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  1. Article ; Online: Accelerated Musculoskeletal Magnetic Resonance Imaging.

    Yoon, Min A / Gold, Garry E / Chaudhari, Akshay S

    Journal of magnetic resonance imaging : JMRI

    2023  

    Abstract: With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been ... ...

    Abstract With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.
    Language English
    Publishing date 2023-12-29
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 1146614-5
    ISSN 1522-2586 ; 1053-1807
    ISSN (online) 1522-2586
    ISSN 1053-1807
    DOI 10.1002/jmri.29205
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  2. Book: Arthritis in color

    Bruno, Michael H. / Mosher, Timothy J. / Gold, Garry E.

    advanced imaging of arthritis

    2009  

    Author's details Michael A. Bruno ; Timothy J. Mosher ; Garry E. Gold
    Keywords Arthritis, Rheumatoid / diagnosis ; Osteoarthritis / diagnosis ; Diagnostic Imaging / methods ; Magnetic Resonance Imaging / methods ; Arthritis/Imaging
    Subject code 616.7220754
    Language English
    Size XII, 221 S. : Ill., graph. Darst.
    Publisher Saunders Elsevier
    Publishing place Philadelphia, Pa. u.a.
    Publishing country United States
    Document type Book
    Note Includes bibliographical references
    HBZ-ID HT015977535
    ISBN 978-1-4160-4722-3 ; 1-4160-4722-0
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    2009 A 3006 order for loan Magazin

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  3. Article ; Online: A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.

    Barbieri, Marco / Hooijmans, Melissa T / Moulin, Kevin / Cork, Tyler E / Ennis, Daniel B / Gold, Garry E / Kogan, Feliks / Mazzoli, Valentina

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 8253

    Abstract: This work presents a deep learning approach for rapid and accurate muscle water ... ...

    Abstract This work presents a deep learning approach for rapid and accurate muscle water T
    MeSH term(s) Algorithms ; Water ; Calibration ; Deep Learning ; Magnetic Resonance Imaging/methods ; Muscles/diagnostic imaging ; Phantoms, Imaging ; Image Processing, Computer-Assisted/methods ; Brain
    Chemical Substances Water (059QF0KO0R)
    Language English
    Publishing date 2024-04-08
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-58812-2
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  4. Article ; Online: Correction to: Multimodal positron emission tomography (PET) imaging in non-oncologic musculoskeletal radiology.

    Kogan, Feliks / Yoon, Daehyun / Teeter, Matthew G / Chaudhari, Abhijit J / Hales, Laurel / Barbieri, Marco / Gold, Garry E / Vainberg, Yael / Goyal, Ananya / Watkins, Lauren

    Skeletal radiology

    2024  

    Language English
    Publishing date 2024-04-01
    Publishing country Germany
    Document type Published Erratum
    ZDB-ID 527592-1
    ISSN 1432-2161 ; 0364-2348
    ISSN (online) 1432-2161
    ISSN 0364-2348
    DOI 10.1007/s00256-024-04667-7
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  5. Article ; Online: Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage.

    Hall, Mary E / Wang, Adam S / Gold, Garry E / Levenston, Marc E

    Journal of the Royal Society, Interface

    2022  Volume 19, Issue 193, Page(s) 20220403

    Abstract: The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that ... ...

    Abstract The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.
    MeSH term(s) Animals ; Cartilage, Articular/diagnostic imaging ; Cartilage, Articular/metabolism ; Cattle ; Contrast Media/metabolism ; Contrast Media/pharmacology ; Tomography, X-Ray Computed/methods
    Chemical Substances Contrast Media
    Language English
    Publishing date 2022-08-03
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2156283-0
    ISSN 1742-5662 ; 1742-5689
    ISSN (online) 1742-5662
    ISSN 1742-5689
    DOI 10.1098/rsif.2022.0403
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  6. Article ; Online: Automatic estimation of knee effusion from limited MRI data.

    Raman, Sandhya / Gold, Garry E / Rosen, Matthew S / Sveinsson, Bragi

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 3155

    Abstract: Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a ... ...

    Abstract Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5-30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a potentially useful assessment tool.
    MeSH term(s) Exudates and Transudates/diagnostic imaging ; Female ; Humans ; Knee/diagnostic imaging ; Knee Joint/diagnostic imaging ; Magnetic Resonance Angiography/methods ; Magnetic Resonance Imaging/methods ; Male ; Neural Networks, Computer ; Osteoarthritis, Knee/diagnostic imaging ; Radiography ; Synovitis/diagnostic imaging
    Language English
    Publishing date 2022-02-24
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-07092-9
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  7. Article ; Online: Automatic estimation of knee effusion from limited MRI data

    Sandhya Raman / Garry E. Gold / Matthew S. Rosen / Bragi Sveinsson

    Scientific Reports, Vol 12, Iss 1, Pp 1-

    2022  Volume 11

    Abstract: Abstract Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the ... ...

    Abstract Abstract Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5–30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2022-02-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: A method for measuring B

    Barbieri, Marco / Chaudhari, Akshay S / Moran, Catherine J / Gold, Garry E / Hargreaves, Brian A / Kogan, Feliks

    Magnetic resonance in medicine

    2022  Volume 89, Issue 2, Page(s) 577–593

    Abstract: Purpose: To develop and validate a method for : Methods: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee ... ...

    Abstract Purpose: To develop and validate a method for
    Methods: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences.
    Results: The proposed method for measuring
    Conclusion: The proposed method may allow B0 correction for qDESS
    MeSH term(s) Humans ; Magnetic Resonance Imaging/methods ; Phantoms, Imaging ; Knee/diagnostic imaging ; Knee Joint/diagnostic imaging ; Water
    Chemical Substances Water (059QF0KO0R)
    Language English
    Publishing date 2022-09-25
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 605774-3
    ISSN 1522-2594 ; 0740-3194
    ISSN (online) 1522-2594
    ISSN 0740-3194
    DOI 10.1002/mrm.29465
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  9. Article: Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans.

    Hall, Mary E / Black, Marianne S / Gold, Garry E / Levenston, Marc E

    Quantitative imaging in medicine and surgery

    2021  Volume 12, Issue 1, Page(s) 1–14

    Abstract: Background: This study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary ... ...

    Abstract Background: This study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary blurring increased.
    Methods: A 2D watershed algorithm was applied to CT arthrograms of 3 bovine stifle joints taken 3, 8, 18, and 33 minutes after iohexol injection and used to segment tibial cartilage. Thickness measurements were compared to a reference standard thickness measurement and the 3-minute time point scan.
    Results: 77.2% of cartilage thickness measurements were within 0.2 mm (1 voxel) of the thickness calculated in the reference scan at the 3-minute time point. 42% fewer voxels could be segmented from the 33-minute scan than the 3-minute scan due to diffusion of the contrast agent out of the joint space and into the cartilage, leading to blurring of the cartilage boundary. The traced watershed lines were closer to the location of the cartilage surface in areas where tissues were in direct contact with each other (cartilage-cartilage or cartilage-meniscus contact).
    Conclusions: The use of watershed dam lines to guide cartilage segmentation shows promise for identifying cartilage boundaries from CT arthrograms in areas where soft tissues are in direct contact with each other.
    Language English
    Publishing date 2021-12-14
    Publishing country China
    Document type Journal Article
    ZDB-ID 2653586-5
    ISSN 2223-4306 ; 2223-4292
    ISSN (online) 2223-4306
    ISSN 2223-4292
    DOI 10.21037/qims-20-1062
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  10. Article ; Online: Advanced MRI Approaches for Evaluating Common Lower Extremity Injuries in Basketball Players: Current and Emerging Techniques.

    Rubin, Elka B / Schmidt, Andrew M / Koff, Matthew F / Kogan, Feliks / Gao, Kenneth / Majumdar, Sharmila / Potter, Hollis / Gold, Garry E

    Journal of magnetic resonance imaging : JMRI

    2023  

    Abstract: Magnetic resonance imaging (MRI) can provide accurate and non-invasive diagnoses of lower extremity injuries in athletes. Sport-related injuries commonly occur in and around the knee and can affect the articular cartilage, patellar tendon, hamstring ... ...

    Abstract Magnetic resonance imaging (MRI) can provide accurate and non-invasive diagnoses of lower extremity injuries in athletes. Sport-related injuries commonly occur in and around the knee and can affect the articular cartilage, patellar tendon, hamstring muscles, and bone. Sports medicine physicians utilize MRI to evaluate and diagnose injury, track recovery, estimate return to sport timelines, and assess the risk of recurrent injury. This article reviews the current literature and describes novel developments of quantitative MRI tools that can further advance our understanding of sports injury diagnosis, prevention, and treatment while minimizing injury risk and rehabilitation time. Innovative approaches for enhancing the early diagnosis and treatment of musculoskeletal injuries in basketball players span a spectrum of techniques. These encompass the utilization of T
    Language English
    Publishing date 2023-10-18
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 1146614-5
    ISSN 1522-2586 ; 1053-1807
    ISSN (online) 1522-2586
    ISSN 1053-1807
    DOI 10.1002/jmri.29019
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