Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images

Knee Bone and Cartilage Segmentation Based on a 3D Deep Neural Network Using Adversarial Loss for Prior Shape Constraint

Fast and accurate segmentation of knee bone and cartilage on MRI images is becoming increasingly important in the orthopaedic area, as the segmentation is an essential prerequisite step to a patient-specific diagnosis, optimising implant design and preoperative and intraoperative planning. However, manual segmentation is time-intensive and subjected to inter- and intra-observer variations. Hence, in this study, a three-dimensional (3D) deep neural network using adversarial loss was proposed to automatically segment the knee bone in a resampled image volume in order to enlarge the contextual information and incorporate prior shape constraints. A restoration network was proposed to further improve the bone segmentation accuracy by restoring the bone segmentation back to the original resolution. A conventional U-Net-like network was used to segment the cartilage. The ultimate results were the combination of the bone and cartilage outcomes through post-processing. The quality of the proposed method was thoroughly assessed using various measures for the dataset from the Grand Challenge Segmentation of Knee Images 2010 (SKI10), together with a comparison with a baseline network U-Net. A fine-tuned U-Net-like network can achieve state-of-the-art results without any post-processing operations. This method achieved a total score higher than 76 in terms of the SKI10 validation dataset. This method showed to be robust to extract bone and cartilage masks from the MRI dataset, even for the pathological case.

Shoulder MR Imaging and MR Arthrography Techniques: New Advances

MR imaging is the standard diagnostic modality that provides a comprehensive and accurate assessment for both osseous and soft-tissue pathologic conditions of the shoulder. This article discusses standard MR imaging and arthrography protocols used routinely in clinical practice, as well as more innovative sequences and reconstruction techniques, facilitated by the increasing availability of high-field-strength magnets and multichannel phased array surface coils and incorporation of artificial intelligence. These exciting innovations allow for a more detailed and diagnostic imaging assessment, improvements in image quality, and more rapid image acquisition.

Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images

BACKGROUND

The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement of ACT on magnetic resonance imaging (MRI) (2) use ML to provide reference data on ACT in healthy knees, and (3) identify whether demographic variables impact these results.

METHODS

Patients recruited into the Osteoarthritis Initiative with a radiographic Kellgren-Lawrence grade of 0 or 1 with 3D double-echo steady-state MRIs were included and their gender, age, and body mass index were collected. Using a validated ML algorithm, 2 orthogonal points on each femoral condyle were identified (distal and posterior) and ACT was measured on each MRI. Site-specific ACT was compared using paired t-tests, and multivariate regression was used to investigate the risk-adjusted effect of each demographic variable on ACT.

RESULTS

A total of 3910 MRI were included. The average femoral ACT was 2.34 mm (standard deviation, 0.71; 95% confidence interval, 0.95-3.73). In multivariate analysis, distal-medial (-0.17 mm) and distal-lateral cartilage (-0.32 mm) were found to be thinner than posterior-lateral cartilage, while posterior-medial cartilage was found to be thicker (0.21 mm). In addition, female sex was found to negatively impact cartilage thickness (OR, -0.36; all values: P < .001).

CONCLUSION

ML was effectively used to automate the segmentation and measurement of cartilage thickness on a large number of MRIs of healthy knees to provide normative data on the variation in ACT in this population. We further report patient variables that can influence ACT. Further validation will determine whether this technique represents a powerful new tool for tracking the impact of medical intervention on the progression of articular cartilage degeneration.

SUSAN: segment unannotated image structure using adversarial network

PURPOSE

To describe and evaluate a segmentation method using joint adversarial and segmentation convolutional neural network to achieve accurate segmentation using unannotated MR image datasets.

THEORY AND METHODS

A segmentation pipeline was built using joint adversarial and segmentation network. A convolutional neural network technique called cycle-consistent generative adversarial network (CycleGAN) was applied as the core of the method to perform unpaired image-to-image translation between different MR image datasets. A joint segmentation network was incorporated into the adversarial network to obtain additional functionality for semantic segmentation. The fully automated segmentation method termed as SUSAN was tested for segmenting bone and cartilage on 2 clinical knee MR image datasets using images and annotated segmentation masks from an online publicly available knee MR image dataset. The segmentation results were compared using quantitative segmentation metrics with the results from a supervised U-Net segmentation method and 2 registration methods. The Wilcoxon signed-rank test was used to evaluate the value difference of quantitative metrics between different methods.

RESULTS

The proposed method SUSAN provided high segmentation accuracy with results comparable to the supervised U-Net segmentation method (most quantitative metrics having P > 0.05) and significantly better than a multiatlas registration method (all quantitative metrics having P < 0.001) and a direct registration method (all quantitative metrics having P< 0.0001) for the clinical knee image datasets. SUSAN also demonstrated the applicability for segmenting knee MR images with different tissue contrasts.

CONCLUSION

SUSAN performed rapid and accurate tissue segmentation for multiple MR image datasets without the need for sequence specific segmentation annotation. The joint adversarial and segmentation network and training strategy have promising potential applications in medical image segmentation.

Deep convolutional neural network for segmentation of knee joint anatomy

PURPOSE

To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation.

METHODS

A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data.

RESULTS

The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non-specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm.

CONCLUSION

The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well-suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning-based segmentation method has promising potential applications in musculoskeletal imaging.

Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging

PURPOSE

To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint.

METHODS

A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts.

RESULTS

The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions.

CONCLUSION

The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Test-retest reliability of tibiofemoral joint space width measurements made using a low-dose standing CT scanner

OBJECTIVE

To determine the test-retest reliability of knee joint space width (JSW) measurements made using standing CT (SCT) imaging.

SUBJECTS AND METHODS

This prospective two-visit study included 50 knees from 30 subjects (66% female; mean ± SD age 58.2 ± 11.3 years; BMI 29.1 ± 5.6 kg/m²; 38% KL grade 0-1). Tibiofemoral geometry was obtained from bilateral, approximately 20° fixed-flexed SCT images acquired at visits 2 weeks apart. For each compartment, the total joint area was defined as the area with a JSW <10 mm. The summary measurements of interest were the percentage of the total joint area with a JSW less than 0.5-mm thresholds between 2.0 and 5.0 mm in each tibiofemoral compartment. Test-retest reliability of the summary JSW measurements was assessed by intraclass correlation coefficients (ICC 2,1) for the percentage area engaged at each threshold of JSW and root-mean-square errors (RMSE) were calculated to assess reproducibility.

RESULTS

The ICCs were excellent for each threshold assessed, ranging from 0.95 to 0.97 for the lateral and 0.90 to 0.97 for the medial compartment. RMSE ranged from 1.1 to 7.2% for the lateral and from 3.1 to 9.1% for the medial compartment, with better reproducibility at smaller JSW thresholds.

CONCLUSION

The knee joint positioning protocol used demonstrated high day-to-day reliability for SCT 3D tibiofemoral JSW summary measurements repeated 2 weeks apart. Low-dose SCT provides a great deal of information about the joint while maintaining high reliability, making it a suitable alternative to plain radiographs for evaluating JSW in people with knee OA.

Evaluation of Automated Volumetric Cartilage Quantification for Hip Preservation Surgery

Automating the process of femoroacetabular cartilage identification from magnetic resonance imaging (MRI) images has important implications to guiding clinical care by providing a temporal metric that allows for optimizing the timing for joint preservation surgery. In this paper, we evaluate a new automated cartilage segmentation method using a time trial, segmented volume comparison, overlap metrics, and Euclidean distance mapping. We report interrater overlap metrics using the true fast imaging with steady-state precession MRI sequence of 0.874, 0.546, and 0.704 for the total overlap, union overlap, and mean overlap, respectively. This method was 3.28× faster than manual segmentation. This technique provides clinicians with volumetric cartilage information that is useful for optimizing the timing for joint preservation procedures.

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.

Reliability of semiautomated computational methods for estimating tibiofemoral contact stress in the Multicenter Osteoarthritis Study

Recent findings suggest that contact stress is a potent predictor of subsequent symptomatic osteoarthritis development in the knee. However, much larger numbers of knees (likely on the order of hundreds, if not thousands) need to be reliably analyzed to achieve the statistical power necessary to clarify this relationship. This study assessed the reliability of new semiautomated computational methods for estimating contact stress in knees from large population-based cohorts. Ten knees of subjects from the Multicenter Osteoarthritis Study were included. Bone surfaces were manually segmented from sequential 1.0 Tesla magnetic resonance imaging slices by three individuals on two nonconsecutive days. Four individuals then registered the resulting bone surfaces to corresponding bone edges on weight-bearing radiographs, using a semi-automated algorithm. Discrete element analysis methods were used to estimate contact stress distributions for each knee. Segmentation and registration reliabilities (day-to-day and interrater) for peak and mean medial and lateral tibiofemoral contact stress were assessed with Shrout-Fleiss intraclass correlation coefficients (ICCs). The segmentation and registration steps of the modeling approach were found to have excellent day-to-day (ICC 0.93-0.99) and good inter-rater reliability (0.84-0.97). This approach for estimating compartment-specific tibiofemoral contact stress appears to be sufficiently reliable for use in large population-based cohorts.

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.

Quantitative cartilage imaging in knee osteoarthritis

Quantitative measures of cartilage morphology (i.e., thickness) represent potentially powerful surrogate endpoints in osteoarthritis (OA). These can be used to identify risk factors of structural disease progression and can facilitate the clinical efficacy testing of structure modifying drugs in OA. This paper focuses on quantitative imaging of articular cartilage morphology in the knee, and will specifically deal with different cartilage morphology outcome variables and regions of interest, the relative performance and relationship between cartilage morphology measures, reference values for MRI-based knee cartilage morphometry, imaging protocols for measurement of cartilage morphology (including those used in the Osteoarthritis Initiative), sensitivity to change observed in knee OA, spatial patterns of cartilage loss as derived by subregional analysis, comparison of MRI changes with radiographic changes, risk factors of MRI-based cartilage loss in knee OA, the correlation of MRI-based cartilage loss with clinical outcomes, treatment response in knee OA, and future directions of the field.

In vivo high-resolution MRI (7T) of femoro-tibial cartilage changes in the rat anterior cruciate ligament transection model of osteoarthritis: a cross-sectional study

OBJECTIVE

To assess OA-related changes in mean compartmental femorotibial cartilage thickness in rat knees by three-dimensional (3D) MRI (7T).

METHODS

MRI was performed in vivo at 7T on OA and untouched contralateral knee joints. Gradient Echo Fast Imaging 3D MR images were acquired sequentially in surgically induced OA (D0) in 40 Wistar rats (anterior cruciate ligament transection). Mean femoral (trochlear, lateral and medial) and tibial (lateral and medial) cartilage thicknesses were quantified from a 2D MRI slide in weight-bearing areas and from a 3D MRI data set. At each time-point [Day (D)8, D14, D21, D40 and D60], eight animals (16 knees) were sacrificed for concomitant histomorphometry.

RESULTS

As body weight dramatically increased throughout the experiment (+150%, baseline vs endpoint), all compartmental mean cartilage thicknesses noticeably decreased (D8, D14) and then remained relatively stable. Femoral compartments in OA knees were thinner at the end of the experiment than in contralateral age-matched knees. Conversely, lateral and medial tibial cartilages were thicker than controls. Histological correlation was significant only in untouched healthy cartilages (3D better than 2D).

CONCLUSIONS

3D MRI (7T) enables in vivo monitoring of compartmental changes in OA-related femorotibial rat cartilage thickness vs contralateral age-matched knees.

Magnetic resonance imaging-based semiquantitative and quantitative assessment in osteoarthritis

Whole organ magnetic resonance imaging (MRI)-based semiquantitative (SQ) assessment of knee osteoarthritis (OA), based on reliable scoring methods and expert reading, has become a powerful research tool in OA. SQ morphologic scoring has been applied to large observational cross-sectional and longitudinal epidemiologic studies as well as interventional clinical trials. SQ whole organ scoring analyzes all joint structures that are potentially relevant as surrogate outcome measures of OA and potential disease modification, including cartilage, subchondral bone, osteophytes, intra- and periarticular ligaments, menisci, synovial lining, cysts, and bursae. Resources needed for SQ scoring rely on the MRI protocol, image quality, experience of the expert readers, method of documentation, and the individual scoring system that will be applied. The first part of this article discusses the different available OA whole organ scoring systems, focusing on MRI of the knee, and also reviews alternative approaches. Rheumatologists are made aware of artifacts and differential diagnoses when applying any of the SQ scoring systems. The second part focuses on quantitative approaches in OA, particularly measurement of (subregional) cartilage loss. This approach allows one to determine minute changes that occur relatively homogeneously across cartilage structures and that are not apparent to the naked eye. To this end, the cartilage surfaces need to be segmented by trained users using specialized software. Measurements of knee cartilage loss based on water-excitation spoiled gradient recalled echo acquisition in the steady state, fast low-angle shot, or double-echo steady-state imaging sequences reported a 1% to 2% decrease in cartilage thickness annually, and a high degree of spatial heterogeneity of cartilage thickness changes in femorotibial subregions between subjects. Risk factors identified by quantitative measurement technology included a high body mass index, meniscal extrusion and meniscal tears, knee malalignment, advanced radiographic OA grade, bone marrow alterations, and focal cartilage lesions.

Computer input devices: neutral party or source of significant error in manual lesion segmentation?

Lesion segmentation involves outlining the contour of an abnormality on an image to distinguish boundaries between normal and abnormal tissue and is essential to track malignant and benign disease in medical imaging for clinical, research, and treatment purposes. A laser optical mouse and a graphics tablet were used by radiologists to segment 12 simulated reference lesions per subject in two groups (one group comprised three lesion morphologies in two sizes, one for each input device for each device two sets of six, composed of three morphologies in two sizes each). Time for segmentation was recorded. Subjects completed an opinion survey following segmentation. Error in contour segmentation was calculated using root mean square error. Error in area of segmentation was calculated compared to the reference lesion. 11 radiologists segmented a total of 132 simulated lesions. Overall error in contour segmentation was less with the graphics tablet than with the mouse (P < 0.0001). Error in area of segmentation was not significantly different between the tablet and the mouse (P = 0.62). Time for segmentation was less with the tablet than the mouse (P = 0.011). All subjects preferred the graphics tablet for future segmentation (P = 0.011) and felt subjectively that the tablet was faster, easier, and more accurate (P = 0.0005). For purposes in which accuracy in contour of lesion segmentation is of the greater importance, the graphics tablet is superior to the mouse in accuracy with a small speed benefit. For purposes in which accuracy of area of lesion segmentation is of greater importance, the graphics tablet and mouse are equally accurate.

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.

Hip cartilage thickness measurement accuracy improvement

Accurate measurement of the distance separating two adjacent sheet structures, such as femoral cartilage and acetabular cartilage in the hip joint is important in evaluation of osteoarthritis. A new method, insensitive to the influence of adjacent sheet structures, was developed to improve the accuracy of hip cartilage thickness measurement. A theoretical simulation for investigating the influence of adjacent sheet structures on the accuracy of cartilage thickness measurement in MR images was performed. The thickness is defined as the distance between zero-crossings of the second directional derivatives along the sheet surface normal direction. The simulation measurement showed considerable underestimation in thickness measurement occurred due to the influence of the adjacent sheet. A new method based on a model of the MR imaging process to eliminate the influence of adjacent sheet structure was developed and tested using phantoms and two cadaveric human hip joint MR scans. The new method reduced the influence of the adjacent sheet structure was more accurate than the conventional method for measuring hip cartilage thickness.

[Evaluation of cartilage defects in the knee: validity of clinical, magnetic-resonance-imaging and radiological findings compared with arthroscopy]

BACKGROUND

The study was aimed to evaluate the validity of clinical, radiological and MRI examination for cartilage defects of the knee compared with arthroscopic finding.

METHODS

Seven-hundred seventy-two patients who were suffering from knee pain over more than 3 months were evaluated clinical (grinding-sign) and with radiography and magnetic resonance imaging (MRI) and subsequent arthroscopy.

RESULTS

The grinding sign had a sensitivity of 0.39. The association of a positive grinding test with high grade cartilage defects was significant (p<0.000). In 97.4% an intact chondral surface correlated with a normal radiological finding. Subchondral sclerosis, exophytes and a joint space narrowing was significantly associated with high grade cartilage defects (p<0.000). The accuracy of MRI was 59.5%. The MRI resulted in an overestimation in 36.6% and an underestimation in 3.9%. False-positive results were significant more often assessed in low-grade cartilage defects (p<0.000).

CONCLUSIONS

Clinical signs, x-ray imaging and MRI correlate with arthroscopic findings in cases of deep cartilage lesions. In intact or low-grade degenerated cartilage often results an overestimating of these findings.

Automatic morphometric cartilage quantification in the medial tibial plateau from MRI for osteoarthritis grading

OBJECTIVE

To evaluate whether a novel, fully automatic, morphometric cartilage quantification framework is suitable for assessing level of knee osteoarthritis (OA) in clinical trials.

METHOD

The population was designed with a normal population and groups with varying degree of OA of both sexes and at ages from 21 to 78. Posterior-anterior X-rays were acquired in semi-flexed, load-bearing position. The radiographic signs of OA were evaluated based on the Kellgren and Lawrence score (KL) and the joint space width (JSW) was measured. Turbo 3D T1 magnetic resonance imaging (MRI) scans were acquired with resolution 0.7x0.7x0.8mm(3) from a 0.18T scanner. The morphometric cartilage quantification from MRI resulted in volume, surface area, thickness and surface curvature for the medial tibial cartilage compartment. These quantifications were evaluated against JSW with respect to precision and ability to separate healthy subjects from OA subjects.

RESULTS

The automatic, morphometric cartilage quantifications allowed fairly precise measurements with scan-rescan coefficient of variations (CVs) in the range from 3.4% to 6.3%. All quantifications, including JSW, allowed separation of the groups of healthy and OA subjects. However, for separation of the healthy from the borderline cases (KL 0 vs KL 1), only the Cartilage Curvature quantification allowed statistically significant separation (P<0.01).

CONCLUSION

The novel morphometric framework shows promise for use in clinical trials. The ability of the Cartilage Curvature quantification to detect the early stages of OA and the effectiveness of the focal thickness Q10 measure are particularly noteworthy. Furthermore, these results may indirectly support that low-field MRI may be a low-cost option for clinical trials.

Semiautomated digital analysis of knee joint space width using MR images

OBJECTIVE

The goal of this study was to (a) develop a semiautomated computer algorithm to measure knee joint space width (JSW) from magnetic resonance (MR) images using standard imaging techniques and (b) evaluate the reproducibility of the algorithm.

DESIGN

Using a standard clinical imaging protocol, bilateral knee MR images were obtained twice within a 2-week period from 17 asymptomatic research participants. Images were analyzed to determine the variability of the measurements performed by the program compared with the variability of manual measurements.

RESULTS

Measurement variability of the computer algorithm was considerably smaller than the variability of manual measurements. The average difference between two measurements of the same slice performed with the computer algorithm by the same user was 0.004 +/- 0.07 mm for the tibiofemoral joint (TF) and 0.009 +/- 0.11 mm for the patellofemoral joint (PF) compared with an average of 0.12 +/- 0.22 mm TF and 0.13 +/- 0.29 mm PF, respectively, for the manual method. Interuser variability of the computer algorithm was also considerably smaller, with an average difference of 0.004 +/- 0.1 mm TF and 0.0006 +/- 0.1 mm PF compared with 0.38 +/- 0.59 mm TF and 0.31 +/- 0.66 mm PF obtained using a manual method. The between-day reproducibility was larger but still within acceptable limits at 0.09 +/- 0.39 mm TF and 0.09 +/- 0.51 mm PF. This technique has proven consistently reproducible on a same slice base,while the reproducibility comparing different acquisitions of the same subject was larger. Longitudinal reproducibility improvement needs to be addressed through acquisition protocol improvements.

CONCLUSION

A semiautomated method for measuring knee JSW from MR images has been successfully developed.

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

OBJECTIVE

Validation of a new fast software technique to segment the cartilage on knee magnetic resonance (MR) acquisitions. Large studies of knee osteoarthritis (OA) will require fast and reproducible methods to quantify cartilage changes for knee MR data. In this report we document and measure the reproducibility and reader time of a software-based technique to quantify the volume and thickness of articular cartilage on knee MR images.

METHODS

The software was tested on a set of duplicate sagittal three-dimensional (3D) dual echo steady state (DESS) acquisitions from 15 (8 OA, 7 normal) subjects. The repositioning, inter-reader, and intra-reader reproducibility of the cartilage volume (VC) and thickness (ThC) were measured independently as well as the reader time for each cartilage plate. The root-mean square coefficient of variation (RMSCoV) was used as metric to quantify the reproducibility of VC and mean ThC.

RESULTS

The repositioning RMSCoV was as follows: VC=2.0% and ThC=1.2% (femur), VC=2.9% and ThC=1.6% (medial tibial plateau), VC=5.5% and ThC=2.4% (lateral tibial plateau), and VC=4.6% and ThC=2.3% (patella). RMSCoV values were higher for the inter-reader reproducibility (VC: 2.5-8.6%) (ThC: 1.9-5.2%) and lower for the intra-reader reproducibility (VC: 1.6-2.5%) (ThC: 1.2-1.9%). The method required an average of 75.4min per knee.

CONCLUSIONS

We have documented a fast reproducible semi-automated software method to segment articular cartilage on knee MR acquisitions.

Quantitative MRI of cartilage and bone: degenerative changes in osteoarthritis

Magnetic resonance imaging (MRI) and quantitative image analysis technology has recently started to generate a great wealth of quantitative information on articular cartilage and bone physiology, pathophysiology and degenerative changes in osteoarthritis. This paper reviews semiquantitative scoring of changes of articular tissues (e.g. WORMS = whole-organ MRI scoring or KOSS = knee osteoarthritis scoring system), quantification of cartilage morphology (e.g. volume and thickness), quantitative measurements of cartilage composition (e.g. T2, T1rho, T1Gd = dGEMRIC index) and quantitative measurement of bone structure (e.g. app. BV/TV, app. TbTh, app. Tb.N, app. Tb.Sp) in osteoarthritis. For each of these fields we describe the hardware and MRI sequences available, the image analysis systems and techniques used to derive semiquantitative and quantitative parameters, the technical accuracy and precision of the measurements reported to date and current results from cross-sectional and longitudinal studies in osteoarthritis. Moreover, the paper summarizes studies that have compared MRI-based measurements with radiography and discusses future perspectives of quantitative MRI in osteoarthritis. In summary, the above methodologies show great promise for elucidating the pathophysiology of various tissues and identifying risk factors of osteoarthritis, for developing structure modifying drugs (DMOADs) and for combating osteoarthritis with new and better therapy.

Magnetic resonance imaging (MRI) of articular cartilage in knee osteoarthritis (OA): morphological assessment

OBJECTIVE

Magnetic resonance imaging (MRI) is a three-dimensional imaging technique with unparalleled ability to evaluate articular cartilage. This report reviews the current status of morphological assessment of cartilage with quantitative MRI (qMRI), and its relevance for identifying disease status, and monitoring progression and treatment response in knee osteoarthritis (OA).

METHOD

An international panel of experts in MRI of knee OA, with direct experience in the analysis of cartilage morphology with qMRI, reviewed the existing published and unpublished data on the subject, and debated the findings at the OMERACT-OARSI Workshop on Imaging technologies (December 2002, Bethesda, MA) with scientists and clinicians from academia, the pharmaceutical industry and the regulatory agencies. This report reviews (1) MRI pulse sequence considerations for morphological analysis of articular cartilage; (2) techniques for segmenting cartilage; (3) semi-quantitative scoring of cartilage status; and (4) technical validity (accuracy), precision (reproducibility) and sensitivity to change of quantitative measures of cartilage morphology.

RESULTS

Semi-quantitative scores of cartilage status have been shown to display adequate reliability, specificity and sensitivity, and to detect lesion progression at reasonable observation periods (1-2 years). Quantitative assessment of cartilage morphology (qMRI), with fat-suppressed gradient echo sequences, and appropriate image analysis techniques, displays high accuracy and adequate precision (e.g., root-mean-square standard deviation medial tibia=61 microl) for cross-sectional and longitudinal studies in OA patients. Longitudinal studies suggest that changes of cartilage volume of the order of -4% to -6% occur per annum in OA in most knee compartments (e.g., -90 microl in medial tibia). Annual changes in cartilage volume exceed the precision errors and appear to be associated with clinical symptoms as well as with time to knee arthroplasty.

CONCLUSIONS

MRI provides reliable and quantitative data on cartilage status throughout most compartments of the knee, with robust acquisition protocols for multi-center trials now being available. MRI of cartilage has tremendous potential for large scale epidemiological studies of OA progression, and for clinical trials of treatment response to structure modifying OA drugs.