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

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.

Highlights in Pathophysiology of the Musculoskeletal System

The intention of the present Special Issue is to focus on the latest research in the musculoskeletal system, with an emphasis on the molecular mechanisms underlying its pathophysiology, as well as innovative diagnostic tools and therapeutic perspectives [...]

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.

Evaluation of Sodium Relaxation Times and Concentrations in the Achilles Tendon Using MRI

Sodium magnetic resonance imaging (MRI) can be used to evaluate the change in the proteoglycan content in Achilles tendons (ATs) of patients with different AT pathologies by measuring the ²³Na signal-to-noise ratio (SNR). As ²³Na SNR alone is difficult to compare between different studies, because of the high influence of hardware configurations and sequence settings on the SNR, we further set out to measure the apparent tissue sodium content (aTSC) in the AT as a better comparable parameter. Ten healthy controls and one patient with tendinopathy in the AT were examined using a clinical 3 Tesla (T) MRI scanner in conjunction with a dual tuned ¹H/²³Na surface coil to measure ²³Na SNR and aTSC in their ATs. ²³Na T₁ and T₂* of the AT were also measured for three controls to correct for different relaxation behavior. The results were as follows: ²³Na SNR = 11.7 ± 2.2, aTSC = 82.2 ± 13.9 mM, ²³Na T₁ = 20.4 ± 2.4 ms, ²³Na T_2s* = 1.4 ± 0.4 ms, and ²³Na T_2l* = 13.9 ± 0.8 ms for the whole AT of healthy controls with significant regional differences. These are the first reported aTSCs and ²³Na relaxation times for the AT using sodium MRI and may serve for future comparability in different studies regarding examinations of diseased ATs with sodium MRI.