Antti Joukainen

Purpose: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data. Methods: Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images. Results: The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available. Conclusion: Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.

Objectives: Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size. Hence, they do not allow clinical quantification of bone microstructure, which plays an important role in osteoarthritis, osteoporosis and fracture risk. For maxillofacial imaging, changes in small mineralized structures are important for dental, periodontal and ossicular chain diagnostics as well as treatment planning. Deep learning (DL)-based super-resolution (SR) models could allow for better evaluation of these microstructural details. In this study, we demonstrate a widely applicable method for increasing the spatial resolution of clinical CT images using DL, which only requires training on a limited set of data that are easy to acquire in a laboratory setting from e.g. cadaver knees. Our models are assessed rigorously for technical image quality, ability to predict bone microstructure, as well as clinical image quality of the knee, wrist, ankle and dentomaxillofacial region. Materials and methods: Knee tissue blocks from five cadavers and six total knee replacement patients as well as 14 extracted teeth from eight patients were scanned using micro-computed tomography. The images were used as training data for the developed DL-based SR technique, inspired by previous studies on single-image SR. The technique was benchmarked with an ex vivo test set, consisting of 52 small osteochondral samples imaged with clinical and laboratory CT scanners, to quantify bone morphometric parameters. A commercially available quality assurance phantom was imaged with a clinical CT device, and the technical image quality was quantified with a modulation transfer function. To visually assess the clinical image quality, CBCT studies from wrist, knee, ankle, and maxillofacial region were enhanced with SR and contrasted to interpolated images. A dental radiologist and dental surgeon reviewed maxillofacial CBCT studies of nine patients and corresponding SR predictions. Results: The SR models yielded a higher Pearson correlation to bone morphological parameters on the ex vivo test set compared to the use of a conventional image processing pipeline. The phantom analysis confirmed a higher spatial resolution on the images enhanced by the SR approach. A statistically significant increase of spatial resolution was seen in the third, fourth, and fifth line pair patterns. However, the predicted grayscale values of line pair patterns exceeded those of uniform areas. Musculoskeletal CBCT images showed more details on SR predictions compared to interpolation. Averaging predictions on orthogonal planes improved visual quality on perpendicular planes but could smear the details for morphometric analysis. SR in dental imaging allowed to visualize smaller mineralized structures in the maxillofacial region, however, some artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores in all categories for both CBCT and SR. Although not statistically significant, the dental radiologist slightly preferred the original CBCT images. The dental surgeon scored one of the SR models slightly higher compared to CBCT. The interrater variability κ was mostly low to fair. The source code ( https://doi.org/10.5281/zenodo.8041943 ) and pretrained SR networks ( https://doi.org/10.17632/4xvx4p9tzv.1 ) are publicly available. Conclusions: Utilizing experimental laboratory imaging modalities in model training could allow pushing the spatial resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT imaging. Implications of SR include higher patient throughput, more precise diagnostics, and disease interventions at an earlier state. However, the grayscale distribution of the images is modified, and the predictions are limited to depicting the mineralized structures rather than estimating density or tissue composition. Finally, while the musculoskeletal images showed promising results, a larger maxillofacial dataset would be recommended for training SR models in dental applications.

Objective Rate of shoulder instability surgery (SIS) has increased in different specific populations. We analysed nationwide trend of SIS in adults in Finland between 1997 and 2014. Design A retrospective register study. Setting National Hospital Discharge Register of Finland. Participants A total of 22 550 adult patients with SIS in Finland (1997–2014). Primary and secondary outcome measures Analysis included appropriate diagnosis (International Classification of Diseases 10) and procedure coding combinations applicable for SIS. The primary outcome variable was the incidence of SIS per 100 000 person-years, and the secondary outcomes were the study year, sex, age groups (18–29, 30–49 and over 50 years of age) and the type of hospital (public or private). Results The overall nationwide rate of SIS in adults increased 177% between 1997 and 2014 in Finland. The rate was the lowest (13/100 000 person-years) in 1997, and the peak rate (40/100 000 person-years) was noted in 2007. The increase in rate was rapid between 1997 and 2007, after which the rate became stable. During the study period, the highest increases were noted in the young adults age group (270%), and especially in the middle-aged group who were operated on in private hospitals (930%). Conclusions The rate of SIS increased almost threefold in Finland from 1997 to 2014. The increase was most significant in young and middle-aged adults (18–50 years), in men, and in private hospitals.

The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R² change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

Dual contrast micro computed tomography (CT) shows potential for detecting articular cartilage degeneration. However, the performance of conventional CT systems is limited by beam hardening, low image resolution (full‐body CT), and long acquisition times (conventional microCT). Therefore, to reveal the full potential of the dual contrast technique for imaging cartilage composition we employ the technique using synchrotron microCT. We hypothesize that the above‐mentioned limitations are overcome with synchrotron microCT utilizing monochromatic X‐ray beam and fast image acquisition. Human osteochondral samples (n = 41, four cadavers) were immersed in a contrast agent solution containing two agents (cationic CA4+ and non‐ionic gadoteridol) and imaged with synchrotron microCT at an early diffusion time point (2 h) and at diffusion equilibrium (72 h) using two monochromatic X‐ray energies (32 and 34 keV). The dual contrast technique enabled simultaneous determination of CA4+ (i.e., proteoglycan content) and gadoteridol (i.e., water content) partitions within cartilage. Cartilage proteoglycan content and biomechanical properties correlated significantly (0.327 < r < 0.736, p < 0.05) with CA4+ partition in superficial and middle zones at both diffusion time points. Normalization of the CA4+ partition with gadoteridol partition within the cartilage significantly (p < 0.05) improved the detection sensitivity for human osteoarthritic cartilage proteoglycan content, biomechanical properties, and overall condition (Mankin, Osteoarthritis Research Society International, and International Cartilage Repair Society grading systems). The dual energy technique combined with the dual contrast agent enables assessment of human articular cartilage proteoglycan content and biomechanical properties based on CA4+ partition determined using synchrotron microCT. Additionally, the dual contrast technique is not limited by the beam hardening artifact of conventional CT systems. © 2019 The Authors. Journal of Orthopaedic Research® published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 38:563–573, 2020

Objective: To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT). Design: Osteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, Ø = 2 mm; n = 45, Ø = 4 mm) were imaged using CEμCT with phosphotungstic acid-staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depthwise and subjected to dimensionally reduced Local Binary Pattern-textural feature analysis. Regularized Ridge and Logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (Ø = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (Ø = 4 mm samples). The performance was assessed using Spearman’s correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC). Results: Highest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearman’s correlations on all zones. For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71→0.62, AUC = 0.77→0.63) and increased in DZ (AP = 0.50→0.83, AUC = 0.72→0.72). Conclusion: We showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CEμCT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.

Infrapatellar fat pad (IFP) has recently emerged as a potential source of inflammation in knee arthropathies. It has been proposed to be one source of adipocytokines, fatty acids (FA), and FA-derived lipid mediators that could contribute to the pathophysiological processes in the knee joint. Alterations in synovial fluid (SF) lipid composition have been linked to both osteoarthritis (OA) and rheumatoid arthritis (RA). The aim of the present study was to compare the FA signatures in the IFP and SF of RA and OA patients. Pairs of IFP and SF samples were collected from the same knees of RA (n = 10) and OA patients (n = 10) undergoing total joint replacement surgery. Control SF samples (n = 6) were harvested during diagnostic or therapeutic arthroscopic knee surgery unrelated to RA or OA. The FA composition in the total lipids of IFP and SF was determined by gas chromatography with flame ionization and mass spectrometric detection. Arthropathies resulted in a significant reduction in the SF proportions of n-6 polyunsaturated FA (PUFA), more pronouncedly in OA than in RA. OA was also characterized with reduced percentages of 22:6n-3 and lower product/precursor ratios of n-3 PUFA. The proportions of total monounsaturated FA increased in both RA and OA SF. Regarding IFP, RA patients had lower proportions of 20:4n-6, total n-6 PUFA, and 22:6n-3, as well as lower product/precursor ratios of n-3 PUFA compared to OA patients. The average chain length of SF FA decreased in both diagnoses and the double bond index in OA. The observed complex alterations in the FA signatures could have both contributed to but also limited the inflammatory processes and cartilage destruction in the RA and OA knees.