Synthetic CT in Musculoskeletal Disorders

Lombardi, Alecio; Ma, Yajun; Jang, Hyungseok; Jerban, Saeed; Du, Jiang; Chang, Eric Y.; Chung, Christine B.

doi:10.1097/rli.0000000000000916

Cited by 13 publications

(6 citation statements)

References 128 publications

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“…65 Bound Water Biomarker Using Inversion Recovery Ultrashort Echo Time IR-UTE sequences can suppress the long-T2 signal, potentially from PW and fat, and then image BW in bone. [68][69][70] Comparing the IR-UTE signal from bone with an external reference standard can estimate BW content. 57,58,62,64,71 BW content quantification based on the IR-UTE sequence requires efficient nulling of the PW signal.…”

Section: Total Water Biomarker Using Basic Ultrashort Echo Timementioning

confidence: 99%

Bone Biomarkers Based on Magnetic Resonance Imaging

Jerban,

Jang,

Chang

et al. 2024

Semin Musculoskelet Radiol

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Magnetic resonance imaging (MRI) is increasingly used to evaluate the microstructural and compositional properties of bone. MRI-based biomarkers can characterize all major compartments of bone: organic, water, fat, and mineral components. However, with a short apparent spin-spin relaxation time (T2*), bone is invisible to conventional MRI sequences that use long echo times. To address this shortcoming, ultrashort echo time MRI sequences have been developed to provide direct imaging of bone and establish a set of MRI-based biomarkers sensitive to the structural and compositional changes of bone. This review article describes the MRI-based bone biomarkers representing total water, pore water, bound water, fat fraction, macromolecular fraction in the organic matrix, and surrogates for mineral density. MRI-based morphological bone imaging techniques are also briefly described.

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Section: Total Water Biomarker Using Basic Ultrashort Echo Timementioning

confidence: 99%

Bone Biomarkers Based on Magnetic Resonance Imaging

Jerban,

Jang,

Chang

et al. 2024

Semin Musculoskelet Radiol

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“…Over the past decades, pulse sequence acceleration has marked several milestones, including parallel imaging, [13][14][15][16] simultaneous multislice acquisition, [17][18][19][20] compressed sensing, 8,[21][22][23][24][25][26][27] and bidirectional 3-dimensional high-resolution isotropic acquisition schemes. 22,[28][29][30][31][32][33][34] Each technique has been gainfully applied to musculoskeletal MRI and advanced patient care [8][9][10][11][12][13][14][15][16][17][18][19] through optimizing signal-to-noise ratio, imaging artifacts, spatial resolution, and reconstruction time. However, neither technique fundamentally influenced the signal-tonoise interdependence of temporal resolution (pulse sequence acquisition speed), spatial resolution (image detail), and contrast resolution (musculoskeletal tissue contrast).…”

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confidence: 99%

“…MRI opened up the possibility for noninvasive diagnosis of a broad range of soft tissue abnormalities, including menisci, ligaments, muscles, tendons, fibrocartilage, articular cartilage, and bone marrow. Without MRI, the development of modern musculoskeletal imaging would not have been possible 1–12 …”

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confidence: 99%

Artificial Intelligence–Driven Ultra-Fast Superresolution MRI

2022

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Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence acceleration methods enable 4-fold turbo spin echo pulse sequence acceleration in clinical practice; however, at this speed, conventional image reconstruction approaches the signal-to-noise limits of temporal, spatial, and contrast resolution. Novel deep learning image reconstruction methods can minimize signal-to-noise interdependencies to better advantage than conventional image reconstruction, leading to unparalleled gains in image speed and quality when combined with parallel imaging and simultaneous multislice acquisition. The enormous potential of deep learning-based image reconstruction promises to facilitate the 10-fold acceleration of the turbo spin echo pulse sequence, equating to a total acquisition time of 2-3 minutes for entire MRI examinations of joints without sacrificing spatial resolution or image quality. Current investigations aim for a better understanding of stability and failure modes of image reconstruction networks, validation of network reconstruction performance with external data sets, determination of diagnostic performances with independent reference standards, establishing generalizability to other centers, scanners, field strengths, coils, and anatomy, and building publicly available benchmark data sets to compare methods and foster innovation and collaboration between the clinical and image processing community. In this article, we review basic concepts of deep learning-based acquisition and image reconstruction techniques for accelerating and improving the quality of musculoskeletal MRI, commercially available and developing deep learning-based MRI solutions, superresolution, denoising, generative adversarial networks, and combined strategies for deep learning-driven ultra-fast superresolution musculoskeletal MRI. This article aims to equip radiologists and imaging scientists with the necessary practical knowledge and enthusiasm to meet this exciting new era of musculoskeletal MRI.

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“…8 Drs Alecio Lombardi and Christine Chung from the University of California San Diego describe the techniques for MRI-based synthetic CT with a systematic review of its clinical applications for detecting musculoskeletal disorders. 9 Drs Brendan Eck and Xiaojuan Li from the Cleveland Clinic provide a comprehensive overview of quantitative MRI techniques applied to evaluate articular cartilage, skeletal muscle, joint inflammation, and biomechanics. 10 Dr Iman Khodarahmi from the New York University Grossman School of Medicine in New York City discusses the physics background and clinical capabilities of modern low-field MRI for a broad spectrum of musculoskeletal applications.…”

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confidence: 99%

“…Dr Dana Lin from the New York University Grossman School of Medicine in New York City summarizes current deep learning approaches for ultrafast musculoskeletal MRI and describes the path to 10-fold accelerated turbo spin echo MRI 8 . Drs Alecio Lombardi and Christine Chung from the University of California San Diego describe the techniques for MRI-based synthetic CT with a systematic review of its clinical applications for detecting musculoskeletal disorders 9 . Drs Brendan Eck and Xiaojuan Li from the Cleveland Clinic provide a comprehensive overview of quantitative MRI techniques applied to evaluate articular cartilage, skeletal muscle, joint inflammation, and biomechanics 10 .…”

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confidence: 99%

Scientific Advances and Technical Innovations in Musculoskeletal Radiology

Fritz

Runge

2022

Invest Radiol

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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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Synthetic CT in Musculoskeletal Disorders

Cited by 13 publications

References 128 publications

Bone Biomarkers Based on Magnetic Resonance Imaging

Bone Biomarkers Based on Magnetic Resonance Imaging

Artificial Intelligence–Driven Ultra-Fast Superresolution MRI

Scientific Advances and Technical Innovations in Musculoskeletal Radiology

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