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Fritz J, Rashidi A, de Cesar Netto C. Magnetic Resonance Imaging of Total Ankle Arthroplasty: State-of-The-Art Assessment of Implant-Related Pain and Dysfunction. Clin Podiatr Med Surg 2024; 41:619-647. [PMID: 39237176 DOI: 10.1016/j.cpm.2024.04.001] [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] [Indexed: 09/07/2024]
Abstract
Total ankle arthroplasty (TAA) is an effective alternative for treating patients with end-stage ankle degeneration, improving mobility, and providing pain relief. Implant survivorship is constantly improving; however, complications occur. Many causes of pain and dysfunction after total ankle arthroplasty can be diagnosed accurately with clinical examination, laboratory, radiography, and computer tomography. However, when there are no or inconclusive imaging findings, magnetic resonance imaging (MRI) is highly accurate in identifying and characterizing bone resorption, osteolysis, infection, osseous stress reactions, nondisplaced fractures, polyethylene damage, nerve injuries and neuropathies, as well as tendon and ligament tears. Multiple vendors offer effective, clinically available MRI techniques for metal artifact reduction MRI of total ankle arthroplasty. This article reviews the MRI appearances of common TAA implant systems, clinically available techniques and protocols for metal artifact reduction MRI of TAA implants, and the MRI appearances of a broad spectrum of TAA-related complications.
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Affiliation(s)
- Jan Fritz
- Department of Orthopedic Surgery, Division of Foot and Ankle Surgery, Duke University, Durham, NC, USA.
| | - Ali Rashidi
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Floor, Rm 313, New York, NY 10016, USA
| | - Cesar de Cesar Netto
- Department of Radiology, Molecular Imaging Program at StanDepartment of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
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Vosshenrich J, Fritz J. [Accelerated musculoskeletal magnetic resonance imaging with deep learning-based image reconstruction at 0.55 T-3 T]. RADIOLOGIE (HEIDELBERG, GERMANY) 2024; 64:758-765. [PMID: 38864874 PMCID: PMC11422270 DOI: 10.1007/s00117-024-01325-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/14/2024] [Indexed: 06/13/2024]
Abstract
CLINICAL/METHODICAL ISSUE Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice. STANDARD RADIOLOGICAL METHODS MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast. METHODOLOGICAL INNOVATIONS Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors. PERFORMANCE The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy. ACHIEVEMENTS Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested. PRACTICAL RECOMMENDATIONS Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.
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Affiliation(s)
- Jan Vosshenrich
- Department of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA.
- Klinik für Radiologie und Nuklearmedizin, Universitätsspital Basel, Petersgraben 4, 4031, Basel, Schweiz.
| | - Jan Fritz
- Department of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA
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Ensle F, Abel F, Lohezic M, Obermüller C, Guggenberger R. Deep learning reconstruction for optimized bone assessment in zero echo time MR imaging of the knee. Eur J Radiol 2024; 179:111663. [PMID: 39142010 DOI: 10.1016/j.ejrad.2024.111663] [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: 03/20/2024] [Revised: 06/29/2024] [Accepted: 08/02/2024] [Indexed: 08/16/2024]
Abstract
PURPOSE To evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla. METHODS This retrospective study included 48 consecutive exams of 46 patients (23 females) who underwent clinically indicated knee MRI at 1.5 Tesla. Standard imaging protocol comprised a sagittal prescribed, isotropic ZTE sequence. ZTE image reconstruction was performed with a standard-of-care (non-DL) and prototype DLRecon method. Exams were divided into subsets with and without osseous pathology based on the radiology report. Using a 4-point scale, two blinded readers qualitatively graded features of bone depiction including artifacts and conspicuity of pathology including diagnostic certainty in the respective subsets. Quantitatively, one reader measured signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone. Comparative analyses were conducted to assess the differences between the reconstruction methods. In addition, interreader agreement was calculated for the qualitative gradings. RESULTS DLRecon significantly improved gradings for bone depiction relative to non-DL reconstruction (all, p < 0.05), while there was no significant difference with regards to artifacts (both, median score of 0; p = 0.058). In the subset with pathologies, conspicuity of pathology and diagnostic confidence were also scored significantly higher in DLRecon compared to non-DL (median 3 vs 2; p ≤ 0.03). Interreader agreement ranged from moderate to almost-perfect (κ = 0.54-0.88). Quantitatively, DLRecon demonstrated significantly enhanced CNR and SNR of bone compared to non-DL (p < 0.001). CONCLUSION ZTE MRI with DLRecon improved bone depiction in the knee, compared to non-DL. Additionally, DLRecon increased conspicuity of osseous findings together with diagnostic certainty.
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Affiliation(s)
- Falko Ensle
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| | - Frederik Abel
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Carina Obermüller
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roman Guggenberger
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Fritz B, de Cesar Netto C, Fritz J. Multiaxial 3D MRI of the Ankle: Advanced High-Resolution Visualization of Ligaments, Tendons, and Articular Cartilage. Clin Podiatr Med Surg 2024; 41:685-706. [PMID: 39237179 DOI: 10.1016/j.cpm.2024.04.004] [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] [Indexed: 09/07/2024]
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.
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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.
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Warstadt M, Winegar B, Shah LM. Imaging of Cervical Spine Trauma: Update of Techniques and Clinical Relevance. Clin Spine Surg 2024:01933606-990000000-00361. [PMID: 39315684 DOI: 10.1097/bsd.0000000000001677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/09/2024] [Accepted: 08/13/2024] [Indexed: 09/25/2024]
Abstract
Imaging of cervical spine trauma most commonly begins with computed tomography (CT) for initial osseous and basic soft tissue evaluation, followed by magnetic resonance imaging (MRI) for complementary evaluation of the neural structures (i.e., spinal cord, nerves) and soft tissues (i.e., ligaments). Although CT and conventional MRI sequences have been the mainstay of trauma imaging for decades, there have been significant advances in CT processing, imaging sequences and techniques made possible by hardware and software development, and artificial intelligence. These advancements may provide advantages in increasing sensitivity for detection of pathology as well as in decreasing imaging and interpretation time. Unquestionably, the most important role of imaging is to provide information to help direct patient care, including diagnosis, next steps in treatment plan, and prognosis. As such, there has been a growing body of research investigating the clinical relevance of imaging findings to clinical outcomes in the setting of spinal cord injury. This article will focus on these recent advances in imaging of cervical spinal trauma.
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Affiliation(s)
- Melissa Warstadt
- Department of Radiology, University of Utah, 30 N Mario Capecchi Dr. Salt Lake City, UT
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Chung CB, Pathria MN, Resnick D. MRI in MSK: is it the ultimate examination? Skeletal Radiol 2024; 53:1727-1735. [PMID: 38277028 DOI: 10.1007/s00256-024-04601-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [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
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Eckstein F, Walter-Rittel TC, Chaudhari AS, Brisson NM, Maleitzke T, Duda GN, Wisser A, Wirth W, Winkler T. The design of a sample rapid magnetic resonance imaging (MRI) acquisition protocol supporting assessment of multiple articular tissues and pathologies in knee osteoarthritis. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100505. [PMID: 39183946 PMCID: PMC11342198 DOI: 10.1016/j.ocarto.2024.100505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 07/21/2024] [Indexed: 08/27/2024] Open
Abstract
Objective This expert opinion paper proposes a design for a state-of-the-art magnetic resonance image (MRI) acquisition protocol for knee osteoarthritis clinical trials in early and advanced disease. Semi-quantitative and quantitative imaging endpoints are supported, partly amendable to automated analysis. Several (peri-) articular tissues and pathologies are covered, including synovitis. Method A PubMed literature search was conducted, with focus on the past 5 years. Further, osteoarthritis imaging experts provided input. Specific MRI sequences, orientations, spatial resolutions and parameter settings were identified to align with study goals. We strived for implementation on standard clinical scanner hardware, with a net acquisition time ≤30 min. Results Short- and long-term longitudinal MRIs should be obtained at ≥1.5T, if possible without hardware changes during the study. We suggest a series of gradient- and spin-echo-sequences, supporting MOAKS, quantitative analysis of cartilage morphology and T2, and non-contrast-enhanced depiction of synovitis. These sequences should be properly aligned and positioned using localizer images. One of the sequences may be repeated in each participant (re-test), optimally at baseline and follow-up, to estimate within-study precision. All images should be checked for quality and protocol-adherence as soon as possible after acquisition. Alternative approaches are suggested that expand on the structural endpoints presented. Conclusions We aim to bridge the gap between technical MRI acquisition guides and the wealth of imaging literature, proposing a balance between image acquisition efficiency (time), safety, and technical/methodological diversity. This approach may entertain scientific innovation on tissue structure and composition assessment in clinical trials on disease modification of knee osteoarthritis.
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Affiliation(s)
- Felix Eckstein
- Research Program for Musculoskeletal Imaging, Center for Anatomy & Cell Biology, Paracelsus Medical University, Salzburg, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Thula Cannon Walter-Rittel
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany
| | | | - Nicholas M. Brisson
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Tazio Maleitzke
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Musculoskeletal Surgery, Berlin, Germany
- Trauma Orthopaedic Research Copenhagen Hvidovre (TORCH), Department of Orthopaedic Surgery, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Georg N. Duda
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Berlin Movement Diagnostics (BeMoveD), Center for Musculoskeletal Surgery, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
| | - Anna Wisser
- Research Program for Musculoskeletal Imaging, Center for Anatomy & Cell Biology, Paracelsus Medical University, Salzburg, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Wolfgang Wirth
- Research Program for Musculoskeletal Imaging, Center for Anatomy & Cell Biology, Paracelsus Medical University, Salzburg, Austria
- Ludwig Boltzmann Institute for Arthritis and Rehabilitation (LBIAR), Paracelsus Medical University, Salzburg, Austria
- Chondrometrics GmbH, Freilassing, Germany
| | - Tobias Winkler
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Julius Wolff Institute, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Center for Musculoskeletal Surgery, Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
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Vosshenrich J, Koerzdoerfer G, Fritz J. Modern acceleration in musculoskeletal MRI: applications, implications, and challenges. Skeletal Radiol 2024; 53:1799-1813. [PMID: 38441617 DOI: 10.1007/s00256-024-04634-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/20/2024] [Accepted: 02/22/2024] [Indexed: 08/09/2024]
Abstract
Magnetic resonance imaging (MRI) is crucial for accurately diagnosing a wide spectrum of musculoskeletal conditions due to its superior soft tissue contrast resolution. However, the long acquisition times of traditional two-dimensional (2D) and three-dimensional (3D) fast and turbo spin-echo (TSE) pulse sequences can limit patient access and comfort. Recent technical advancements have introduced acceleration techniques that significantly reduce MRI times for musculoskeletal examinations. Key acceleration methods include parallel imaging (PI), simultaneous multi-slice acquisition (SMS), and compressed sensing (CS), enabling up to eightfold faster scans while maintaining image quality, resolution, and safety standards. These innovations now allow for 3- to 6-fold accelerated clinical musculoskeletal MRI exams, reducing scan times to 4 to 6 min for joints and spine imaging. Evolving deep learning-based image reconstruction promises even faster scans without compromising quality. Current research indicates that combining acceleration techniques, deep learning image reconstruction, and superresolution algorithms will eventually facilitate tenfold accelerated musculoskeletal MRI in routine clinical practice. Such rapid MRI protocols can drastically reduce scan times by 80-90% compared to conventional methods. Implementing these rapid imaging protocols does impact workflow, indirect costs, and workload for MRI technologists and radiologists, which requires careful management. However, the shift from conventional to accelerated, deep learning-based MRI enhances the value of musculoskeletal MRI by improving patient access and comfort and promoting sustainable imaging practices. This article offers a comprehensive overview of the technical aspects, benefits, and challenges of modern accelerated musculoskeletal MRI, guiding radiologists and researchers in this evolving field.
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Affiliation(s)
- Jan Vosshenrich
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
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Zijlstra F, While PT. Deep-learning-based image reconstruction with limited data: generating synthetic raw data using deep learning. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01193-4. [PMID: 39207581 DOI: 10.1007/s10334-024-01193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 07/15/2024] [Accepted: 07/22/2024] [Indexed: 09/04/2024]
Abstract
OBJECT Deep learning has shown great promise for fast reconstruction of accelerated MRI acquisitions by learning from large amounts of raw data. However, raw data is not always available in sufficient quantities. This study investigates synthetic data generation to complement small datasets and improve reconstruction quality. MATERIALS AND METHODS An adversarial auto-encoder was trained to generate phase and coil sensitivity maps from magnitude images, which were combined into synthetic raw data. On a fourfold accelerated MR reconstruction task, deep-learning-based reconstruction networks were trained with varying amounts of training data (20 to 160 scans). Test set performance was compared between baseline experiments and experiments that incorporated synthetic training data. RESULTS Training with synthetic raw data showed decreasing reconstruction errors with increasing amounts of training data, but importantly this was magnitude-only data, rather than real raw data. For small training sets, training with synthetic data decreased the mean absolute error (MAE) by up to 7.5%, whereas for larger training sets the MAE increased by up to 2.6%. DISCUSSION Synthetic raw data generation improved reconstruction quality in scenarios with limited training data. A major advantage of synthetic data generation is that it allows for the reuse of magnitude-only datasets, which are more readily available than raw datasets.
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Affiliation(s)
- Frank Zijlstra
- Department of Radiology and Nuclear Medicine, St Olav's University Hospital, Postboks 3250 Torgarden, 7006, Trondheim, Norway.
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway.
| | - Peter Thomas While
- Department of Radiology and Nuclear Medicine, St Olav's University Hospital, Postboks 3250 Torgarden, 7006, Trondheim, Norway
- Department of Circulation and Medical Imaging, NTNU-Norwegian University of Science and Technology, Trondheim, Norway
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Smekens C, Beirinckx Q, Bosmans F, Vanhevel F, Snoeckx A, Sijbers J, Jeurissen B, Janssens T, Van Dyck P. Deep Learning-Enhanced Accelerated 2D TSE and 3D Superresolution Dixon TSE for Rapid Comprehensive Knee Joint Assessment. Invest Radiol 2024:00004424-990000000-00251. [PMID: 39190787 DOI: 10.1097/rli.0000000000001118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Abstract
OBJECTIVES The aim of this study was to evaluate the use of a multicontrast deep learning (DL)-reconstructed 4-fold accelerated 2-dimensional (2D) turbo spin echo (TSE) protocol and the feasibility of 3-dimensional (3D) superresolution reconstruction (SRR) of DL-enhanced 6-fold accelerated 2D Dixon TSE magnetic resonance imaging (MRI) for comprehensive knee joint assessment, by comparing image quality and diagnostic performance with a conventional 2-fold accelerated 2D TSE knee MRI protocol. MATERIALS AND METHODS This prospective, ethics-approved study included 19 symptomatic adult subjects who underwent knee MRI on a clinical 3 T scanner. Every subject was scanned with 3 DL-enhanced acquisition protocols in a single session: a clinical standard 2-fold in-plane parallel imaging (PI) accelerated 2D TSE-based protocol (5 sequences, 11 minutes 23 seconds) that served as a reference, a DL-reconstructed 4-fold accelerated 2D TSE protocol combining 2-fold PI and 2-fold simultaneous multislice acceleration (5 sequences, 6 minutes 24 seconds), and a 3D SRR protocol based on DL-enhanced 6-fold accelerated (ie, 3-fold PI and 2-fold simultaneous multislice) 2D Dixon TSE MRI (6 anisotropic 2D Dixon TSE acquisitions rotated around the phase-encoding axis, 6 minutes 24 seconds). This resulted in a total of 228 knee MRI scans comprising 21,204 images. Three readers evaluated all pseudonymized and randomized images in terms of image quality using a 5-point Likert scale. Two of the readers (musculoskeletal radiologists) additionally evaluated anatomical visibility and diagnostic confidence to assess normal and pathological knee structures with a 5-point Likert scale. They recorded the presence and location of internal knee derangements, including cartilage defects, meniscal tears, tears of ligaments, tendons and muscles, and bone injuries. The statistical analysis included nonparametric Friedman tests, and interreader and intrareader agreement assessment using the weighted Fleiss-Cohen kappa (κ) statistic. P values of less than 0.05 were considered statistically significant. RESULTS The evaluated DL-enhanced 4-fold accelerated 2D TSE protocol provided very similar image quality and anatomical visibility to the standard 2D TSE protocol, whereas the 3D SRR Dixon TSE protocol scored less in terms of overall image quality due to reduced edge sharpness and the presence of artifacts (P < 0.001). Subjective signal-to-noise ratio, contrast resolution, fluid brightness, and fat suppression were good to excellent for all protocols. For 1 reader, the Dixon method of the 3D SRR protocol provided significantly better fat suppression than the spectral fat saturation applied in the standard 2D TSE protocol (P < 0.05). The visualization of knee structures with 3D SRR Dixon TSE was very similar to the standard protocol, except for cartilage, tendons, and bone, which were affected by the presence of reconstruction and aliasing artifacts (P < 0.001). The diagnostic confidence of both readers was high for all protocols and all knee structures, except for cartilage and tendons. The standard 2D TSE protocol showed a significantly higher diagnostic confidence for assessing tendons than 3D SRR Dixon TSE MRI (P < 0.01). The interreader and intrareader agreement for the assessment of internal knee derangements using any of the 3 protocols was substantial to almost perfect (κ = 0.67-1.00). For cartilage, the interreader agreement was substantial for DL-enhanced accelerated 2D TSE (κ = 0.79) and almost perfect for standard 2D TSE (κ = 0.98) and 3D SRR Dixon TSE (κ = 0.87). For menisci, the interreader agreement was substantial for 3D SRR Dixon TSE (κ = 0.70-0.80) and substantial to almost perfect for standard 2D TSE (κ = 0.80-0.99) and DL-enhanced 2D TSE (κ = 0.87-1.00). Moreover, the total acquisition time was reduced by 44% when using the DL-enhanced accelerated 2D TSE or 3D SRR Dixon TSE protocol instead of the conventional 2D TSE protocol. CONCLUSIONS The presented DL-enhanced 4-fold accelerated 2D TSE protocol provides image quality and diagnostic performance similar to the standard 2D protocol. Moreover, the 3D SRR of DL-enhanced 6-fold accelerated 2D Dixon TSE MRI is feasible for multicontrast 3D knee MRI as its diagnostic performance is comparable to standard 2-fold accelerated 2D knee MRI. However, reconstruction and aliasing artifacts need to be further addressed to guarantee a more reliable visualization and assessment of cartilage, tendons, and bone. Both the 2D and 3D SRR DL-enhanced protocols enable a 44% faster examination compared with conventional 2-fold accelerated routine 2D TSE knee MRI and thus open new paths for more efficient clinical 2D and 3D knee MRI.
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Affiliation(s)
- Céline Smekens
- From the imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium (C.S., Q.B., J.S., B.J.); Siemens Healthcare NV/SA, Groot-Bijgaarden, Belgium (C.S., T.J.); Department of Radiology, Antwerp University Hospital, Antwerp, Belgium (F.B., F.V., A.S., P.V.D.); and MIRA, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium (A.S., P.V.D.)
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Singh SB, Sarrami AH, Gatidis S, Varniab ZS, Chaudhari A, Daldrup-Link HE. Applications of Artificial Intelligence for Pediatric Cancer Imaging. AJR Am J Roentgenol 2024; 223:e2431076. [PMID: 38809123 DOI: 10.2214/ajr.24.31076] [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] [Indexed: 05/30/2024]
Abstract
Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric cancers are rare, and imaging technologies are evolving rapidly, leading to insufficient data of a particular type to effectively train these algorithms. The small market size of pediatric patients compared with adult patients could also contribute to this challenge, as market size is a driver of commercialization. This review provides an overview of the current state of AI applications for pediatric cancer imaging, including applications for medical image acquisition, processing, reconstruction, segmentation, diagnosis, staging, and treatment response monitoring. Although current developments are promising, impediments due to the diverse anatomies of growing children and nonstandardized imaging protocols have led to limited clinical translation thus far. Opportunities include leveraging reconstruction algorithms to achieve accelerated low-dose imaging and automating the generation of metric-based staging and treatment monitoring scores. Transfer learning of adult-based AI models to pediatric cancers, multiinstitutional data sharing, and ethical data privacy practices for pediatric patients with rare cancers will be keys to unlocking the full potential of AI for clinical translation and improving outcomes for these young patients.
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Affiliation(s)
- Shashi B Singh
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Amir H Sarrami
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Sergios Gatidis
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Zahra S Varniab
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
| | - Akshay Chaudhari
- Department of Radiology, Integrative Biomedical Imaging Informatics (IBIIS), Stanford University School of Medicine, Stanford University, Stanford, CA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford University, Stanford, CA
| | - Heike E Daldrup-Link
- Department of Radiology, Division of Pediatric Radiology, Stanford University School of Medicine, 1201 Welch Rd, Stanford, CA 94305
- Department of Pediatrics, Pediatric Hematology-Oncology, Lucile Packard Children's Hospital, Stanford University, Stanford, CA
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Feuerriegel GC, Goller SS, von Deuster C, Sutter R. Inflammatory Knee Synovitis: Evaluation of an Accelerated FLAIR Sequence Compared With Standard Contrast-Enhanced Imaging. Invest Radiol 2024; 59:599-604. [PMID: 38329824 DOI: 10.1097/rli.0000000000001065] [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: 02/10/2024]
Abstract
OBJECTIVES The aim of this study was to assess the diagnostic value and accuracy of a deep learning (DL)-accelerated fluid attenuated inversion recovery (FLAIR) sequence with fat saturation (FS) in patients with inflammatory synovitis of the knee. MATERIALS AND METHODS Patients with suspected knee synovitis were retrospectively included between January and September 2023. All patients underwent a 3 T knee magnetic resonance imaging including a DL-accelerated noncontrast FLAIR FS sequence (acquisition time: 1 minute 38 seconds) and a contrast-enhanced (CE) T1-weighted FS sequence (acquisition time: 4 minutes 50 seconds), which served as reference standard. All knees were scored by 2 radiologists using the semiquantitative modified knee synovitis score, effusion synovitis score, and Hoffa inflammation score. Diagnostic confidence, image quality, and image artifacts were rated on separate Likert scales. Wilcoxon signed rank test was used to compare the semiquantitative scores. Interreader and intrareader reproducibility were calculated using Cohen κ. RESULTS Fifty-five patients (mean age, 52 ± 17 years; 28 females) were included in the study. Twenty-seven patients (49%) had mild to moderate synovitis (synovitis score 6-13), and 17 patients (31%) had severe synovitis (synovitis score >14). No signs of synovitis were detected in 11 patients (20%) (synovitis score <5). Semiquantitative assessment of the whole knee synovitis score showed no significant difference between the DL-accelerated FLAIR sequence and the CE T1-weighted sequence (mean FLAIR score: 10.69 ± 8.83, T1 turbo spin-echo FS: 10.74 ± 10.32; P = 0.521). Both interreader and intrareader reproducibility were excellent (range Cohen κ [0.82-0.96]). CONCLUSIONS Assessment of inflammatory knee synovitis using a DL-accelerated noncontrast FLAIR FS sequence was feasible and equivalent to CE T1-weighted FS imaging.
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Affiliation(s)
- Georg C Feuerriegel
- From the Department of Radiology, Balgrist University Hospital, Faculty of Medicine, University of Zurich, Zurich, Switzerland (G.C.F., S.S.G., R.S.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Zurich, Switzerland (C.v.D.); and Swiss Center for Musculoskeletal Imaging, Balgrist Campus, Zurich, Switzerland (C.v.D.)
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Bosbach WA, Merdes KC, Jung B, Montazeri E, Anderson S, Mitrakovic M, Daneshvar K. Deep Learning Reconstruction of Accelerated MRI: False-Positive Cartilage Delamination Inserted in MRI Arthrography Under Traction. Top Magn Reson Imaging 2024; 33:e0313. [PMID: 39016321 DOI: 10.1097/rmr.0000000000000313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 05/28/2024] [Indexed: 07/18/2024]
Abstract
OBJECTIVES The radiological imaging industry is developing and starting to offer a range of novel artificial intelligence software solutions for clinical radiology. Deep learning reconstruction of magnetic resonance imaging data seems to allow for the acceleration and undersampling of imaging data. Resulting reduced acquisition times would lead to greater machine utility and to greater cost-efficiency of machine operations. MATERIALS AND METHODS Our case shows images from magnetic resonance arthrography under traction of the right hip joint from a 30-year-old, otherwise healthy, male patient. RESULTS The undersampled image data when reconstructed by a deep learning tool can contain false-positive cartilage delamination and false-positive diffuse cartilage defects. CONCLUSIONS In the future, precision of this novel technology will have to be put to thorough testing. Bias of systems, in particular created by the choice of training data, will have to be part of those assessments.
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Affiliation(s)
- Wolfram A Bosbach
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Switzerland
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Jeon K, Park WY, Kahn CE, Nagy P, You SC, Yoon SH. Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility. Invest Radiol 2024:00004424-990000000-00232. [PMID: 38985896 DOI: 10.1097/rli.0000000000001106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
ABSTRACT Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.
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Affiliation(s)
- Kyulee Jeon
- From the Department of Biomedical Systems Informatics, Yonsei University, Seoul, South Korea (K.J., S.C.Y.); Institution for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea (K.J., S.C.Y.); Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD (W.Y.P., P.N.); Department of Radiology, University of Pennsylvania, Philadelphia, PA (C.E.K.); and Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea (S.H.Y.)
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Marth AA, von Deuster C, Sommer S, Feuerriegel GC, Goller SS, Sutter R, Nanz D. Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T. Invest Radiol 2024:00004424-990000000-00230. [PMID: 38960863 DOI: 10.1097/rli.0000000000001095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
OBJECTIVES The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms. MATERIALS AND METHODS This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria. RESULTS The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions (P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality (P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration (P = 0.087), image noise of T1-weighted images at 2-fold acceleration (P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration (P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions (P ≥ 0.082). CONCLUSIONS This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.
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Affiliation(s)
- Adrian Alexander Marth
- From the Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Zurich, Switzerland (A.A.M., C.v.D., S.S., D.N.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (A.A.M., G.C.F., S.S.G., R.S.); Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Zurich, Switzerland (C.v.D., S.S.); and Medical Faculty, University of Zurich, Zurich, Switzerland (R.S., D.N.)
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16
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Lin DJ, Doshi AM, Fritz J, Recht MP. Designing Clinical MRI for Enhanced Workflow and Value. J Magn Reson Imaging 2024; 60:29-39. [PMID: 37795927 DOI: 10.1002/jmri.29038] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/18/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023] Open
Abstract
MRI is an expensive and traditionally time-intensive modality in imaging. With the paradigm shift toward value-based healthcare, radiology departments must examine the entire MRI process cycle to identify opportunities to optimize efficiency and enhance value for patients. Digital tools such as "frictionless scheduling" prioritize patient preference and convenience, thereby delivering patient-centered care. Recent advances in conventional and deep learning-based accelerated image reconstruction methods have reduced image acquisition time to such a degree that so-called nongradient time now constitutes a major percentage of total room time. For this reason, architectural design strategies that reconfigure patient preparation processes and decrease the turnaround time between scans can substantially impact overall throughput while also improving patient comfort and privacy. Real-time informatics tools that provide an enterprise-wide overview of MRI workflow and Picture Archiving and Communication System (PACS)-integrated instant messaging can complement these efforts by offering transparent, situational data and facilitating communication between radiology team members. Finally, long-term investment in training, recruiting, and retaining a highly skilled technologist workforce is essential for building a pipeline and team of technologists committed to excellence. Here, we highlight various opportunities for optimizing MRI workflow and enhancing value by offering many of our own on-the-ground experiences and conclude by anticipating some of the future directions for process improvement and innovation in clinical MR imaging. EVIDENCE LEVEL: N/A TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Dana J Lin
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
| | - Ankur M Doshi
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
| | - Jan Fritz
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
| | - Michael P Recht
- Department of Radiology, NYU Grossman School of Medicine/NYU Langone Health, New York, New York, USA
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Matsumoto S, Tsuboyama T, Onishi H, Fukui H, Honda T, Wakayama T, Wang X, Matsui T, Nakamoto A, Ota T, Kiso K, Osawa K, Tomiyama N. Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance. Invest Radiol 2024; 59:479-488. [PMID: 37975732 DOI: 10.1097/rli.0000000000001047] [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: 11/19/2023]
Abstract
OBJECTIVE The aim of this study was to evaluate the impact of ultra-high-resolution acquisition and deep learning reconstruction (DLR) on the image quality and diagnostic performance of T2-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging of the rectum. MATERIALS AND METHODS This prospective study included 34 patients who underwent magnetic resonance imaging (MRI) for initial staging or restaging of rectal tumors. The following 4 types of oblique axial PROPELLER images perpendicular to the tumor were obtained: a standard 3-mm slice thickness with conventional reconstruction (3-CR) and DLR (3-DLR), and 1.2-mm slice thickness with CR (1.2-CR) and DLR (1.2-DLR). Three radiologists independently evaluated the image quality and tumor extent by using a 5-point scoring system. Diagnostic accuracy was evaluated in 22 patients with rectal cancer who underwent surgery after MRI without additional neoadjuvant therapy (median interval between MRI and surgery, 22 days). The signal-to-noise ratio and tissue contrast were measured on the 4 types of PROPELLER imaging. RESULTS 1.2-DLR imaging showed the best sharpness, overall image quality, and rectal and lesion conspicuity for all readers ( P < 0.01). Of the assigned scores for tumor extent, extramural venous invasion (EMVI) scores showed moderate agreement across the 4 types of PROPELLER sequences in all readers (intraclass correlation coefficient, 0.60-0.71). Compared with 3-CR imaging, the number of cases with MRI-detected extramural tumor spread was significantly higher with 1.2-DLR imaging (19.0 ± 2.9 vs 23.3 ± 0.9, P = 0.03), and the number of cases with MRI-detected EMVI was significantly increased with 1.2-CR, 3-DLR, and 1.2-DLR imaging (8.0 ± 0.0 vs 9.7 ± 0.5, 11.0 ± 2.2, and 12.3 ± 1.7, respectively; P = 0.02). For the diagnosis of histopathologic extramural tumor spread, 3-CR and 1.2-CR had significantly higher specificity than 3-DLR and 1.2-DLR imaging (0.75 and 0.78 vs 0.64 and 0.58, respectively; P = 0.02), and only 1.2-CR had significantly higher accuracy than 3-CR imaging (0.83 vs 0.79, P = 0.01). The accuracy of MRI-detected EMVI with reference to pathological EMVI was significantly lower for 3-CR and 3-DLR compared with 1.2-CR (0.77 and 0.74 vs 0.85, respectively; P < 0.01), and was not significantly different between 1.2-CR and 1.2-DLR (0.85 vs 0.80). Using any pathological venous invasion as the reference standard, the accuracy of MRI-detected EMVI was significantly the highest with 1.2-DLR, followed by 1.2-CR, 3-CR, and 3-DLR (0.71 vs 0.67 vs 0.59 vs 0.56, respectively; P < 0.01). The signal-to-noise ratio was significantly highest with 3-DLR imaging ( P < 0.05). There were no significant differences in tumor-to-muscle contrast between the 4 types of PROPELLER imaging. CONCLUSIONS Ultra-high-resolution PROPELLER T2-weighted imaging of the rectum combined with DLR improved image quality, increased the number of cases with MRI-detected extramural tumor spread and EMVI, but did not improve diagnostic accuracy with respect to pathology in rectal cancer, possibly because of false-positive MRI findings or false-negative pathologic findings.
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Affiliation(s)
- Shohei Matsumoto
- From the Department of Radiology, Osaka University Graduate School of Medicine, Osaka, Japan (S.M., T.T., H.O., H.F., T.H., A.N., T.O., K.K., K.O., N.T.); MR Collaboration and Development, GE Healthcare, Tokyo, Japan (T.W.); MR Collaboration and Development, GE Healthcare, Austin, TX (X.W.); and Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan (T.M.)
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Mayerhoefer ME, Shepherd TM, Weber M, Leithner D, Woo S, Pan JW, Pardoe HR. Sexual Dimorphism of Radiomic Features in the Brain: An Exploratory Study Using 700 μm MP2RAGE MRI at 7 T. Invest Radiol 2024:00004424-990000000-00223. [PMID: 38896439 DOI: 10.1097/rli.0000000000001088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
OBJECTIVES The aim of this study was to determine whether MRI radiomic features of key cerebral structures differ between women and men, and whether detection of such differences depends on the image resolution. MATERIALS AND METHODS Ultrahigh resolution (UHR) 3D MP2RAGE (magnetization-prepared 2 rapid acquisition gradient echo) T1-weighted MR images (voxel size, 0.7 × 0.7 × 0.7 mm3) of the brain of 30 subjects (18 women and 12 men; mean age, 39.0 ± 14.8 years) without abnormal findings on MRI were retrospectively included. MRI was performed on a whole-body 7 T MR system. A convolutional neural network was used to segment the following structures: frontal cortex, frontal white matter, thalamus, putamen, globus pallidus, caudate nucleus, and corpus callosum. Eighty-seven radiomic features were extracted respectively: gray-level histogram (n = 18), co-occurrence matrix (n = 24), run-length matrix (n = 16), size-zone matrix (n = 16), and dependence matrix (n = 13). Feature extraction was performed at UHR and, additionally, also after resampling to 1.4 × 1.4 × 1.4 mm3 voxel size (standard clinical resolution). Principal components (PCs) of radiomic features were calculated, and independent samples t tests with Cohen d as effect size measure were used to assess differences in PCs between women and men for the different cerebral structures. RESULTS At UHR, at least a single PC differed significantly between women and men in 6/7 cerebral structures: frontal cortex (d = -0.79, P = 0.042 and d = -1.01, P = 0.010), frontal white matter (d = -0.81, P = 0.039), thalamus (d = 1.43, P < 0.001), globus pallidus (d = 0.92, P = 0.020), caudate nucleus (d = -0.83, P = 0.039), and corpus callosum (d = -0.97, P = 0.039). At standard clinical resolution, only a single PC extracted from the corpus callosum differed between sexes (d = 1.05, P = 0.009). CONCLUSIONS Nonnegligible differences in radiomic features of several key structures of the brain exist between women and men, and need to be accounted for. Very high spatial resolution may be required to uncover and further investigate the sexual dimorphism of brain structures on MRI.
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Affiliation(s)
- Marius E Mayerhoefer
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY (M.E.M., T.M.S., D.L., S.W.); Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria (M.E.M., M.W.); Florey Institute of Neuroscience and Mental Health, Melbourne, Australia (H.R.P.); Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, NY (H.R.P.); and Department of Radiology, University of Missouri Columbia, Columbia, MO (J.W.P.)
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Schlicht F, Vosshenrich J, Donners R, Seifert AC, Fenchel M, Nickel D, Obmann M, Harder D, Breit HC. Advanced deep learning-based image reconstruction in lumbar spine MRI at 0.55 T - Effects on image quality and acquisition time in comparison to conventional deep learning-based reconstruction. Eur J Radiol Open 2024; 12:100567. [PMID: 38711678 PMCID: PMC11070664 DOI: 10.1016/j.ejro.2024.100567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/08/2024] Open
Abstract
Objectives To evaluate an optimized deep leaning-based image post-processing technique in lumbar spine MRI at 0.55 T in terms of image quality and image acquisition time. Materials and methods Lumbar spine imaging was conducted on 18 patients using a 0.55 T MRI scanner, employing conventional (CDLR) and advanced (ADLR) deep learning-based post-processing techniques. Two musculoskeletal radiologists visually evaluated the images using a 5-point Likert scale to assess image quality and resolution. Quantitative assessment in terms of signal intensities (SI) and contrast ratios was performed by region of interest measurements in different body-tissues (vertebral bone, intervertebral disc, spinal cord, cerebrospinal fluid and autochthonous back muscles) to investigate differences between CDLR and ADLR sequences. Results The images processed with the advanced technique (ADLR) were rated superior to the conventional technique (CDLR) in terms of signal/contrast, resolution, and assessability of the spinal canal and neural foramen. The interrater agreement was moderate for signal/contrast (ICC = 0.68) and good for resolution (ICC = 0.77), but moderate for spinal canal and neuroforaminal assessability (ICC = 0.55). Quantitative assessment showed a higher contrast ratio for fluid-sensitive sequences in the ADLR images. The use of ADLR reduced image acquisition time by 44.4%, from 14:22 min to 07:59 min. Conclusions Advanced deep learning-based image reconstruction algorithms improve the visually perceived image quality in lumbar spine imaging at 0.55 T while simultaneously allowing to substantially decrease image acquisition times. Clinical relevance Advanced deep learning-based image post-processing techniques (ADLR) in lumbar spine MRI at 0.55 T significantly improves image quality while reducing image acquisition time.
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Affiliation(s)
- Felix Schlicht
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
| | - Jan Vosshenrich
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
| | - Ricardo Donners
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
| | - Alina Carolin Seifert
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
| | - Matthias Fenchel
- Siemens Healthcare GmbH, Magnetic Resonance, Allee am Röthelheimpark 2, Erlangen 91052, Germany
| | - Dominik Nickel
- Siemens Healthcare GmbH, Magnetic Resonance, Allee am Röthelheimpark 2, Erlangen 91052, Germany
| | - Markus Obmann
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
| | - Dorothee Harder
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
| | - Hanns-Christian Breit
- Department of Radiology, University Hospital Basel, Petersgraben 4, Basel 4031, Switzerland
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Singh S, Mohajer B, Wells SA, Garg T, Hanneman K, Takahashi T, AlDandan O, McBee MP, Jawahar A. Imaging Genomics and Multiomics: A Guide for Beginners Starting Radiomics-Based Research. Acad Radiol 2024; 31:2281-2291. [PMID: 38286723 DOI: 10.1016/j.acra.2024.01.024] [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: 10/30/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024]
Abstract
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
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Affiliation(s)
- Shiva Singh
- Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland
| | - Bahram Mohajer
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Shane A Wells
- Radiology, University of Michigan, Ann Arbor, Michigan
| | - Tushar Garg
- Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland
| | - Kate Hanneman
- Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Omran AlDandan
- Department of Radiology, Imam Abdulrahman Bin Faisal University, College of Medicine: Dammam, Eastern, Saudi Arabia
| | - Morgan P McBee
- Radiology and Radiological Sciences, Medical University of South Carolina, Charleston, South Carolina
| | - Anugayathri Jawahar
- Radiology, Northwestern University-Feinberg School of Medicine, 800, Arkes Pavilion, 676 N St. Clair St, Chicago, IL 60611.
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Ni M, He M, Yang Y, Wen X, Zhao Y, Gao L, Yan R, Xu J, Zhang Y, Chen W, Jiang C, Li Y, Zhao Q, Wu P, Li C, Qu J, Yuan H. Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective. Eur Radiol 2024; 34:3046-3058. [PMID: 37932390 DOI: 10.1007/s00330-023-10368-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/29/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVE To investigate the potential applicability of AI-assisted compressed sensing (ACS) in knee MRI to enhance and optimize the scanning process. METHODS Volunteers and patients with sports-related injuries underwent prospective MRI scans with a range of acceleration techniques. The volunteers were subjected to varied ACS acceleration levels to ascertain the most effective level. Patients underwent scans at the determined optimal 3D-ACS acceleration level, and 3D compressed sensing (CS) and 2D parallel acquisition technology (PAT) scans were performed. The resultant 3D-ACS images underwent 3.5 mm/2.0 mm multiplanar reconstruction (MPR). Experienced radiologists evaluated and compared the quality of images obtained by 3D-ACS-MRI and 3D-CS-MRI, 3.5 mm/2.0 mm MPR and 2D-PAT-MRI, diagnosed diseases, and compared the results with the arthroscopic findings. The diagnostic agreement was evaluated using Cohen's kappa correlation coefficient, and both absolute and relative evaluation methods were utilized for objective assessment. RESULTS The study involved 15 volunteers and 53 patients. An acceleration factor of 10.69 × was identified as optimal. The quality evaluation showed that 3D-ACS provided poorer bone structure visualization, and improved cartilage visualization and less satisfactory axial images with 3.5 mm/2.0 mm MPR than 2D-PAT. In terms of objective evaluation, the relative evaluation yielded satisfactory results across different groups, while the absolute evaluation revealed significant variances in most features. Nevertheless, high levels of diagnostic agreement (κ: 0.81-0.94) and accuracy (0.83-0.98) were observed across all diagnoses. CONCLUSION ACS technology presents significant potential as a replacement for traditional CS in 3D-MRI knee scans, allowing thinner MPRs and markedly faster scans without sacrificing diagnostic accuracy. CLINICAL RELEVANCE STATEMENT 3D-ACS-MRI of the knee can be completed in the 160 s with good diagnostic consistency and image quality. 3D-MRI-MPR can replace 2D-MRI and reconstruct images with thinner slices, which helps to optimize the current MRI examination process and shorten scanning time. KEY POINTS • AI-assisted compressed sensing technology can reduce knee MRI scan time by over 50%. • 3D AI-assisted compressed sensing MRI and related multiplanar reconstruction can replace traditional accelerated MRI and yield thinner 2D multiplanar reconstructions. • Successful application of 3D AI-assisted compressed sensing MRI can help optimize the current knee MRI process.
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Affiliation(s)
- Ming Ni
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Miao He
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Yuxin Yang
- United Imaging Research Institute of Intelligent Imaging, Beijing, People's Republic of China
| | - Xiaoyi Wen
- Institute of Statistics and Big Data, Renmin University of China, Beijing, People's Republic of China
| | - Yuqing Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Lixiang Gao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Ruixin Yan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Jiajia Xu
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yarui Zhang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Wen Chen
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Chenyu Jiang
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Yali Li
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Qiang Zhao
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China
| | - Peng Wu
- United Imaging Healthcare Co, Shanghai, People's Republic of China
| | - Chunlin Li
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Junda Qu
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, People's Republic of China.
- Beijing Key Laboratory of Fundamental Research On Biomechanics in Clinical Application, Capital Medical University, Beijing, People's Republic of China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Capital Medical University, Beijing, People's Republic of China.
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, People's Republic of China.
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22
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Zhang Y, Ye Z, Xia C, Tan Y, Zhang M, Lv X, Tang J, Li Z. Clinical Applications and Recent Updates of Simultaneous Multi-slice Technique in Accelerated MRI. Acad Radiol 2024; 31:1976-1988. [PMID: 38220568 DOI: 10.1016/j.acra.2023.12.032] [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: 10/29/2023] [Revised: 12/15/2023] [Accepted: 12/19/2023] [Indexed: 01/16/2024]
Abstract
Simultaneous multi-slice (SMS) is a magnetic resonance imaging (MRI) acceleration technique that utilizes multi-band radio-frequency pulses to simultaneously excite and encode multiple slices. Currently, SMS has been widely studied and applied in the MRI examination to reduce acquisition time, which can significantly improve the examination efficiency and patient throughput. Moreover, SMS technique can improve spatial resolution, which is of great value in disease diagnosis, treatment response monitoring, and prognosis prediction. This review will briefly introduce the technical principles of SMS, and summarize its current clinical applications. More importantly, we will discuss the recent technical progress and future research direction of SMS, hoping to highlight the clinical value and scientific potential of this technique.
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Affiliation(s)
- Yiteng Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zheng Ye
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Chunchao Xia
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Yuqi Tan
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Meng Zhang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Xinyang Lv
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Jing Tang
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Zhenlin Li
- Department of Radiology, West China Hospital of Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
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23
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Ensle F, Kaniewska M, Lohezic M, Guggenberger R. Enhanced bone assessment of the shoulder using zero-echo time MRI with deep-learning image reconstruction. Skeletal Radiol 2024:10.1007/s00256-024-04690-8. [PMID: 38658419 DOI: 10.1007/s00256-024-04690-8] [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] [Received: 01/22/2024] [Revised: 04/07/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVES To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings. METHODS In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm. Exams were classified into three subgroups: no pathological findings, degenerative changes, and posttraumatic changes, respectively. Two blinded readers performed bone assessment on a 4-point scale (0-poor, 3-perfect) by qualitatively grading image quality features and delineation of osseous pathologies including diagnostic confidence in the respective subgroups. Quantitatively, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone were measured. Qualitative variables were compared using the Wilcoxon signed-rank test for ordinal data and the McNemar test for dichotomous variables; quantitative measures were compared with Student's t-testing. RESULTS DLRecon scored significantly higher than SOC in all visual metrics of image quality (all, p < 0.03), except in the artifact category (p = 0.37). DLRecon also received superior qualitative scores for delineation of osseous pathologies and diagnostic confidence (p ≤ 0.03). Quantitatively, DLRecon achieved superior CNR (95 CI [1.4-3.1]) and SNR (95 CI [15.3-21.5]) of bone than SOC (p < 0.001). CONCLUSION DLRecon enhanced image quality in ZTE MRI and improved delineation of osseous pathologies, allowing for increased diagnostic confidence in bone assessment.
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Affiliation(s)
- Falko Ensle
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland.
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland.
| | - Malwina Kaniewska
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
| | | | - Roman Guggenberger
- Diagnostic and Interventional Radiology, University Hospital Zurich, University Zurich, Zurich, Switzerland
- University of Zurich (UZH), Raemistrasse 100, CH-8091, Zurich, Switzerland
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24
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Chen W, Lim LJR, Lim RQR, Yi Z, Huang J, He J, Yang G, Liu B. Artificial intelligence powered advancements in upper extremity joint MRI: A review. Heliyon 2024; 10:e28731. [PMID: 38596104 PMCID: PMC11002577 DOI: 10.1016/j.heliyon.2024.e28731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024] Open
Abstract
Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.
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Affiliation(s)
- Wei Chen
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Lincoln Jian Rong Lim
- Department of Medical Imaging, Western Health, Footscray Hospital, Victoria, Australia
- Department of Surgery, The University of Melbourne, Victoria, Australia
| | - Rebecca Qian Ru Lim
- Department of Hand & Reconstructive Microsurgery, Singapore General Hospital, Singapore
| | - Zhe Yi
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
| | - Jiaxing Huang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Jia He
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Ge Yang
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Bo Liu
- Department of Hand Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, China
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25
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Chaban YV, Vosshenrich J, McKee H, Gunasekaran S, Brown MJ, Atalay MK, Heye T, Markl M, Woolen SA, Simonetti OP, Hanneman K. Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action. J Magn Reson Imaging 2024; 59:1149-1167. [PMID: 37694980 DOI: 10.1002/jmri.28994] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 09/12/2023] Open
Abstract
The environmental impact of magnetic resonance imaging (MRI) has recently come into focus. This includes its enormous demand for electricity compared to other imaging modalities and contamination of water bodies with anthropogenic gadolinium related to contrast administration. Given the pressing threat of climate change, addressing these challenges to improve the environmental sustainability of MRI is imperative. The purpose of this review is to discuss the challenges, opportunities, and the need for action to reduce the environmental impact of MRI and prepare for the effects of climate change. The approaches outlined are categorized as strategies to reduce greenhouse gas (GHG) emissions from MRI during production and use phases, approaches to reduce the environmental impact of MRI including the preservation of finite resources, and development of adaption plans to prepare for the impact of climate change. Co-benefits of these strategies are emphasized including lower GHG emission and reduced cost along with improved heath and patient satisfaction. Although MRI is energy-intensive, there are many steps that can be taken now to improve the environmental sustainability of MRI and prepare for the effects of climate change. On-going research, technical development, and collaboration with industry partners are needed to achieve further reductions in MRI-related GHG emissions and to decrease the reliance on finite resources. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Yuri V Chaban
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jan Vosshenrich
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Hayley McKee
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Suvai Gunasekaran
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Maura J Brown
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael K Atalay
- Department of Diagnostic Imaging, Alpert Medical School of Brown University, Providence, Rhode Island, USA
| | - Tobias Heye
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA
| | - Sean A Woolen
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | | | - Kate Hanneman
- Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada
- Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada
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26
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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.
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Affiliation(s)
- Benjamin Fritz
- Abteilung für Radiologie, Universitätsklinik Balgrist, Forchstr. 340, 8008, Zürich, Schweiz.
- Medizinische Fakultät, Universität Zürich, Zürich, Schweiz.
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27
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Jarraya M, Guermazi A, Roemer FW. Osteoarthritis year in review 2023: Imaging. Osteoarthritis Cartilage 2024; 32:18-27. [PMID: 37879600 DOI: 10.1016/j.joca.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/24/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE This narrative review summarizes the original research in the field of in vivo osteoarthritis (OA) imaging between 1 January 2022 and 1 April 2023. METHODS A PubMed search was conducted using the following several terms pertaining to OA imaging, including but not limited to "Osteoarthritis / OA", "Magnetic resonance imaging / MRI", "X-ray" "Computed tomography / CT", "artificial intelligence /AI", "deep learning", "machine learning". This review is organized by topics including the anatomical structure of interest and modality, AI, challenges of OA imaging in the context of clinical trials, and imaging biomarkers in clinical trials and interventional studies. Ex vivo and animal studies were excluded from this review. RESULTS Two hundred and forty-nine publications were relevant to in vivo human OA imaging. Among the articles included, the knee joint (61%) and MRI (42%) were the predominant anatomical area and imaging modalities studied. Marked heterogeneity of structural tissue damage in OA knees was reported, a finding of potential relevance to clinical trial inclusion. The use of AI continues to rise rapidly to be applied in various aspect of OA imaging research but a lack of generalizability beyond highly standardized datasets limit interpretation and wide-spread application. No pharmacologic clinical trials using imaging data as outcome measures have been published in the period of interest. CONCLUSIONS Recent advances in OA imaging continue to heavily weigh on the use of AI. MRI remains the most important modality with a growing role in outcome prediction and classification.
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Affiliation(s)
- Mohamed Jarraya
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Ali Guermazi
- Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA.
| | - Frank W Roemer
- Department of Radiology, VA Boston Healthcare System, Boston University School of Medicine, Boston, MA, USA; Department of Radiology, Universitätsklinikum Erlangen & Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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28
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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.
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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.)
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29
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Dalili D, Isaac A, Fritz J. Selective MR neurography-guided lumbosacral plexus perineural injections: techniques, targets, and territories. Skeletal Radiol 2023; 52:1929-1947. [PMID: 37495713 DOI: 10.1007/s00256-023-04384-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/02/2023] [Accepted: 06/02/2023] [Indexed: 07/28/2023]
Abstract
The T12 to S4 spinal nerves form the lumbosacral plexus in the retroperitoneum, providing sensory and motor innervation to the pelvis and lower extremities. The lumbosacral plexus has a wide range of anatomic variations and interchange of fibers between nerve anastomoses. Neuropathies of the lumbosacral plexus cause a broad spectrum of complex pelvic and lower extremity pain syndromes, which can be challenging to diagnose and treat successfully. In their workup, selective nerve blocks are employed to test the hypothesis that a lumbosacral plexus nerve contributes to a suspected pelvic and extremity pain syndrome, whereas therapeutic perineural injections aim to alleviate pain and paresthesia symptoms. While the sciatic and femoral nerves are large in caliber, the iliohypogastric and ilioinguinal, genitofemoral, lateral femoral cutaneous, anterior femoral cutaneous, posterior femoral cutaneous, obturator, and pudendal nerves are small, measuring a few millimeters in diameter and have a wide range of anatomic variants. Due to their minuteness, direct visualization of the smaller lumbosacral plexus branches can be difficult during selective nerve blocks, particularly in deeper pelvic locations or larger patients. In this setting, the high spatial and contrast resolution of interventional MR neurography guidance benefits nerve visualization and targeting, needle placement, and visualization of perineural injectant distribution, providing a highly accurate alternative to more commonly used ultrasonography, fluoroscopy, and computed tomography guidance for perineural injections. This article offers a practical guide for MR neurography-guided lumbosacral plexus perineural injections, including interventional setup, pulse sequence protocols, lumbosacral plexus MR neurography anatomy, anatomic variations, and injection targets.
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Affiliation(s)
- Danoob Dalili
- Academic Surgical Unit, Southwest London Elective Orthopaedic Centre (SWLEOC), Dorking Road, Epsom, KT18 7EG, London, UK
- Department of Radiology, Epsom and St Hellier University Hospitals NHS Trust, Dorking Road, Epsom, London, KT18 7EG, UK
| | - Amanda Isaac
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Jan Fritz
- Department of Radiology, New York University Grossman School of Medicine, NY, USA.
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30
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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.
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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.
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31
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Fritz J, Rashidi A, de Cesar Netto C. Magnetic Resonance Imaging of Total Ankle Arthroplasty: State-of-The-Art Assessment of Implant-Related Pain and Dysfunction. Foot Ankle Clin 2023; 28:463-492. [PMID: 37536814 DOI: 10.1016/j.fcl.2023.05.012] [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
Total ankle arthroplasty (TAA) is an effective alternative for treating patients with end-stage ankle degeneration, improving mobility, and providing pain relief. Implant survivorship is constantly improving; however, complications occur. Many causes of pain and dysfunction after total ankle arthroplasty can be diagnosed accurately with clinical examination, laboratory, radiography, and computer tomography. However, when there are no or inconclusive imaging findings, magnetic resonance imaging (MRI) is highly accurate in identifying and characterizing bone resorption, osteolysis, infection, osseous stress reactions, nondisplaced fractures, polyethylene damage, nerve injuries and neuropathies, as well as tendon and ligament tears. Multiple vendors offer effective, clinically available MRI techniques for metal artifact reduction MRI of total ankle arthroplasty. This article reviews the MRI appearances of common TAA implant systems, clinically available techniques and protocols for metal artifact reduction MRI of TAA implants, and the MRI appearances of a broad spectrum of TAA-related complications.
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Affiliation(s)
- Jan Fritz
- Department of Orthopedic Surgery, Division of Foot and Ankle Surgery, Duke University, Durham, NC, USA.
| | - Ali Rashidi
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Floor, Rm 313, New York, NY 10016, USA
| | - Cesar de Cesar Netto
- Department of Radiology, Molecular Imaging Program at StanDepartment of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA
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32
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Van Dyck P, Vandenrijt J, Vande Vyvere T, Snoeckx A, Heusdens CHW. Analysis of Discordant Findings between 3T Magnetic Resonance Imaging and Arthroscopic Evaluation of the Knee Meniscus. J Clin Med 2023; 12:5667. [PMID: 37685734 PMCID: PMC10489133 DOI: 10.3390/jcm12175667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
Numerous studies have assessed the performance of magnetic resonance imaging (MRI) in detecting tears of the knee menisci using arthroscopy results as the gold standard, but few have concentrated on the nature of discordant findings. The purpose of this study was to analyze the discordances between 3T MRI and arthroscopic evaluation of the knee meniscus. Medical records of 112 patients who underwent 3T MRI and subsequent arthroscopy of the knee were retrospectively analyzed to determine the accuracy of diagnoses of meniscal tear. Compared with arthroscopy, there were 22 false-negative and 14 false-positive MR interpretations of meniscal tear occurring in 32 patients. Images with errors in diagnosis were retrospectively reviewed by two musculoskeletal radiologists in consensus and all errors were categorized as either unavoidable, equivocal or as interpretation error. Of 36 MR diagnostic errors, there were 16 (44%) unavoidable, 5 (14%) interpretation errors and 15 (42%) equivocal for meniscal tear. The largest categories of errors were unavoidable false-positive MRI diagnoses (71%) and equivocal false-negative MRI diagnoses (50%). All meniscal tears missed by MRI were treated with partial meniscectomy (n = 14) or meniscal repair (n = 8). Discordant findings between 3T MRI and arthroscopic evaluation of the knee meniscus remain a concern and primarily occur due to unavoidable and equivocal errors. Clinicians involved in the diagnosis and treatment of patients with meniscal tears should understand why and how the findings seen on knee MRI and arthroscopy may sometimes differ.
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Affiliation(s)
- Pieter Van Dyck
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, 2650 Edegem, Belgium; (T.V.V.); (A.S.)
- Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium;
| | - Jasper Vandenrijt
- Department of Orthopedics, Antwerp University Hospital, Drie Eikenstraat 655, 2650 Edegem, Belgium;
| | - Thijs Vande Vyvere
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, 2650 Edegem, Belgium; (T.V.V.); (A.S.)
- Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium;
| | - Annemiek Snoeckx
- Department of Radiology, Antwerp University Hospital, Drie Eikenstraat 655, 2650 Edegem, Belgium; (T.V.V.); (A.S.)
- Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium;
| | - Christiaan H. W. Heusdens
- Faculty of Medicine and Health Sciences, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium;
- Department of Orthopedics, Antwerp University Hospital, Drie Eikenstraat 655, 2650 Edegem, Belgium;
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33
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Debs P, Fayad LM. The promise and limitations of artificial intelligence in musculoskeletal imaging. FRONTIERS IN RADIOLOGY 2023; 3:1242902. [PMID: 37609456 PMCID: PMC10440743 DOI: 10.3389/fradi.2023.1242902] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 07/26/2023] [Indexed: 08/24/2023]
Abstract
With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.
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Affiliation(s)
- Patrick Debs
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD, United States
| | - Laura M. Fayad
- The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins Medical Institutions, Baltimore, MD, United States
- Department of Orthopaedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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34
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Wijnen JP, Seiberlich N, Golay X. Will standardization kill innovation? MAGMA (NEW YORK, N.Y.) 2023; 36:525-528. [PMID: 37632642 DOI: 10.1007/s10334-023-01115-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 08/01/2023] [Indexed: 08/28/2023]
Affiliation(s)
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, 1500 E. Medical Center Dr., Ann Arbor, MI, 48109, USA.
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35
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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: 12] [Impact Index Per Article: 12.0] [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.
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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.)
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36
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Recht MP, White LM, Fritz J, Resnick DL. Advances in Musculoskeletal Imaging: Recent Developments and Predictions for the Future. Radiology 2023; 308:e230615. [PMID: 37642575 DOI: 10.1148/radiol.230615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Affiliation(s)
- Michael P Recht
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
| | - Lawrence M White
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
| | - Jan Fritz
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
| | - Donald L Resnick
- From the Department of Radiology, NYU Grossman School of Medicine, 660 First Ave, 3rd Floor, New York, NY 10016 (M.P.R., J.F.); Department of Medical Imaging, University Health Network, Sinai Health System and Women's College Hospital, Toronto, Canada (L.M.W.); and Department of Radiology, UCSD Teleradiology and Education Center, La Jolla, Calif (D.L.R.)
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Zibetti MVW, Menon RG, de Moura HL, Zhang X, Kijowski R, Regatte RR. Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications. J Magn Reson Imaging 2023; 58:44-60. [PMID: 37010113 PMCID: PMC10323700 DOI: 10.1002/jmri.28689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/06/2023] [Accepted: 03/06/2023] [Indexed: 04/04/2023] Open
Abstract
Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state-of-the-art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio-frequency pulse sequences, and deep learning-based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Marcelo V. W. Zibetti
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Rajiv G. Menon
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Hector L. de Moura
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Xiaoxia Zhang
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Richard Kijowski
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Ravinder R. Regatte
- Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
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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.
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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.
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Park EH, de Cesar Netto C, Fritz J. MRI in Acute Ankle Sprains: Should We Be More Aggressive with Indications? Foot Ankle Clin 2023; 28:231-264. [PMID: 37137621 DOI: 10.1016/j.fcl.2023.01.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Acute ankle sprains are common sports injuries. MRI is the most accurate test for assessing the integrity and severity of ligament injuries in acute ankle sprains. However, MRI may not detect syndesmotic and hindfoot instability, and many ankle sprains are treated conservatively, questioning the value of MRI. In our practice, MRI adds value in confirming the absence or presence of ankle sprain-associated hindfoot and midfoot injuries, especially when clinical examinations are challenging, radiographs are inconclusive, and subtle instability is suspected. This article reviews and illustrates the MRI appearances of the spectrum of ankle sprains and associated hindfoot and midfoot injuries.
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Affiliation(s)
- Eun Hae Park
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1St Ave, 3rd Floor, New York, NY 10016, USA; Department of Radiology, Jeonbuk National University Medical School, Jeonju, Republic of Korea
| | - Cesar de Cesar Netto
- Department of Orthopaedics and Rehabilitation, University of Iowa, 200 Hawkins Dr, Iowa City, IA 52242, USA
| | - Jan Fritz
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, 660 1St Ave, 3rd Floor, New York, NY 10016, USA.
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40
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Govindan B, Sabri MA, Hai A, Banat F, Haija MA. A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach. Pharmaceutics 2023; 15:868. [PMID: 36986729 PMCID: PMC10058002 DOI: 10.3390/pharmaceutics15030868] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 03/10/2023] Open
Abstract
The new era of nanomedicine offers significant opportunities for cancer diagnostics and treatment. Magnetic nanoplatforms could be highly effective tools for cancer diagnosis and treatment in the future. Due to their tunable morphologies and superior properties, multifunctional magnetic nanomaterials and their hybrid nanostructures can be designed as specific carriers of drugs, imaging agents, and magnetic theranostics. Multifunctional magnetic nanostructures are promising theranostic agents due to their ability to diagnose and combine therapies. This review provides a comprehensive overview of the development of advanced multifunctional magnetic nanostructures combining magnetic and optical properties, providing photoresponsive magnetic platforms for promising medical applications. Moreover, this review discusses various innovative developments using multifunctional magnetic nanostructures, including drug delivery, cancer treatment, tumor-specific ligands that deliver chemotherapeutics or hormonal agents, magnetic resonance imaging, and tissue engineering. Additionally, artificial intelligence (AI) can be used to optimize material properties in cancer diagnosis and treatment, based on predicted interactions with drugs, cell membranes, vasculature, biological fluid, and the immune system to enhance the effectiveness of therapeutic agents. Furthermore, this review provides an overview of AI approaches used to assess the practical utility of multifunctional magnetic nanostructures for cancer diagnosis and treatment. Finally, the review presents the current knowledge and perspectives on hybrid magnetic systems as cancer treatment tools with AI models.
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Affiliation(s)
- Bharath Govindan
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Department of Chemistry, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Muhammad Ashraf Sabri
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Abdul Hai
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Fawzi Banat
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Mohammad Abu Haija
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
- Advanced Materials Chemistry Center (AMCC), Khalifa University of Science and Technology, Abu Dhabi P.O. Box 127788, United Arab Emirates
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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.
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Fritz B, Fritz J. MR Imaging–Ultrasonography Correlation of Acute and Chronic Foot and Ankle Conditions. Magn Reson Imaging Clin N Am 2023; 31:321-335. [PMID: 37019553 DOI: 10.1016/j.mric.2023.01.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Foot and ankle injuries are common musculoskeletal disorders. In the acute setting, ligamentous injuries are most common, whereas fractures, osseous avulsion injuries, tendon and retinaculum tears, and osteochondral injuries are less common. The most common chronic and overuse injuries include osteochondral and articular cartilage defects, tendinopathies, stress fractures, impingement syndromes, and neuropathies. Common forefoot conditions include traumatic and stress fractures, metatarsophalangeal and plantar plate injuries and degenerations, intermittent bursitis, and perineural fibrosis. Ultrasonography is well-suited for evaluating superficial tendons, ligaments, and muscles. MR imaging is best for deeper-located soft tissue structures, articular cartilage, and cancellous bone.
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Scientific Advances and Technical Innovations in Musculoskeletal Radiology. Invest Radiol 2023; 58:1-2. [PMID: 36484774 DOI: 10.1097/rli.0000000000000930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
ABSTRACT Decades of technical innovations have propelled musculoskeletal radiology through an astonishing evolution. New artificial intelligence and deep learning methods capitalize on many past innovations in magnetic resonance imaging (MRI) to reach unprecedented speed, image quality, and new contrasts. Similarly exciting developments in computed tomography (CT) include clinically applicable molecular specificity and substantially improved spatial resolution of musculoskeletal structures and diseases. This special issue of Investigative Radiology comprises a collection of expert summaries and reviews on the most impactful innovations and cutting-edge topics in musculoskeletal radiology, including radiomics and deep learning methods for musculoskeletal disease detection, high-resolution MR neurography, deep learning-driven ultra-fast musculoskeletal MRI, MRI-based synthetic CT, quantitative MRI, modern low-field MRI, 7.0 T MRI, dual-energy CT, cone beam CT, kinematic CT, and synthetic contrast generation in musculoskeletal MRI.
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