Translation of morphological and functional musculoskeletal imaging

Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook

Osteoarthritis (OA) affects more than 500 millions people worldwide and places an enormous economic and medical burden on patients and healthcare systems. The knee is the most commonly affected joint. However, there is no effective early diagnostic method for OA. The main pathological feature of OA is cartilage degeneration. Owing to the poor regenerative ability of chondrocytes, early detection of OA and prompt intervention are extremely important. The T2 relaxation time indicates changes in cartilage composition and responds to alterations in the early cartilage matrix. T2 mapping does not require contrast agents or special equipment, so it is widely used. Radiomics analysis methods are used to construct diagnostic or predictive models based on information extracted from clinical images. Owing to the development of artificial intelligence methods, radiomics has made excellent progress in segmentation and model construction. In this review, we summarize the progress of T2 mapping radiomics research methods in terms of T2 map acquisition, image postprocessing, and OA diagnosis or predictive model construction.

Quantative MRI predicts tendon mechanical behavior, collagen composition, and organization

Quantitative magnetic resonance imaging (qMRI) measures have provided insights into the composition, quality, and structure-function of musculoskeletal tissues. Low signal-to-noise ratio has limited application to tendon. Advances in scanning sequences and sample positioning have improved signal from tendon allowing for evaluation of structure and function. The purpose of this study was to elucidate relationships between tendon qMRI metrics (T1, T2, T1ρ and diffusion tensor imaging [DTI] metrics) with tendon tissue mechanics, collagen concentration and organization. Sixteen human Achilles tendon specimens were collected, imaged with qMRI, and subjected to mechanical testing with quantitative polarized light imaging. T2 values were related to tendon mechanics [peak stress (rsp  = 0.51, p = 0.044), equilibrium stress (rsp  = 0.54, p = 0.033), percent relaxation (rsp  = -0.55, p = 0.027), hysteresis (rsp  = -0.64, p = 0.007), linear modulus (rsp  = 0.67, p = 0.009)]. T1ρ had a statistically significant relationship with percent relaxation (r = 0.50, p = 0.048). Collagen content was significantly related to DTI measures (range of r = 0.56-0.62). T2 values from a single slice of the midportion of human Achilles tendons were strongest predictors of tendon tensile mechanical metrics. DTI diffusivity indices (mean diffusivity, axial diffusivity, radial diffusivity) were strongly correlated with collagen content. These findings build on a growing body of literature supporting the feasibility of qMRI to characterize tendon tissue and noninvasively measure tendon structure and function. Statement of Clinical Significance: Quantitative MRI can be applied to characterize tendon tissue and is a noninvasive measure that relates to tendon composition and mechanical behavior.

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

Objectives

Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.

Methods

The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC).

Results

Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87).

Conclusion

Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously.

Key Points

• Subchondral bone plays a role in the osteoarthritis disease processes.

• MRI radiomics is a potential method for quantifying changes in subchondral bone.

• Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07951-5.

A novel MRI compatible mouse fracture model to characterize and monitor bone regeneration and tissue composition

Over the last years, murine in vivo magnetic resonance imaging (MRI) contributed to a new understanding of tissue composition, regeneration and diseases. Due to artefacts generated by the currently used metal implants, MRI is limited in fracture healing research so far. In this study, we investigated a novel MRI-compatible, ceramic intramedullary fracture implant during bone regeneration in mice. Three-point-bending revealed a higher stiffness of the ceramic material compared to the metal implants. Electron microscopy displayed a rough surface of the ceramic implant that was comparable to standard metal devices and allowed cell attachment and growth of osteoblastic cells. MicroCT-imaging illustrated the development of the callus around the fracture site indicating a regular progressing healing process when using the novel implant. In MRI, different callus tissues and the implant could clearly be distinguished from each other without any artefacts. Monitoring fracture healing using MRI-compatible implants will improve our knowledge of callus tissue regeneration by 3D insights longitudinal in the same living organism, which might also help to reduce the consumption of animals for future fracture healing studies, significantly. Finally, this study may be translated into clinical application to improve our knowledge about human bone regeneration.

Quantitative µMRI and PLM study of rabbit humeral and femoral head cartilage at sub-10 µm resolutions

This study aimed to establish the baseline characteristics in humeral and femoral cartilage in rabbit, using quantitative magnetic resonance imaging (MRI) relaxation times (T2, T1ρ, and T1) at 9.75 and 70-82 µm pixel resolutions, and quantitative polarized light microscopy (PLM) measures (retardation, angle) at 1.0 and 4.0 µm pixel resolutions. Five intact (i.e., unopened) shoulder joints (the scapula and humeral heads) and three femoral heads of the hip joints from five healthy rabbits were imaged in MRI at 70-82 µm resolution. Thirteen cartilage-bone specimens were harvested from these joints and imaged in µMRI at 9.75 µm resolution. Subsequently, quantitative PLM study of these specimens enabled the examination of the fibril orientation and organization in both intact joints and individual specimens. Quantitative MRI relaxation data and PLM fibril structural data show distinct features in tissue properties at different depths of cartilage, different in individual histological zones. The thicknesses of the histological zones in µMRI and PLM were successfully obtained. This is the first correlated and quantitative MRI and PLM study of rabbit cartilage at sub-10 µm resolutions, which benefits future investigation of osteoarthritis using the rabbit model. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 38:1052-1062, 2020.

What does digitalization hold for the creation of real-world evidence?

Health-related information is increasingly being collected and stored digitally. These data, either structured or unstructured, are becoming the ubiquitous assets that might enable us to comprehensively map out a patient's health journey from an asymptomatic state of wellness to disease onset and its trajectory. These new data could provide rich real-world evidence for better clinical care and research, if they can be accessed, linked and analyzed-all of which are possible. In this review, these opportunities will be explored through a case vignette of a patient with OA, followed by discussion on how this digitalized real-world evidence could best be utilized, as well as the challenges of data access, quality and maintaining public trust.