Detection of early cartilage degeneration in the tibiotalar joint using 3 T gagCEST imaging: a feasibility study

Accelerating multipool CEST MRI of Parkinson's disease using deep learning-based Z-spectral compressed sensing

PURPOSE

To develop a deep learning-based approach to reduce the scan time of multipool CEST MRI for Parkinson's disease (PD) while maintaining sufficient prediction accuracy.

METHOD

A deep learning approach based on a modified one-dimensional U-Net, termed Z-spectral compressed sensing (CS), was proposed to recover dense Z-spectra from sparse ones. The neural network was trained using simulated Z-spectra generated by the Bloch equation with various parameter settings. Its feasibility and effectiveness were validated through numerical simulations and in vivo rat brain experiments, compared with commonly used linear, pchip, and Lorentzian interpolation methods. The proposed method was applied to detect metabolism-related changes in the 6-hydroxydopamine PD model with multipool CEST MRI, including APT, CEST@2 ppm, nuclear Overhauser enhancement, direct saturation, and magnetization transfer, and the prediction performance was evaluated by area under the curve.

RESULTS

The numerical simulations and in vivo rat-brain experiments demonstrated that the proposed method could yield superior fidelity in retrieving dense Z-spectra compared with existing methods. Significant differences were observed in APT, CEST@2 ppm, nuclear Overhauser enhancement, and direct saturation between the striatum regions of wild-type and PD models, whereas magnetization transfer exhibited no significant difference. Receiver operating characteristic analysis demonstrated that multipool CEST achieved better predictive performance compared with individual pools. Combined with Z-spectral CS, the scan time of multipool CEST MRI can be reduced to 33% without distinctly compromising prediction accuracy.

CONCLUSION

The integration of Z-spectral CS with multipool CEST MRI can enhance the prediction accuracy of PD and maintain the scan time within a reasonable range.

Comparison of compositional MRI techniques to quantify the regenerative potential of articular cartilage: a preclinical minipig model after osteochondral defect treatments with autologous mesenchymal stromal cells and unseeded scaffolds

Background

The field of orthopedics seeks effective, safer methods for evaluating articular cartilage regeneration. Despite various treatment innovations, non-invasive, contrast-free full quantitative assessments of hyaline articular cartilage's regenerative potential using compositional magnetic resonance (MR) sequences remain challenging. In this context, our aim was to investigate the effectiveness of different MR sequences for quantitative assessment of cartilage and to compare them with the current gold standard delayed gadolinium-enhanced MR imaging of cartilage (dGEMRIC) measurements.

Methods

We employed ex vivo imaging in a preclinical minipig model to assess knee cartilage regeneration. Standardized osteochondral defects were drilled in the proximal femur of the specimens (n=14), which were divided into four groups. Porcine collagen scaffolds seeded with autologous adipose-derived stromal cells (ASC), autologous bone marrow stromal cells (BMSC), and unseeded scaffolds (US) were implanted in femoral defects. Furthermore, there was a defect group which received no treatment. After 6 months, the specimens were examined using different compositional MR methods, including the gold standard dGEMRIC as well as T1, T2, T2*, and T1ρ techniques. The statistical evaluation involved comparing the defect region with the uninjured tibia and femur cartilage layers and all measurements were performed on a clinical 3T MR Scanner.

Results

In the untreated defect group, we observed significant differences in the defect region, with dGEMRIC values significantly lower (404.86±64.2 ms, P=0.018) and T2 times significantly higher (44.24±2.75 ms, P<0.001). Contrastingly, in all three treatment groups (ASC, BMSC, US), there were no significant differences among the three regions in the dGEMRIC sequence, suggesting successful cartilage regeneration. However, T1, T2*, and T1ρ sequences failed to detect such differences, highlighting their lower sensitivity for cartilage regeneration.

Conclusions

As expected, dGEMRIC is well suited for monitoring cartilage regeneration. Interestingly, T2 imaging also proved to be a reliable cartilage imaging technique and thus offers a contrast agent-free alternative to the former gold standard for subsequent in vivo studies investigating the cartilage regeneration potential of different treatment modalities.

Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTRasym) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations. Recent noise-reduction algorithms like principal component analysis (PCA), nonlocal mean filtering (NLM), and block matching combined with 3D filtering (BM3D) have shown promise, as there is a burgeoning interest in the utilization of neural networks (NNs), particularly autoencoders, for imaging denoising. This study uses the Bloch-McConnell equations, which allow for the synthetic generation of CEST images and explores NNs efficacy in denoising these images. Using synthetically generated phantoms, autoencoders were created, and their performance was compared with traditional denoising methods using various datasets. The results underscored the superior performance of NNs, notably the ResUNet architectures, in noise identification and abatement compared to analytical approaches across a wide noise gamut. This superiority was particularly pronounced at elevated noise intensities in the in vitro data. Notably, the neural architectures significantly improved the PSNR values, achieving up to 35.0, while some traditional methods struggled, especially in low-noise reduction scenarios. However, the application to the in vivo data presented challenges due to varying noise profiles. This study accentuates the potential of NNs as robust denoising tools, but their translation to clinical settings warrants further investigation.

Articular Cartilage-From Basic Science Structural Imaging to Non-Invasive Clinical Quantitative Molecular Functional Information for AI Classification and Prediction

This review presents the changes that the imaging of articular cartilage has undergone throughout the last decades. It highlights that the expectation is no longer to image the structure and associated functions of articular cartilage but, instead, to devise methods for generating non-invasive, function-depicting images with quantitative information that is useful for detecting the early, pre-clinical stage of diseases such as primary or post-traumatic osteoarthritis (OA/PTOA). In this context, this review summarizes (a) the structure and function of articular cartilage as a molecular imaging target, (b) quantitative MRI for non-invasive assessment of articular cartilage composition, microstructure, and function with the current state of medical diagnostic imaging, (c), non-destructive imaging methods, (c) non-destructive quantitative articular cartilage live-imaging methods, (d) artificial intelligence (AI) classification of degeneration and prediction of OA progression, and (e) our contribution to this field, which is an AI-supported, non-destructive quantitative optical biopsy for early disease detection that operates on a digital tissue architectural fingerprint. Collectively, this review shows that articular cartilage imaging has undergone profound changes in the purpose and expectations for which cartilage imaging is used; the image is becoming an AI-usable biomarker with non-invasive quantitative functional information. This may aid in the development of translational diagnostic applications and preventive or early therapeutic interventions that are yet beyond our reach.

Biomechanical Effects of Chronic Ankle Instability on the Talar Cartilage Matrix: The Value of T1ρ Relaxation Mapping Without and With Mechanical Loading

BACKGROUND

T1ρ mapping has been proposed for the detection of early cartilage degeneration associated with chronic ankle instability (CAI). However, there are limited data surrounding the influence of ankle loading on T1ρ relaxation.

PURPOSE

To evaluate T1ρ relaxation times of talar cartilage, as an indicator of early degenerative changes, associated with CAI and to investigate the influence of acute axial in situ loading on T1ρ values in CAI patients and healthy controls.

STUDY TYPE

Prospective.

SUBJECTS

A total of 9 patients (age = 21.8 ± 2.5 years, male/female = 2/7) with chronic ankle instability and 18 healthy control subjects (age = 22.8 ± 3.6 years, male/female = 5/13).

FIELD STRENGTH

3 T.

SEQUENCE

3D gradient echo fast low-angle shot (FLASH) sequence augmented with a variable spin-lock preparation period.

ASSESSMENT

Ankle T1ρ mapping was performed without and with axial loading of 500 N. The talar cartilage was segmented in five coronal slices covering the central talocrural joint. Median talar T1ρ values were separately calculated for the medial and lateral facets.

STATISTICAL TESTS

Mann-Whitney U and Wilcoxon signed-rank tests, significance level: P < 0.05.

RESULTS

For the combined cohorts, the statistical analysis yielded significantly lower T1ρ values with loading compared to the no-load measurement for both the lateral (no load: [51.0 ± 4.0] msec, load: [49.5 ± 5.4] msec) as well as the medial compartment (no load: [50.0 ± 5.4] msec, load: [47.8 ± 6.8] msec). In the unloaded scans, the CAI patients showed significantly increased talar T1ρ values ([53.0 ± 7.4] mse ) compared to the healthy control subjects ([48.8 ± 4.1] msec) in the medial compartment.

DATA CONCLUSION

Increased talar T1ρ relaxation times in CAI patients compared to healthy controls suggest that T1ρ relaxation is a sensitive biomarker for CAI-induced early-stage cartilage degeneration. However, the load-induced T1ρ change did not prove to be a viable marker for the altered biomechanical properties of the hyaline talar cartilage.

LEVEL OF EVIDENCE

2 LEVEL OF EFFICACY: Stage 2.

Adaptive IoU Thresholding for Improving Small Object Detection: A Proof-of-Concept Study of Hand Erosions Classification of Patients with Rheumatic Arthritis on X-ray Images

In recent years, much research evaluating the radiographic destruction of finger joints in patients with rheumatoid arthritis (RA) using deep learning models was conducted. Unfortunately, most previous models were not clinically applicable due to the small object regions as well as the close spatial relationship. In recent years, a new network structure called RetinaNets, in combination with the focal loss function, proved reliable for detecting even small objects. Therefore, the study aimed to increase the recognition performance to a clinically valuable level by proposing an innovative approach with adaptive changes in intersection over union (IoU) values during training of Retina Networks using the focal loss error function. To this end, the erosion score was determined using the Sharp van der Heijde (SvH) metric on 300 conventional radiographs from 119 patients with RA. Subsequently, a standard RetinaNet with different IoU values as well as adaptively modified IoU values were trained and compared in terms of accuracy, mean average accuracy (mAP), and IoU. With the proposed approach of adaptive IoU values during training, erosion detection accuracy could be improved to 94% and an mAP of 0.81 ± 0.18. In contrast Retina networks with static IoU values achieved only an accuracy of 80% and an mAP of 0.43 ± 0.24. Thus, adaptive adjustment of IoU values during training is a simple and effective method to increase the recognition accuracy of small objects such as finger and wrist joints.

Lorentzian-Corrected Apparent Exchange-Dependent Relaxation (LAREX) Ω-Plot Analysis-An Adaptation for qCEST in a Multi-Pool System: Comprehensive In Silico, In Situ, and In Vivo Studies

Based on in silico, in situ, and in vivo studies, this study aims to develop a new method for the quantitative chemical exchange saturation transfer (qCEST) technique considering multi-pool systems. To this end, we extended the state-of-the-art apparent exchange-dependent relaxation (AREX) method with a Lorentzian correction (LAREX). We then validated this new method with in situ and in vivo experiments on human intervertebral discs (IVDs) using the Kendall-Tau correlation coefficient. In the in silico experiments, we observed significant deviations of the AREX method as a function of the underlying exchange rate (kba) and fractional concentration (fb) compared to the ground truth due to the influence of other exchange pools. In comparison to AREX, the LAREX-based Ω-plot approach yielded a substantial improvement. In the subsequent in situ and in vivo experiments on human IVDs, no correlation to the histological reference standard or Pfirrmann classification could be found for the fb (in situ: τ = −0.17 p = 0.51; in vivo: τ = 0.13 p = 0.30) and kba (in situ: τ = 0.042 p = 0.87; in vivo: τ = −0.26 p = 0.04) of Glycosaminoglycan (GAG) with AREX. In contrast, the influence of interfering pools could be corrected by LAREX, and a moderate to strong correlation was observed for the fractional concentration of GAG for both in situ (τ = −0.71 p = 0.005) and in vivo (τ = −0.49 p < 0.001) experiments. The study presented here is the first to introduce a new qCEST method that enables qCEST imaging in systems with multiple proton pools.

Chemical Exchange Saturation Transfer for Lactate-Weighted Imaging at 3 T MRI: Comprehensive In Silico, In Vitro, In Situ, and In Vivo Evaluations

Based on in silico, in vitro, in situ, and in vivo evaluations, this study aims to establish and optimize the chemical exchange saturation transfer (CEST) imaging of lactate (Lactate-CEST—LATEST). To this end, we optimized LATEST sequences using Bloch−McConnell simulations for optimal detection of lactate with a clinical 3 T MRI scanner. The optimized sequences were used to image variable lactate concentrations in vitro (using phantom measurements), in situ (using nine human cadaveric lower leg specimens), and in vivo (using four healthy volunteers after exertional exercise) that were then statistically analyzed using the non-parametric Friedman test and Kendall Tau-b rank correlation. Within the simulated Bloch−McConnell equations framework, the magnetization transfer ratio asymmetry (MTRasym) value was quantified as 0.4% in the lactate-specific range of 0.5−1 ppm, both in vitro and in situ, and served as the imaging surrogate of the lactate level. In situ, significant differences (p < 0.001) and strong correlations (τ = 0.67) were observed between the MTRasym values and standardized intra-muscular lactate concentrations. In vivo, a temporary increase in the MTRasym values was detected after exertional exercise. In this bench-to-bedside comprehensive feasibility study, different lactate concentrations were detected using an optimized LATEST imaging protocol in vitro, in situ, and in vivo at 3 T, which prospectively paves the way towards non-invasive quantification and monitoring of lactate levels across a broad spectrum of diseases.

AcidoCEST-UTE MRI Reveals an Acidic Microenvironment in Knee Osteoarthritis

A relationship between an acidic pH in the joints, osteoarthritis (OA), and pain has been previously demonstrated. Acidosis Chemical Exchange Saturation Transfer (acidoCEST) indirectly measures the extracellular pH through the assessment of the exchange of protons between amide groups on iodinated contrast agents and bulk water. It is possible to estimate the extracellular pH in the osteoarthritic joint using acidoCEST MRI. However, conventional MR sequences cannot image deep layers of cartilage, meniscus, ligaments, and other musculoskeletal tissues that present with short echo time and fast signal decay. Ultrashort echo time (UTE) MRI, on the other hand, has been used successfully to image those joint tissues. Here, our goal is to compare the pH measured in the knee joints of volunteers without OA and patients with severe OA using acidoCEST-UTE MRI. Patients without knee OA and patients with severe OA were examined using acidoCEST-UTE MRI and the mean pH of cartilage, meniscus, and fluid was calculated. Additionally, the relationship between the pH measurements and the Knee Injury and Osteoarthritis Outcome Score (KOOS) was investigated. AcidoCEST-UTE MRI can detect significant differences in the pH of knee cartilage, meniscus, and fluid between joints without and with OA, with OA showing lower pH values. In addition, symptoms and knee-joint function become worse at lower pH measurements.

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6−33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10−35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28−43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

Quantification of Sodium Relaxation Times and Concentrations as Surrogates of Proteoglycan Content of Patellar CARTILAGE at 3T MRI

Sodium MRI has the potential to depict cartilage health accurately, but synovial fluid can influence the estimation of sodium parameters of cartilage. Therefore, this study aimed to reduce the impact of synovial fluid to render the quantitative compositional analyses of cartilage tissue technically more robust. Two dedicated protocols were applied for determining sodium T1 and T2* relaxation times. For each protocol, data were acquired from 10 healthy volunteers and one patient with patellar cartilage damage. Data recorded with multiple repetition times for T1 measurement and multi-echo data acquired with an additional inversion recovery pulse for T2* measurement were analysed using biexponential models to differentiate longitudinal relaxation components of cartilage (T1,car) and synovial fluid (T1,syn), and short (T2s*) from long (T2l*) transversal relaxation components. Sodium relaxation times and concentration estimates in patellar cartilage were successfully determined: T1,car = 14.5 ± 0.7 ms; T1,syn = 37.9 ± 2.9 ms; c(T1-protocol) = 200 ± 48 mmol/L; T2s* = 0.4 ± 0.1 ms; T2l* = 12.6 ± 0.7 ms; c(T2*-protocol) = 215 ± 44 mmol/L for healthy volunteers. In conclusion, a robust determination of sodium relaxation times is possible at a clinical field strength of 3T to quantify sodium concentrations, which might be a valuable tool to determine cartilage health.

Accuracy of MRI-Based Talar Cartilage Thickness Measurement and Talus Bone and Cartilage Modeling: Comparison with Ground-Truth Laser Scan Measurements

OBJECTIVE

The purpose of this work was to compare measurements of talar cartilage thickness and cartilage and bone surface geometry from clinically feasible magnetic resonance imaging (MRI) against high-accuracy laser scan models. Measurement of talar bone and cartilage geometry from MRI would provide useful information for evaluating cartilage changes, selecting osteochondral graft sources or creating patient-specific joint models.

DESIGN

Three-dimensional (3D) bone and cartilage models of 7 cadaver tali were created using (1) manual segmentation of high-resolution volumetric sequence 3T MR images and (2) laser scans. Talar cartilage thickness was compared between the laser scan- and MRI-based models for the dorsal, medial, and lateral surfaces. The laser scan- and MRI-based cartilage and bone surface models were compared using model-to-model distance.

RESULTS

Average cartilage thickness within the dorsal, medial, and lateral surfaces were 0.89 to 1.05 mm measured with laser scanning, and 1.10 to 1.22 mm measured with MRI. MRI-based thickness was 0.16 to 0.32 mm higher on average in each region. The average absolute surface-to-surface differences between laser scan- and MRI-based bone and cartilage models ranged from 0.16 to 0.22 mm for bone (MRI bone models smaller than laser scan models) and 0.35 to 0.38 mm for cartilage (MRI bone models larger than laser scan models).

CONCLUSIONS

This study demonstrated that cartilage and bone 3D modeling and measurement of average cartilage thickness on the dorsal, medial, and lateral talar surfaces using MRI were feasible and provided similar model geometry and thickness values to ground-truth laser scan-based measurements.

In-Situ Cartilage Functionality Assessment Based on Advanced MRI Techniques and Precise Compartmental Knee Joint Loading through Varus and Valgus Stress

Stress MRI brings together mechanical loading and MRI in the functional assessment of cartilage and meniscus, yet lacks basic scientific validation. This study assessed the response-to-loading patterns of cartilage and meniscus incurred by standardized compartmental varus and valgus loading of the human knee joint. Eight human cadaveric knee joints underwent imaging by morphologic (i.e., proton density-weighted fat-saturated and 3D water-selective) and quantitative (i.e., T1ρ and T2 mapping) sequences, both unloaded and loaded to 73.5 N, 147.1 N, and 220.6 N of compartmental pressurization. After manual segmentation of cartilage and meniscus, morphometric measures and T2 and T1ρ relaxation times were quantified. CT-based analysis of joint alignment and histologic and biomechanical tissue measures served as references. Under loading, we observed significant decreases in cartilage thickness (p < 0.001 (repeated measures ANOVA)) and T1ρ relaxation times (p = 0.001; medial meniscus, lateral tibia; (Friedman test)), significant increases in T2 relaxation times (p ≤ 0.004; medial femur, lateral tibia; (Friedman test)), and adaptive joint motion. In conclusion, varus and valgus stress MRI induces meaningful changes in cartilage and meniscus secondary to compartmental loading that may be assessed by cartilage morphometric measures as well as T2 and T1ρ mapping as imaging surrogates of tissue functionality.

Longitudinal T2 Mapping and Texture Feature Analysis in the Detection and Monitoring of Experimental Post-Traumatic Cartilage Degeneration

Background: Traumatic cartilage injuries predispose articulating joints to focal cartilage defects and, eventually, posttraumatic osteoarthritis. Current clinical-standard imaging modalities such as morphologic MRI fail to reliably detect cartilage trauma and to monitor associated posttraumatic degenerative changes with oftentimes severe prognostic implications. Quantitative MRI techniques such as T2 mapping are promising in detecting and monitoring such changes yet lack sufficient validation in controlled basic research contexts. Material and Methods: 35 macroscopically intact cartilage samples obtained from total joint replacements were exposed to standardized injurious impaction with low (0.49 J, n = 14) or high (0.98 J, n = 14) energy levels and imaged before and immediately, 24 h, and 72 h after impaction by T2 mapping. Contrast, homogeneity, energy, and variance were quantified as features of texture on each T2 map. Unimpacted controls (n = 7) and histologic assessment served as reference. Results: As a function of impaction energy and time, absolute T2 values, contrast, and variance were significantly increased, while homogeneity and energy were significantly decreased. Conclusion: T2 mapping and texture feature analysis are sensitive diagnostic means to detect and monitor traumatic impaction injuries of cartilage and associated posttraumatic degenerative changes and may be used to assess cartilage after trauma to identify “cartilage at risk”.

Aggrecan, the Primary Weight-Bearing Cartilage Proteoglycan, Has Context-Dependent, Cell-Directive Properties in Embryonic Development and Neurogenesis: Aggrecan Glycan Side Chain Modifications Convey Interactive Biodiversity

This review examines aggrecan's roles in developmental embryonic tissues, in tissues undergoing morphogenetic transition and in mature weight-bearing tissues. Aggrecan is a remarkably versatile and capable proteoglycan (PG) with diverse tissue context-dependent functional attributes beyond its established role as a weight-bearing PG. The aggrecan core protein provides a template which can be variably decorated with a number of glycosaminoglycan (GAG) side chains including keratan sulphate (KS), human natural killer trisaccharide (HNK-1) and chondroitin sulphate (CS). These convey unique tissue-specific functional properties in water imbibition, space-filling, matrix stabilisation or embryonic cellular regulation. Aggrecan also interacts with morphogens and growth factors directing tissue morphogenesis, remodelling and metaplasia. HNK-1 aggrecan glycoforms direct neural crest cell migration in embryonic development and is neuroprotective in perineuronal nets in the brain. The ability of the aggrecan core protein to assemble CS and KS chains at high density equips cartilage aggrecan with its well-known water-imbibing and weight-bearing properties. The importance of specific arrangements of GAG chains on aggrecan in all its forms is also a primary morphogenetic functional determinant providing aggrecan with unique tissue context dependent regulatory properties. The versatility displayed by aggrecan in biodiverse contexts is a function of its GAG side chains.