous, ligamentous, and osseocartilaginous injuries. Statistical analyses included k-based intermethod agreements and diagnostic performance testing. P < .05 was considered indicative of a statistically significant difference. Results A total of 252 adults were evaluated (mean age ± standard deviation, 47 years ± 17; 134 men). Among the participants, 104 (mean age, 42 years ± 18; 57 women) were in the 1.5-T arm and 148 (mean age, 46 years ± 17; 87 men) were in the 3.0-T arm. Twenty-nine participants (mean age, 38 years ± 12; 15 men) in the 1.5-T arm and 42 (mean age, 41 years ± 16; 24 men) in the 3.0-T arm underwent arthroscopy a mean of 45 days ± 31 and 45 days ± 22 after MRI, respectively. Intermethod agreements were good at 1.5 T (κ >0.71 [95% CI: 0.56, 0.83]) and very good at 3.0 T (κ >0.85 [95% CI: 0.69, 0.96]). The diagnostic performances of corresponding 5-minute and 10-minute MRI protocols were similar for 1.5 T, with areas under the receiver operating characteristic curve (AUCs) greater than 0.78 (95% CI: 0.71, 0.84) (P > .32), and 3.0 T, with AUCs greater than 0.83 (95% CI: 0.78, 0.88) (P > .32). Conclusion Comparisons of 5-minute five-sequence simultaneous multislice- and parallel imaging (PI)-accelerated and 10-minute five-sequence PI-accelerated turbo spin-echo MRI of the knee suggest similar performances at 1.5 and 3.0 T. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Subhas in this issue.
Collapse
Affiliation(s)
- Filippo Del Grande
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Ali Rashidi
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Rodrigo Luna
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Marco Delcogliano
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Steven E Stern
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Danoob Dalili
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| | - Jan Fritz
- From the Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Md (F.D.G., A.R., R.L., D.D.); Department of Radiology, Ospedale Regionale di Lugano, Lugano, Switzerland (F.D.G.), Department of Orthopedic Surgery, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland (M.D.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Nuffield Orthopedic Center, Oxford University Hospitals NHS Foundation Trust, Oxford, England (D.D.); and Department of Radiology, Grossman School of Medicine, New York University, 660 1st Ave, 3rd Floor, Room 313, New York, NY 10016 (J.F.)
| |
Collapse
|
32
|
Rapid Musculoskeletal MRI in 2021: Clinical Application of Advanced Accelerated Techniques. AJR Am J Roentgenol 2021; 216:718-733. [DOI: 10.2214/ajr.20.22902] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
|
33
|
Luna R, Fritz J, Del Grande F, Ahlawat S, Fayad LM. Determination of skeletal tumor extent: is an isotropic T1-weighted 3D sequence adequate? Eur Radiol 2020; 31:3138-3146. [PMID: 33179165 DOI: 10.1007/s00330-020-07394-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 10/08/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To test the hypothesis that an accelerated, T1-weighted 3D CAIPIRINHA SPACE sequence with isotropic voxel size offers a similar performance to conventional T1-weighted 2D TSE (turbo spin echo) for the evaluation of bone tumor extent and characteristics. METHODS Thirty-four patients who underwent 3-T MRI with 3DT1 (CAIPIRINHA SPACE TSE) and 2DT1 (TSE) were included. Sequence acquisition time was reported. Two radiologists independently evaluated each technique for tumor location, size/length, tumor-to-joint distance, signal intensity, margin/extraosseous extension, and signal-to-noise (SNR) and contrast-to-noise (CNR) ratios. RESULTS Tumors were located in long (20/36, 55.5%) and pelvic (16/36, 44.4%) bones. 3DT1 sequence required an average acquisition time of 235 s (± 42 s, range 156-372), while two plane 2DT1 sequences combined (coronal and axial) had an average acquisition time of 381 s (± 73 s, range 312-523). There was no difference in the measurements of tumor length and tumor-to-joint distance (p = 0.95) between 3DT1 and 2DT1 images. Tumors were hypointense (17/36, 47.2% vs 17/36, 47.2%), isointense (12/36, 33.3% vs 12/36, 33.3%), or hyperintense (7/36, 19.4% vs 7/36, 19.4%) on 3DT1 vs 2DT1, respectively. Assessment of tumor margins and extraosseous extension was similar, and there was no difference in tumor SNR or CNR (p > 0.05). CONCLUSIONS An accelerated 3D CAIPIRINHA SPACE T1 sequence provides comparable assessments of intramedullary bone tumor extent and similar tumor characteristics to conventional 2DT1 MRI. For the assessment of bone tumors, the isotropic volume acquisition and multiplanar reformation capability of the 3DT1 datasets can obviate the need for 2DT1 acquisitions in multiple planes. KEY POINTS • 3DT1 offers an equivalent performance to 2DT1 for the assessment of bone tumor characteristics, with faster and higher resolution capability, obviating the need for acquiring 2DT1 in multiple planes. • There was no difference in the measurements of tumor length and tumor-to-joint distance obtained on 3DT1 and 2DT1 images. • There was no difference in signal-to-noise ratio (SNR) or contrast-to-noise ratio (CNR) measures between 3DT1 and 2DT1.
Collapse
Affiliation(s)
- Rodrigo Luna
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Jan Fritz
- Grossman School of Medicine, NYU Langone Health, 550 First Avenue, New York, NY, 10016, USA
| | - Filippo Del Grande
- Servizio si Radiologia del Sottoceneri, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland
| | - Shivani Ahlawat
- Division of Musculoskeletal Imaging, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA
| | - Laura M Fayad
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins School of Medicine, Baltimore, MD, 21287, USA.
| |
Collapse
|
34
|
Chaudhari AS, Kogan F, Pedoia V, Majumdar S, Gold GE, Hargreaves BA. Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis. J Magn Reson Imaging 2020; 52:1321-1339. [PMID: 31755191 PMCID: PMC7925938 DOI: 10.1002/jmri.26991] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 12/16/2022] Open
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.
Collapse
Affiliation(s)
| | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Valentina Pedoia
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Sharmila Majumdar
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
- Center of Digital Health Innovation (CDHI), University of California San Francisco, San Francisco, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| |
Collapse
|
35
|
Abbreviated Musculoskeletal MRI Protocols: Point-Improved Patient Access and Reduced Health Care Costs. AJR Am J Roentgenol 2020; 216:33-34. [PMID: 32603225 DOI: 10.2214/ajr.20.24004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
36
|
Lee S, Lee GY, Kim S, Park YB, Lee HJ. Clinical utility of fat-suppressed 3-dimensional controlled aliasing in parallel imaging results in higher acceleration sampling perfection with application optimized contrast using different flip angle evolutions MRI of the knee in adults. Br J Radiol 2020; 93:20190725. [PMID: 32516546 DOI: 10.1259/bjr.20190725] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To compare htree-dimensional CAIPIRINHA SPACE and two-dimensional turbo spin echo (2D TSE) MRI in the diagnosis of knee pathology in symptomatic adult patients. METHODS From February to September in 2018, 120 patients who underwent a knee MRI using both 3D CAIPIRINHA SPACE and 2D TSE MRI were enrolled. The signal-to-noise ratios (SNRs) and contrast-to-noise ratio (CNR) of the 2D and 3D MRI were compared using a paired t-test. Two radiologists independently evaluated both 2D and 3D MRI images using scoring systems for the menisci, ligaments, and cartilage. Intermethod, inter- and intrareader agreements were determined using an intraclass correlation coefficient (ICC). The diagnostic performance of both methods was measured in 44 patients with arthroscopy. RESULTS The mean scan time of 3D CAIPIRINHA SPACE MRI (4' 43") was shorter than that of 2D TSE MRI (17' 27"). The mean SNR and CNR of 3D CAIPIRINHA SPACE was higher than those of 2D TSE MRI (mean difference, 3.97 of SNR and 1.58 of CNR; p < 0.001 and p = .038, respectively). Intermethod (ICC, 0.84-1.0) and inter-reader (ICC, 0.75-0.97), and intra-reader agreements (ICC, 0.87-1.0) were good or excellent. The diagnostic accuracy of 3D CAIPIRINHA SPACE sequence was equal for ligament (95.5%) and better for meniscal and cartilage evaluation (84.1% each), compared to 2D TSE MRI (79.5% each). CONCLUSION The fat-suppressed 3D CAIPIRINHA SPACE MRI maybe useful in clinical practice for the evaluation of the knee in place of the 2D conventional MRI protocol. ADVANCES IN KNOWLEDGE 1. The 3D CAIPIRINHA SPACE MRI of the knee joint may be acceptable to be used in clinical practice showing comparable imaging quality compared to conventional 2D TSE MRI.2. Compared with arthroscopic findings as the gold-standard, the diagnostic performance of 3D CAIPIRINHA SPACE MRI was equal or better for knee joint evaluation than that of 2D TSE MRI, as well as with shorter scan time.
Collapse
Affiliation(s)
- Seungho Lee
- Department of the Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Guen Young Lee
- Department of the Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Sujin Kim
- Department of the Radiology, Chung-Ang University Hospital, Seoul, Korea
| | - Yong-Beom Park
- Department of the orthopedic Surgery, Chung-Ang University Hospital, Seoul, Korea
| | - Han-Jun Lee
- Department of the orthopedic Surgery, Chung-Ang University Hospital, Seoul, Korea
| |
Collapse
|
37
|
Stern C, Galley J, Fröhlich S, Peterhans L, Spörri J, Sutter R. Distal Femoral Cortical Irregularity at Knee MRI: Increased Prevalence in Youth Competitive Alpine Skiers. Radiology 2020; 296:411-419. [PMID: 32544036 DOI: 10.1148/radiol.2020192589] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Background Tumor-like cortical irregularities at the posterior distal femur are common incidental findings in adolescents, but the origin of these irregularities is debated. Purpose To compare the prevalence of distal femoral cortical irregularities (DFCIs) at different tendon attachment sites in youth competitive alpine skiers with that in young adults. Materials and Methods In this secondary analysis of a prospective trial, unenhanced 3-T knee MRI scans obtained in youth competitive alpine skiers were compared with images in control participants of the same age from 2014 to 2019 (Cantonal Ethics Committee Zurich registry number: KEK-ZH-2017-01395) for presence of DFCIs at the femoral attachment of the medial head of the gastrocnemius muscle (MHG) and/or lateral head of the gastrocnemius muscle (LHG) and adductor magnus tendon by two radiologists. DFCI size and tendon attachment position were measured. Tendon attachment position and associated MRI findings (meniscus, cartilage, bone marrow edema, joint effusion, ligaments, tendons) were examined for an association with DFCI. Pearson χ2, Student t test, logistic regression, and κ statistics were applied. Results Unilateral knee MRI scans obtained in 105 skiers (mean age, 14.8 years ± 0.6 [standard deviation]; 66 boys) and in 105 control participants (mean age, 14.6 years ± 0.5; 59 boys) were evaluated. DFCIs were found in 61 of 105 skiers (58%; 95% confidence interval [CI]: 48.5%, 67.2%) compared with 28 of 105 control participants (27%; 95% CI: 18.9%, 35.7%) (P < .001). Two skiers had more than one DFCI. Distribution of DFCIs for skiers and control participants was 60 of 63 (95.2%) and 26 of 28 (92.8%) at the MHG, three of 63 (4.8%) and one of 28 (3.6%) at the LHG, and zero of 63 (0%) and one of 28 (3.6%) at the adductor magnus attachment site, respectively. Interreader agreement was almost perfect (κ = 0.87; 95% CI: 0.80, 0.93). The mean size of MHG-related DFCIs in skiers (3.7 mm) was not different compared to the size of those in control participants (3.4 mm) (P = .32), nor was a difference found for the MHG tendon attachment position in knees with DFCI (63.9 mm vs 63.0 mm, P = .83) or without DFCI (63.6 mm vs 62.8 mm, P = .86). Regarding associated MRI findings, increased signal intensity of the MHG tendon showed a significant association with MHG-related DFCI in both groups (P = .01 for both). Conclusion A distal femoral cortical irregularity at the attachment sites of tendons was a frequent incidental finding on knee MRI scans, with an increased prevalence in youth competitive alpine skiers. © RSNA, 2020 Online supplemental material is available for this article.
Collapse
Affiliation(s)
- Christoph Stern
- From the Department of Radiology (C.S., J.G., R.S.), Sports Medical Research Group, Department of Orthopedics (S.F., L.P., J.S.), and University Centre for Prevention and Sports Medicine (S.F., L.P., J.S.), Balgrist University Hospital, Forchstrasse 340, CH-8008, Zürich, Switzerland; and Faculty of Medicine, University of Zurich, Zurich, Switzerland (C.S., J.G., R.S.)
| | - Julien Galley
- From the Department of Radiology (C.S., J.G., R.S.), Sports Medical Research Group, Department of Orthopedics (S.F., L.P., J.S.), and University Centre for Prevention and Sports Medicine (S.F., L.P., J.S.), Balgrist University Hospital, Forchstrasse 340, CH-8008, Zürich, Switzerland; and Faculty of Medicine, University of Zurich, Zurich, Switzerland (C.S., J.G., R.S.)
| | - Stefan Fröhlich
- From the Department of Radiology (C.S., J.G., R.S.), Sports Medical Research Group, Department of Orthopedics (S.F., L.P., J.S.), and University Centre for Prevention and Sports Medicine (S.F., L.P., J.S.), Balgrist University Hospital, Forchstrasse 340, CH-8008, Zürich, Switzerland; and Faculty of Medicine, University of Zurich, Zurich, Switzerland (C.S., J.G., R.S.)
| | - Loris Peterhans
- From the Department of Radiology (C.S., J.G., R.S.), Sports Medical Research Group, Department of Orthopedics (S.F., L.P., J.S.), and University Centre for Prevention and Sports Medicine (S.F., L.P., J.S.), Balgrist University Hospital, Forchstrasse 340, CH-8008, Zürich, Switzerland; and Faculty of Medicine, University of Zurich, Zurich, Switzerland (C.S., J.G., R.S.)
| | - Jörg Spörri
- From the Department of Radiology (C.S., J.G., R.S.), Sports Medical Research Group, Department of Orthopedics (S.F., L.P., J.S.), and University Centre for Prevention and Sports Medicine (S.F., L.P., J.S.), Balgrist University Hospital, Forchstrasse 340, CH-8008, Zürich, Switzerland; and Faculty of Medicine, University of Zurich, Zurich, Switzerland (C.S., J.G., R.S.)
| | - Reto Sutter
- From the Department of Radiology (C.S., J.G., R.S.), Sports Medical Research Group, Department of Orthopedics (S.F., L.P., J.S.), and University Centre for Prevention and Sports Medicine (S.F., L.P., J.S.), Balgrist University Hospital, Forchstrasse 340, CH-8008, Zürich, Switzerland; and Faculty of Medicine, University of Zurich, Zurich, Switzerland (C.S., J.G., R.S.)
| |
Collapse
|
38
|
Update: Klinische Knorpelbildgebung – Teil 1. Radiologe 2019; 59:692-699. [DOI: 10.1007/s00117-019-0561-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
39
|
Chaudhari AS, Stevens KJ, Sveinsson B, Wood JP, Beaulieu CF, Oei EH, Rosenberg JK, Kogan F, Alley MT, Gold GE, Hargreaves BA. Combined 5-minute double-echo in steady-state with separated echoes and 2-minute proton-density-weighted 2D FSE sequence for comprehensive whole-joint knee MRI assessment. J Magn Reson Imaging 2019; 49:e183-e194. [PMID: 30582251 PMCID: PMC7850298 DOI: 10.1002/jmri.26582] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/01/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Clinical knee MRI protocols require upwards of 15 minutes of scan time. PURPOSE/HYPOTHESIS To compare the imaging appearance of knee abnormalities depicted with a 5-minute 3D double-echo in steady-state (DESS) sequence with separate echo images, with that of a routine clinical knee MRI protocol. A secondary goal was to compare the imaging appearance of knee abnormalities depicted with 5-minute DESS paired with a 2-minute coronal proton-density fat-saturated (PDFS) sequence. STUDY TYPE Prospective. SUBJECTS Thirty-six consecutive patients (19 male) referred for a routine knee MRI. FIELD STRENGTH/SEQUENCES DESS and PDFS at 3T. ASSESSMENT Five musculoskeletal radiologists evaluated all images for the presence of internal knee derangement using DESS, DESS+PDFS, and the conventional imaging protocol, and their associated diagnostic confidence of the reading. STATISTICAL TESTS Differences in positive and negative percent agreement (PPA and NPA, respectively) and 95% confidence intervals (CIs) for DESS and DESS+PDFS compared with the conventional protocol were calculated and tested using exact McNemar tests. The percentage of observations where DESS or DESS+PDFS had equivalent confidence ratings to DESS+Conv were tested with exact symmetry tests. Interreader agreement was calculated using Krippendorff's alpha. RESULTS DESS had a PPA of 90% (88-92% CI) and NPA of 99% (99-99% CI). DESS+PDFS had increased PPA of 99% (95-99% CI) and NPA of 100% (99-100% CI) compared with DESS (both P < 0.001). DESS had equivalent diagnostic confidence to DESS+Conv in 94% of findings, whereas DESS+PDFS had equivalent diagnostic confidence in 99% of findings (both P < 0.001). All readers had moderate concordance for all three protocols (Krippendorff's alpha 47-48%). DATA CONCLUSION Both 1) 5-minute 3D-DESS with separated echoes and 2) 5-minute 3D-DESS paired with a 2-minute coronal PDFS sequence depicted knee abnormalities similarly to a routine clinical knee MRI protocol, which may be a promising technique for abbreviated knee MRI. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
Collapse
Affiliation(s)
- Akshay S. Chaudhari
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Kathryn J. Stevens
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Bragi Sveinsson
- Department of Radiology, Stanford University, Stanford, California, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeff P. Wood
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Christopher F. Beaulieu
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Edwin H.G. Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | | | - Feliks Kogan
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Garry E. Gold
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA
| | - Brian A. Hargreaves
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| |
Collapse
|
40
|
Tamir JI, Taviani V, Alley MT, Perkins B, Hart L, Obrien K, Wishah F, Sandberg JK, Anderson MJ, Turek JS, Willke TL, Lustig M, Vasanawala SS. Targeted rapid knee MRI exam using T 2 shuffling. J Magn Reson Imaging 2019; 49:e195-e204. [PMID: 30637847 PMCID: PMC6551292 DOI: 10.1002/jmri.26600] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 11/18/2018] [Accepted: 11/19/2018] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND MRI is commonly used to evaluate pediatric musculoskeletal pathologies, but same-day/near-term scheduling and short exams remain challenges. PURPOSE To investigate the feasibility of a targeted rapid pediatric knee MRI exam, with the goal of reducing cost and enabling same-day MRI access. STUDY TYPE A cost effectiveness study done prospectively. SUBJECTS Forty-seven pediatric patients. FIELD STRENGTH/SEQUENCE 3T. The 10-minute protocol was based on T2 Shuffling, a four-dimensional acquisition and reconstruction of images with variable T2 contrast, and a T1 2D fast spin-echo (FSE) sequence. A distributed, compressed sensing-based reconstruction was implemented on a four-node high-performance compute cluster and integrated into the clinical workflow. ASSESSMENT In an Institutional Review Board-approved study with informed consent/assent, we implemented a targeted pediatric knee MRI exam for assessing pediatric knee pain. Pediatric patients were subselected for the exam based on insurance plan and clinical indication. Over a 2-year period, 47 subjects were recruited for the study and 49 MRIs were ordered. Date and time information was recorded for MRI referral, registration, and completion. Image quality was assessed from 0 (nondiagnostic) to 5 (outstanding) by two readers, and consensus was subsequently reached. STATISTICAL TESTS A Wilcoxon rank-sum test assessed the null hypothesis that the targeted exam times compared with conventional knee exam times were unchanged. RESULTS Of the 49 cases, 20 were completed on the same day as exam referral. Median time from registration to exam completion was 18.7 minutes. Median reconstruction time for T2 Shuffling was reduced from 18.9 minutes to 95 seconds using the distributed implementation. Technical fees charged for the targeted exam were one-third that of the routine clinical knee exam. No subject had to return for additional imaging. DATA CONCLUSION The targeted knee MRI exam is feasible and reduces the imaging time, cost, and barrier to same-day MRI access for pediatric patients. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.
Collapse
Affiliation(s)
- Jonathan I. Tamir
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | - Valentina Taviani
- Global Applied Science Laboratory, GE Healthcare, Menlo Park, California, USA
| | - Marcus T. Alley
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Becki Perkins
- Department of Radiology, Lucile Packard Children’s Hospital, Stanford, California, USA
| | - Lori Hart
- Department of Radiology, Lucile Packard Children’s Hospital, Stanford, California, USA
| | - Kendall Obrien
- Department of Radiology, Lucile Packard Children’s Hospital, Stanford, California, USA
| | - Fidaa Wishah
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Jesse K Sandberg
- Department of Radiology, Stanford University, Stanford, California, USA
| | | | - Javier S. Turek
- Brain-Inspired Computing Lab, Intel Labs, Hillsboro, Oregon, USA
| | | | - Michael Lustig
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California, USA
| | | |
Collapse
|
41
|
Link TM, Patel R. The need for short MRI examinations: A musculoskeletal perspective. J Magn Reson Imaging 2019; 49:e49-e50. [DOI: 10.1002/jmri.26565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 10/16/2018] [Indexed: 12/26/2022] Open
Affiliation(s)
- Thomas M. Link
- Department of Radiology of Biomedical ImagingUniversity of California San Francisco California USA
| | - Rina Patel
- Department of Radiology of Biomedical ImagingUniversity of California San Francisco California USA
| |
Collapse
|