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Age and gender differences in lumbar intervertebral disk strain using mechanical loading magnetic resonance imaging

Menon, Rajiv G; de Moura, Hector L; Kijowski, Richard; Regatte, Ravinder R
The objective of the current study was to investigate age- and gender-related differences in lumbar intervertebral disk (IVD) strain with the use of static mechanical loading and continuous three-dimensional (3D) golden-angle radial sparse parallel (GRASP) MRI. A continuous 3D-GRASP stack-of-stars trajectory of the lumbar spine was performed on a 3-T scanner with static mechanical loading. Compressed sensing reconstruction, motion deformation maps, and Lagrangian strain maps during loading and recovery in the X-, Y-, and Z-directions were calculated for segmented IVD segments from L1/L2 to L5/S1. Mean IVD height was measured at rest. Spearman coefficients were used to evaluate the associations between age and global IVD height and global IVD strain. Mann-Whitney tests were used to compare global IVD height and global IVD strain in males and females. The prospective study enrolled 20 healthy human volunteers (10 males, 10 females; age 34.6 ± 11.4 [mean ± SD], range 22-56 years). Significant increases in compressive strain were observed with age, as evidenced by negative correlations between age and global IVD strain during loading (ρ = -0.76, p = 0.0046) and recovery (ρ = -0.68, p = 0.0251) in the loading X-direction. There was no significant correlation between age and global IVD height, global IVD strain during loading and recovery in the Y-direction, and global IVD strain during loading and recovery in the Z-direction. There were no significant differences between males and females in global IVD height and global IVD strain during loading and recovery in the X-, Y-, and Z-directions. It was concluded that our study demonstrated the significant role aging plays in internal dynamic strains in the lumbar IVD during loading and recovery. Older healthy individuals have reduced IVD stiffness and greater IVD compression during static mechanical loading of the lumbar spine. The GRASP-MRI technique demonstrates the feasibility to identify changes in IVD mechanical properties with early IVD degeneration due to aging.
PMID: 37409683
ISSN: 1099-1492
CID: 5539302

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications

Zibetti, Marcelo V W; Menon, Rajiv G; de Moura, Hector L; Zhang, Xiaoxia; Kijowski, Richard; Regatte, Ravinder R
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.
PMID: 37010113
ISSN: 1522-2586
CID: 5463602

Genicular artery embolization for treatment of knee osteoarthritis pain: Systematic review and meta-analysis

Taslakian, Bedros; Miller, Larry E.; Mabud, Tarub S.; Macaulay, William; Samuels, Jonathan; Attur, Mukundan; Alaia, Erin F.; Kijowski, Richard; Hickey, Ryan; Sista, Akhilesh K.
Objective: Genicular artery embolization (GAE) is a novel, minimally invasive procedure for treatment of knee osteoarthritis (OA). This meta-analysis investigated the safety and effectiveness of this procedure. Design: Outcomes of this systematic review with meta-analysis were technical success, knee pain visual analog scale (VAS; 0"“100 scale), WOMAC Total Score (0"“100 scale), retreatment rate, and adverse events. Continuous outcomes were calculated as the weighted mean difference (WMD) versus baseline. Minimal clinically important difference (MCID) and substantial clinical benefit (SCB) rates were estimated in Monte Carlo simulations. Rates of total knee replacement and repeat GAE were calculated using life-table methods. Results: In 10 groups (9 studies; 270 patients; 339 knees), GAE technical success was 99.7%. Over 12 months, the WMD ranged from −34 to −39 at each follow-up for VAS score and −28 to −34 for WOMAC Total score (all p "‹< "‹0.001). At 12 months, 78% met the MCID for VAS score; 92% met the MCID for WOMAC Total score, and 78% met the SCB for WOMAC Total score. Higher baseline knee pain severity was associated with greater improvements in knee pain. Over 2 years, 5.2% of patients underwent total knee replacement and 8.3% received repeat GAE. Adverse events were minor, with transient skin discoloration as the most common (11.6%). Conclusions: Limited evidence suggests that GAE is a safe procedure that confers improvement in knee OA symptoms at established MCID thresholds. Patients with greater knee pain severity may be more responsive to GAE.
SCOPUS:85162354695
ISSN: 2665-9131
CID: 5549022

Age-Dependent Changes in Knee Cartilage T1 , T2 , and T1p Simultaneously Measured Using MRI Fingerprinting

Kijowski, Richard; Sharafi, Azadeh; Zibetti, Marcelo V W; Chang, Gregory; Cloos, Martijn A; Regatte, Ravinder R
BACKGROUND:Magnetic resonance fingerprinting (MRF) techniques have been recently described for simultaneous multiparameter cartilage mapping of the knee although investigation of their ability to detect early cartilage degeneration remains limited. PURPOSE/OBJECTIVE:relaxation times measured using a three-dimensional (3D) MRF sequence in healthy volunteers. STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:The study group consisted of 24 healthy asymptomatic human volunteers (15 males with mean age 34.9 ± 14.4 years and 9 females with mean age 44.5 ± 13.1 years). FIELD STRENGTH/SEQUENCE/UNASSIGNED:maps of knee cartilage. ASSESSMENT/RESULTS:relaxation times of the knee were measured. STATISTICAL TESTS/METHODS:relaxation times. The value of P < 0.05 was considered statistically significant. RESULTS: = 0.54-0.66). CONCLUSION/CONCLUSIONS:relaxation times simultaneously measured using a 3D-MRF sequence in healthy volunteers showed age-dependent changes in knee cartilage, primarily within the medial compartment.
PMID: 36190187
ISSN: 1522-2586
CID: 5361572

Prediction of total knee replacement using deep learning analysis of knee MRI

Rajamohan, Haresh Rengaraj; Wang, Tianyu; Leung, Kevin; Chang, Gregory; Cho, Kyunghyun; Kijowski, Richard; Deniz, Cem M
Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case-control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs.
PMCID:10147603
PMID: 37117260
ISSN: 2045-2322
CID: 5465642

Radiomics and Deep Learning for Disease Detection in Musculoskeletal Radiology: An Overview of Novel MRI- and CT-Based Approaches

Fritz, Benjamin; Yi, Paul H; Kijowski, Richard; Fritz, Jan
ABSTRACT/UNASSIGNED:Radiomics and machine learning-based methods offer exciting opportunities for improving diagnostic performance and efficiency in musculoskeletal radiology for various tasks, including acute injuries, chronic conditions, spinal abnormalities, and neoplasms. While early radiomics-based methods were often limited to a smaller number of higher-order image feature extractions, applying machine learning-based analytic models, multifactorial correlations, and classifiers now permits big data processing and testing thousands of features to identify relevant markers. A growing number of novel deep learning-based methods describe magnetic resonance imaging- and computed tomography-based algorithms for diagnosing anterior cruciate ligament tears, meniscus tears, articular cartilage defects, rotator cuff tears, fractures, metastatic skeletal disease, and soft tissue tumors. Initial radiomics and deep learning techniques have focused on binary detection tasks, such as determining the presence or absence of a single abnormality and differentiation of benign versus malignant. Newer-generation algorithms aim to include practically relevant multiclass characterization of detected abnormalities, such as typing and malignancy grading of neoplasms. So-called delta-radiomics assess tumor features before and after treatment, with temporal changes of radiomics features serving as surrogate markers for tumor responses to treatment. New approaches also predict treatment success rates, surgical resection completeness, and recurrence risk. Practice-relevant goals for the next generation of algorithms include diagnostic whole-organ and advanced classification capabilities. Important research objectives to fill current knowledge gaps include well-designed research studies to understand how diagnostic performances and suggested efficiency gains of isolated research settings translate into routine daily clinical practice. This article summarizes current radiomics- and machine learning-based magnetic resonance imaging and computed tomography approaches for musculoskeletal disease detection and offers a perspective on future goals and objectives.
PMID: 36070548
ISSN: 1536-0210
CID: 5337042

2D versus 3D MRI of osteoarthritis in clinical practice and research

Walter, Sven S.; Fritz, Benjamin; Kijowski, Richard; Fritz, Jan
Accurately detecting and characterizing articular cartilage defects is critical in assessing patients with osteoarthritis. While radiography is the first-line imaging modality, magnetic resonance imaging (MRI) is the most accurate for the noninvasive assessment of articular cartilage. Multiple semiquantitative grading systems for cartilage lesions in MRI were developed. The Outerbridge and modified Noyes grading systems are commonly used in clinical practice and for research. Other useful grading systems were developed for research, many of which are joint-specific. Both two-dimensional (2D) and three-dimensional (3D) pulse sequences are used to assess cartilage morphology and biochemical composition. MRI techniques for morphological assessment of articular cartilage can be categorized into 2D and 3D FSE/TSE spin-echo and gradient-recalled echo sequences. T2 mapping is most commonly used to qualitatively assess articular cartilage microstructural composition and integrity, extracellular matrix components, and water content. Quantitative techniques may be able to label articular cartilage alterations before morphological defects are visible. Accurate detection and characterization of shallow low-grade partial and small articular cartilage defects are the most challenging for any technique, but where high spatial resolution 3D MRI techniques perform best. This review article provides a practical overview of commonly used 2D and 3D MRI techniques for articular cartilage assessments in osteoarthritis.
SCOPUS:85149786724
ISSN: 0364-2348
CID: 5446692

Repeatability of Quantitative Knee Cartilage T1, T2, and T1ρ Mapping With 3D-MRI Fingerprinting

Zhang, Xiaoxia; de Moura, Hector L.; Monga, Anmol; Zibetti, Marcelo V.W.; Kijowski, Richard; Regatte, Ravinder R.
Background: Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. Purpose: To assess the intra-day and inter-day repeatabilities of knee cartilage T1, T2, and T1ρ maps using a 3D-MRF sequence for simultaneous measurement. Study Type: Prospective. Subjects: Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. Field Strength/Sequence: 3 T/3D-MRF, T1, T2, and T1ρ maps. Assessment: The acquisition of 3D-MRF cartilage (simultaneous acquisition of T1, T2, and T1ρ maps) were acquired using a dictionary pattern-matching approach. Conventional cartilage T1, T2, and T1ρ maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. Statistical Tests: The mean and SD for cartilage T1, T2, and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland"“Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. Results: The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1, T2, and T1ρ, respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. Conclusion: The 3D-MRF sequence had high intra-subject and inter-subject repeatabilities for simultaneously measuring knee cartilage T1, T2, and T1ρ with good agreement with conventional sequences. Evidence Level: 1. Technical Efficacy: Stage 1.
SCOPUS:85175075205
ISSN: 1053-1807
CID: 5616662

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

Hirvasniemi, J; Runhaar, J; van der Heijden, R A; Zokaeinikoo, M; Yang, M; Li, X; Tan, J; Rajamohan, H R; Zhou, Y; Deniz, C M; Caliva, F; Iriondo, C; Lee, J J; Liu, F; Martinez, A M; Namiri, N; Pedoia, V; Panfilov, E; Bayramoglu, N; Nguyen, H H; Nieminen, M T; Saarakkala, S; Tiulpin, A; Lin, E; Li, A; Li, V; Dam, E B; Chaudhari, A S; Kijowski, R; Bierma-Zeinstra, S; Oei, E H G; Klein, S
OBJECTIVES/OBJECTIVE:The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. DESIGN/METHODS:The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). RESULTS:Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. CONCLUSION/CONCLUSIONS:The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.
PMID: 36243308
ISSN: 1522-9653
CID: 5361322

Emerging Technology in Musculoskeletal MRI and CT

Kijowski, Richard; Fritz, Jan
This article provides a focused overview of emerging technology in musculoskeletal MRI and CT. These technological advances have primarily focused on decreasing examination times, obtaining higher quality images, providing more convenient and economical imaging alternatives, and improving patient safety through lower radiation doses. New MRI acceleration methods using deep learning and novel reconstruction algorithms can reduce scanning times while maintaining high image quality. New synthetic techniques are now available that provide multiple tissue contrasts from a limited amount of MRI and CT data. Modern low-field-strength MRI scanners can provide a more convenient and economical imaging alternative in clinical practice, while clinical 7.0-T scanners have the potential to maximize image quality. Three-dimensional MRI curved planar reformation and cinematic rendering can provide improved methods for image representation. Photon-counting detector CT can provide lower radiation doses, higher spatial resolution, greater tissue contrast, and reduced noise in comparison with currently used energy-integrating detector CT scanners. Technological advances have also been made in challenging areas of musculoskeletal imaging, including MR neurography, imaging around metal, and dual-energy CT. While the preliminary results of these emerging technologies have been encouraging, whether they result in higher diagnostic performance requires further investigation.
PMID: 36413131
ISSN: 1527-1315
CID: 5384152