Regular Running Is Related to the Knee Joint Cartilage Structure in Healthy Adults

PURPOSE

The purpose of this study was to determine whether regular running distance and biomechanics are related to medial central femur cartilage (MCFC) structure.

METHODS

The cross-sectional study sample consisted of 1164 runners and nonrunners aged 18-65 yr. Participants completed questionnaires on physical activity and their running history. We performed quantitative magnetic resonance imaging of knee cartilage-T2 relaxation time (T2) mapping (high T2 indicates cartilage degeneration)-and a running biomechanical analysis using a three-dimensional motion capture system. A 14-d monitoring of the physical activity was conducted.

RESULTS

Those aged 35-49 yr were at 84% higher odds of having MCFC T2 in the highest level (85th percentile, P < 0.05) compared with youngest adults indicating that MCFC structures may be altered with aging. Being male was associated with 34% lower odds of having T2 at the highest level ( P < 0.05) compared with females. Nonrunners and runners with the highest weekly running distance were more likely to have a high T2 compared with runners with running distance of 6-20 km·wk -1 ( P < 0.05). In addition, the maximal knee internal adduction moment was associated with a 19% lower odds of having T2 at the highest level ( P < 0.05).

CONCLUSIONS

Females compared with males and a middle-aged cohort compared with the younger cohort seemed to be associated with the degeneration of MCFC structures. Runners who ran 6-20 km·wk -1 were associated with a higher quality of their MCFC compared with highly active individuals and nonrunners. Knee frontal plane biomechanics was related to MCFC structure indicating a possibility of modifying the medial knee collagen fibril network through regular running.

Real-Time Tissue Classification Using a Novel Optical Needle Probe for Biopsy

Core needle biopsy is a part of the histopathological process, which is required for cancerous tissue examination. The most common method to guide the needle inside of the body is ultrasound screening, which in greater part is also the only guidance method. Ultrasound screening requires user experience. Furthermore, patient involuntary movements such as breathing might introduce artifacts and blur the screen. Optically enhanced core needle biopsy probe could potentially aid interventional radiologists during this procedure, providing real-time information on tissue properties close to the needle tip, while it is advancing inside of the body. In this study, we used diffuse optical spectroscopy in a custom-made core needle probe for real-time tissue classification. Our aim was to provide initial characteristics of the smart needle probe in the differentiation of tissues and validate the basic purpose of the probe of informing about breaking into a desired organ. We collected optical spectra from rat blood, fat, heart, kidney, liver, lungs, and muscle tissues. Gathered data were analyzed for feature extraction and evaluation of two machine learning-based classifiers: support vector machine and k-nearest neighbors. Their performances on training data were compared using subject-independent k-fold cross-validation. The best classifier model was chosen and its feasibility for real-time automated tissue recognition and classification was then evaluated. The final model reached nearly 80% of correct real-time classification of rat organs when using the needle probe during real-time classification.

Effects of Bariatric Surgery on Knee Articular Cartilage and Osteoarthritis Symptoms-A 12-Month Follow-Up Using T2 Relaxation Time and WOMAC Osteoarthritis Index

BACKGROUND

Obesity is a significant risk factor for osteoarthritis (OA). The most effective treatment for morbid obesity is bariatric surgery.

PURPOSE

To study the effects of potential surgically induced weight loss on knee articular cartilage and OA symptoms of obese patients over a 12-month follow-up.

STUDY TYPE

Prospective longitudinal cohort study.

SUBJECTS

45 obese patients (38 female, BMI = 42.3 ± 6.5 kg/m2) who underwent gastric bypass (intervention group), and 46 age-matched conservative-care controls (37 female, BMI = 39.8 ± 4.6 kg/m2).

FIELD STRENGTH/SEQUENCE

Multiecho spin echo sequence at 3 T.

ASSESSMENT

Knee cartilage T2 measurements and WOMAC Indices were measured presurgery and after 12 months. The intervention group was split into successful (≥20% total weight loss (TWL)) and unsuccessful (<20% TWL) weight loss groups. T2 and WOMAC indices were also measured in controls at baseline and after 12 months. Changes among the three groups were analyzed.

STATISTICAL TESTS

Analysis of variance (significance level 0.05).

RESULTS

Twenty-six (58%) intervention patients achieved ≥20% TWL. The <20% TWL group demonstrated significantly more T2 reduction in the deep lateral femur over 12 months compared with the ≥20% TWL group (-3.83 ± 8.18 msec vs. 2.47 ± 6.54 msec, respectively), whereas no significant differences were observed on the medial femoral compartment (P = 0.385, P = 0.551, and P = 0.511 for bulk, superficial and deep regions, respectively). Changes in WOMAC indices over 12 months were significantly greater in the ≥20% TWL group compared with controls. In the <20% TWL group, pain significantly improved over 12 months compared with controls, while stiffness and function changes were not statistically significant (P = 0.063 and P = 0.051, respectively).

DATA CONCLUSION

Cartilage matrix, measured by T2, showed improvement on lateral femoral cartilage with <20% TWL compared with ≥20% TWL. Bariatric surgery provided significant improvements in knee symptoms with ≥20% TWL compared with conservative WL. This effect is also seen to some extent with <20% TWL compared with conservative WL.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 4.

Anisotropy of T2 and T1ρ relaxation time in articular cartilage at 3 T

PURPOSE

The anisotropy of R2 and R1ρ relaxation rates in articular cartilage contains information about the collagenous structure of the tissue. Here we determine and study the anisotropic and isotropic components of T2 and T1ρ relaxation parameters in articular cartilage with a clinical 3T MRI device. Furthermore, a visual representation of the topographical variation in anisotropy is given via anisotropy mapping.

METHODS

Eight bovine stifle joints were imaged at 22 orientations with respect to the main magnetic field using T2, continuous-wave (CW) T1ρ, and adiabatic T1ρ mapping sequences. Relaxation rates were separated into isotropic and anisotropic relaxation components using a previously established relaxation anisotropy model. Pixel-wise anisotropy values were determined from the relaxation-time maps using Michelson contrast.

RESULTS

The relaxation rates obtained from the samples displayed notable variation depending on the sample orientation, magnetization preparation, and cartilage layer. R2 demonstrated significant anisotropy, whereas CW-R1ρ (300 Hz) and CW-R1ρ (500 Hz) displayed a low degree of anisotropy. Adiabatic R1ρ was largely isotropic. In the deep cartilage regions, relaxation rates were generally faster and more anisotropic than in the cartilage closer to the tissue surface. The isotropic relaxation rate components were found to have similar values regardless of measurement sequence.

CONCLUSIONS

The fitted relaxation model for T2 and T1ρ demonstrated varying amounts anisotropy, depending on magnetization preparation, and studied the articular cartilage layer. Anisotropy mapping of full joints showed varying amounts of anisotropy depending on the quantitative MRI parameter and topographical location, and in the case of T2, showed systematic changes in anisotropy across cartilage depth.

If you can make it, you can share it - Perspectives on the first DIY-fair at the European congress of medical physics (ECMP, DUBLIN 2022)

The medical physics and engineering community is known for being active in conjuring do-it-yourself (DIY) -solutions to support their clinical and research work. To facilitate the exchange of solutions and ideas, a DIY-fair was held for the first time at the European Congress of Medical Physics (ECMP) in August 2022 in Dublin, Ireland. Altogether 32 contributions were presented, consisting of software, scripts, 3D-printed customized solutions, devices, gadgets and phantoms. All contributions were published in video format on a dedicated YouTube channel, and most were also presented in person at the conference. The fair demonstrated that there is an unmet need for sharing and distributing information on self-created solutions in the medical physics community. The authors propose the creation of a dedicated platform for sharing such content within our community, as well as a continuity of DIY-fairs at future ECMP meetings.

Melting of aqueous NaCl solutions in porous materials: shifted phase transition distribution (SIDI) approach for determining NMR cryoporometry pore size distributions

Nuclear magnetic resonance cryoporometry (NMRC) and differential scanning calorimetry thermoporometry (DSC-TPM) are powerful methods for measuring mesopore size distributions. The methods are based on the fact that, according to the Gibbs-Thomson equation, the melting point depression of a liquid confined to a pore is inversely proportional to the pore size. However, aqueous salt solutions, which inherently exist in a broad range of biological porous materials as well as technological applications such as electrolytes, do not melt at a single temperature. This causes artefacts in the pore size distributions extracted by traditional Gibbs-Thomson analysis of NMRC and DSC-TPM data. Bulk aqueous NaCl solutions are known to have a broad distribution of melting points between the eutectic and pure water phase transition points (252-273 K). Here, we hypothesize that, when aqueous NaCl solution (saline) is confined to a small pore, the whole melting point distribution is shifted toward lower temperatures by the value predicted by the Gibbs-Thomson equation. We show that this so-called shifted phase transition distribution (SIDI) approach removes the artefacts arising from the traditional Gibbs-Thomson analysis and gives correct pore size distributions for saline saturated mesoporous silica gel and controlled pore materials analyzed by NMR cryoporometry. Furthermore, we demonstrate that the method can be used for determining pore sizes in collagen-chondroitin sulphate hydrogels resembling the composition of the extracellular matrix of articular cartilage. It is straightforward to apply the SIDI analysis for DSC-TMP data as well.

Deep learning enables time-efficient soft tissue enhancement in CBCT: Proof-of-concept study for dentomaxillofacial applications

PURPOSE

The use of iterative and deep learning reconstruction methods, which would allow effective noise reduction, is limited in cone-beam computed tomography (CBCT). As a consequence, the visibility of soft tissues is limited with CBCT. The study aimed to improve this issue through time-efficient deep learning enhancement (DLE) methods.

METHODS

Two DLE networks, UNIT and U-Net, were trained with simulated CBCT data. The performance of the networks was tested with three different test data sets. The quantitative evaluation measured the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR) of the DLE reconstructions with respect to the ground truth iterative reconstruction method. In the second assessment, a dentomaxillofacial radiologist assessed the resolution of hard tissue structures, visibility of soft tissues, and overall image quality of real patient data using the Likert scale. Finally, the technical image quality was determined using modulation transfer function, noise power spectrum, and noise magnitude analyses.

RESULTS

The study demonstrated that deep learning CBCT denoising is feasible and time efficient. The DLE methods, trained with simulated CBCT data, generalized well, and DLE provided quantitatively (SSIM/PSNR) and visually similar noise-reduction as conventional IR, but with faster processing time. The DLE methods improved soft tissue visibility compared to the conventional Feldkamp-Davis-Kress (FDK) algorithm through noise reduction. However, in hard tissue quantification tasks, the radiologist preferred the FDK over the DLE methods.

CONCLUSION

Post-reconstruction DLE allowed feasible reconstruction times while yielding improvements in soft tissue visibility in each dataset.

Assessment of articular cartilage of ankle joint in stable and unstable unilateral weber type-B/SER-type ankle fractures shortly after trauma using T2 relaxation time

Background

Early detection of post-traumatic cartilage damage in the ankle joint in magnetic resonance images can be difficult due to disturbances to structures usually appearing over time.

Purpose

To study the articular cartilage of unilateral Weber type-B/SER-type ankle fractures shortly post-trauma using T2 relaxation time.

Material and Methods

Fifty one fractured ankles were gathered from consecutively screened patients, compiled initially for RCT studies, and treated at Oulu University Hospital and classified as stable (n = 28) and unstable fractures (n = 23) based on external-rotation stress test: medial clear space of ≥5 mm was interpreted as unstable. A control group of healthy young individuals (n = 19) was also gathered. All ankles were imaged on average 9 (range: 1 to 25) days after injury on a 3.0T MRI unit for T2 relaxation time assessment, and the cartilage was divided into sub-regions for comparison.

Results

Control group displayed significantly higher T2 values in tibial cartilage compared to stable (six out of nine regions, p-values = .003-.043) and unstable (six out of nine regions, p-values = .001-.037) ankle fractures. No differences were detected in talar cartilage. Also, no differences were observed between stable and unstable fractures in tibial or talar cartilage.

Conclusions

Lower T2 relaxation times of tibial cartilage in fractured ankles suggest intact extra cellular matrix (ECM) of the cartilage. Severity of the ankle fracture, measured by ankle stability, does not seem to increase ECM degradation immediately after trauma.

Independent evaluation of a multi-view multi-task convolutional neural network breast cancer classification model using Finnish mammography screening data

BACKGROUND

Development of deep convolutional neural networks for breast cancer classification has taken significant steps towards clinical adoption. It is though unclear how the models perform for unseen data, and what is required to adapt them to different demographic populations. In this retrospective study, we adopt an openly available pre-trained mammography breast cancer multi-view classification model and evaluate it by utilizing an independent Finnish dataset.

METHODS

Transfer learning was used, and the pre-trained model was finetuned with 8,829 examinations from the Finnish dataset (4,321 normal, 362 malignant and 4,146 benign examinations). Holdout dataset with 2,208 examinations from the Finnish dataset (1,082 normal, 70 malignant and 1,056 benign examinations) was used in the evaluation. The performance was also evaluated on a manually annotated malignant suspect subset. Receiver Operating Characteristic (ROC) and Precision-Recall curves were used to performance measures.

RESULTS

The Area Under ROC [95%CI] values for malignancy classification obtained with the finetuned model for the entire holdout set were 0.82 [0.76, 0.87], 0.84 [0.77, 0.89], 0.85 [0.79, 0.90], and 0.83 [0.76, 0.89] for R-MLO, L-MLO, R-CC and L-CC views respectively. Performance on the malignant suspect subset was slightly better. On the auxiliary benign classification task performance remained low.

CONCLUSIONS

The results indicate that the model performs well also in an out-of-distribution setting. Finetuning allowed the model to adapt to some of the underlying local demographics. Future research should concentrate to identify breast cancer subgroups adversely affecting performance, as it is a requirement for increasing the model's readiness level for a clinical setting.

SPICY: a method for single scan rotating frame relaxometry

T _1ρ is an NMR relaxation mode that is sensitive to low frequency molecular motions, making it an especially valuable tool in biomolecular research. Here, we introduce a new method, SPICY, for measuring T_1ρ relaxation times. In contrast to conventional T_1ρ experiments, in which the sequence is repeated many times to determine the T_1ρ time, the SPICY sequence allows determination of T_1ρ within a single scan, shortening the experiment time remarkably. We demonstrate the method using ¹H T_1ρ relaxation dispersion experiments. Additionally, we combine the sequence with spatial encoding to produce 1D images in a single scan. We show that T_1ρ relaxation times obtained using the single scan approach are in good agreement with those obtained using the traditional experiments.

Effects of a 360° virtual counselling environment on patient anxiety and CCTA process time: A randomised controlled trial

INTRODUCTION

This study investigated whether a 360° virtual counselling environment (360°VCE) was more effective at decreasing patients' anxiety than routine standard of care counselling for patients undergoing coronary computed tomography angiography (CCTA), and if there was any difference in the process times for both of these groups.

METHODS

A total of 86 patients underwent CCTA in this randomised controlled trial. Patients were randomly assigned to intervention and control groups. The 360°VCE was developed using spherical panoramic images and non-immersive 360° technology. The primary outcome, anxiety, was measured using the State-Trait Anxiety Inventory (STAI). The secondary outcome, CCTA process time, was measured from the time of arrival in the department until end of examination.

RESULTS

Pre-scan anxiety was lower among patients in the 360°VCE group immediately before CCTA in comparison to patients in the control group (p = 0.015). Women demonstrated higher levels of anxiety than men in both groups. No between-group differences were discerned in CCTA process time.

CONCLUSION

Access to 360°VCE can reduce patients' pre-CCTA anxiety levels.

IMPLICATIONS FOR PRACTICE

The presented results can be used to improve patient counselling and care, reduce anxiety among patients undergoing CCTA, and optimise the CCTA examination procedure.

Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study

Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial.

Machine Learning Prediction of Collagen Fiber Orientation and Proteoglycan Content From Multiparametric Quantitative MRI in Articular Cartilage

BACKGROUND

Machine learning models trained with multiparametric quantitative MRIs (qMRIs) have the potential to provide valuable information about the structural composition of articular cartilage.

PURPOSE

To study the performance and feasibility of machine learning models combined with qMRIs for noninvasive assessment of collagen fiber orientation and proteoglycan content.

STUDY TYPE

Retrospective, animal model.

ANIMAL MODEL

An open-source single slice MRI dataset obtained from 20 samples of 10 Shetland ponies (seven with surgically induced cartilage lesions followed by treatment and three healthy controls) yielded to 1600 data points, including 10% for test and 90% for train validation.

FIELD STRENGTH/SEQUENCE

A 9.4 T MRI scanner/qMRI sequences: T₁ , T₂ , adiabatic T_1ρ and T_2ρ , continuous-wave T_1ρ and relaxation along a fictitious field (T_RAFF ) maps.

ASSESSMENT

Five machine learning regression models were developed: random forest (RF), support vector regression (SVR), gradient boosting (GB), multilayer perceptron (MLP), and Gaussian process regression (GPR). A nested cross-validation was used for performance evaluation. For reference, proteoglycan content and collagen fiber orientation were determined by quantitative histology from digital densitometry (DD) and polarized light microscopy (PLM), respectively.

STATISTICAL TESTS

Normality was tested using Shapiro-Wilk test, and association between predicted and measured values was evaluated using Spearman's Rho test. A P-value of 0.05 was considered as the limit of statistical significance.

RESULTS

Four out of the five models (RF, GB, MLP, and GPR) yielded high accuracy (R²  = 0.68-0.75 for PLM and 0.62-0.66 for DD), and strong significant correlations between the reference measurements and predicted cartilage matrix properties (Spearman's Rho = 0.72-0.88 for PLM and 0.61-0.83 for DD). GPR algorithm had the highest accuracy (R²  = 0.75 and 0.66) and lowest prediction-error (root mean squared [RMSE] = 1.34 and 2.55) for PLM and DD, respectively.

DATA CONCLUSION

Multiparametric qMRIs in combination with regression models can determine cartilage compositional and structural features, with higher accuracy for collagen fiber orientation than proteoglycan content.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Assessing post-traumatic changes in cartilage using T1ρ dispersion parameters

Degeneration of cartilage can be studied non-invasively with quantitative MRI. A promising parameter for detecting early osteoarthritis in articular cartilage is T_1ρ, which can be tuned via the amplitude of the spin-lock pulse. By measuring T_1ρ at several spin-lock amplitudes, the dispersion of T_1ρ is obtained. The aim of this study is to find out if the dispersion contains diagnostically relevant information complementary to a T_1ρ measurement at a single spin-lock amplitude. To this end, five differently acquired dispersion parameters are utilized; A, B, τ_c, T_1ρ/T₂, and R₂ - R_1ρ. An open dataset of an equine model of post-traumatic cartilage was utilized to assess the T_1ρ dispersion parameters for the evaluation of cartilage degeneration. Firstly, the parameters were compared for their sensitivity in detecting degenerative changes. Secondly, the relationship of the dispersion parameters to histological and biomechanical reference parameters was studied. Parameters A, T_1ρ/T₂, and R₂ - R_1ρ were found to be sensitive to lesion-induced changes in the cartilage within sample. Strong correlations of several dispersion parameters with optical density, as well as with collagen fibril angle were found. Most of the dispersion parameters correlated strongly with individual T_1ρ values. The results suggest that dispersion parameters can in some cases provide a more accurate description of the biochemical composition of cartilage as compared to conventional MRI parameters. However, in most cases the information given by the dispersion parameters is more of a refinement than complementary to conventional quantitative MRI.

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images

OBJECTIVES

The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth.

DESIGN

The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC).

RESULTS

Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57-0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52-0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables.

CONCLUSION

The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint

BACKGROUND

Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time improvement. Contrast synthesis from MRF could significantly decrease the imaging time.

PURPOSE

To improve clinical utility of MRF by synthesizing contrast-weighted MR images from the quantitative data provided by MRF, using U-nets that were trained for the synthesis task utilizing L1- and perceptual loss functions, and their combinations.

STUDY TYPE

Retrospective.

POPULATION

Knee joint MRI data from 184 subjects from Northern Finland 1986 Birth Cohort (ages 33-35, gender distribution not available).

FIELD STRENGTH AND SEQUENCE

A 3 T, multislice-MRF, proton density (PD)-weighted 3D-SPACE (sampling perfection with application optimized contrasts using different flip angle evolution), fat-saturated T2-weighted 3D-space, water-excited double echo steady state (DESS).

ASSESSMENT

Data were divided into training, validation, test, and radiologist's assessment sets in the following way: 136 subjects to training, 3 for validation, 3 for testing, and 42 for radiologist's assessment. The synthetic and target images were evaluated using 5-point Likert scale by two musculoskeletal radiologists blinded and with quantitative error metrics.

STATISTICAL TESTS

Friedman's test accompanied with post hoc Wilcoxon signed-rank test and intraclass correlation coefficient. The statistical cutoff P <0.05 adjusted by Bonferroni correction as necessary was utilized.

RESULTS

The networks trained in the study could synthesize conventional images with high image quality (Likert scores 3-4 on a 5-point scale). Qualitatively, the best synthetic images were produced with combination of L1- and perceptual loss functions and perceptual loss alone, while L1-loss alone led to significantly poorer image quality (Likert scores below 3). The interreader and intrareader agreement were high (0.80 and 0.92, respectively) and significant. However, quantitative image quality metrics indicated best performance for the pure L1-loss.

DATA CONCLUSION

Synthesizing high-quality contrast-weighted images from MRF data using deep learning is feasible. However, more studies are needed to validate the diagnostic accuracy of these synthetic images.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 1.

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative

In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.

Deep learning-based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative

Morphological changes in knee cartilage subregions are valuable imaging-based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double-echo steady-state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0-/12-/24-month visits. Our method performed deep learning-based segmentation of knee cartilage tissues, their subregional division via multi-atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm³ ) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845-0.973 and mean differences = 262-501 mm³ for weight-bearing cartilage volume, and r = 0.770-0.962 and mean differences = 0.513-1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12- and 24-month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage-related imaging biomarkers.

Dipolar Relaxation of Water Protons in the Vicinity of a Collagen-like Peptide

Quantitative magnetic resonance imaging is one of the few available methods for noninvasive diagnosis of degenerative changes in articular cartilage. The clinical use of the imaging data is limited by the lack of a clear association between structural changes at the molecular level and the measured magnetic relaxation times. In anisotropic, collagen-containing tissues, such as articular cartilage, the orientation dependency of nuclear magnetic relaxation can obscure the content of the images. Conversely, if the molecular origin of the phenomenon would be better understood, it would provide opportunities for diagnostics as well as treatment planning of degenerative changes in these tissues. We study the magnitude and orientation dependence of the nuclear magnetic relaxation due to dipole–dipole coupling of water protons in anisotropic, collagenous structures. The water–collagen interactions are modeled with molecular dynamics simulations of a small collagen-like peptide dissolved in water. We find that in the vicinity of the collagen-like peptide, the dipolar relaxation of water hydrogen nuclei is anisotropic, which can result in orientation-dependent relaxation times if the water remains close to the peptide. However, the orientation-dependency of the relaxation is different from the commonly observed magic-angle phenomenon in articular cartilage MRI.

Three Professional Singers' Vocal Tract Dimensions in Operatic Singing, Kulning, and Edge-A Multiple Case Study Examining Loud Singing

OBJECTIVE

A comprehensive understanding of how vocal tract dimensions vary among different types of loud voice productions has not yet been fully formed. This study aims to expand the existing knowledge on the topic.

METHODS

Three trained professional singers together practiced the vocal techniques underlying Opera and Kulning singing styles for one hour and, afterwards, phonated using these techniques on vowel [iː] at pitch C5 (523 Hz), while their vocal tracts were scanned via MRI. One of the participants also produced the samples in the Edge vocal mode using [ɛː]. Several dimensional vocal tract measurements were calculated from the MRIs. Spectral analysis was conducted on the filtered audio recorded during the MRI.

RESULTS

The Operatic technique demonstrated a lower larynx, a larger tongue-palate distance, and larger epilaryngeal and pharyngeal tube diameters compared to Kulning. Edge showed the highest laryngeal position, narrowest pharynx and epilarynx tubes, and the least forward-tilted larynx out of the styles studied. The spectra of Opera and Kulning showed a dominant first harmonic, while in Edge, the second harmonic was the strongest.

CONCLUSIONS

The results shed light on the magnitude of vocal tract changes necessary for genre-typical vocal projection. This information can be pedagogically helpful.

Effectiveness of Digital Counseling Environments on Anxiety, Depression, and Adherence to Treatment Among Patients Who Are Chronically Ill: Systematic Review

Background

Patients who are chronically ill need novel patient counseling methods to support their self-care at different stages of the disease. At present, knowledge of how effective digital counseling is at managing patients’ anxiety, depression, and adherence to treatment seems to be fragmented, and the development of digital counseling will require a more comprehensive view of this subset of interventions.

Objective

This study aims to identify and synthesize the best available evidence on the effectiveness of digital counseling environments at improving anxiety, depression, and adherence to treatment among patients who are chronically ill.

Methods

Systematic searches of the EBSCO (CINAHL), PubMed, Scopus, and Web of Science databases were conducted in May 2019 and complemented in October 2020. The review considered studies that included adult patients aged ≥18 years with chronic diseases; interventions evaluating digital (mobile, web-based, and ubiquitous) counseling interventions; and anxiety, depression, and adherence to treatment, including clinical indicators related to adherence to treatment, as outcomes. Methodological quality was assessed using the standardized Joanna Briggs Institute critical appraisal tool for randomized controlled trials or quasi-experimental studies. As a meta-analysis could not be conducted because of considerable heterogeneity in the reported outcomes, narrative synthesis was used to synthesize the results.

Results

Of the 2056 records screened, 20 (0.97%) randomized controlled trials, 4 (0.19%) pilot randomized controlled trials, and 2 (0.09%) quasi-experimental studies were included. Among the 26 included studies, 10 (38%) digital, web-based interventions yielded significantly positive effects on anxiety, depression, adherence to treatment, and the clinical indicators related to adherence to treatment, and another 18 (69%) studies reported positive, albeit statistically nonsignificant, changes among patients who were chronically ill. The results indicate that an effective digital counseling environment comprises high-quality educational materials that are enriched with multimedia elements and activities that engage the participant in self-care. Because of the methodological heterogeneity of the included studies, it is impossible to determine which type of digital intervention is the most effective for managing anxiety, depression, and adherence to treatment.

Conclusions

This study provides compelling evidence that digital, web-based counseling environments for patients who are chronically ill are more effective than, or at least comparable to, standard counseling methods; this suggests that digital environments could complement standard counseling.

Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis

OBJECTIVE

To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.

DESIGN

We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n  = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Hand-crafted features, based on Local Binary Patterns (LBP), were then extracted to describe the patellar texture. First, a machine learning model (Gradient Boosting Machine) was trained to detect radiographic PFOA from the LBP features. Furthermore, we used end-to-end trained deep convolutional neural networks (CNNs) directly on the texture patches for detecting the PFOA. The proposed classification models were eventually compared with more conventional reference models that use clinical assessments and participant characteristics such as age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of prediction models was assessed using the area under the receiver operating characteristic curve (ROC AUC), the area under the precision-recall (PR) curve -average precision (AP)-, and Brier score in the stratified 5-fold cross validation setting.

RESULTS

Of the 5507 knees, 953 (17.3%) had PFOA. AUC and AP for the strongest reference model including age, sex, BMI, WOMAC score, and tibiofemoral KL grade to predict PFOA were 0.817 and 0.487, respectively. Textural ROI classification using CNN significantly improved the prediction performance (ROC AUC = 0.889, AP = 0.714).

CONCLUSION

We present the first study that analyses patellar bone texture for diagnosing PFOA. Our results demonstrates the potential of using texture features of patella to predict PFOA.

T2 -weighted magnetic resonance imaging texture as predictor of low back pain: A texture analysis-based classification pipeline to symptomatic and asymptomatic cases

Low back pain is a very common symptom and the leading cause of disability throughout the world. Several degenerative imaging findings seen on magnetic resonance imaging are associated with low back pain but none of them is specific for the presence of low back pain as abnormal findings are prevalent among asymptomatic subjects as well. The purpose of this population-based study was to investigate if more specific magnetic resonance imaging predictors of low back pain could be found via texture analysis and machine learning. We used this methodology to classify T₂ -weighted magnetic resonance images from the Northern Finland Birth Cohort 1966 data to symptomatic and asymptomatic groups. Lumbar spine magnetic resonance imaging was performed using a fast spin-echo sequence at 1.5 T. Texture analysis pipeline consisting of textural feature extraction, principal component analysis, and logistic regression classifier was applied to the data to classify them into symptomatic (clinically relevant pain with frequency ≥30 days and intensity ≥6/10) and asymptomatic (frequency ≤7 days, intensity ≤3/10, and no previous pain episodes in the follow-up period) groups. Best classification results were observed applying texture analysis to the two lowest intervertebral discs (L4-L5 and L5-S1), with accuracy of 83%, specificity of 83%, sensitivity of 82%, negative predictive value of 94%, precision of 56%, and receiver operating characteristic area-under-curve of 0.91. To conclude, textural features from T₂ -weighted magnetic resonance images can be applied in low back pain classification.

Generative adversarial networks improve interior computed tomography angiography reconstruction

In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03 ± 0.01), and structural similarity index (SSIM) (0.92 ± 0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03 ± 0.01 and 0.04 ± 0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5 ± 2.6 dB and 28.6 ± 2.6 dB), and SSIM (0.90 ± 0.02 and 0.87 ± 0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%.

Automated detection of patellofemoral osteoarthritis from knee lateral view radiographs using deep learning: data from the Multicenter Osteoarthritis Study (MOST)

OBJECTIVE

To assess the ability of imaging-based deep learning to detect radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.

DESIGN

Knee lateral view radiographs were extracted from The Multicenter Osteoarthritis Study (MOST) public use datasets (n = 18,436 knees). Patellar region-of-interest (ROI) was first automatically detected, and subsequently, end-to-end deep convolutional neural networks (CNNs) were trained and validated to detect the status of patellofemoral OA. Patellar ROI was detected using deep-learning-based object detection method. Atlas-guided visual assessment of PFOA status by expert readers provided in the MOST public use datasets was used as a classification outcome for the models. Performance of classification models was assessed using the area under the receiver operating characteristic curve (ROC AUC) and the average precision (AP) obtained from the Precision-Recall (PR) curve in the stratified 5-fold cross validation setting.

RESULTS

Of the 18,436 knees, 3,425 (19%) had PFOA. AUC and AP for the reference model including age, sex, body mass index (BMI), the total Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, and tibiofemoral Kellgren-Lawrence (KL) grade to detect PFOA were 0.806 and 0.478, respectively. The CNN model that used only image data significantly improved the classifier performance (ROC AUC = 0.958, AP = 0.862).

CONCLUSION

We present the first machine learning based automatic PFOA detection method. Furthermore, our deep learning based model trained on patella region from knee lateral view radiographs performs better at detecting PFOA than models based on patient characteristics and clinical assessments.

Functional and structural properties of human patellar articular cartilage in osteoarthritis

Changes in the fibril-reinforced poroelastic (FRPE) mechanical material parameters of human patellar cartilage at different stages of osteoarthritis (OA) are not known. Further, the patellofemoral joint loading is thought to include more sliding and shear compared to other knee joint locations, thus, the relations between structural and functional changes may differ in OA. Thus, our aim was to determine the patellar cartilage FRPE properties followed by associating them with the structure and composition. Osteochondral plugs (n = 14) were harvested from the patellae of six cadavers. Then, the FRPE material properties were determined, and those properties were associated with proteoglycan content, collagen fibril orientation angle, optical retardation (fibril parallelism), and the state of OA of the samples. The initial fibril network modulus and permeability strain-dependency factor were 72% and 63% smaller in advanced OA samples when compared to early OA samples. Further, we observed a negative association between the initial fibril network modulus and optical retardation (r = -0.537, p < 0.05). We also observed positive associations between 1) the initial permeability and optical retardation (r = 0.547, p < 0.05), and 2) the initial fibril network modulus and optical density (r = 0.670, p < 0.01).These results suggest that the reduced pretension of the collagen fibrils, as shown by the reduced initial fibril network modulus, is linked with the loss of proteoglycans and cartilage swelling in human patellofemoral OA. The characterization of these changes is important to improve the representativeness of knee joint models in tissue and cell scale.

Optimizing iterative reconstruction for quantification of calcium hydroxyapatite with photon counting flat-detector computed tomography: a cardiac phantom study

Purpose: Coronary artery calcium (CAC) scoring with computed tomography (CT) has been proposed as a screening tool for coronary artery disease, but concerns remain regarding the radiation dose of CT CAC scoring. Photon counting detectors and iterative reconstruction (IR) are promising approaches for patient dose reduction, yet the preservation of CAC scores with IR has been questioned. The purpose of this study was to investigate the applicability of IR for quantification of CAC using a photon counting flat-detector. Approach: We imaged a cardiac rod phantom with calcium hydroxyapatite (CaHA) inserts with different noise levels using an experimental photon counting flat-detector CT setup to simulate the clinical CAC scoring protocol. We applied filtered back projection (FBP) and two IR algorithms with different regularization strengths. We compared the air kerma values, image quality parameters [noise magnitude, noise power spectrum, modulation transfer function (MTF), and contrast-to-noise ratio], and CaHA quantification accuracy between FBP and IR. Results: IR regularization strength influenced CAC scores significantly ( p < 0.05 ). The CAC volumes and scores between FBP and IRs were the most similar when the IR regularization strength was chosen to match the MTF of the FBP reconstruction. Conclusion: When the regularization strength is selected to produce comparable spatial resolution with FBP, IR can yield comparable CAC scores and volumes with FBP. Nonetheless, at the lowest radiation dose setting, FBP produced more accurate CAC volumes and scores compared to IR, and no improved CAC scoring accuracy at low dose was demonstrated with the utilized IR methods.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study

OBJECTIVE

To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class.

METHOD

T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier.

RESULTS

LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %.

CONCLUSION

LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Evaluation of articular cartilage with quantitative MRI in an equine model of post-traumatic osteoarthritis

Chondral lesions lead to degenerative changes in the surrounding cartilage tissue, increasing the risk of developing post-traumatic osteoarthritis (PTOA). This study aimed to investigate the feasibility of quantitative magnetic resonance imaging (qMRI) for evaluation of articular cartilage in PTOA. Articular explants containing surgically induced and repaired chondral lesions were obtained from the stifle joints of seven Shetland ponies (14 samples). Three age-matched nonoperated ponies served as controls (six samples). The samples were imaged at 9.4 T. The measured qMRI parameters included T₁ , T₂ , continuous-wave T_1ρ (CWT_1ρ ), adiabatic T_1ρ (AdT_1ρ ), and T_2ρ (AdT_2ρ ) and relaxation along a fictitious field (T_RAFF ). For reference, cartilage equilibrium and dynamic moduli, proteoglycan content and collagen fiber orientation were determined. Mean values and profiles from full-thickness cartilage regions of interest, at increasing distances from the lesions, were used to compare experimental against control and to correlate qMRI with the references. Significant alterations were detected by qMRI parameters, including prolonged T₁ , CWT_1ρ , and AdT_1ρ in the regions adjacent to the lesions. The changes were confirmed by the reference methods. CWT_1ρ was more strongly associated with the reference measurements and prolonged in the affected regions at lower spin-locking amplitudes. Moderate to strong correlations were found between all qMRI parameters and the reference parameters (ρ = -0.531 to -0.757). T₁ , low spin-lock amplitude CWT_1ρ , and AdT_1ρ were most responsive to changes in visually intact cartilage adjacent to the lesions. In the context of PTOA, these findings highlight the potential of T₁ , CWT_1ρ , and AdT_1ρ in evaluation of compositional and structural changes in cartilage.

Running and Physical Activity in an Air-Polluted Environment: The Biomechanical and Musculoskeletal Protocol for a Prospective Cohort Study 4HAIE (Healthy Aging in Industrial Environment-Program 4)

Far too little attention has been paid to health effects of air pollution and physical (in)activity on musculoskeletal health. The purpose of the Healthy aging in industrial environment study (4HAIE) is to investigate the potential impact of physical activity in highly polluted air on musculoskeletal health. A total of 1500 active runners and inactive controls aged 18–65 will be recruited. The sample will be recruited using quota sampling based on location (the most air-polluted region in EU and a control region), age, sex, and activity status. Participants will complete online questionnaires and undergo a two-day baseline laboratory assessment, including biomechanical, physiological, psychological testing, and magnetic resonance imaging. Throughout one-year, physical activity data will be collected through Fitbit monitors, along with data regarding the incidence of injuries, air pollution, psychological factors, and behavior collected through a custom developed mobile application. Herein, we introduce a biomechanical and musculoskeletal protocol to investigate musculoskeletal and neuro-mechanical health in this 4HAIE cohort, including a design for controlling for physiological and psychological injury factors. In the current ongoing project, we hypothesize that there will be interactions of environmental, biomechanical, physiological, and psychosocial variables and that these interactions will cause musculoskeletal diseases/protection.

Air gap technique is recommended in axiolateral hip radiographs

Purpose

To investigate the replacement of conventional grid by air gap in axiolateral hip radiographs. The optimal air gap distance was studied with respect to radiation dose and image quality using phantom images, as well as 26 patient axiolateral hip radiographs.

Methods

The CDRAD phantom, along with polymethylmethacrylate slabs with thicknesses of 10.0, 14.6, and 20.0 cm was employed. The inverse image quality index and dose area product (DAP), as well as their combination, so called figure‐of‐merit (FOM) parameter, were evaluated for these images, with air gaps from 20 to 50 cm in increments of 10 cm. Images were compared to those acquired using a conventional grid utilized in hip radiography. Radiation dose was measured and kept constant at the surface of the detector by using a reference dosimeter. Verbal consent was asked from 26 patients to participate to the study. Air gap distances from 20 to 50 cm and tube current‐time products from 8 to 50 mAs were employed. Exposure index, DAP, as well as patient height and weight were recorded. Two radiologists evaluated the image quality of 26 hip axiolateral projection images on a 3‐point nondiagnostic — good/sufficiently good — too good scale. Source‐to‐image distance of 200 cm and peak tube voltage of 90 kVp were used in both studies.

Results and conclusion

Based on the phantom study, it is possible to reduce radiation dose by replacing conventional grid with air gap without compromising image quality. The optimal air gap distance appears to be 30 cm, based on the FOM analysis. Patient study corroborates this observation, as sufficiently good image quality was found in 24 of 26 patient radiographs, with 7 of 26 images obtained with 30 cm air gap. Thus, air gap method, with an air gap distance of 30 cm, is recommended in axiolateral hip radiography.

Multiparametric MR imaging reveals early cartilage degeneration at 2 and 8 weeks after ACL transection in a rabbit model

In this study, the rabbit model with anterior cruciate ligament transection (ACLT) was used to investigate early degenerative changes in cartilage using multiparametric quantitative magnetic resonance imaging (qMRI). ACLT was surgically induced in the knees of skeletally mature New Zealand White rabbits (n = 14). ACL transected and contralateral knee compartments-medial femur, lateral femur, medial tibia, and lateral tibia-were harvested 2 (n = 8) and 8 weeks (n = 6) postsurgery. Twelve age-matched nonoperated rabbits served as control. qMRI was conducted at 9.4 T and included relaxation times T₁ , T₂ , continuous-wave T_1ρ (CWT_1ρ ), adiabatic T_1ρ (AdT_1ρ ), adiabatic T_2ρ (AdT_2ρ ), and relaxation along a fictitious field (T_RAFF ). For reference, quantitative histology and biomechanical measurements were carried out. Posttraumatic changes were primarily noted in the superficial half of the cartilage. Prolonged T₁ , T₂ , CWT_1ρ , and AdT_1ρ were observed in the lateral femur 2 and 8 weeks post-ACLT, compared with the corresponding control and contralateral groups (P < .05). Collagen orientation was significantly altered in the lateral femur at 2 weeks post-ACLT compared with the corresponding control group. In the medial femur, all the studied relaxation time parameters, except T_RAFF , were increased 8 weeks post-ACLT, as compared with the corresponding contralateral and control groups (P < .05). Similarly, significant proteoglycan loss was observed in the medial femur at 8 weeks following surgery (P < .05). Multiparametric MRI demonstrated early degenerative changes primarily in the superficial cartilage with T₁ , T₂ , CWT_1ρ , and AdT_1ρ sensitive to cartilage changes at 2 weeks after surgery.

Interior photon counting computed tomography for quantification of coronary artery calcium: pre-clinical phantom study

Computed tomography (CT) is the reference method for cardiac imaging, but concerns have been raised regarding the radiation dose of CT examinations. Recently, photon counting detectors (PCDs) and interior tomography, in which the radiation beam is limited to the organ-of-interest, have been suggested for patient dose reduction. In this study, we investigated interior PCD-CT (iPCD-CT) for non-enhanced quantification of coronary artery calcium (CAC) using an anthropomorphic torso phantom and ex vivo coronary artery samples. We reconstructed the iPCD-CT measurements with filtered back projection (FBP), iterative total variation (TV) regularization, padded FBP, and adaptively detruncated FBP and adaptively detruncated TV. We compared the organ doses between conventional CT and iPCD-CT geometries, assessed the truncation and cupping artifacts with iPCD-CT, and evaluated the CAC quantification performance of iPCD-CT. With approximately the same effective dose between conventional CT geometry (0.30 mSv) and interior PCD-CT with 10.2 cm field-of-view (0.27 mSv), the organ dose of the heart was increased by 52.3% with interior PCD-CT when compared to CT. Conversely, the organ doses to peripheral and radiosensitive organs, such as the stomach (55.0% reduction), were often reduced with interior PCD-CT. FBP and TV did not sufficiently reduce the truncation artifact, whereas padded FBP and adaptively detruncated FBP and TV yielded satisfactory truncation artifact reduction. Notably, the adaptive detruncation algorithm reduced truncation artifacts effectively when it was combined with reconstruction detrending. With this approach, the CAC quantification accuracy was good, and the coronary artery disease grade reclassification rate was particularly low (5.6%). Thus, our results confirm that CAC quantification can be performed with the interior CT geometry, that the artifacts are effectively reduced with suitable interior reconstruction methods, and that interior tomography provides efficient patient dose reduction.

Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis

OBJECTIVE

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA).

DESIGN

Bilateral posterior-anterior knee radiographs were analyzed from the baseline of Osteoarthritis Initiative (OAI) (9012 knee radiographs) and Multicenter Osteoarthritis Study (MOST) (3,644 knee radiographs) datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. Subsequently, we built logistic regression models to identify and compare the performances of several texture descriptors and each ROI placement method using 5-fold cross validation. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset. We used area under the receiver operating characteristic curve (ROC AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results.

RESULTS

We found that the adaptive ROI improves the classification performance (OA vs non-OA) over the commonly-used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, Local Binary Pattern (LBP) yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820].

CONCLUSION

Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA.

Effects of progressive aquatic resistance training on symptoms and quality of life in women with knee osteoarthritis: A secondary analysis

OBJECTIVE

To conduct a secondary analysis to study the effects, those 4 months of aquatic resistance training have on self-assessed symptoms and quality of life in post-menopausal women with mild knee osteoarthritis (OA), after the intervention and after a 12-month follow-up period.

METHODS

A total of 87 post-menopausal volunteer women, aged 60-68 years, with mild knee OA were recruited in a randomized, controlled, 4-month aquatic training trial (RCT) and randomly assigned to an intervention (n = 43) and a control (n = 44) group. The intervention group participated in 48 supervised aquatic resistance training sessions over 4 months while the control group maintained their usual level of physical activity. Additionally, 77 participants completed the 12-month post-intervention follow-up period. Self-assessed symptoms were estimated using the OA-specific Western Ontario and McMaster University Osteoarthritis Index (WOMAC) and Health-related Quality of life (HRQoL) using the generic Short-form Health Survey (SF-36).

RESULTS

After 4 months of aquatic resistance training, there was a significant decrease in the stiffness dimension of WOMAC -8.5 mm (95% CI = -14.9 to -2.0, P = .006) in the training group compared to the controls. After the cessation of the training, this benefit was no longer observed during the 12-month follow-up. No between-group differences were observed in any of the SF-36 dimensions.

CONCLUSIONS

The results of this study show that participation in an intensive aquatic resistance training program did not have any short- or long-term impact on pain and physical function or quality of life in women with mild knee OA. However, a small short-term decrease in knee stiffness was observed.

Framework for Photon Counting Quantitative Material Decomposition

In this paper, the accuracy of material decomposition (MD) using an energy discriminating photon counting detector was studied. An MD framework was established and validated using calcium hydroxyapatite (CaHA) inserts of known densities (50 mg/cm³, 100 mg/cm³, 250 mg/cm³, 400 mg/cm³), and diameters (1.2, 3.0, and 5.0 mm). These inserts were placed in a cardiac rod phantom that mimics a tissue equivalent heart and measured using an experimental photon counting detector cone beam computed tomography (PCD-CBCT) setup. The quantitative coronary calcium scores (density, mass, and volume) obtained from the MD framework were compared with the nominal values. In addition, three different calibration techniques, signal-to-equivalent thickness calibration (STC), polynomial correction (PC), and projected equivalent thickness calibration (PETC) were compared to investigate the effect of the calibration method on the quantitative values. The obtained MD estimates agreed well with the nominal values for density (mass) with mean absolute percent errors (MAPEs) 8 ± 11% (9 ± 15%) and 4 ± 6% (9 ± 14%) for STC and PETC calibration methods, respectively. PC displayed large MAPEs for density (27 ± 9%), and mass (25 ± 12%). Volume estimation resulted in large deviations between true and measured values with notable MAPEs for STC (40 ± 90%), PC (40 ± 80%), and PETC (40 ± 90%). The framework demonstrated the feasibility of quantitative CaHA mass and density scoring using PCD-CBCT.

Defective WNT signaling may protect from articular cartilage deterioration - a quantitative MRI study on subjects with a heterozygous WNT1 mutation

OBJECTIVE

WNT signaling is of key importance in chondrogenesis and defective WNT signaling may contribute to the pathogenesis of osteoarthritis and other cartilage diseases. Biochemical composition of articular cartilage in patients with aberrant WNT signaling has not been studied. Our objective was to assess the knee articular cartilage in WNT1 mutation-positive individuals using a 3.0T MRI unit to measure cartilage thickness, relaxation times, and texture features.

DESIGN

Cohort comprised mutation-positive (N = 13; age 17-76 years) and mutation-negative (N = 13; 16-77 years) subjects from two Finnish families with autosomal dominant WNT1 osteoporosis due to a heterozygous missense mutation c.652T>G (p.C218G) in WNT1. All subjects were imaged with a 3.0T MRI unit and assessed for cartilage thickness, T2 and T1ρ relaxation times, and T2 texture features contrast, dissimilarity and homogeneity of T2 relaxation time maps in six regions of interest (ROIs) in the tibiofemoral cartilage.

RESULTS

All three texture features showed opposing trends with age between the groups in the medial tibiofemoral cartilage (P = 0.020-0.085 for the difference of the regression coefficients), the mutation-positive individuals showing signs of cartilage preservation. No significant differences were observed in the lateral tibiofemoral cartilage. Cartilage thickness and means of T2 relaxation time did not differ between groups. Means of T1ρ relaxation time were significantly different in one ROI but the regression analysis displayed no differences.

CONCLUSIONS

Our results show less age-related cartilage deterioration in the WNT1 mutation-positive than the mutation-negative subjects. This suggests, that the WNT1 mutation may alter cartilage turnover and even have a potential cartilage-preserving effect.

An Automatic Regularization Method: An Application for 3-D X-Ray Micro-CT Reconstruction Using Sparse Data

X-ray tomography is a reliable tool for determining the inner structure of 3-D object with penetrating X-rays. However, traditional reconstruction methods, such as Feldkamp-Davis-Kress (FDK), require dense angular sampling in the data acquisition phase leading to long measurement times, especially in X-ray micro-tomography to obtain high-resolution scans. Acquiring less data using greater angular steps is an obvious way for speeding up the process and avoiding the need to save huge data sets. However, computing 3-D reconstruction from such a sparsely sampled data set is difficult because the measurement data are usually contaminated by errors, and linear measurement models do not contain sufficient information to solve the problem in practice. An automatic regularization method is proposed for robust reconstruction, based on enforcing sparsity in the 3-D shearlet transform domain. The inputs of the algorithm are the projection data and a priori known expected degree of sparsity, denoted as . The number C_pr can be calibrated from a few dense-angle reconstructions and fixed. Human subchondral bone samples were tested, and morphometric parameters of the bone reconstructions were then analyzed using standard metrics. The proposed method is shown to outperform the baseline algorithm (FDK) in the case of sparsely collected data. The number of X-ray projections can be reduced up to 10% of the total amount 300 projections over 180° with uniform angular step while retaining the quality of the reconstruction images and of the morphometric parameters.

Patient radiation dose and fluoroscopy time during ERCP: a single-center, retrospective study of influencing factors

OBJECTIVES

Recently, both the number and the complexity with associated increased technical difficulty of therapeutic ERCP procedures have significantly increased resulting in longer procedural and fluoroscopy times. During ERCP, the patient is exposed to ionizing radiation and the consequent radiation dose depends on multiple factors. The aim of this study was to identify factors affecting fluoroscopy time and radiation dose in patients undergoing ERCP.

MATERIALS AND METHODS

Data related to patient demographics, procedural characteristics and radiation exposure in ERCP procedures (n = 638) performed between August 2013 and August 2015 was retrospectively reviewed and analyzed. Statistically significant factors identified by univariate analyses were included in multivariate analysis with fluoroscopy time (FT) and dose area product (DAP) as dependent variables. Effective dose (ED) was estimated from DAP measurements using conversion coefficient.

RESULTS

The factors independently associated with increased DAP during ERCP were age, gender, radiographer, complexity level of ERCP, cannulation difficulty grade, bile duct injury and biliary stent placement. In multivariate analysis the endoscopist, the complexity level of ERCP, cannulation difficulty grade, pancreatic duct leakage, bile duct dilatation and brushing were identified as predictors for a longer FT. The mean DAP, FT, number of acquired images and ED for all ERCP procedures were 2.33 Gy·cm², 1.84 min, 3 and 0.61 mSv, respectively.

CONCLUSIONS

Multiple factors had an effect on DAP and FT in ERCP. The awareness of these factors may help to predict possible prolonged procedures causing a higher radiation dose to the patient and thus facilitate the use of appropriate precautions.

Assessment of meniscus with adiabatic T1ρ and T2ρ relaxation time in asymptomatic subjects and patients with mild osteoarthritis: a feasibility study

OBJECTIVE

To investigate the ability of magnetic resonance imaging (MRI) adiabatic relaxation times in the rotating frame (adiabatic T_1ρ and T_2ρ) to detect structural alterations in meniscus tissue of mild OA patients and asymptomatic volunteers.

METHOD

MR images of 24 subjects (age range: 50-67 years, 12 male), including 12 patients with mild osteoarthritis (OA) (Kellgren-Lawrence (KL) = 1, 2) and 12 asymptomatic volunteers, were acquired using a 3 T clinical MRI system. Morphological assessment was performed using semiquantitative MRI OA Knee Score (MOAKS). Adiabatic T_1ρ and T_2ρ (AdT_1ρ, AdT_2ρ) relaxation time maps were calculated in regions of interest (ROIs) containing medial and lateral horns of menisci. The median relaxation time values of the ROIs were compared between subjects classified based on radiographic findings and MOAKS evaluations.

RESULTS

MOAKS assessment of patients and volunteers indicated the presence of meniscal and cartilage lesions in both groups. For the combined cohort group, prolonged AdT_1ρ was observed in the posterior horn of the medial meniscus (PHMED) in subjects with MOAKS meniscal tear (P < 0.05). AdT_2ρ was statistically significantly longer in PHMED of subjects with MOAKS full-thickness cartilage loss (P < 0.05). After adjusting for multiple comparisons, differences in medians of observed AdT_1ρ and AdT_2ρ values between mild OA patients and asymptomatic volunteers did not reach statistical significance.

CONCLUSION

AdT_1ρ and AdT_2ρ measurements have the potential to identify changes in structural composition of meniscus tissue associated with meniscal tear and cartilage loss in a cohort group of mild OA patients and asymptomatic volunteers.

Estimation of the Effect of Body Weight on the Development of Osteoarthritis Based on Cumulative Stresses in Cartilage: Data from the Osteoarthritis Initiative

Evaluation of the subject-specific biomechanical effects of obesity on the progression of OA is challenging. The aim of this study was to create 3D MRI-based finite element models of the knee joints of seven obese subjects, who had developed OA at 4-year follow-up, and of seven normal weight subjects, who had not developed OA at 4-year follow-up, to test the sensitivity of cumulative maximum principal stresses in cartilage in quantitative risk evaluation of the initiation and progression of knee OA. Volumes of elements with cumulative stresses over 5 MPa in tibial cartilage were significantly (p < 0.05) larger in obese subjects as compared to normal weight subjects. Locations of high peak cumulative stresses at the baseline in most of the obese subjects showed a good agreement with the locations of the cartilage loss and MRI scoring at follow-up. Simulated weight loss (to body mass index 24 kg/m2) in obese subjects led to significant reduction of the highest cumulative stresses in tibial and femoral cartilages. The modeling results suggest that an analysis of cumulative stresses could be used to evaluate subject-specific effects of obesity and weight loss on cartilage responses and potential risks for the progression of knee OA.

Physical Activity Is Related with Cartilage Quality in Women with Knee Osteoarthritis

PURPOSE

To study the relationship between 12-month leisure-time physical activity (LTPA) level and changes in estimated biochemical composition of tibiofemoral cartilage in postmenopausal women with mild knee osteoarthritis (OA).

METHODS

Originally, 87 volunteer postmenopausal women, age 60 to 68 yr, with mild knee OA (Kellgren Lawrence I/II and knee pain) participated in a randomized controlled, 4-month aquatic training trial (RCT), after which 76 completed the 12-month postintervention follow-up period. Self-reported LTPA was collected along the 12-month period using a diary from which MET task hours per month were calculated. Participants were divided into MET task hour tertiles: 1, lowest (n = 25); 2 = middle (n = 25) and 3 = highest (n = 26). The biochemical composition of the cartilage was estimated using transverse relaxation time (T2) mapping sensitive to the properties of the collagen network and delayed gadolinium-enhanced magnetic resonance imaging of the cartilage (dGEMRIC index) sensitive to the cartilage glycosaminoglycan content. Secondary outcomes were cardiorespiratory fitness, isometric knee extension and flexion force, and the knee injury and OA outcome questionnaire.

RESULTS

During the 12-month follow-up period, there was a significant linear relationship between higher LTPA level and increased dGEMRIC index changes in the posterior region of interest (ROI) of the lateral (P = 0.003 for linearity) and medial (P = 0.006) femoral cartilage. Furthermore, these changes were seen in the posterior lateral femoral cartilage superficial (P = 0.004) and deep (P = 0.007) ROI and in the posterior medial superficial ROI (P < 0.001). There was no linear relationship between LTPA level and other measured variables.

CONCLUSIONS

These results suggest that higher LTPA level is related to regional increases in estimated glycosaminoglycan content of tibiofemoral cartilage in postmenopausal women with mild knee OA as measured with dGEMRIC index during a 12-month period.

Association between radiography-based subchondral bone structure and MRI-based cartilage composition in postmenopausal women with mild osteoarthritis

OBJECTIVE

Our aim was to investigate the relation between radiograph-based subchondral bone structure and cartilage composition assessed with delayed gadolinium enhanced magnetic resonance imaging of cartilage (dGEMRIC) and T₂ relaxation time.

DESIGN

Ninety-three postmenopausal women (Kellgren-Lawrence grade 0: n = 13, 1: n = 26, 2: n = 54) were included. Radiograph-based bone structure was assessed using entropy of the Laplacian-based image (E_Lap) and local binary patterns (E_LBP), homogeneity indices of the local angles (HI_Angles,mean, HI_Angles,Perp, HI_Angles,Paral), and horizontal (FD_Hor) and vertical fractal dimensions (FD_Ver). Mean dGEMRIC index and T₂ relaxation time of tibial cartilage were calculated to estimate cartilage composition.

RESULTS

HI_Angles,mean (r_s = -0.22) and HI_Angles,Paral (r_s = -0.24) in medial subchondral bone were related (P < 0.05) to dGEMRIC index of the medial tibial cartilage. E_Lap (r_s = -0.23), FD_Hor,0.34 mm (r = 0.21) and FD_Ver,0.68 mm (r = 0.24) in medial subchondral bone were related (P < 0.05) to T₂ relaxation time values of the medial tibial cartilage. FD_Hor at different scales in lateral subchondral bone were related (P < 0.01) to dGEMRIC index (r = 0.29-0.41) and T₂ values of lateral tibial cartilage (r = -0.28 to -0.36). FD_Ver at larger scales were related (P < 0.05) to dGEMRIC index (r = 0.24-0.25) and T₂ values of lateral tibial cartilage (r = -0.21). HI_Angles,Paral (r = -0.25) and FD_Ver,0.68 mm (r_s = 0.22) in the lateral tibial trabecular bone were related (P < 0.05) to dGEMRIC index of the lateral tibial cartilage.

CONCLUSION

Our results support the presumption that several tissues are affected in the early osteoarthritis (OA). Furthermore, they indicate that the detailed analysis of radiographs may serve as a complementary imaging tool for OA studies.

Magnetic resonance imaging (MRI)-defined cartilage degeneration and joint pain are associated with poor physical function in knee osteoarthritis - the Oulu Knee Osteoarthritis study

OBJECTIVE

The main aim was to investigate the associations between Magnetic Resonance Imaging (MRI)-defined structural pathologies of the knee and physical function.

DESIGN

A cohort study with frequency matching on age and sex with eighty symptomatic subjects with knee pain and suspicion or diagnosis of knee osteoarthritis (OA) and 57 asymptomatic subjects was conducted. The subjects underwent knee MRI, and the severity of structural changes was graded by MRI Osteoarthritis Knee Score (MOAKS) in separate knee locations. WOMAC function subscores were recorded and physical function tests (20-m and 5-min walk, stair ascending and descending, timed up & go and repeated sit-to-stand tests) performed. The association between MRI-defined structural pathologies and physical function tests and WOMAC function subscores were evaluated by linear regression analysis with adjustment for demographic factors, other MRI-features and pain with using effect size (ES) as a measure of the magnitude of an association.

RESULTS

Cartilage degeneration showed significant association with poor physical performance in TUG-, stair ascending and descending-, 20-m- and 5-min walk-tests (ESs in the subjects with cartilage degeneration anywhere between 0.134 [95%CI 0.037-0.238] and 0.224 [0.013-0.335]) and with increased WOMAC function subscore (ES in the subjects with cartilage degeneration anywhere 0.088 [0.012-0.103]). Also, lateral meniscus maceration and extrusion were associated with poor performance in stair ascending test (ESs 0.067 [0.008-0.163] and 0.077 [0.012-0.177]).

CONCLUSIONS

After adjustments cartilage degeneration was associated with both decreased self-reported physical function and poor performance in the physical function tests. Furthermore, subjects with lateral meniscus maceration and extrusions showed significantly worse performance in stair ascending tests.

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint

Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.