3D MRI of Articular Cartilage

Time-efficient combined morphologic and quantitative joint MRI: an in situ study of standardized knee cartilage defects in human cadaveric specimens

BACKGROUND

Quantitative techniques such as T2 and T1ρ mapping allow evaluating the cartilage and meniscus. We evaluated multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry (MIXTURE) sequences with turbo spin-echo (TSE) contrast and additional parameter maps versus reference TSE sequences in an in situ model of human cartilage defects.

METHODS

Standardized cartilage defects of 8, 5, and 3 mm in diameter were created in the lateral femora of ten human cadaveric knee specimens (81 ± 10 years old; nine males, one female). MIXTURE sequences providing proton density-weighted fat-saturated images and T2 maps or T1-weighted images and T1ρ maps as well as the corresponding two- and three-dimensional TSE reference sequences were acquired before and after defect creation (3-T scanner; knee coil). Defect delineability, bone texture, and cartilage relaxation times were quantified. Appropriate parametric or non-parametric tests were used.

RESULTS

Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences (p ≤ 0.47). After defect creation, relaxation times significantly increased in the central femur (T2pre = 51 ± 4 ms [mean ± standard deviation] versus T2post = 56 ± 4 ms; p = 0.002) and all regions combined (T1ρpre = 40 ± 4 ms versus T1ρpost = 43 ± 4 ms; p = 0.004).

CONCLUSIONS

MIXTURE permitted time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1ρ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defects and bone texture, were comparable between MIXTURE and reference sequences.

RELEVANCE STATEMENT

Equally time-efficient and versatile, the MIXTURE sequence platform combines morphologic imaging using familiar contrasts, excellent image correspondence versus corresponding reference sequences and quantitative mapping information, thereby increasing the diagnostic value beyond mere morphology.

KEY POINTS

• Combined morphologic and quantitative MIXTURE sequences are based on three-dimensional TSE contrasts. • MIXTURE sequences were studied in an in situ human cartilage defect model. • Morphologic image features, i.e., defect delineabilty and bone texture, were investigated. • Morphologic image features were similar between MIXTURE and reference sequences. • MIXTURE allowed time-efficient simultaneous morphologic and quantitative knee joint assessment.

Gaining Insight into Updated MR Imaging for Quantitative Assessment of Cartilage Injury in Knee Osteoarthritis

PURPOSE OF THE REVIEW

Knee Osteoarthritis (KOA) entails progressive cartilage degradation, reviewed via MRI for morphology, biochemical composition, and microtissue alterations, discussing clinical advantages, limitations, and research applicability.

RECENT FINDINGS

Compositional MRI, like T2/T2* mapping, T1rho mapping, gagCEST, dGEMRIC, sodium imaging, diffusion-weighted imaging, and diffusion-tensor imaging, provide insights into cartilage injury in KOA. These methods quantitatively measure collagen, glycosaminoglycans, and water content, revealing important information about biochemical compositional and microstructural alterations. Innovative techniques like hybrid multi-dimensional MRI and diffusion-relaxation correlation spectrum imaging show potential in depicting initial cartilage changes at a sub-voxel level. Integration of automated image analysis tools addressed limitations in manual cartilage segmentation, ensuring robust and reproducible assessments of KOA cartilage. Compositional MRI techniques reveal microstructural changes in cartilage. Multi-dimensional MR imaging assesses biochemical alterations in KOA-afflicted cartilage, aiding early degeneration identification. Integrating artificial intelligence enhances cartilage analysis, optimal diagnostic accuracy for early KOA detection and monitoring.

Imaging of the Ankle Ligaments and Cartilage Injuries as an Aid to Ankle Preservation Surgery

Ankle sprains are among the most common musculoskeletal injuries and can lead to ankle ligament and cartilage injuries. Imaging plays an important role in differentiating ligament injuries from other causes of ankle pain such as fractures, osteochondral lesions or tendon injuries that helps guide further management. Chronic untreated ankle ligamentous and cartilage injuries can further progress to ankle osteoarthritis, hence the need for timely diagnosis and treatment. Surgical treatment is often required in patients not responding to conservative treatment and ranges from repair and reconstruction procedures for ligament injuries to arthroscopic debridement and repair procedures for cartilage injuries. Cartilage defects and deficiency may be augmented depending on the extent of cartilage loss and associated subchondral changes on MRI. Awareness of operative techniques utilized is essential to interpret imaging findings in postoperative settings.

Quantification of cartilage and subchondral bone cysts on knee specimens based on a spectral photon-counting computed tomography

Spectral photon-counting computed tomography (SPCCT) is a new technique with the capability to provide mono-energetic (monoE) images with high signal to noise ratio. We demonstrate the feasibility of SPCCT to characterize at the same time cartilage and subchondral bone cysts (SBCs) without contrast agent in osteoarthritis (OA). To achieve this goal, 10 human knee specimens (6 normal and 4 with OA) were imaged with a clinical prototype SPCCT. The monoE images at 60 keV with isotropic voxels of 250 × 250 × 250 µm3 were compared with monoE synchrotron radiation CT (SR micro-CT) images at 55 keV with isotropic voxels of 45 × 45 × 45 µm3 used as benchmark for cartilage segmentation. In the two OA knees with SBCs, the volume and density of SBCs were evaluated in SPCCT images. In 25 compartments (lateral tibial (LT), medial tibial, (MT), lateral femoral (LF), medial femoral and patella), the mean bias between SPCCT and SR micro-CT analyses were 101 ± 272 mm3 for cartilage volume and 0.33 mm ± 0.18 for mean cartilage thickness. Between normal and OA knees, mean cartilage thicknesses were found statistically different (0.005 < p < 0.04) for LT, MT and LF compartments. The 2 OA knees displayed different SBCs profiles in terms of volume, density, and distribution according to size and location. SPCCT with fast acquisitions is able to characterize cartilage morphology and SBCs. SPCCT can be used potentially as a new tool in clinical studies in OA.

7 T Musculoskeletal MRI: Fundamentals and Clinical Implementation

ABSTRACT

This review summarizes the current state-of-the-art of musculoskeletal 7 T magnetic resonance imaging (MRI), the associated technological challenges, and gives an overview of current and future clinical applications of 1 H-based 7 T MRI. The higher signal-to-noise ratio at 7 T is predominantly used for increased spatial resolution and thus the visualization of anatomical details or subtle lesions rather than to accelerate the sequences. For musculoskeletal MRI, turbo spin echo pulse sequences are particularly useful, but with altered relaxation times, B1 inhomogeneity, and increased artifacts at 7 T; specific absorption rate limitation issues quickly arise for turbo spin echo pulse sequences. The development of dedicated pulse sequence techniques in the last 2 decades and the increasing availability of specialized coils now facilitate several clinical musculoskeletal applications. 7 T MRI is performed in vivo in a wide range of applications for the knee joint and other anatomical areas, such as ultra-high-resolution nerve imaging or bone trabecular microarchitecture imaging. So far, however, it has not been shown systematically whether the higher field strength compared with the established 3 T MRI systems translates into clinical advantages, such as an early-stage identification of tissue damage allowing for preventive therapy or an influence on treatment decisions and patient outcome. At the moment, results tend to suggest that 7 T MRI will be reserved for answering specific, targeted musculoskeletal questions rather than for a broad application, as is the case for 3 T MRI. Future data regarding the implementation of clinical use cases are expected to clarify if 7 T musculoskeletal MRI applications with higher diagnostic accuracy result in patient benefits compared with MRI at lower field strengths.

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

ABSTRACT

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.