A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.

Analysis of Hand Joint Space Morphology in Women and Men with Hereditary Hemochromatosis

Hereditary hemochromatosis (HH) causes unbalanced iron deposition in many organs including the joints leading to severe cartilage loss and bone damage in the metacarpophalangeal joints (MCPJ). High-resolution peripheral quantitative computed tomography (HR-pQCT) and its joint space width (JSW) quantification algorithm quantifies in vivo 3D joint morphology. We therefore aimed to (i) determine feasibility and performance of the JSW algorithm in HH, (ii) quantify joint space morphology, and (iii) investigate the relationship between morphological and clinical parameters in HH. Here, we performed an exploratory study on 24 HH patients and sex- and age-matched controls using HR-pQCT imaging of MCPJ. Mineralized bone structure was automatically segmented from the grayscale image data and periosteal surface bone masks and joint space masks were generated. Mean, minimal, and maximal joint space width (JSW; JSW.MIN; JSW.MAX), JSW heterogeneity (JSW.SD), JSW asymmetry (JSW.AS), and joint space volume (JSV) were computed. Demographics and, for HH patients, disease-specific parameters were recorded. Segmentation of JS was very good with 79.7% of MCPJs successfully segmented at first attempt and 20.3% requiring semi-manual correction. HH men showed larger JSV at all MCPs (+ 25.4% < JSV <  + 41.8%, p < 0.05), larger JSW.MAX at MCP 3-4 (+ 14%, 0.006 < p < 0.062), and wider JSW (+ 13%, p = 0.043) at MCP 4 relative to HH women. Compared to controls, both HH men and HH women showed larger JSW.AS and smaller JSW.MIN at all MCP levels, reaching significance for HH men at MCP 2 and 3 (JSW.AS: + 323% < JSW.AS <  + 359%, 0.020 < p < 0.043; JSW.MIN: - 216% < JSW.MIN < - 225%, p < 0.043), and for women at MCP 3 (JSW.AS: + 180%, p = 0.025; JSW.MIN: - 41.8%, p = 0.022). Time since HH diagnosis was correlated positively with MCP 4 JSW.AS and JSW.SD (0.463 < ρ < 0.499, p < 0.040), and the number of phlebotomies since diagnosis was correlated with JSW.SD at all MCPs (0.432 < ρ < 0.535, p < 0.050). HR-pQCT-based JSW quantification in MCPJ of HH patients is feasible, performs well even in narrow JS, and allows to define the microstructural joint burden of HH.

Identifying Skeletal Maturity from X-rays using Deep Neural Networks

Skeletal maturity estimation is routinely evaluated by pediatrics and radiologists to assess growth and hormonal disorders. Methods integrated with regression techniques are incompatible with low-resolution digital samples and generate bias, when the evaluation protocols are implemented for feature assessment on coarse X-Ray hand images. This paper proposes a comparative analysis between two deep neural network architectures, with the base models such as Inception-ResNet-V2 and Xception-pre-trained networks. Based on 12,611 hand X-Ray images of RSNA Bone Age database, Inception-ResNet-V2 and Xception models have achieved R-Squared value of 0.935 and 0.942 respectively. Further, in the same order, the MAE accomplished by the two models are 12.583 and 13.299 respectively, when subjected to very few training instances with negligible chances of overfitting.

Application of ImageJ software for quantification of Hand Joint Space Narrowing in Patients with Rheumatoid Arthritis

BACKGROUND

ImageJ software is used to quantify the joint space width (JSW) of hand and wrist in patients with rheumatoid arthritis (RA) as well as in the healthy control group.

METHOD

Forty-one RA patients and 31 healthy controls are included in this study. All of 72 participants underwent digital radiography of the bilateral hand and wrist, then all the images were opened by ImageJ software to measure the width of wrist and hand joint space (total 2160 joints). Joint space narrowing (JSN) was defined if the width was less than the mean - 2SD of the control group.

RESULT

The mean JSW of all sites of wrist and hand joints of RA patients was significantly reduced as compared to those in the control group (p<0.001). There were 37/41 (90.24%) RA patients who had JSN in at least one joint in hand or wrist. In total, 70.89% of joints on the right and 68.46% of joints on the left wrist and hand had JSN.

CONCLUSION

ImageJ software was simple and convenient , which helps rheumatologists quantify the width of joint space for diagnosis and follow-up in RA patients.

Computer-Based Radiographic Quantification of Joint Space Narrowing Progression Using Sequential Hand Radiographs: Validation Study in Rheumatoid Arthritis Patients from Multiple Institutions

We have developed a refined computer-based method to detect joint space narrowing (JSN) progression with the joint space narrowing progression index (JSNPI) by superimposing sequential hand radiographs. The purpose of this study is to assess the validity of a computer-based method using images obtained from multiple institutions in rheumatoid arthritis (RA) patients. Sequential hand radiographs of 42 patients (37 females and 5 males) with RA from two institutions were analyzed by a computer-based method and visual scoring systems as a standard of reference. The JSNPI above the smallest detectable difference (SDD) defined JSN progression on the joint level. The sensitivity and specificity of the computer-based method for JSN progression was calculated using the SDD and a receiver operating characteristic (ROC) curve. Out of 314 metacarpophalangeal joints, 34 joints progressed based on the SDD, while 11 joints widened. Twenty-one joints progressed in the computer-based method, 11 joints in the scoring systems, and 13 joints in both methods. Based on the SDD, we found lower sensitivity and higher specificity with 54.2 and 92.8%, respectively. At the most discriminant cutoff point according to the ROC curve, the sensitivity and specificity was 70.8 and 81.7%, respectively. The proposed computer-based method provides quantitative measurement of JSN progression using sequential hand radiographs and may be a useful tool in follow-up assessment of joint damage in RA patients.

Radiographic temporal subtraction analysis can detect finger joint space narrowing progression in rheumatoid arthritis with clinical low disease activity

Background Recent papers suggest that finger joints with positive synovial vascularity (SV) assessed by ultrasonography under clinical low disease activity (CLDA) in rheumatoid arthritis (RA) patients may cause joint space narrowing (JSN) progression. Purpose To investigate the performance of a computer-based method by directly comparing with the conventional scoring method in terms of the detectability of JSN progression in hand radiography of RA patients with CLDA. Material and Methods Fifteen RA patients (13 women, 2 men) with long-term sustained CLDA of >2 years were included. Radiological progression of finger joints was measured or scored using the computer-based method which can detect JSN progression between two radiographic images as the joint space difference index (JSDI), as well as the Genant-modified Sharp score (GSS). We also quantitatively assessed SV of these joints using ultrasonography. Results Out of 270 joints, we targeted 259 finger joints after excluding nine damaged joints (four ankylosis, three complete luxation, and two subluxation) and two improved joints according to the GSS results. The JSDI of finger joints with JSN progression was significantly higher than those without JSN progression ( P = 0.018). The JSDI of finger joints with ultrasonographic SV was significantly higher than those without ultrasonographic SV ( P = 0.004). Progression in JSDI showed stronger associations with ultrasonographic SV than progression in GSS (odds ratio [95% confidence interval]: 7.19 [3.37-15.36] versus 5.84 [2.76-12.33]). Conclusion The computer-based method was comparable to the conventional scoring method regarding the detectability of JSN progression in RA patients with CLDA.

Fully automated joint space width measurement and digital X-ray radiogrammetry in early RA

Objectives

To study fully automated digital joint space width (JSW) and bone mineral density (BMD) in relation to a conventional radiographic scoring method in early rheumatoid arthritis (eRA).

Methods

Radiographs scored by the modified Sharp van der Heijde score (SHS) in patients with eRA were acquired from the SWEdish FarmacOTherapy study. Fully automated JSW measurements of bilateral metacarpals 2, 3 and 4 were compared with the joint space narrowing (JSN) score in SHS. Multilevel mixed model statistics were applied to calculate the significance of the association between ΔJSW and ΔBMD over 1 year, and the JSW differences between damaged and undamaged joints as evaluated by the JSN.

Results

Based on 576 joints of 96 patients with eRA, a significant reduction from baseline to 1 year was observed in the JSW from 1.69 (±0.19) mm to 1.66 (±0.19) mm (p<0.01), and BMD from 0.583 (±0.068) g/cm² to 0.566 (±0.074) g/cm² (p<0.01). A significant positive association was observed between ΔJSW and ΔBMD over 1 year (p<0.0001). On an individual joint level, JSWs of undamaged (JSN=0) joints were wider than damaged (JSN>0) joints: 1.68 mm (95% CI 1.70 to 1.67) vs 1.54 mm (95% CI 1.63 to 1.46). Similarly the unadjusted multilevel model showed significant differences in JSW between undamaged (1.68 mm (95% CI 1.72 to 1.64)) and damaged joints (1.63 mm (95% CI 1.68 to 1.58)) (p=0.0048). This difference remained significant in the adjusted model: 1.66 mm (95% CI 1.70 to 1.61) vs 1.62 mm (95% CI 1.68 to 1.56) (p=0.042).

Conclusions

To measure the JSW with this fully automated digital tool may be useful as a quick and observer-independent application for evaluating cartilage damage in eRA.

Trial registration number

NCT00764725.

Semi-Automated Quantification of Finger Joint Space Narrowing Using Tomosynthesis in Patients with Rheumatoid Arthritis

The purpose of the study is to validate the semi-automated method using tomosynthesis images for the assessment of finger joint space narrowing (JSN) in patients with rheumatoid arthritis (RA), by using the semi-quantitative scoring method as the reference standard. Twenty patients (14 females and 6 males) with RA were included in this retrospective study. All patients underwent radiography and tomosynthesis of the bilateral hand and wrist. Two rheumatologists and a radiologist independently scored JSN with two modalities according to the Sharp/van der Heijde score. Two observers independently measured joint space width on tomosynthesis images using an in-house semi-automated method. More joints with JSN were revealed with tomosynthesis score (243 joints) and the semi-automated method (215 joints) than with radiography (120 joints), and the associations between tomosynthesis scores and radiography scores were demonstrated (P < 0.001). There was significant, negative correlation between measured joint space width and tomosynthesis scores with r = -0.606 (P < 0.001) in metacarpophalangeal joints and r = -0.518 (P < 0.001) in proximal interphalangeal joints. Inter-observer and intra-observer agreement of the semi-automated method using tomosynthesis images was in almost perfect agreement with intra-class correlation coefficient (ICC) values of 0.964 and 0.963, respectively. The semi-automated method using tomosynthesis images provided sensitive, quantitative, and reproducible measurement of finger joint space in patients with RA.

Sensitivity-to-change and validity of semi-automatic joint space width measurements in hand osteoarthritis: a follow-up study

OBJECTIVE

To assess sensitivity-to-change and validity of longitudinal quantitative semi-automatic joint space width (JSW) measurements and to compare this method with semi-quantitative joint space narrowing (JSN) scoring in hand osteoarthritis (OA) patients.

DESIGN

Baseline and 2-year follow-up radiographs of 56 hand OA patients (mean age 62 years, 86% women) were used. JSN was scored 0-3 using the Osteoarthritis Research Society International atlas and JSW was quantified in millimetres (mm) in the second to fifth distal, proximal interphalangeal and metacarpal joints (DIPJs, PIPJs, MCPJs). Sensitivity-to-change was evaluated by calculating Standardized Response Means (SRMs). Change in JSW or JSN above the Smallest Detectable Difference (SDD) defined progression on joint level. To assess construct validity, progressed joints were compared by cross-tabulation and by associating baseline ultrasound variables with progression (using generalized estimating equations, adjusting for age and sex).

RESULTS

The JSW method detected statistically significant mean changes over 2.6 years (-0.027 mm (95%CI -0.01; -0.04), -0.024 mm (-0.01; -0.03), -0.021 mm (-0.01; -0.03) for DIPJs, PIPJs, MCPJs, respectively). Sensitivity-to-change was low (SRMs: 0.174, 0.168, 0.211, respectively). 9.1% (121/1336) of joints progressed in JSW, but 3.6% (48/1336) widened. 83 (6.2%) joints progressed in JSW only, 36 (2.7%) in JSN only and 37 (2.8%) in both methods. Progression in JSW showed weaker associations with baseline inflammatory ultrasound features than progression in JSN.

CONCLUSIONS

Assessment of progression in hand OA defined by JSW measurements is possible, but performs less well than progression defined by JSN scoring. Therefore, the value of JSW measurements in hand OA clinical trials remains questionable.

Radiographic quantifications of joint space narrowing progression by computer-based approach using temporal subtraction in rheumatoid wrist

OBJECTIVE

To investigate the validity of a computer-based method using temporal subtraction in carpal joints of patients with rheumatoid arthritis (RA), which can detect the difference in joint space between two images with the joint space difference index (JSDI).

METHODS

The study consisted of 43 patients with RA (39 females and 4 males) who underwent radiography at baseline and at 1-year follow-up. The joint space narrowing (JSN) of carpal joints on bilateral hand radiographs was assessed by our computer-based method, using the Sharp/van der Heijde method as the standard of reference. We compared the JSDI of joints with JSN progression in the follow-up period with that of those without JSN progression. In addition, we examined whether there is a significant difference in JSDI in terms of laterality or topology of the joint.

RESULTS

The JSDI of joints with JSN progression was significantly higher than that of those without JSN progression (Mann-Whitney U test, p < 0.001). There was no statistically significant difference in the JSDI between the left and right carpal joints, which was analysed for five different joints altogether and each joint separately (Mann-Whitney U test, p > 0.05). There was statistically significant difference in JSDI among different joints (Kruskal-Wallis test, p = 0.003).

CONCLUSION

These results suggest that our computer-based method may be useful to recognize the JSN progression on radiographs of rheumatoid wrists.

ADVANCES IN KNOWLEDGE

The computer-based temporal subtraction method can detect the JSN progression in the wrist, which is the single most commonly involved site in RA.

Performance of computer-based analysis using temporal subtraction to assess joint space narrowing progression in rheumatoid patients

Our computer-based method can detect the chronological change in joint space width between baseline and follow-up images as the joint space difference index (JSDI). The aim of this study was to verify the sensitivity and specificity of our computer-based method in assessment of joint space narrowing progression in rheumatoid patients. Twenty-seven patients (24 women and 3 men) with rheumatoid arthritis underwent radiography of the bilateral hand at baseline and at 1 year. The joint space narrowing (JSN) of a total of 252 metacarpophalangeal (MCP) joints and 229 carpal joints was assessed by our computer-based method, setting the Sharp/van der Heijde method as the gold standard. We constructed a receiver operating characteristic curve by using the Sharp/van der Heijde method as the gold standard and set the optimal cutoff on JSDI for MCP, carpal, and MCP/carpal joints. We then calculated the sensitivity and specificity for each cutoff in assessment of JSN progression. At the most discriminant cutoff, the sensitivity and specificity of the computer-based method for MCP joints was 78.6 versus 85.3 %, respectively (AUC = 0.837; P < 0.001). Carpal joints revealed a lower sensitivity and specificity with 64.7 and 86.8 % (AUC = 0.775; P < 0.001). Furthermore, the sensitivity and specificity for MCP/carpal joints was 71.0 versus 83.6 %, respectively (AUC = 0.778; P < 0.001). The computer-based method presented a reliable assessment of JSN progression with high sensitivity and specificity and may be useful in follow-up assessment of the joint damage in rheumatoid patients.

Computer-based measurements of joint space analysis using metacarpal morphometry in hand radiograph for evaluation of rheumatoid arthritis

AIM AND OBJECTIVES

The aim and objectives are as follows: (i) to perform an automated segmentation of the hand from radiographs using a dual tree complex wavelet-based watershed algorithm; ii) to compare the measured statistical features of the joint space of the hand using gray level co-occurrence matrix (GLCM) method with standard diagnostic parameters of rheumatoid arthritis (RA).

METHODS

Fifty-three patients with RA and 17 age- and sex-matched healthy controls were included in the study. The erythrocyte sedimentation rate (ESR), C-reactive protein, rheumatoid factor, health assessment questionnaire score (HAQ), disease activity score (DAS) and hand radiographs of all the subjects were obtained. Joint space width and cortical thickness were measured in metacarpophalangeal joints (MCP) and metacarpal bone semi-automatically using MIMICS software. Dual tree complex wavelet transform-based watershed algorithm was applied for automated segmentation, and feature extraction was performed using the GLCM method in hand radiographs of the total population.

RESULTS

In the RA group (n = 53), the joint space width measured in the MCP1, MCP3, MCP5 of the hand were reduced significantly (P < 0.01) by 16.4%, 15.6%, and 17.5%, respectively compared to the normal group (n = 17). The measured combined cortical thickness at the second, third and fourth metacarpal bones of the hand were reduced significantly (P < 0.01) by 9.5%, 12% and 8%, respectively in the RA group compared to the normal group.

CONCLUSION

The dual tree complex wavelet transform-based watershed algorithm provided effective segmentation in the digitized hand radiographs. The standard diagnostic parameters for RA were highly correlated with measured statistical features at MCP3 hand joint in the total population studied.

Global localization of 3D anatomical structures by pre-filtered Hough forests and discrete optimization

The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively.

Automatic and Quantitative Measurement of Collagen Gel Contraction Using Model-Guided Segmentation

Quantitative measurement of collagen gel contraction plays a critical role in the field of tissue engineering because it provides spatial-temporal assessment (e.g., changes of gel area and diameter during the contraction process) reflecting the cell behaviors and tissue material properties. So far the assessment of collagen gels relies on manual segmentation, which is time-consuming and suffers from serious intra- and inter-observer variability. In this study, we propose an automatic method combining various image processing techniques to resolve these problems. The proposed method first detects the maximal feasible contraction range of circular references (e.g., culture dish) and avoids the interference of irrelevant objects in the given image. Then, a three-step color conversion strategy is applied to normalize and enhance the contrast between the gel and background. We subsequently introduce a deformable circular model (DCM) which utilizes regional intensity contrast and circular shape constraint to locate the gel boundary. An adaptive weighting scheme was employed to coordinate the model behavior, so that the proposed system can overcome variations of gel boundary appearances at different contraction stages. Two measurements of collagen gels (i.e., area and diameter) can readily be obtained based on the segmentation results. Experimental results, including 120 gel images for accuracy validation, showed high agreement between the proposed method and manual segmentation with an average dice similarity coefficient larger than 0.95. The results also demonstrated obvious improvement in gel contours obtained by the proposed method over two popular, generic segmentation methods.

Quantitative in vivo HR-pQCT imaging of 3D wrist and metacarpophalangeal joint space width in rheumatoid arthritis

In this technique development study, high-resolution peripheral quantitative computed tomography (HR-pQCT) was applied to non-invasively image and quantify 3D joint space morphology of the wrist and metacarpophalangeal (MCP) joints of patients with rheumatoid arthritis (RA). HR-pQCT imaging (82 μm voxel-size) of the dominant hand was performed in patients with diagnosed rheumatoid arthritis (RA, N = 16, age: 52.6 ± 12.8) and healthy controls (CTRL, N = 7, age: 50.1 ± 15.0). An automated computer algorithm was developed to segment wrist and MCP joint spaces. The 3D distance transformation method was applied to spatially map joint space width, and summarized by the mean joint space width (JSW), minimal and maximal JSW (JSW.MIN, JSW.MAX), asymmetry (JSW.AS), and distribution (JSW.SD)-a measure of joint space heterogeneity. In vivo precision was determined for each measure by calculating the smallest detectable difference (SDD) and root mean square coefficient of variation (RMSCV%) of repeat scans. Qualitatively, HR-pQCT images and pseudo-color JSW maps showed global joint space narrowing, as well as regional and focal abnormalities in RA patients. In patients with radiographic JSN at an MCP, JSW.SD was two-fold greater vs. CTRL (p < 0.01), and JSW.MIN was more than two-fold lower (p < 0.001). Similarly, JSW.SD was significantly greater in the wrist of RA patients vs. CTRL (p < 0.05). In vivo precision was highest for JSW (SDD: 100 μm, RMSCV: 2.1%) while the SDD for JSW.MIN and JSW.SD were 370 and 110 μm, respectively. This study suggests that in vivo quantification of 3D joint space morphology from HR-pQCT, could improve early detection of joint damage in rheumatological diseases.

The usefulness of computer-aided joint space analysis in the assessment of rheumatoid arthritis

OBJECTIVE

Computer-aided joint space analysis (CAJSA) is a newly developed technique for the measurement of radiogeometrically detectable joint space widths of the metacarpal-phalangeal (JSD-MCP) and proximal-interphalangeal articulations (JSD-PIP). The aim of this study was to verify the sensitivity and specificity of these CAJSA measurements in the assessment of established RA.

METHODS

Four hundred and fifty-eight participants (248 healthy subjects, 210 RA patients) underwent computerized semi-automated measurements of the JSD-MCP and JSD-PIP articulations (CAJSA, Radiogrammetry Kit, Version 1.3.6) based on digitally performed radiographs. The Sharp joint space narrowing score was also performed to determine RA-related joint space narrowing.

RESULTS

The significant severity-dependent reduction for JSD-MCP was -44.0% and for JSD-PIP, -25.94% between Sharp scores 0 and 3. The sensitivity and specificity of JSD-MCP (total) was 88.1% versus 77.8%, respectively (AUC = 0.920; P < 0.001). Furthermore, JSD-PIP (total) revealed a lower sensitivity and specificity with 61.4% and 88.7% (AUC = 0.878; P < 0.001).

CONCLUSION

The CAJSA method presented a reliable assessment of disease-related joint space narrowing in patients suffering from RA with excellent sensitivity and specificity. By providing quantitative data, other scoring methods could be significantly improved, and thereby the accuracy of the diagnosis and a better therapeutic evaluation could be achieved.

Reproducibility and influence of hand rotation on computer-aided joint space analysis

OBJECTIVES

Computer-aided joint space analysis (CAJSA) is a recently developed, semi-automated tool to aid in the measurement of joint space margins based on hand radiographs. The objective of this study is to verify the potential effect of hand rotation during X-ray imaging on the measurement of joint space width using CAJSA and to evaluate the reproducibility of the CAJSA technique in healthy subjects and in patients with rheumatoid arthritis.

METHODS

All joint space distance (JSD) measurements were performed using CAJSA-technology at the metacarpophalangeal articulation based on conventional and digital hand radiographs.

RESULTS

I. Hand rotation showed an effect on the reproducibility with CV ranging between 0.39% (angulation 1°) and 1.66% (angulation 19°). II. Regarding the overall reproducibility of the CAJSA method, the intra-radiograph reproducibility of JSD was calculated with CV=0.54% for conventional images and CV=0.38% for digital images. The inter-radiograph reproducibility error was observed with CV=0.66% (conventional images) and CV=0.63% (digital images). III. The study revealed a reproducibility for CAJSA measurements in RA ranging between 0.37% (JSD-MCP ring finger; van der Heijde-modification of the Sharp method score 1) to 1.37% (JSD-MCP index finger; van der Heijde-modification of the Sharp method score 3).

CONCLUSION

CAJSA measures JSD at the metacarpophalangeal articulation with high reproducibility in healthy subjects and in patients with differing stages of rheumatoid arthritis. Additional findings show that hand rotation during X-ray imaging has an impact on the CAJSA measurements; thus avoiding acquisition of hand radiographs with a rotation error of more than 15 degrees can be recommended.

First assessment of three-dimensional quantitative photoacoustic tomography for in vivo detection of osteoarthritis in the finger joints

PURPOSE

The purpose of this pilot clinical study is to assess three-dimensional (3-D) quantitative photoacoustic tomography (qPAT) for in vivo detection of osteoarthritis (OA) in the finger joints.

METHODS

All subject data were handled in compliance with the rules and regulations concerning the privacy and security of protected health information under HIPAA. Seven female subjects (two OA patients and five healthy controls) entered the study and their distal interphalangeal (DIP) joints were examined by a 3-D photoacoustic scanner. 3-D optical absorption coefficient images of all the photoacoustically examined joints were recovered using a 3-D qPAT reconstruction algorithm.

RESULTS

The recovered quantitative photoacoustic images revealed obvious difference in the optical absorption coefficient of the joint cavity (cartilage and synovial fluid) between the OA and healthy joints. Quantitative analysis of the joints also indicated an apparent difference in the recovered joint spacing between the OA and healthy subjects.

CONCLUSION

This initial clinical evaluation suggests that it is feasible to detect osteoarthritis in the finger joints with our 3-D qPAT approach, which has paved the way to further statistically evaluate the diagnostic performance of the 3-D qPAT approach in comparison with radiography or magnetic resonance imaging (MRI) on a sample of hand osteoarthritis.

Feasibility study of semi-automated measurements of finger joint space widths

The purpose of this study is to evaluate technical feasibility based on image capturing conditions (film-focus distance (FFD), film sensitivity, film brand, exposure level and tube voltage) that potentially alter radiographs and consequently may influence the semi-automated measurement of joint space distance (JSD) by computer-aided joint space analysis (CAJSA) in rheumatoid arthritis and osteoarthritis. The radiographs of a left hand (deceased man) were acquired under systematically changing image capturing conditions (exposure level: 4-8 mAs; FFD: 90-130 cm; film sensitivity: 200/400 and tube voltage: 40-52 kV with different image modalities: conventional radiographs, original digital radiographs, digital print-outs). All JSD-measurements were performed with the CAJSA-technology (Radiogrammetry Kit, Version 1.3.6; Sectra; Sweden) at the metacarpal-phalangeal articulation. JSD-analysis was not influenced by changes of FFD, exposure level, film sensitivity or film brand. JSD showed significant variation caused by tube voltage (conventional: CV = 1.913% for Agfa and CV = 2.448% for Kodak; digital: CV = 0.741% for Philips print-outs and CV = 0.620% with original digital images versus CV = 2.185% for Siemens print-outs and 0.951% with original digital images). Computer-aided joint space analysis for JSD-measurements is unaffected by the following image capturing parameters: film-focus distance, film sensitivity, film brand and exposure level. An influence of tube voltage was detected in a lesser extent for original digital images compared to the printed digital as well as conventional versions. Consequently, a standardized tube voltage is essential for accurate reproductions of CAJSA-measurements in rheumatoid arthritis and osteoarthritis.

Computational radiology in skeletal radiography

Recent years have brought rapid developments in computational image analysis in musculo-skeletal radiology. Meanwhile the algorithms have reached a maturity that makes initial clinical use feasible. Applications range from joint space measurement to erosion quantification, and from fracture detection to the assessment of alignment angles. Current results of computational image analysis in radiography are very promising, but some fundamental issues remain to be clarified, among which the definition of the optimal trade off between automatization and operator-dependency, the integration of these tools into clinical work flow and last not least the proof of incremental clinical benefit of these methods.

Assessement of rheumatic diseases with computational radiology: current status and future potential

In recent years, several computational image analysis methods to assess disease progression in rheumatic diseases were presented. This review article explains the basics of these methods as well as their potential application in rheumatic disease monitoring, it covers radiography, sonography as well as magnetic resonance imaging in quantitative analysis frameworks.

Automatic assessment of the knee alignment angle on full-limb radiographs

In this study a fully automatic assessment of the knee alignment angles in full-limb radiographs was developed and compared to manual standard of reference measurements in a prospective manner. The data consisted of 28 knees which were gathered from total-leg radiographs of 15 patients (12 males and 3 females with a mean age of 29.4+/-6.9 years) consecutively. For statistical evaluation, a leave-one-out cross-validation was performed. The pattern recognition and consequently the fully automatic assessment were successful in all patients. The automatically measured angles highly correlated with the standard of reference (r=0.989). The mean absolute difference was 0.578 degrees (95% CI: 0.399-0.757 degrees ). 82% of the angles differed less than 1 degrees from the standard of reference, 46% differed less than 0.5 degrees and 31% differed less than 0.2 degrees . The automatic method showed a high agreement between repeated measurements (+0.515 degrees to -0.429 degrees ). The automatic assessment of alignment angles in full-limb radiographs were equal to the manual assessment. No measurement related user interaction was necessary to achieve results.

Implementation of Z-Scores as an Age- and Sex-independent Parameter for Estimating Joint Space Widths in Rheumatoid Arthritis

Objective.

To compare normative data of joint space distances (JSD) with the JSD of patients with rheumatoid arthritis (RA) as measured by computer-aided joint space analysis (CAJSA) at the metacarpophalangeal (MCP) articulations, and to differentiate age- and sex-related alterations from the disease-related joint space narrowing.

Methods.

In total, 256 healthy subjects and 248 patients with verified RA (following revised ACR criteria) underwent computerized semiautomated measurements of JSD (CAJSA, version 1.3.6) at the MCP articulation (JSD-MCP) based on digital radiographs. The Z-score, a comparative parameter that differentiates joint space alterations caused by RA-related cartilage destruction from age- and sex-related changes, was calculated.

Results.

Our data showed a relationship between measured joint space widths (MCP total and MCP thumb to little finger) and age for healthy subjects and also the RA group. The RA group revealed an age-related joint space narrowing that was surpassed by the RA-related narrowing of joint space widths classified by Sharp joint space narrowing score and resulting in smaller Z-scores for RA patients.

Conclusion.

The CAJSA technique seems to distinguish age-related JSD changes in healthy volunteers from RA-induced alterations. In addition the Z-score was also able to differentiate RA-dependent narrowing of JSD. Calculation of the Z-scores based on sex- and age-specific reference data may facilitate earlier identification of patients with RA, allowing initiation of a more optimal, individually adapted therapeutic strategy.

Automatic quantification of joint space narrowing and erosions in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic disease that affects and potentially destroys the joints of the appendicular skeleton. The precise and reproducible quantification of the progression of joint space narrowing and the erosive bone destructions caused by RA is crucial during treatment and in imaging biomarkers in clinical trials. Current manual scoring methods exhibit high interreader variability, even after intensive training, and thus, impede the efficient monitoring of the disease. We propose a fully automatic quantitative assessment of the radiographic changes that result from RA, to increase the accuracy, reproducibility, and speed of image interpretation. Initial joint location estimates are obtained by local linear mappings based on texture features. Bone contours are delineated by active shape models comprised of statistical models of bone shape and local texture. These models are refined by snakes which increase the accuracy and allow for a fitting of pathological deviations from the training population. The method then measures joint space widths and detects erosions on the bone contour. Joint space widths are measured with a coefficient of variation of 2%-7% for repeated measurements and erosion detection exhibits an area under the receiver operating characteristic (ROC) curve of 0.89. Model landmarks serve as a reference system along the contour. These landmarks enable the definition of joint regions and more specific follow-up monitoring. The automatic quantification allows for a remote analysis, relevant for multicenter clinical trials, and reduces the workload of clinical experts since parts of the process can be managed by nonexpert personnel.