Reliability of an efficient MRI-based method for estimation of knee cartilage volume using surface registration

A reproducibility study of knee cartilage volume and thickness values derived by fully automatic segmentation based on three-dimensional dual-echo in steady state data from 1.5 T and 3 T magnetic resonance imaging

OBJECTIVE

To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data.

METHODS

The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan-rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared.

RESULTS

No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan-rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland-Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences.

CONCLUSION

The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory.

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.

A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative

Knee osteoarthritis (OA) is a degenerative joint disease that is prevalent in advancing age. The pathology of OA disease is still unclear, and there are no effective interventions that can completely alter the OA disease process. Magnetic resonance (MR) image evaluation is sensitive for depicting early changes of knee OA, and therefore important for early clinical intervention for relieving the symptom. Automated cartilage segmentation based on MR images is a vital step in experimental longitudinal studies to follow-up the patients and prospectively define a new quantitative marker from OA progression. In this paper, we develop a deep learning-based coarse-to-fine approach for automated knee bone, cartilage, and meniscus segmentation with high computational efficiency. The proposed method is evaluated using two-fold cross-validation on 507 MR volumes (81,120 slices) with OA from the Osteoarthritis Initiative (OAI)1 dataset. The mean dice similarity coefficients (DSCs) of femoral bone (FB), tibial bone (TB), femoral cartilage (FC), and tibial cartilage (TC) separately are 99.1%, 98.2%, 90.9%, and 85.8%. The time of segmenting each patient is 12 s, which is fast enough to be used in clinical practice. Our proposed approach may provide an automated toolkit to help computer-aided quantitative analyses of OA images.

Vector Field Convolution-Based B-Spline Deformation Model for 3D Segmentation of Cartilage in MRI

In this paper, a novel 3D vector field convolution (VFC)-based B-spline deformation model is proposed for accurate and robust cartilage segmentation. Firstly, the anisotropic diffusion method is utilized for noise reduction, and the Sinc interpolation method is employed for resampling. Then, to extract the rough cartilage, features derived from

A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods

This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage compartment from 3-D MR imaging and used principal component analysis (PCA) analysis to process the feature set. Four machine learning methods (artificial neural network (ANN), support vector machine, random forest, and naïve Bayes) were employed to predict the progression of OA, which was measured by the change of Kellgren and Lawrence (KL) grade, Joint Space Narrowing on Medial compartment (JSM) grade, and Joint Space Narrowing on Lateral compartment (JSL) grade. To examine the different effects of medial and lateral informative locations, we have divided the 36-D feature set into a 18-D medial feature set and a 18-D lateral feature set and run the experiment on four classifiers separately. Experiment results showed that the medial feature set generated better prediction performance than the lateral feature set, while using the total 36-D feature set generated the best. PCA analysis is helpful in feature space reduction and performance improvement. For KL grade prediction, the best performance was achieved by ANN with AUC = 0.761 and F-measure = 0.714. For JSM grade prediction, the best performance was achieved by random forest with AUC = 0.785 and F-measure = 0.743, while for JSL grade prediction, the best performance was achieved by ANN with AUC = 0.695 and F-measure = 0.796. As experiment results showing that the informative locations on medial compartment provide more distinguishing features than informative locations on the lateral compartment, it could be considered to select more points from the medial compartment while reducing the number of points from the lateral compartment to improve clinical CDI design.

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

OBJECTIVES

To evaluate the accuracy of magnetic resonance imaging measurements of cartilage tissue-mimicking phantoms and to determine a combination of magnetic resonance imaging parameters to optimize accuracy while minimizing scan time.

METHOD

Edge dimensions from 4 rectangular agar phantoms ranging from 10.5 to 14.5 mm in length and 1.25 to 5.5 mm in width were independently measured by two readers using a steel ruler. Coronal T1 spin echo (T1 SE), fast spoiled gradient-recalled echo (FSPGR) and multiplanar gradient-recalled echo (GRE MPGR) sequences were used to obtain phantom images on a 1.5-T scanner.

RESULTS

Inter- and intra-reader reliability were high for both direct measurements and for magnetic resonance imaging measurements of phantoms. Statistically significant differences were noted between the mean direct measurements and the mean magnetic resonance imaging measurements for phantom 1 when using a GRE MPGR sequence (512x512 pixels, 1.5-mm slice thickness, 5:49 min scan time), while borderline differences were noted for T1 SE sequences with the following parameters: 320x320 pixels, 1.5-mm slice thickness, 6:11 min scan time; 320x320 pixels, 4-mm slice thickness, 6:11 min scan time; and 512x512 pixels, 1.5-mm slice thickness, 9:48 min scan time. Borderline differences were also noted when using a FSPGR sequence with 512x512 pixels, a 1.5-mm slice thickness and a 3:36 min scan time.

CONCLUSIONS

FSPGR sequences, regardless of the magnetic resonance imaging parameter combination used, provided accurate measurements. The GRE MPGR sequence using 512x512 pixels, a 1.5-mm slice thickness and a 5:49 min scan time and, to a lesser degree, all tested T1 SE sequences produced suboptimal accuracy when measuring the widest phantom.

Development of a Rapid Cartilage Damage Quantification Method for the Lateral Tibiofemoral Compartment Using Magnetic Resonance Images: Data from the Osteoarthritis Initiative

The purpose of this study was to expand and validate the cartilage damage index (CDI) to detect cartilage damage in the lateral tibiofemoral compartment. We used an iterative 3-step process to develop and validate the lateral CDI: development (100 knees), testing (80 knees), and validation (100 knees). The validation set included 100 knees from the Osteoarthritis Initiative that was enriched to include all grades of lateral joint space narrowing (JSN, 0–3). Measurement of the CDI was rapid at 7.4 (s.d. 0.73) minutes per knee pair (baseline and follow-up of one knee). The intratester reliability is good (intraclass correlation coefficient (3, 1 model) = 0.86 to 0.98). At baseline, knees with greater KL grade and lateral JSN had a lower mean CDI (i.e., greater cartilage damage). Baseline lateral CDI is associated with both lateral JSW (r = 0.81 to 0.85, p < 0.01) and HKA (r = −0.30 to −0.33, p < 0.05). The SRM is good (lateral femur SRM = −0.76; lateral tibia SRM = −0.73; lateral tibiofemoral total SRM = −0.87). The lateral tibiofemoral CDI quantification allows for rapid evaluation and is reliable and responsive, with good construct validity. It may be an efficient method to measure lateral tibiofemoral articular cartilage in large clinical and epidemiologic studies.

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.

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

Imaging of hip in osteoarthritis (OA) has seen considerable progress in the past decade, with the introduction of new techniques that may be more sensitive to structural disease changes. The purpose of this expert opinion, consensus driven recommendation is to provide detail on how to apply hip imaging in disease modifying clinical 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, artifacts associated with various MRI sequences); quality assurance/control procedures; measurement methods; measurement performance (reliability, responsiveness, and validity); recommendations for trials; and research recommendations.

Development of a rapid knee cartilage damage quantification method using magnetic resonance images

BACKGROUND

Cartilage morphometry based on magnetic resonance images (MRIs) is an emerging outcome measure for clinical trials among patients with knee osteoarthritis (KOA). However, current methods for cartilage morphometry take many hours per knee and require extensive training on the use of the associated software. In this study we tested the feasibility, reliability, and construct validity of a novel osteoarthritis cartilage damage quantification method (Cartilage Damage Index [CDI]) that utilizes informative locations on knee MRIs.

METHODS

We selected 102 knee MRIs from the Osteoarthritis Initiative that represented a range of KOA structural severity (Kellgren Lawrence [KL] Grade 0 - 4). We tested the intra- and inter-tester reliability of the CDI and compared the CDI scores against different measures of severity (radiographic joint space narrowing [JSN] grade, KL score, joint space width [JSW]) and static knee alignment, both cross-sectionally and longitudinally.

RESULTS

Determination of the CDI took on average14.4 minutes (s.d. 2.1) per knee pair (baseline and follow-up of one knee). Repeatability was good (intra- and inter-tester reliability: intraclass correlation coefficient >0.86). The mean CDI scores related to all four measures of osteoarthritis severity (JSN grade, KL score, JSW, and knee alignment; all p values < 0.05). Baseline JSN grade and knee alignment also predicted subsequent 24-month longitudinal change in the CDI (p trends <0.05). During 24 months, knees with worsening in JSN or KL grade (i.e. progressors) had greater change in CDI score.

CONCLUSIONS

The CDI is a novel knee cartilage quantification method that is rapid, reliable, and has construct validity for assessment of medial tibiofemoral osteoarthritis structural severity and its progression. It has the potential to addresses the barriers inherent to studies requiring assessment of cartilage damage on large numbers of knees, and as a biomarker for knee osteoarthritis progression.

Methodologies for semiquantitative evaluation of hip osteoarthritis by magnetic resonance imaging: approaches based on the whole organ and focused on active lesions

OBJECTIVE

As a wider variety of therapeutic options for osteoarthritis (OA) becomes available, there is an increasing need to objectively evaluate disease severity on magnetic resonance imaging (MRI). This is more technically challenging at the hip than at the knee, and as a result, few systematic scoring systems exist. The OMERACT (Outcome Measures in Rheumatology) filter of truth, discrimination, and feasibility can be used to validate image-based scoring systems. Our objective was (1) to review the imaging features relevant to the assessment of severity and progression of hip OA; and (2) to review currently used methods to grade these features in existing hip OA scoring systems.

METHODS

A systematic literature review was conducted. MEDLINE keyword search was performed for features of arthropathy (such as hip + bone marrow edema or lesion, synovitis, cyst, effusion, cartilage, etc.) and scoring system (hip + OA + MRI + score or grade), with a secondary manual search for additional references in the retrieved publications.

RESULTS

Findings relevant to the severity of hip OA include imaging markers associated with inflammation (bone marrow lesion, synovitis, effusion), structural damage (cartilage loss, osteophytes, subchondral cysts, labral tears), and predisposing geometric factors (hip dysplasia, femoral-acetabular impingement). Two approaches to the semiquantitative assessment of hip OA are represented by Hip OA MRI Scoring System (HOAMS), a comprehensive whole organ assessment of nearly all findings, and the Hip Inflammation MRI Scoring System (HIMRISS), which selectively scores only active lesions (bone marrow lesion, synovitis/effusion). Validation is presently confined to limited assessment of reliability.

CONCLUSION

Two methods for semiquantitative assessment of hip OA on MRI have been described and validation according to the OMERACT Filter is limited to evaluation of reliability.

Responsiveness and reliability of MRI in knee osteoarthritis: a meta-analysis of published evidence

OBJECTIVE

To summarize literature on the responsiveness and reliability of MRI-based measures of knee osteoarthritis (OA) structural change.

METHODS

A literature search was conducted using articles published up to the time of the search, April 2009. 1338 abstracts obtained with this search were preliminarily screened for relevance and of these, 243 were selected for data extraction. For this analysis we extracted data on reliability and responsiveness for every reported synovial joint tissue as it relates to MRI measurement in knee OA. Reliability was defined by inter- and intra-reader intra-class correlation (ICC), or coefficient of variation, or kappa statistics. Responsiveness was defined as standardized response mean (SRM) - ratio of mean of change over time divided by standard deviation of change. Random-effects models were used to pool data from multiple studies.

RESULTS

The reliability analysis included data from 84 manuscripts. The inter-reader and intra-reader ICC were excellent (range 0.8-0.94) and the inter-reader and intra-reader kappa values for quantitative and semi-quantitative measures were all moderate to excellent (range 0.52-0.88). The lowest value (kappa=0.52) corresponded to semi-quantitative synovial scoring intra-reader reliability and the highest value (ICC=0.94) for semi-quantitative cartilage morphology. The responsiveness analysis included data from 42 manuscripts. The pooled SRM for quantitative measures of cartilage morphometry for the medial tibiofemoral joint was -0.86 (95% confidence intervals (CI) -1.26 to -0.46). The pooled SRM for the semi-quantitative measurement of cartilage morphology for the medial tibiofemoral joint was 0.55 (95% CI 0.47-0.64). For the quantitative analysis, SRMs are negative because the quantitative value, indicating a loss of cartilage, goes down. For the semi-quantitative analysis, SRMs indicating a loss in cartilage are positive (increase in score).

CONCLUSION

MRI has evolved substantially over the last decade and its strengths include the ability to visualize individual tissue pathologies, which can be measured reliably and with good responsiveness using both quantitative and semi-quantitative techniques.

Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee

In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.

In vivo cartilage contact deformation of human ankle joints under full body weight

Quantitative data on in vivo deformation of articular cartilage is important for understanding the articular joint function and the etiology of degenerative joint diseases such as osteoarthritis. This study experimentally determined the in vivo cartilage thickness distribution and articular cartilage contact strain distribution in human ankle joints under full body weight loading conditions using a combined dual fluoroscopic and magnetic resonance imaging technique. The average cartilage thickness with the joint non-weight bearing was found to be 1.43 mm +/- 0.15 mm and 1.42 mm +/- 0.18 mm in the distal tibial and proximal talar cartilage layers, respectively. During weight bearing on a single leg, the strain distribution data revealed that 42.4% +/- 15.7% of the contact area had contact strain higher than 15% in the ankle joint. Peak cartilage contact strain reached 34.5% +/- 7.3%. This quantitative data on in vivo human cartilage morphology and deformation demonstrated that the cartilage may undergo large deformations under the loading conditions experienced in human ankle joints during daily activities. The in vivo cartilage contact deformation can be used as displacement boundary conditions in three-dimensional (3D) finite element models of the joint to calculate in vivo 3D articular cartilage contact stress/strain distributions.

A technique for regional analysis of femorotibial cartilage thickness based on quantitative magnetic resonance imaging

The objective of this work was to develop a methodology for measuring cartilage thickness in anatomically based subregions in the tibial and in the central weight-bearing femoral cartilage from magnetic resonance (MR) images. The tibial plateau was divided into a central area of the total subchondral bone area (tAB), and anterior, posterior, internal, and external subregions surrounding it. In the weight-bearing femoral condyles, central, internal, and external subregions were determined. The Euclidean distance between the tAB and cartilage surface was used for determining cartilage thickness. The reproducibility of the method was evaluated on test-retest data sets of 12 participants (six healthy, six with osteoarthritis). The subregion size was varied systematically to study the influence on the reproducibility. The size of the subregions was highly consistent under conditions of repositioning (standard deviation 0.0%-0.3%). The precision errors for regional mean cartilage thickness measurements ranged from 19 microm (1.5%) to 84 microm (4.7%). The computation of regional cartilage thickness values from segmented MR images is shown to be highly reproducible and robust under conditions of joint repositioning. In longitudinal studies, this technique may substantially enhance the ability of quantitative MRI to monitor structural changes in osteoarthritis at narrow time intervals.

Determination of real-time in-vivo cartilage contact deformation in the ankle joint

The knowledge of real-time in-vivo cartilage deformation is important for understanding of cartilage function and biomechanical factors that may relate to cartilage degeneration. This study investigated cartilage contact area and peak contact compressive strain of four healthy human ankle joints as a function of time using a combined magnetic resonance (MR) and dual-orthogonal fluoroscopic imaging technique. Each ankle was subjected to a different constant loading (between 700 and 820 N). The cartilage contact deformation was obtained from the first second to 300 s after the load was applied. In all ankle joints studied in this paper, contact strains increased to 24-38% at first 20 s after loading. Beyond 20 s, the change of cartilage contact deformation was relatively small and varied in a rate close to zero beyond 50 s. These data indicated that the cartilage contact areas and contact strain could raise dramatically right after loading and reach a relatively stable condition within 1 min after constant loading. The history of cartilage deformation determined in this study may provide a real-time boundary condition for 3D finite element simulation of in vivo cartilage contact stress in the joint as a function of time.

Automatic segmentation of the bone and extraction of the bone-cartilage interface from magnetic resonance images of the knee

The accurate segmentation of the articular cartilages from magnetic resonance (MR) images of the knee is important for clinical studies and drug trials into conditions like osteoarthritis. Currently, segmentations are obtained using time-consuming manual or semi-automatic algorithms which have high inter- and intra-observer variabilities. This paper presents an important step towards obtaining automatic and accurate segmentations of the cartilages, namely an approach to automatically segment the bones and extract the bone-cartilage interfaces (BCI) in the knee. The segmentation is performed using three-dimensional active shape models, which are initialized using an affine registration to an atlas. The BCI are then extracted using image information and prior knowledge about the likelihood of each point belonging to the interface. The accuracy and robustness of the approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images. The (femur, tibia, patella) bone segmentation had a median Dice similarity coefficient of (0.96, 0.96, 0.89) and an average point-to-surface error of 0.16 mm on the BCI. The extracted BCI had a median surface overlap of 0.94 with the real interface, demonstrating its usefulness for subsequent cartilage segmentation or quantitative analysis.

Accuracy and reliability of MRI vs. laboratory measurements in an ex vivo porcine model of arthritic cartilage loss

PURPOSE

To quantify the accuracy of magnetic resonance imaging (MRI) measurement of change in cartilage volume due to thin linear excisions, simulating arthritic cartilage losses, by comparison with laboratory volume measurements in an ex vivo porcine model.

MATERIALS AND METHODS

We scanned 15 porcine patellae by T1-weighted spoiled gradient echo (SPGR) MRI at baseline and after excision of up to three thin layers of articular cartilage. Excised fragment volume was determined from density and weight. Postexcision scans were "fused" to the baseline scan by three-dimensional (3D) registration. This allowed automated recalculation of the remaining cartilage volume within a baseline region of interest (ROI) following each excision. We compared MRI estimates of change in cartilage volume to direct laboratory measurement of fragment volume.

RESULTS

Our 38 excised fragments averaged 0.16 mL, or approximately 7% of cartilage volume. MRI and laboratory estimates of total cartilage volume loss differed by 1.6% +/- 13.2% (mean, coefficient of variation [CV]). Accuracy was +/-0.1 mL for 95% of scans.

CONCLUSION

MRI estimates of small changes in porcine patellar cartilage volume were unbiased, reliable, and accurate to 0.1 mL. Despite a proportionately high error in the very thin fragments tested, achievement of similar accuracy in vivo would be adequate to detect approximately two years of osteoarthritic cartilage loss.