Novel fast semi-automated software to segment cartilage for knee MR acquisitions

Quantitative measurement of cartilage morphology in osteoarthritis: current knowledge and future directions

Quantitative measures of cartilage morphology ("cartilage morphometry") extracted from high resolution 3D magnetic resonance imaging (MRI) sequences have been shown to be sensitive to osteoarthritis (OA)-related change and also to treatment interventions. Cartilage morphometry is therefore nowadays widely used as outcome measure for observational studies and randomized interventional clinical trials. The objective of this narrative review is to summarize the current status of cartilage morphometry in OA research, to provide insights into aspects relevant for the design of future studies and clinical trials, and to give an outlook on future developments. It covers the aspects related to the acquisition of MRIs suitable for cartilage morphometry, the analysis techniques needed for deriving quantitative measures from the MRIs, the quality assurance required for providing reliable cartilage measures, and the appropriate participant recruitment criteria for the enrichment of study cohorts with knees likely to show structural progression. Finally, it provides an overview over recent clinical trials that relied on cartilage morphometry as a structural outcome measure for evaluating the efficacy of disease-modifying OA drugs (DMOAD).

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

Prevalence of radiographic hip dysplasia in Japanese population-based study

OBJECTIVES

The purpose of this study was to measure the indices of radiographic developmental dysplasia of the hip (DDH) in a cross-sectional study of an elderly Japanese population.

METHODS

Hip radiographs of 427 informed, voluntary Japanese community-dwelling individuals (279 female and 148 male) aged 50-96 years-old were obtained from Miyagawa village in Japan through a health screening. The hip radiographs were measured by a custom-written, semi-automated MATLAB program. The center edge (CE) angle, acetabular roof obliquity (ARO), acetabular head index (AHI), and minimum joint space width (mJSW) were measured. We examined the associations between gender, side-of-hip, and age group on radiographic DDH and hip osteoarthritis (OA).

RESULTS

The mean CE angle was 31.0°. The mean ARO was 5.8°. The mean AHI was 88.2%. The mean mJSW was 4.0 mm. Of the total population, 29.9% had DDH and 4.0% had hip OA. Of those who had hip OA, 41.2% were secondary OA, and 58.8% were primary OA. The relationship between DDH and OA was not significant.

CONCLUSION

DDH is unlikely to be an important cause of hip OA in the present population-based study.

Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI

In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model's performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.

Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative

OBJECTIVES

Accurate and efficient knee cartilage and bone segmentation are necessary for basic science, clinical trial, and clinical applications. This work tested a multi-stage convolutional neural network framework for MRI image segmentation.

MATERIALS AND METHODS

Stage 1 of the framework coarsely segments images outputting probabilities of each voxel belonging to the classes of interest: 4 cartilage tissues, 3 bones, 1 background. Stage 2 segments overlapping sub-volumes that include Stage 1 probability maps concatenated to raw image data. Using six fold cross-validation, this framework was tested on two datasets comprising 176 images [88 individuals in the Osteoarthritis Initiative (OAI)] and 60 images (15 healthy young men), respectively.

RESULTS

On the OAI segmentation dataset, the framework produces cartilage segmentation accuracies (Dice similarity coefficient) of 0.907 (femoral), 0.876 (medial tibial), 0.913 (lateral tibial), and 0.840 (patellar). Healthy cartilage accuracies are excellent (femoral = 0.938, medial tibial = 0.911, lateral tibial = 0.930, patellar = 0.955). Average surface distances are less than in-plane resolution. Segmentations take 91 ± 11 s per knee.

DISCUSSION

The framework learns to automatically segment knee cartilage tissues and bones from MR images acquired with two sequences, producing efficient, accurate quantifications at varying disease severities.

Analyzing Femorotibial Cartilage Thickness Using Anatomically Standardized Maps: Reproducibility and Reference Data

Alterations in cartilage thickness (CTh) are a hallmark of knee osteoarthritis, which remain difficult to characterize at high resolution, even with modern magnetic resonance imaging (MRI), due to a paucity of standardization tools. This study aimed to assess a computational anatomy method producing standardized two-dimensional femorotibial CTh maps. The method was assessed with twenty knees, processed following three common experimental scenarios. Cartilage thickness maps were obtained for the femorotibial cartilages by reconstructing bone and cartilage mesh models in tree-dimension, calculating three-dimensional CTh maps, and anatomically standardizing the maps. The intra-operator accuracy (median (interquartile range, IQR) of -0.006 (0.045) mm), precision (0.152 (0.070) mm), entropy (7.02 (0.71) and agreement (0.975 (0.020))) results suggested that the method is adequate to capture the spatial variations in CTh and compare knees at varying osteoarthritis stages. The lower inter-operator precision (0.496 (0.132) mm) and agreement (0.808 (0.108)) indicate a possible loss of sensitivity to detect differences in a setting with multiple operators. The results confirmed the promising potential of anatomically standardized maps, with the lower inter-operator reproducibility stressing the need to coordinate operators. This study also provided essential reference data and indications for future research using CTh maps.

Quantification in Musculoskeletal Imaging Using Computational Analysis and Machine Learning: Segmentation and Radiomics

Although still limited in clinical practice, quantitative analysis is expected to increase the value of musculoskeletal (MSK) imaging. Segmentation aims at isolating the tissues and/or regions of interest in the image and is crucial to the extraction of quantitative features such as size, signal intensity, or image texture. These features may serve to support the diagnosis and monitoring of disease. Radiomics refers to the process of extracting large amounts of features from radiologic images and combining them with clinical, biological, genetic, or any other type of complementary data to build diagnostic, prognostic, or predictive models. The advent of machine learning offers promising prospects for automatic segmentation and integration of large amounts of data. We present commonly used segmentation methods and describe the radiomics pipeline, highlighting the challenges to overcome for adoption in clinical practice. We provide some examples of applications from the MSK literature.

Favourable alignment outcomes with MRI-based patient-specific instruments in total knee arthroplasty

PURPOSE

Patient-specific instruments (PSIs) are already in relatively common use, and their post-operative radiographic results are equal to those for total knee arthroplasty (TKA) with conventional instrumentation. PSI use requires a preoperative MRI scan, CT scan, or a combination of MRI and a long-leg standing radiograph. However, there is no consensus as to which of these modalities, MRI or CT, is the preferred imaging modality when performing TKA with PSIs.

METHODS

This systematic literature review and meta-analysis studied the differences in alignment outliers between CT- and MRI-based PSI for TKA. A search of the Cochrane Database of Systematic Reviews, MEDLINE/PubMed and Embase was conducted, without restriction on date of publication. Only level I evidence studies written in English that included TKA with the use of MRI- and CT-based PSI were selected. A meta-analysis was then performed of the rate of outliers in the biomechanical axis and individual femoral and tibial component alignment. Where considerable heterogeneity among studies was present or the data did not provide sufficient information for performing the meta-analysis, a qualitative synthesis was undertaken.

RESULTS

Twelve randomized controlled trials, studying 841 knees, were eligible for data extraction and meta-analysis. MRI-based PSI resulted in a significantly lower proportion of coronal plane outliers with regard to the lateral femoral component (OR 0.52, 95% CI 0.30-0.89, P = 0.02), without significant heterogeneity (n.s.). There were no significant differences regarding the biomechanical axis or frontal femoral and individual tibial component alignment.

CONCLUSION

This systematic review and meta-analysis demonstrate that alignment with MRI-based PSI is at least as good as, if not better than, that with CT-based PSI. To prevent for malalignment, MRI should be the imaging modality of choice when performing TKA surgery with PSI.

LEVEL OF EVIDENCE

I.

Quantitative measurement of medial femoral knee cartilage volume - analysis of the OA Biomarkers Consortium FNIH Study cohort

OBJECTIVE

Large studies of knee osteoarthritis (KOA) require well-characterized efficient methods to assess progression. We previously developed the local-area cartilage segmentation (LACS) software method, to measure cartilage volume on magnetic resonance imaging (MRI) scans. The present study further validates this method in a larger patient cohort and assesses predictive validity in a case-control study.

METHOD

The OA Biomarkers Consortium FNIH Project, a case-control study of KOA progression nested within the Osteoarthritis Initiative (OAI), includes 600 subjects in four subgroups based on radiographic and pain progression. Our software tool measured change in medial femoral cartilage volume in a central weight-bearing region. Different sized regions of cartilage were assessed to explore their sensitivity to change. The readings were performed on MRI scans at the baseline and 24-month visits. We used standardized response means (SRMs) for responsiveness and logistic regression for predictive validity.

RESULTS

Cartilage volume change was associated strongly with radiographic progression (odds ratios (OR) = 4.66; 95% confidence intervals (CI) = 2.85-7.62). OR were significant but of lesser magnitude for the combined radiographic and pain progression outcome (OR = 1.70; 95% CI = 1.40-2.07). For the full 600 subjects, theSRM was -0.51 for the largest segmented area. Smaller areas of cartilage segmentation were also able to predict the case-control status. The average reader time for the largest area was less than 20 min per scan. Smaller areas could be assessed with less reader time.

CONCLUSION

We demonstrated that the LACS method is fast, responsive, and associated with radiographic and pain progression, and is appropriate for existing and future large studies of KOA.

A radiological analysis of the difference between MRI- and CT-based patient-specific matched guides for total knee arthroplasty from the same manufacturer: a randomised controlled trial

AIMS

This prospective randomised controlled trial was designed to evaluate the outcome of both the MRI- and CT-based patient-specific matched guides (PSG) from the same manufacturer.

PATIENTS AND METHODS

A total of 137 knees in 137 patients (50 men, 87 women) were included, 67 in the MRI- and 70 in the CT-based PSG group. Their mean age was 68.4 years (47.0 to 88.9). Outcome was expressed as the biomechanical limb alignment (centre hip-knee-ankle: HKA-axis) achieved post-operatively, the position of the individual components within 3° of the pre-operatively planned alignment, correct planned implant size and operative data (e.g. operating time and blood loss).

RESULTS

The patient demographics (e.g. age, body mass index), correct planned implant size and operative data were not significantly different between the two groups. The proportion of outliers in the coronal and sagittal plane ranged from 0% to 21% in both groups. Only the number of outliers for the posterior slope of the tibial component showed a significant difference (p = 0.004) with more outliers in the CT group (n = 9, 13%) than in the MRI group (0%).

CONCLUSION

The post-operative HKA-axis was comparable in the MRI- and CT-based PSGs, but there were significantly more outliers for the posterior slope in the CT-based PSGs.

TAKE HOME MESSAGE

Alignment with MRI-based PSG is at least as good as, if not better, than that of the CT-based PSG, and is the preferred imaging modality when performing TKA with use of PSG. Cite this article: Bone Joint J 2016;98-B:786-92.

OARSI Clinical Trials Recommendations: Knee imaging in clinical trials in osteoarthritis

Significant advances have occurred in our understanding of the pathogenesis of knee osteoarthritis (OA) and some recent trials have demonstrated the potential for modification of the disease course. The purpose of this expert opinion, consensus driven exercise is to provide detail on how one might use and apply knee imaging in knee OA trials. It includes information on acquisition methods/techniques (including guidance on positioning for radiography, sequence/protocol recommendations/hardware for magnetic resonance imaging (MRI)); commonly encountered problems (including positioning, hardware and coil failures, sequences artifacts); quality assurance (QA)/control procedures; measurement methods; measurement performance (reliability, responsiveness, validity); recommendations for trials; and research recommendations.

Local area cartilage segmentation: a semiautomated novel method of measuring cartilage loss in knee osteoarthritis

OBJECTIVE

To assess the responsiveness and reader time of a novel semiautomated tool to detect knee cartilage loss over 2 years in subjects with knee osteoarthritis.

METHODS

A total of 122 subjects from the Osteoarthritis Initiative progression cohort were selected. A reader used the software method to segment cartilage on double-echo steady-state sequence scans in the medial compartment of the femur from the baseline and 24-month visits. Change in cartilage volume (ΔV) was measured at a fixed weight-bearing (WB) location with respect to the 3-dimensional coordinate system based on cylindrical coordinates. Change was measured for 5 regions of varying WB surface area centered on the fixed point. The average change (ΔV), the SD of ΔV, and the standardized response mean (SRM) are reported.

RESULTS

The SRM was −0.52 for the largest region and decreased in magnitude as smaller regions of cartilage were probed. The average evaluation time was <20 minutes per knee compartment, split approximately evenly between a technician and a trained reader.

CONCLUSION

The results establish that measurement of cartilage loss in a local region can be done efficiently and that the resultant measures are responsive to loss of cartilage over time. The coordinate system can potentially be used to objectively examine and establish a consistent location for all knees that is most responsive to change in cartilage volume. This technique can provide rapidly an objective quantitative measure of cartilage loss and could substantially reduce study costs for large trials and data sets.

Imaging research results from the osteoarthritis initiative (OAI): a review and lessons learned 10 years after start of enrolment

The Osteoarthritis Initiative (OAI) is a multicentre, prospective, observational, cohort study of knee osteoarthritis (OA) that began recruitment in 2004. The OAI provides public access to clinical and image data, enabling researchers to examine risk factors/predictors and the natural history of knee OA incidence and progression, and the qualification of imaging and other biomarkers. In this narrative review, we report imaging findings and lessons learned 10 years after enrolment has started. A literature search for full text articles published from the OAI was performed up to 31 December 2013 using Pubmed and the OAI web page. We summarise the rationale, design and imaging protocol of the OAI, and the history of OAI publications. We review studies from early partial, and later full OAI public data releases. The latter are structured by imaging method and tissue, reviewing radiography and then MRI findings on cartilage morphology, cartilage lesions and composition (T2), bone, meniscus, muscle and adipose tissue. Finally, analyses directly comparing findings from MRI and radiography are summarised. Ten years after the first participants were enrolled and first papers published, the OAI has become an invaluable resource to the OA research community. It has fuelled novel methodological approaches of analysing images, and has provided a wealth of information on OA pathophysiology. Continued collection and public release of long-term observations will help imaging measures to gain scientific and regulatory acceptance as 'prognostic' or 'efficacy of intervention' biomarkers, potentially enabling shorter and more efficient clinical trials that can test structure-modifying therapeutic interventions (NCT00080171).

Quantitative measurement of femoral condyle cartilage in the knee by MRI: validation study by multireaders

PURPOSE

To determine reproducibility of the femoral condyle cartilage volume (CV) in cross-sectional and longitudinal studies using various 3D imaging techniques at 1.5 T and 3 T.

MATERIALS AND METHODS

In 21 subjects with osteoarthritis, magnetic resonance imaging (MRI) including four different sequences (sagittal 3D fat suppressed spoiled gradient-echo [SPGR] at 1.5 T, fat suppressed fast low angle shot [FLASH] at 3 T, water-excitation dual echo steady state [DESS] at 3 T, and water-excitation multiecho data image combination [MEDIC] at 3 T) were acquired at baseline and ∼1 year later. The CV measured using semiautomated segmentation software by three readers was analyzed.

RESULTS

The mean of the interclass correlation coefficient between each reader from SPGR, FLASH, DESS, and MEDIC was 0.899, 0.948, 0.943, and 0.954, respectively. The mean CV (×10(4) mm(3) ) measured by each reader from SPGR/FLASH/DESS/MEDIC sequences was the following in this order: 1.34/1.52/1.50/1.35, 1.21/1.43/1.40/1.27, 1.22/1.37/1.36/1.22, and 1.17/1.36/1.35/1.21 by readers 1, 2, 3 (first analysis), and 3 (second analysis), respectively. There was no statistically significant difference in CV between any readers in any sequences. The CV measured on FLASH and DESS tended to be greater than that on SPGR or MEDIC.

CONCLUSION

Inter- and intraobserver reproducibility of cartilage segmentation using semiautomated software was validated. Although there was no statistical significance, there was a tendency of under- or overestimating CV by each sequence.

Non-traumatic anterior cruciate ligament abnormalities and their relationship to osteoarthritis using morphological grading and cartilage T2 relaxation times: data from the Osteoarthritis Initiative (OAI)

OBJECTIVES

The aim of this work was to study anterior cruciate ligament (ACL) degeneration in relation to MRI-based morphological knee abnormalities and cartilage T2 relaxation times in subjects with symptomatic osteoarthritis.

METHODS

Two radiologists screened the right knee MRI of 304 randomly selected participants in the Osteoarthritis Initiative cohort with symptomatic OA, for ACL abnormalities. Of the 52 knees with abnormalities, 28 had mucoid degeneration, 12 had partially torn ACLs, and 12 had completely torn ACLs. Fifty-three randomly selected subjects with normal ACLs served as controls. Morphological knee abnormalities were graded using the WORMS score. Cartilage was segmented and compartment-specific T2 values were calculated.

RESULTS

Compared to normal ACL knees, those with ACL abnormalities had a greater prevalence of, and more severe, cartilage, meniscal, bone marrow, subchondral cyst, and medial collateral ligament lesions (all p < 0.05). T2 measurements did not significantly differ by ACL status.

CONCLUSIONS

ACL abnormalities were associated with more severe degenerative changes, likely because of greater joint instability. T2 measurements may not be well suited to assess advanced cartilage degeneration.

Three-dimensional double-echo steady-state (3D-DESS) magnetic resonance imaging of the knee: establishment of flip angles for evaluation of cartilage at 1.5 T and 3.0 T

BACKGROUND

The effect of flip angle (FA) on synovial fluid and cartilage signal and on image contrast using three-dimensional double-echo steady-state (3D-DESS) sequence have only been performed with 1.0-T but not with 1.5-T or 3.0-T scanners.

PURPOSE

To identify the FA that gives the maximum synovial fluid and cartilage values, and to identify the FA at which maximum values of synovial fluid-cartilage contrast-to-noise ratio (CNR) in 3D-DESS sequences when 1.5-T and 3.0-T scanners are used.

MATERIAL AND METHODS

Using 3D-DESS with water-excitation pulse, mid-sagittal plane images of the knees of 10 healthy volunteers (5 men, 5 women; age range, 21-42 years) were obtained with FA varying from 10° to 90°. Synovial fluid signals, cartilage signals, and background were measured at each FA, and the FA that gave the highest synovial fluid and cartilage values was obtained. Synovial fluid-cartilage CNR was also calculated, and the FA that gave the largest CNR was obtained.

RESULTS

At 1.5 T, the maximum synovial fluid signal was at FA 90°, and the maximum cartilage signal was at FA 30°. Synovial fluid-cartilage CNR was highest at FA 90° (P < 0.05). At 3.0 T, the maximum synovial fluid signal was at FA 90°, and the maximum cartilage signal was at FA 20°. Synovial fluid-cartilage CNR was highest at FA 90° (P < 0.05).

CONCLUSION

In order to improve the visibility of cartilage itself, FA settings of 30° at 1.5 T and 20° at 3.0 T are apparently ideal. For observing the cartilage surface, the most effective FA setting is 90° for both 1.5 T and 3.0 T.

Equivalence and precision of knee cartilage morphometry between different segmentation teams, cartilage regions, and MR acquisitions

OBJECTIVE

To compare precision and evaluate equivalence of femorotibial cartilage volume (VC) and mean cartilage thickness over total area of bone (ThCtAB.Me) from independent segmentation teams using identical Magnetic Resonance (MR) images from three series: sagittal 3D Dual Echo in the Steady State (DESS), coronal multi-planar reformat (DESS-MPR) of DESS and coronal 3D Fast Low Angle SHot (FLASH).

DESIGN

Nineteen subjects underwent test-retest MR imaging at 3 T. Four teams segmented the cartilage using prospectively defined plate regions and rules. Mixed models analysis of the pooled data were used to evaluate the effect of acquisition, team and plate on precision and Pearson correlations and mixed models were used to evaluate equivalence.

RESULTS

Segmentation team differences dominated measurement variability in most cartilage regions for all image series. Precision of VC and ThCtAB.Me differed significantly by team and cartilage plate, but not between FLASH and DESS. Mean values of VC and ThCtAB.Me differed by team (P < 0.05) for DESS, FLASH and DESS-MPR. FLASH VC was 4-6% larger than DESS in the medial tibia and lateral central femur, and FLASH ThCtAB.Me was 5-6% larger in the medial tibia, but 4-8% smaller in the medial central femur. Correlations between DESS and FLASH for VC and ThCtAB.Me were high (r = 0.90-0.97), except for DESS vs FLASH medial central femur ThCtAB.Me (r = 0.81-0.83).

CONCLUSIONS

Cartilage morphology metrics from different image contrasts had similar precision, were generally equivalent, and may be combined for cross-sectional analyses if potential systematic offsets are accounted for. Data from different teams should not be pooled unless equivalence is demonstrated for cartilage metrics of interest.

Unsupervised segmentation and quantification of anatomical knee features: data from the Osteoarthritis Initiative

This paper presents a fully automated method for segmenting articular knee cartilage and bone from in vivo 3-D dual echo steady state images. The magnetic resonance imaging (MRI) datasets were obtained from the Osteoarthritis Initiative (OAI) pilot study and include longitudinal images from controls and subjects with knee osteoarthritis (OA) scanned twice at each visit (baseline, 24 month). Initially, human experts segmented six MRI series. Five of the six resultant sets served as reference atlases for a multiatlas segmentation algorithm. The methodology created precise knee segmentations that were used to extract articular cartilage volume, surface area, and thickness as well as subchondral bone plate curvature. Comparison to manual segmentation showed Dice similarity coefficient (DSC) of 0.88 and 0.84 for the femoral and tibial cartilage. In OA subjects, thickness measurements showed test-retest precision ranging from 0.014 mm (0.6%) at the femur to 0.038 mm (1.6%) at the femoral trochlea. In the same population, the curvature test-retest precision ranged from 0.0005 mm(-1) (3.6%) at the femur to 0.0026 mm(-1) (11.7%) at the medial tibia. Thickness longitudinal changes showed OA Pearson correlation coefficient of 0.94 for the femur. In conclusion, the fully automated segmentation methodology produces reproducible cartilage volume, thickness, and shape measurements valuable for the study of OA progression.

The evolution of articular cartilage imaging and its impact on clinical practice

Over the past four decades, articular cartilage imaging has developed rapidly. Imaging now plays a critical role not only in clinical practice and therapeutic decisions but also in the basic research probing our understanding of cartilage physiology and biomechanics.

Quantification of cartilage loss in local regions of knee joints using semi-automated segmentation software: analysis of longitudinal data from the Osteoarthritis Initiative (OAI)

INTRODUCTION

Quantitative cartilage morphometry is a valuable tool to assess osteoarthritis (OA) progression. Current methodologies generally evaluate cartilage morphometry in a full or partial sub-region of the cartilage plates. This report describes the evaluation of a semi-automated cartilage segmentation software tool capable of quantifying cartilage loss in a local indexed region.

METHODS

We examined the baseline and 24-month follow-up MRI image sets of twenty-four subjects from the progression cohort of Osteoarthritis Initiative (OAI), using the Kellgren-Lawrence (KL) score of 3 at baseline as the inclusion criteria. A radiologist independently marked a single region of local thinning for each subject, and three additional readers, blinded to time point, segmented the cartilage using a semi-automated software method. Each baseline-24-month segmentation pair was then registered in 3D and the change in cartilage volume was measured.

RESULTS

After 3D registration, the change in cartilage volume was calculated in specified regions centered at the marked point, and for the entire medial compartment of femur. The responsiveness was quantified using the standardized response mean (SRM) values and the percentage of subjects that showed a loss in cartilage volume. The most responsive measure of change was SRM=-1.21, and was found for a region of 10mm from the indexed point.

DISCUSSION

The results suggest that measurement of cartilage loss in a local region is superior to larger areas and to the total plate. There also may be an optimal region size (10mm from an indexed point) in which to measure change. In principle, the method is substantially faster than segmenting entire plates or sub-regions.

Comparison of radiographic joint space width with magnetic resonance imaging cartilage morphometry: analysis of longitudinal data from the Osteoarthritis Initiative

OBJECTIVE

Magnetic resonance imaging (MRI) and radiography are established imaging modalities for the assessment of knee osteoarthritis (OA). The objective of our study was to compare the responsiveness of radiographic joint space width (JSW) with MRI-derived measures of cartilage morphometry for OA progression in participants from the Osteoarthritis Initiative (OAI).

METHODS

This study examined the baseline and 12-month visits of a subset of 150 subjects from the OAI. Measurement of radiographic JSW was facilitated by the use of automated software that delineated the femoral and tibial margins of the joint. Measures of medial compartment minimum JSW and JSW at fixed locations were compared with cartilage morphometry measures derived from MRI. The results were stratified by Kellgren/Lawrence (K/L) scale grade and by tibiofemoral anatomic axis angle. In order to examine the relative responsiveness of various techniques, we calculated the standardized response mean (SRM) between the 2 visits.

RESULTS

The SRM for radiographic JSW measured at the optimal location was -0.32 compared with -0.39 for the most responsive MRI measure. For the subgroup with a K/L scale grade of 2 or 3, the most responsive SRM values were -0.34 for radiographic JSW and -0.42 for MRI.

CONCLUSION

Our study demonstrates that new measures using a software analysis of digital knee radiographic images are comparable with MRI in detecting OA progression, and potentially superior when considering the cost-effectiveness of the 2 imaging modalities.

Anatomically anchored template-based level set segmentation: application to quadriceps muscles in MR images from the Osteoarthritis Initiative

In this paper, we present a semi-automated segmentation method for magnetic resonance images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely, sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback-Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were as follows: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; and vastus medialis, 0.69 ± 0.16.

Intra- and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method

OBJECTIVE

We developed a semi-automated method based on a graph-cuts algorithm for segmentation and volumetric measurements of the cartilage from high-resolution knee magnetic resonance (MR) images from the Osteoarthritis Initiative (OAI) database and assessed the intra- and inter-observer reproducibility of measurements obtained via this method.

DESIGN

MR image sets from 20 subjects of varying Kellgren-Lawrence (KL) grades (from 0 to IV) on fixed flexion knee radiographs were selected from the baseline double-echo and steady-state (DESS) knee MR images in the OAI database (0.B.1 Imaging Data set). Two trained radiologists independently performed the segmentation of knee cartilage twice using the semi-automated method. The volumes of segmented cartilage were computed and compared. The intra- and inter-observer reproducibility were determined by means of the coefficient of variation (CV%) of repeated cartilage segmented volume measurements. The subjects were also divided into the low- (0, I or II) and high-KL (III or IV) groups. The differences in cartilage volume measurements and CV% within and between the observers were tested with t tests.

RESULTS

The mean (+/-SD) intra-observer CV% for the 20 cases was 1.29 (+/-1.05)% for observer 1 and 1.67 (+/-1.14)% for observer 2, while the mean (+/-SD) inter-observer CV% was 1.31 (+/-1.26)% for session 1 and 1.79 (+/-1.72)% for session 2. There was no significant difference between the two intra-observer CV%'s (P=0.272) and between the two inter-observer CV%'s (P=0.353). The mean intra-observer CV% of the low-KL group was significantly smaller than that for the high-KL group for observer 1 (0.83 vs 1.86%: P=0.025). The segmentation processing times used by the two observers were significantly different (observer 1 vs 2): (mean 49+/-12 vs 33+/-6min) for session 1 and (49+/-8 vs 32+/-8min) for session 2.

CONCLUSION

The semi-automated graph-cuts method allowed us to segment and measure cartilage from high-resolution 3T MR images of the knee with high intra- and inter-observer reproducibility in subjects with varying severity of OA.

Magnetic resonance image segmentation using semi-automated software for quantification of knee articular cartilage---initial evaluation of a technique for paired scans

PURPOSE

Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets.

METHODS

Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm x 0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%).

RESULTS

For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me.

CONCLUSION

Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA.

The osteoarthritis initiative: report on the design rationale for the magnetic resonance imaging protocol for the knee

OBJECTIVES

To report on the process and criteria for selecting acquisition protocols to include in the osteoarthritis initiative (OAI) magnetic resonance imaging (MRI) study protocol for the knee.

METHODS

Candidate knee MR acquisition protocols identified from the literature were first optimized at 3Tesla (T). Twelve knees from 10 subjects were scanned one time with each of 16 acquisitions considered most likely to achieve the study goals and having the best optimization results. The resultant images and multi-planar reformats were evaluated for artifacts and structural discrimination of articular cartilage at the cartilage-fluid, cartilage-fat, cartilage-capsule, cartilage-meniscus and cartilage-cartilage interfaces.

RESULTS

The five acquisitions comprising the final OAI MRI protocol were assembled based on the study goals for the imaging protocol, the image evaluation results and the need to image both knees within a 75 min time slot, including positioning. For quantitative cartilage morphometry, fat-suppressed, 3D dual-echo in steady state (DESS) acquisitions appear to provide the best universal cartilage discrimination.

CONCLUSIONS

The OAI knee MRI protocol provides imaging data on multiple articular structures and features relevant to knee OA that will support a broad range of existing and anticipated measurement methods while balancing requirements for high image quality and consistency against the practical considerations of a large multi-center cohort study. Strengths of the final knee MRI protocol include cartilage quantification capabilities in three planes due to multi-planar reconstruction of a thin slice, high spatial resolution 3D DESS acquisition and the multiple, non-fat-suppressed image contrasts measured during the T2 relaxation time mapping acquisition.

Accuracy of MRI vs CT imaging with particular reference to patient specific templates for total knee replacement surgery

BACKGROUND

The present study compares the accuracy of MRI and CT imaging for the manufacture of patient-specific templates for total knee replacement surgery.

METHODS

A total of 10 ovine knees were imaged using MRI and CT scanners. Each set of images was reconstructed in 3D and then used to manufacture physical models of each bone, using rapid prototyping technology. After imaging the soft tissues were removed and specific measurements of the bony anatomy compared with measurements from the MRI and CT models.

RESULTS

Bone models generated from MRI scans were dimensionally less accurate than those generated from CT scans. Furthermore, the bone models generated from MRI scans were visibly inferior to those generated from the CT scans.

CONCLUSIONS

Current MRI scans do not offer a viable alternative to CT. Although adaptation of the template system to accommodate MRI imaging is possible, the changes required are neither practical nor desirable.

Semiautomated three-dimensional segmentation software to quantify carpal bone volume changes on wrist CT scans for arthritis assessment

Rapid progression of joint destruction is an indication of poor prognosis in patients with rheumatoid arthritis. Computed tomography (CT) has the potential to serve as a gold standard for joint imaging since it provides high resolution three-dimensional (3D) images of bone structure. The authors have developed a method to quantify erosion volume changes on wrist CT scans. In this article they present a description and validation of the methodology using multiple scans of a hand phantom and five human subjects. An anthropomorphic hand phantom was imaged with a clinical CT scanner at three different orientations separated by a 30-deg angle. A reader used the semiautomated software tool to segment the individual carpal bones of each CT scan. Reproducibility was measured as the root-mean-square standard deviation (RMMSD) and coefficient of variation (CoV) between multiple measurements of the carpal volumes. Longitudinal erosion progression was studied by inserting simulated erosions in a paired second scan. The change in simulated erosion size was calculated by performing 3D image registration and measuring the volume difference between scans in a region adjacent to the simulated erosion. The RMSSD for the total carpal volumes was 21.0 mm3 (CoV = 1.3%) for the phantom, and 44.1 mm3 (CoV = 3.0%) for the in vivo subjects. Using 3D registration and local volume difference calculations, the RMMSD was 1.0-3.0 mm3 The reader time was approximately 5 min per carpal bone. There was excellent agreement between the measured and simulated erosion volumes. The effect of a poorly measured volume for a single erosion is mitigated by the large number of subjects that would comprise a clinical study and that there will be many erosions measured per patient. CT promises to be a quantifiable tool to measure erosion volumes and may serve as a gold standard that can be used in the validation of other modalities such as magnetic resonance imaging.

Non-invasive in vivo quantification of the medial tibial cartilage thickness progression in an osteoarthritis rabbit model with quantitative 3D high resolution micro-MRI

OBJECTIVE

To develop a quantitative non-invasive in vivo three-dimensional (3D) high resolution (HR) micro-magnetic resonance imaging (microMRI) protocol to measure the medial tibial cartilage thickness (MT.ThC) in the normal rabbit and in the anterior cruciate ligament transection (ACLT) rabbit model of osteoarthritis and quantify the progression of MT.ThC.

METHODS

The left knee of 10 control and 40 operated rabbits was imaged in vivo with a 7T microMRI system at 3 and 5 months after ACLT. A 3D fast low angle short (FLASH) fat-suppressed MRI protocol was implemented leading to 44x176 microm(3) spatial resolution and to 44 microm(3) isotropic voxel after cubic interpolation. Semi-automatic MT.ThC measurements were made in 3D, in four different locations, in vivo and longitudinally in both groups. At 5 months, gross macroscopy, visual analogical evaluation of the cartilage and histology were compared to the MR-based MT.ThC.

RESULTS

At 3 and 5 months, the MT.ThC measured in the minimum interbone distance area was the thinnest MR-based MT.ThC. It was significantly lower in the operated group and among the four evaluated MT.ThC, it was the most discriminative between the normal and the operated groups (P<0.05). The MT.ThC measured in the minimum interbone distance area was also the most sensitive to change in the operated group (66.4% MT.ThC loss, P=0.003) while no significant changes were observed in the control group.

CONCLUSION

Quantitative 3D HR microMRI allowed for non-invasive longitudinal MT.ThC measurements in four different locations in both the normal and the operated rabbits. We concluded the MT.ThC measured in the minimum interbone distance area reflected the severity of the disease and was the most effective to measure the progression of the medial tibial cartilage destruction.

An MRI derived articular cartilage visualization framework

OBJECTIVE

We present a multi-dimensional framework for the visualization of femoral articular cartilage. The framework comprises methods for visualizing and quantifying changes in cartilage thickness and surface morphology derived from MRI based cartilage segmentation. Adequate visualization of cartilage allows accurate and clinically meaningful assessment of cartilage surface morphology and thickness. In current practice the routine use of conventional 2D MR images provides limited qualitative information and is inconvenient because the imaged volume has to be reviewed slice by slice.

METHOD

A Graphical User Interface (GUI) that encapsulates the framework described above was developed. In the first stage of the analysis MR images of the knee are segmented to delineate cartilage boundaries. Cartilage thicknesses are subsequently measured. The detected points and corresponding thickness data are utilized to produce a visualization framework.

RESULTS

The system was tested using data from six example patients. The spatial distribution of cartilage on the articular surface was visualized using a 3D WearMap. The 2D WearMap allowed the entire cartilage surface to be studied at once. Quantitative interaction with the 2D WearMap was assisted by the ability to ascertain cartilage surface dimensions and TrackBack from a point of interest to the original MR image. As a result, the detection of wear patterns and lesions was efficiently carried out.

CONCLUSION

A means of quantitatively visualizing cartilage defects non-invasively is presented. This stands to reduce clinician reporting times, as well as allowing quantitative follow-up that facilitates osteoarthritis (OA) screening and planning/evaluating interventions.