Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation

Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86

A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images

none6openFiorentino, Maria Chiara; Cipolletta, Edoardo; Filippucci, Emilio; Grassi, Walter; Frontoni, Emanuele; Moccia, SaraFiorentino, Maria Chiara; Cipolletta, Edoardo; Filippucci, Emilio; Grassi, Walter; Frontoni, Emanuele; Moccia, Sar

Imaging of Osteoarthritis

Osteoarthritis (OA) is the most prevalent joint disorder in the elderly, and there is no effective treatment. Imaging is essential for evaluating the synovial joint structures (including cartilage, meniscus, subchondral bone marrow and synovium) for diagnosis, prognosis, and follow-up. This article describes the roles and limitations of both conventional radiography and magnetic resonance (MR) imaging, and considers the use of other modalities (eg, ultrasonography, nuclear medicine, computed tomography [CT], and CT/MR arthrography) in clinical practice and OA research. The emphasis throughout is on OA of the knee. This article emphasizes research developments and literature evidence published since 2008

A new quantitative 3D approach to imaging of structural joint disease.

Imaging of joints with 2D radiography has not been able to detect therapeutic success in research trials while 3D imaging, used regularly in the clinic, has not been approved for this purpose. We present a new 3D approach to this challenge called joint space mapping (JSM) that measures joint space width in 3D from standard clinical computed tomography (CT) data, demonstrating its analysis steps, technical validation, and reproducibility. Using high resolution peripheral quantitative CT as gold standard, we show a marginal over-estimation in accuracy of +0.13 mm and precision of ±0.32 mm. Inter-operator reproducibility bias was near-zero at -0.03 mm with limits of agreement ±0.29 mm and a root mean square coefficient of variation 7.5%. In a technical advance, we present results from across the hip joint in 3D with optimum validation and reproducibility metrics shown at inner joint regions. We also show JSM versatility using different imaging data sets and discuss potential applications. This 3D mapping approach provides information with greater sensitivity than reported for current radiographic methods that could result in improved patient stratification and treatment monitoring

Three-dimensional Ultrasound Imaging For Quantifying Knee Cartilage Volume

Arthritis is the most common chronic health condition in Canada, with the most common form being osteoarthritis (OA). There is a great clinical need for an objective imaging-based point-of-care tool to assess OA status, progression, and response to treatment. This thesis aims to validate a handheld mechanical three-dimensional (3D) ultrasound (US) device against the current clinical standard of magnetic resonance imaging (MRI) for quantifying femoral articular cartilage (FAC) volume. Knee images of 25 healthy volunteers were acquired using 3D US and 3.0 Tesla MRI scans. Two raters manually segmented the trochlear FAC during separate sessions to assess intra- and inter-rater reliabilities. The results demonstrated that 3D US has excellent reliability and strong concurrent validity with MRI for measuring healthy FAC volume. 3D US is a promising, inexpensive, and widely accessible imaging modality that will enable clinicians and researchers to obtain additional information without added complexity or discomfort to patients

Image-based biomechanical models of the musculoskeletal system

Finite element modeling is a precious tool for the investigation of the biomechanics of the musculoskeletal system. A key element for the development of anatomically accurate, state-of-the art finite element models is medical imaging. Indeed, the workflow for the generation of a finite element model includes steps which require the availability of medical images of the subject of interest: segmentation, which is the assignment of each voxel of the images to a specific material such as bone and cartilage, allowing for a three-dimensional reconstruction of the anatomy; meshing, which is the creation of the computational mesh necessary for the approximation of the equations describing the physics of the problem; assignment of the material properties to the various parts of the model, which can be estimated for example from quantitative computed tomography for the bone tissue and with other techniques (elastography, T1rho, and T2 mapping from magnetic resonance imaging) for soft tissues. This paper presents a brief overview of the techniques used for image segmentation, meshing, and assessing the mechanical properties of biological tissues, with focus on finite element models of the musculoskeletal system. Both consolidated methods and recent advances such as those based on artificial intelligence are described

Accuracy of magnetic resonance imaging for measuring maturing cartilage: A phantom study

OBJECTIVES: To evaluate the accuracy of magnetic resonance imaging measurements of cartilage tissue-mimicking phantoms and to determine a combination of magnetic resonance imaging parameters to optimize accuracy while minimizing scan time. METHOD: Edge dimensions from 4 rectangular agar phantoms ranging from 10.5 to 14.5 mm in length and 1.25 to 5.5 mm in width were independently measured by two readers using a steel ruler. Coronal T1 spin echo (T1 SE), fast spoiled gradient-recalled echo (FSPGR) and multiplanar gradient-recalled echo (GRE MPGR) sequences were used to obtain phantom images on a 1.5-T scanner. RESULTS: Inter- and intra-reader reliability were high for both direct measurements and for magnetic resonance imaging measurements of phantoms. Statistically significant differences were noted between the mean direct measurements and the mean magnetic resonance imaging measurements for phantom 1 when using a GRE MPGR sequence (512x512 pixels, 1.5-mm slice thickness, 5:49 min scan time), while borderline differences were noted for T1 SE sequences with the following parameters: 320x320 pixels, 1.5-mm slice thickness, 6:11 min scan time; 320x320 pixels, 4-mm slice thickness, 6:11 min scan time; and 512x512 pixels, 1.5-mm slice thickness, 9:48 min scan time. Borderline differences were also noted when using a FSPGR sequence with 512x512 pixels, a 1.5-mm slice thickness and a 3:36 min scan time. CONCLUSIONS: FSPGR sequences, regardless of the magnetic resonance imaging parameter combination used, provided accurate measurements. The GRE MPGR sequence using 512x512 pixels, a 1.5-mm slice thickness and a 5:49 min scan time and, to a lesser degree, all tested T1 SE sequences produced suboptimal accuracy when measuring the widest phantom

Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

A Phase II Trial of Lutikizumab, an Anti–Interleukin‐1α/β Dual Variable Domain Immunoglobulin, in Knee Osteoarthritis Patients With Synovitis

Objective: To assess the efficacy and safety of the anti–interleukin‐1α/β (anti–IL‐1α/β) dual variable domain immunoglobulin lutikizumab (ABT‐981) in patients with knee osteoarthritis (OA) and evidence of synovitis. Methods: Patients (n = 350; 347 analyzed) with Kellgren/Lawrence grade 2–3 knee OA and synovitis (determined by magnetic resonance imaging [MRI] or ultrasound) were randomized to receive placebo or lutikizumab 25, 100, or 200 mg subcutaneously every 2 weeks for 50 weeks. The coprimary end points were change from baseline in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score at week 16 and change from baseline in MRI‐assessed synovitis at week 26. Results: The WOMAC pain score at week 16 had improved significantly versus placebo with lutikizumab 100 mg (P = 0.050) but not with the 25 mg or 200 mg doses. Beyond week 16, the WOMAC pain score was reduced in all groups but was not significantly different between lutikizumab‐treated and placebo‐treated patients. Changes from baseline in MRI‐assessed synovitis at week 26 and other key symptom‐ and most structure‐related end points at weeks 26 and 52 were not significantly different between the lutikizumab and placebo groups. Injection site reactions, neutropenia, and discontinuations due to neutropenia were more frequent with lutikizumab versus placebo. Reductions in neutrophil and high‐sensitivity C‐reactive protein levels plateaued with lutikizumab 100 mg, with further reductions not observed with the 200 mg dose. Immunogenic response to lutikizumab did not meaningfully affect systemic lutikizumab concentrations. Conclusion: The limited improvement in the WOMAC pain score and the lack of synovitis improvement with lutikizumab, together with published results from trials of other IL‐1 inhibitors, suggest that IL‐1 inhibition is not an effective analgesic/antiinflammatory therapy in most patients with knee OA and associated synovitis