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Purpose: The anisotropy of R2 and R1ρ relaxation rates in articular cartilage contains information about the collagenous structure of the tissue. Here we determine and study the anisotropic and isotropic components of T2 and T1ρ relaxation parameters in articular cartilage with a clinical 3T MRI device. Furthermore, a visual representation of the topographical variation in anisotropy is given via anisotropy mapping. Methods: Eight bovine stifle joints were imaged at 22 orientations with respect to the main magnetic field using T2, continuous‐wave (CW) T1ρ, and adiabatic T1ρ mapping sequences. Relaxation rates were separated into isotropic and anisotropic relaxation components using a previously established relaxation anisotropy model. Pixel‐wise anisotropy values were determined from the relaxation‐time maps using Michelson contrast. Results: The relaxation rates obtained from the samples displayed notable variation depending on the sample orientation, magnetization preparation, and cartilage layer. R2 demonstrated significant anisotropy, whereas CW‐R1ρ (300 Hz) and CW‐R1ρ (500 Hz) displayed a low degree of anisotropy. Adiabatic R1ρ was largely isotropic. In the deep cartilage regions, relaxation rates were generally faster and more anisotropic than in the cartilage closer to the tissue surface. The isotropic relaxation rate components were found to have similar values regardless of measurement sequence. Conclusions: The fitted relaxation model for T2 and T1ρ demonstrated varying amounts anisotropy, depending on magnetization preparation, and studied the articular cartilage layer. Anisotropy mapping of full joints showed varying amounts of anisotropy depending on the quantitative MRI parameter and topographical location, and in the case of T2, showed systematic changes in anisotropy across cartilage depth. [ABSTRACT FROM AUTHOR]

The aim of the present study was to examine the effects of attentional focus instructions on acute changes in the transverse relaxation time (T2) of the femorotibial cartilage and in cartilage volume during repeated drop‐jump landings. Ten healthy females (Mage = 20.4 ± 0.8 years) performed a drop landing task from a 50 cm high box over the course of 3 days (50 repetitions each day) across three attentional focus conditions: external focus (EF: focus on landing as soft as possible), internal focus (IF: focus on bending your knees when you land), and control (CON: no‐focus instruction), which was counterbalanced across focus conditions. T2 mapping and the volume of femorotibial cartilage were determined from magnetic resonance imaging scans at 1.5 T for the dominant knee before and after completing the drop landings in each attentional focus condition per day. Results indicated a smaller change in cartilage T2 relaxation time and volumetry in the central load‐bearing lateral cartilage under the EF, compared to IF and CON. Moreover, the change in T2 and cartilage volume was greater for lateral tibial cartilage as compared to femoral cartilage and was independent of attentional focus instructions. No significant acute quantitative changes were observed in the medial compartment. The peak vertical ground reaction force was found to be the lowest under the EF, compared to IF and CON. These findings suggest that external focus of attention may reduce cartilage load, potentially aiding in the control or management of cartilage injuries during landing in female athletes. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast‐weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time. Purpose: To improve clinical utility of MRF by synthesizing contrast‐weighted MR images from the quantitative data provided by MRF, using U‐nets that were trained for the synthesis task utilizing L1‐ and perceptual loss functions, and their combinations. Study Type: Retrospective. Population: Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33–35, gender distribution not available). Field Strength and Sequence: A 3 T, multislice‐MRF, proton density (PD)‐weighted 3D‐SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat‐saturated T2‐weighted 3D‐space, water‐excited double echo steady state (DESS). Assessment: Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5‐point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics. Statistical Tests: Friedman's test accompanied with post hoc Wilcoxon signed‐rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized. Results: The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3–4 on a 5‐point scale). Qualitatively, the best synthetic images were produced with combination of L1‐ and perceptual loss functions and perceptual loss alone, while L1‐loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1‐loss. Data Conclusion: Synthesizing high‐quality contrast‐weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images. Evidence Level: 4. Technical Efficacy: Stage 1. [ABSTRACT FROM AUTHOR]

Background: Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage. Purpose: To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content. Study Type: Retrospective, animal model. Animal Model: An open‐source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation. Field Strength/Sequence: A 9.4 T MRI scanner/qMRI sequences: T1, T2, adiabatic T1ρ and T2ρ, continuous‐wave T1ρ and relaxation along a fictitious field (TRAFF) maps. Assessment: Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross‐validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively. Statistical Tests: Normality was tested using Shapiro–Wilk test, and association between predicted and measured values was evaluated using Spearman's Rho test. A P‐value of 0.05 was considered as the limit of statistical significance. Results: Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy (R2 = 0.68–0.75 for PLM and 0.62–0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman's Rho = 0.72–0.88 for PLM and 0.61–0.83 for DD). GPR algorithm had the highest accuracy (R2 = 0.75 and 0.66) and lowest prediction‐error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively. Data Conclusion: Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content. Evidence Level: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

In this study, we developed a gray level co‐occurrence matrix‐based 3D texture analysis method for dual‐echo steady‐state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36‐ and 72‐month follow‐ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren‐Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA. [ABSTRACT FROM AUTHOR]

This article is a review of various quantitative MRI methods for assessing cartilage. The authors address a concern regarding the interpretation of T1ρ mapping as an indirect measure of glycosaminoglycan (GAG) content in cartilage. While T1ρ has been correlated with GAG loss in some studies, it is important to recognize that variations in T1ρ values can also reflect changes in other tissue components, such as collagen structure. The authors suggest that attributing T1ρ changes solely to GAG alterations may misguide researchers and clinicians, and they encourage a more accurate understanding of the factors influencing T1ρ. [Extracted from the article]

Morphological changes in knee cartilage subregions are valuable imaging‐based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double‐echo steady‐state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0‐/12‐/24‐month visits. Our method performed deep learning‐based segmentation of knee cartilage tissues, their subregional division via multi‐atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm3) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845–0.973 and mean differences = 262–501 mm3 for weight‐bearing cartilage volume, and r = 0.770–0.962 and mean differences = 0.513–1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12‐ and 24‐month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage‐related imaging biomarkers. [ABSTRACT FROM AUTHOR]

Chondral lesions lead to degenerative changes in the surrounding cartilage tissue, increasing the risk of developing post‐traumatic osteoarthritis (PTOA). This study aimed to investigate the feasibility of quantitative magnetic resonance imaging (qMRI) for evaluation of articular cartilage in PTOA. Articular explants containing surgically induced and repaired chondral lesions were obtained from the stifle joints of seven Shetland ponies (14 samples). Three age‐matched nonoperated ponies served as controls (six samples). The samples were imaged at 9.4 T. The measured qMRI parameters included T1, T2, continuous‐wave T1ρ (CWT1ρ), adiabatic T1ρ (AdT1ρ), and T2ρ (AdT2ρ) and relaxation along a fictitious field (TRAFF). For reference, cartilage equilibrium and dynamic moduli, proteoglycan content and collagen fiber orientation were determined. Mean values and profiles from full‐thickness cartilage regions of interest, at increasing distances from the lesions, were used to compare experimental against control and to correlate qMRI with the references. Significant alterations were detected by qMRI parameters, including prolonged T1, CWT1ρ, and AdT1ρ in the regions adjacent to the lesions. The changes were confirmed by the reference methods. CWT1ρ was more strongly associated with the reference measurements and prolonged in the affected regions at lower spin‐locking amplitudes. Moderate to strong correlations were found between all qMRI parameters and the reference parameters (ρ = −0.531 to −0.757). T1, low spin‐lock amplitude CWT1ρ, and AdT1ρ were most responsive to changes in visually intact cartilage adjacent to the lesions. In the context of PTOA, these findings highlight the potential of T1, CWT1ρ, and AdT1ρ in evaluation of compositional and structural changes in cartilage. [ABSTRACT FROM AUTHOR]

In this study, the rabbit model with anterior cruciate ligament transection (ACLT) was used to investigate early degenerative changes in cartilage using multiparametric quantitative magnetic resonance imaging (qMRI). ACLT was surgically induced in the knees of skeletally mature New Zealand White rabbits (n = 14). ACL transected and contralateral knee compartments—medial femur, lateral femur, medial tibia, and lateral tibia—were harvested 2 (n = 8) and 8 weeks (n = 6) postsurgery. Twelve age‐matched nonoperated rabbits served as control. qMRI was conducted at 9.4 T and included relaxation times T1, T2, continuous‐wave T1ρ (CWT1ρ), adiabatic T1ρ (AdT1ρ), adiabatic T2ρ (AdT2ρ), and relaxation along a fictitious field (TRAFF). For reference, quantitative histology and biomechanical measurements were carried out. Posttraumatic changes were primarily noted in the superficial half of the cartilage. Prolonged T1, T2, CWT1ρ, and AdT1ρ were observed in the lateral femur 2 and 8 weeks post‐ACLT, compared with the corresponding control and contralateral groups (P <.05). Collagen orientation was significantly altered in the lateral femur at 2 weeks post‐ACLT compared with the corresponding control group. In the medial femur, all the studied relaxation time parameters, except TRAFF, were increased 8 weeks post‐ACLT, as compared with the corresponding contralateral and control groups (P <.05). Similarly, significant proteoglycan loss was observed in the medial femur at 8 weeks following surgery (P <.05). Multiparametric MRI demonstrated early degenerative changes primarily in the superficial cartilage with T1, T2, CWT1ρ, and AdT1ρ sensitive to cartilage changes at 2 weeks after surgery. [ABSTRACT FROM AUTHOR]

Background: Early cartilage changes in knee osteoarthritis (OA) can be assessed by both intravenous (i.v.) and intra-articular (i.a.) delayed gadolinium-enhanced MRI of cartilage (dGEMRIC).Purpose: To examine the relationship between i.a. dGEMRIC and delayed gadolinium-enhanced MRI of menisci (dGEMRIM), and to investigate if the approach can be used to assess the morphological degeneration of menisci in obese patients with knee OA.Study Type: Cross-sectional.Population: Eighty-five obese patients with knee OA.Field Strength/sequences: 1.5T. Inversion recovery sequence with four inversion times.Assessment: T1 relaxation times were calculated for posterior weight-bearing femoral cartilage and the posterior horns of the menisci. Meniscus degeneration sum score (0-2) was assessed as increased signal/no signal (1/0) and tear/no tear (1/0).Statistical Tests: T1 relaxation times were compared using Student's t-test. Comparison of cartilage and meniscus T1 relaxation times was done by regression analysis. Analysis of variance (ANOVA) was used for comparison of meniscal T1 relaxation times among the three summed morphological scores (0-2). Statistical analyses were performed with a level of significance at 0.05.Results: For lateral menisci, morphology sum scores of 0, 1, and 2 were found in 13, 58, and 14 patients and for medial menisci in 2, 30, and 30 patients, respectively. Mean T1 relaxation times were 441 msec, 480 msec, and 497 msec for cartilage, lateral menisci, and medial menisci, respectively. T1 relaxation times for the menisci were similar (P = 0.53), and a weak correlation was found between dGEMRIC and dGEMRIM in the lateral compartments (R = 0.26). Comparing dGEMRIM between different morphology sum scores showed no differences (P > 0.4).Data Conclusion: I.a. dGEMRIM showed no correlation between the degree of meniscal degeneration and meniscus T1 relaxation times. I.a. dGEMRIM do not seem to deliver useful information about meniscus degeneration to be suitable for clinical applications, but i.a. dGEMRIC may still be considered an alternative contrast-saving method for cartilage.Level Of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:1700-1706. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate adiabatic T1ρ and T2ρ of articular cartilage in symptomatic osteoarthritis (OA) patients and asymptomatic volunteers, and to determine their association with magnetic resonance imaging (MRI)-based structural abnormalities in cartilage and bone.Materials and Methods: A total of 24 subjects (age range: 50-68 years; 12 female) were enrolled, including 12 early OA patients and 12 volunteers with normal joint function. Patients and volunteers underwent 3T MRI. T2 , adiabatic T1ρ , and T2ρ relaxation times of knee articular cartilage were measured. Proton density (PD)- and T1 -weighted MR image series were also obtained and separately evaluated for morphological changes using the MRI OA Knee Scoring (MOAKS) system. Comparisons using the Mann-Whitney nonparametric test were performed after dividing the study participants according to physical symptoms as determined by Western Ontario and McMaster Universities (WOMAC) score or presence of cartilage lesions, bone marrow lesions, or osteophytes.Results: Elevated adiabatic T1ρ and T2ρ relaxation times of articular cartilage were associated with cartilage loss (P = 0.024-0.047), physical symptoms (0.0068-0.035), and osteophytes (0.0039-0.027). Elevated adiabatic T1ρ was also associated with bone marrow lesions (0.033).Conclusion: Preliminary data suggest that elevated adiabatic T1ρ and T2ρ of cartilage are associated with morphological abnormalities of cartilage and bone, and thus may be applicable for in vivo OA research and diagnostics.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:678-689. [ABSTRACT FROM AUTHOR]

Purpose The aim of the present work was to validate and optimize adiabatic T1ρ and T2ρ mapping for in vivo measurements of articular cartilage at 3 Tesla (T). Methods Phantom and in vivo experiments were systematically performed on a 3T clinical system to evaluate the sequences using hyperbolic secant HS1 and HS4 pulses. R1ρ and R2ρ relaxation rates were studied as a function of agarose and chondroitin sulfate concentration and pulse duration. Optimal in vivo protocol was determined by imaging the articular cartilage of two volunteers and varying the sequence parameters, and successively applied in eight additional subjects. Reproducibility was assessed in phantoms and in vivo. Results Relaxation rates depended on agarose and chondroitin sulfate concentration. The sequences were able to generate relaxation time maps with pulse lengths of 8 and 6 ms for HS1 and HS4, respectively. In vivo findings were in good agreement with the phantoms. The implemented adiabatic T1ρ and T2ρ sequences demonstrated regional variation in relaxation time maps of femorotibial cartilage. Reproducibility in phantoms and in vivo was good to excellent for both adiabatic T1ρ and T2ρ. Conclusions The findings indicate that sequences are suitable for quantitative in vivo assessment of articular cartilage at 3 T. Magn Reson Med 77:1265-1275, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]