Three-dimensional analysis and visualization of regional MR signal intensity distribution of articular cartilage

Medical Engineering & Physics 24 (2002) 219–227 www.elsevier.com/locate/medengphy

Technical note

Three-dimensional analysis and visualization of regional MR signal intensity distribution of articular cartilage Jan Hohe a,b, Sonja Faber c, Roland Muehlbauer a, Maximilian Reiser c, Karl-Hans Englmeier b, Felix Eckstein a,∗ a

Musculoskeletal Research Group, Institute of Anatomy, Ludwig- Maximilians Universita¨t, Mu¨nchen, Pettenkoferstr. 11, D-80336 Mu¨nchen, Germany b Institut fu¨r Medizinische Informatik und Systemforschung, GSF Research Center, Neuherberg, Ingolsta¨dter Landstr. 1, D-85764 Oberschleißheim, Germany c Institute for Diagnostic Radiology, Klinikum der LMU Mu¨nchen (Großhadern), Marchioninistr. 15, D-81377 Mu¨nchen, Germany Received 29 November 2001; accepted 15 January 2002

Abstract The aim of this study was to develop a technique for analyzing and visualizing the regional, three-dimensional signal intensity distribution of articular cartilage in MR images, as a potential surrogate marker of structural or biochemical alterations in early osteoarthritis. Exemplary MR-images of human patellae were acquired at a resolution of 1.5 × 0.31 × 0.31 mm3, using a gradientecho sequence with water excitation, and by combining three data sets to secondary images of proton density. After segmentation of the cartilage outlines, these were transferred to the other images. Contiguous slices were automatically divided into sub-regions that extend from the surface to the bone interface (layers) as well as from medial to lateral (sections). The signal intensity was then calculated and projected onto a three-dimensional representation of the articular surface, either by averaging through the depth (sections) or by visualizing the signal intensity at distinct levels in depth (layers). The exemplary data indicate that the reproducibility for regional analyses is in the same range as for the entire patellar cartilage, and that the distribution patterns of proton density delineated with MRI are in agreement with the literature. In conjunction with suitable MR protocols, this post-processing technique has potential to allow for detection and quantification of early degenerative processes in cartilage, before macro-morphological lesions occur.  2002 IPEM. Published by Elsevier Science Ltd. All rights reserved. Keywords: Articular cartilage; Magnetic resonance imaging; Signal intensity; Proton density; Three-dimensional reconstruction; Cartilage biochemistry

1. Introduction Magnetic resonance imaging (MRI) offers the unique opportunity to assess the properties of articular cartilage non-invasively. So far the technique has been validated with respect to the assessment of macro-morphological parameters of articular cartilage, such as volume and thickness [1–10]. However, the limitation of these outcome variables is that the cartilage has already been seriously damaged when morphological changes become detectable. At this state of disease, successful therapeutic intervention is less promising.

∗ Corresponding author. Tel.: +49-89-5160-4847; fax: +49-895160-4802.

However, MRI also offers the possibility to gain information on the microstructural/biochemical composition of articular cartilage. Alterations of its main molecular constituents (interstitial water, proteoglycans, collagen) are believed to be the earliest signs of osteoarthritis [11– 17]. Reliable detection of these first alterations may help to intervene early and to improve the therapeutic outcome. The possibility to longitudinally and quantitatively assess local structural changes is not only important in the context of monitoring disease progression, but can also for objective evaluation of the effectiveness of different types of chondroprotective treatment [17]. Several investigators have demonstrated a quantitative relationship between the signal intensity of the articular cartilage displayed in MR images, and its

1350-4533/02/$22.00  2002 IPEM. Published by Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 0 - 4 5 3 3 ( 0 2 ) 0 0 0 0 6 - 1

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microstructural/biochemical composition. It has been suggested that the water content of the cartilage may be estimated by determining the T2 [18–20]. Alternatively, Selby and co-workers have proposed a technique for assessing its proton density (PD) from gradient– echo sequences with different echo times and flip angles [21]. They found good agreement with a direct measurement of the weight fraction of interstitial water in specimens. Wolff et al. [22] and others [23–29] utilized the magnetization transfer (MT) contrast to obtain information on cartilage macromolecules (collagen, proteoglycan). Whereas Kim and co-workers suggested [23–25] that collagen was responsible for the MT effect, other investigators found a relevant contribution from the proteoglycans [28,29]. It appears feasible to gain relevant information on cartilage macromolecules from MR images obtained with and without an MT prepulse [26,27]. Paul and co-workers [30] reported a correlation between the signal intensity variations in spin echo sequences and the proteoglycan distribution of the cartilage. More recently, it has been suggested that gadolinium diethyl-enetriaminepentaacetic acid-enhanced (Gd-DTPA) MR imaging can be used for assessing the glycosaminoglycan content [31-34]. Others have proposed sodium imaging for the same purpose [35–38]. These MRI-based techniques potentially allow to produce images, in which the signal intensity is related to the concentration and/or structure of certain biochemical components of the cartilage matrix. Subjective grading of MR signal intensity distributions in series of 2D images is, however, impractical and subjective. Particularly in the context of basic research, epidemiological, or clinical studies, reliable post-processing methods are required for statistical output of the regional distribution of the signal intensity values throughout the cartilage plate. In a previous study [39], we have presented a method for analyzing the proton density and MT coefficients of knee joint cartilage plates on a global (entire cartilage plate) level. This has the limitation that only extensive biochemical and structural changes can be detected, but not those that only affect a certain layer or region of the cartilage plate. The purpose of the present study was therefore to develop a technique for quantitative analysis of the structural composition of the cartilage on a regional level, both throughout its depth (articular surface to bone cartilage interface) and throughout the joint surface. Regional signal intensity values will be computed and visualized using a 3D-model of the cartilage plate. We will use a water excitation gradient echo sequence as an example of a data set, for which signal intensity values are derived from a single image acquisition. Proton density images will be used as an example, in which signal intensity values are derived from multiple acquisitions that are transformed into a secondary image. Please note that the aim of the study was not to validate imaging

protocols for biochemical analysis of articular cartilage, but to present a post-processing technique that can be applied to any 3D MR data set containing potential information on cartilage biochemistry and structure. This post-processing technique should permit, for the first time, to three-dimensionally visualize and quantify local changes of MR signal intensity—as potential surrogate markers of structural or biochemical alterations in early OA—that are confined to certain layers or regions of the cartilage plate only.

2. Material and methods The general methodology developed can be divided into 5 steps, the current paper focussing on step 5, in particular: 1. Acquisition of a 3D image data set for morphological identification of the cartilage. 2. Segmentation of the cartilage from images obtained in step 1. 3. Acquisition of 3D image data sets containing information on cartilage composition. 4. Matching of 3D data sets obtained in step 3 to segmented cartilage of step 2. 5. Analysis of regional signal intensities throughout the depth of the tissue (layers: surface to bone interface) and throughout the joint surface (sections: e.g. medial to lateral). Since the method is potentially sensitive to motion artifacts during and in between image acquisition, we first applied the technique to three knee joint specimens (age 50–65 yr), in which such artifacts cannot occur. In a next step, the technique was applied to three healthy volunteers (age 21–28 yr). The precision (reproducibility) of the technique was assessed in two additional volunteers (see below). Acquisition of 3D image data sets: A clinical 1.5 T MR scanner (Magnetom Vision, Siemens, Erlangen, Germany) was used. Image acquisition was first with a 3D spoiled gradient echo sequence (FLASH 3D) with water excitation (WE sequence), which provides excellent contrast between the cartilage and the surrounding tissue [40–42]. The slice thickness was 1.5 mm (transverse orientation), and the in-plane resolution 0.31 mm (512 × 512 matrix). The repetition time (TR) was 17.2 ms, the echo time (TE) 6.6 ms, and the flip angle (FA) 20°. To gain 3D image data sets of the patellar cartilage that include information about the proton density (PD), we acquired another three primary data sets at the same spatial resolution, the repetition time (TR) being 32.2 ms. The echo time (TE) and flip angle (FA) were varied

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as described by Selby et al. [21], in order to exclude the tissue dependent T1- and T2-times and to compute secondary images of the proton density (TE1: 8.7 ms/FA1: 12°; TE2: 8.7 ms/FA2: 40°; TE3: 17.4 ms/FA3: 40°). These images will be addressed as the PD data set. The total imaging time (all protocols) was 40 min. The three knee joint specimens had been kept frozen and were thawed to room temperature 48 h before image acquisition. The knees of the volunteers were fixed within a collar and padded with sandbags during imaging, to minimize motion artifacts. This was important, because the PD data sets are computed from the three primary MRI sequences, by matching these to the first data set on a voxel by voxel basis [39]. A water phantom was attached to the knees at the medial edge of the patella. In this way, it was possible to compare the PD values with the known signal intensity of water. To estimate the reproducibility of the technique, the entire protocol was acquired three times in two volunteers, with 4 week intervals between acquisitions. The precision was calculated as the coefficient of variation (CV%=SD/mean) of three repeated measurements. Analysis of regional signal intensities throughout the cartilage: The patellar cartilage outline was segmented with a previously described B-spline Snake algorithm [8]. Small movement artifacts in the volunteers were corrected, by applying a 3D matching technique, to recover the pixel by pixel correspondence [39]. The regional signal intensity analysis was divided into 3 steps: 1. Automated calculation of cartilage sub-regions and calculation of the normalized signal intensity in each of the sub-regions 2. Reconstruction of the patellar cartilage plate as a 3D model, based on triangle facets 3. Projection of the regional signal intensities (SI) onto the surface of the 3D model, with the SI values encoded in different colors ad 1: The cartilage plates were divided into three different layers (superficial, middle and deep zone relative to the articular surface), since varying ultrastructural characteristics have been described from the surface to the interface [43]. Ultrastructural variations have also been described for different areas of the joint surface, namely weight-bearing and non-weight-bearing areas [44]. Therefore, the cartilage surface was also subdivided into different sections (medial to lateral, superior to inferior). This resulted in a row- and column-like subdivision of the segmented cartilage in each MR image (Fig. 1), where the number of rows and columns can be selected freely by the user. Vertical sections (columns) were automatically determined by dividing both the articular surface and bone interface surfaces into equal sections,

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Fig. 1. Example of automated subdivision of a segmented patellar cartilage in one slice, showing sections and layers.

and by then connecting the corresponding points on each surface (Fig. 1). Horizontal zones (rows, layers) were determined by calculating parallel intermediate lines between the cartilage surface and the cartilage bone interface. This was achieved by additionally dividing connecting lines between the surfaces (Fig. 1). The intersections between the intermediate horizontal lines and the vertical sections determined the sub-regions for the SI analysis (Fig. 1). By increasing the number of layers or sections, a more detailed assessment of the regional SI is possible. In turn, however, this reduces the number of voxels included in each sub-region and thus increases the sensitivity to noise. A threshold for the minimum voxel number per area of interest can therefore be defined by the user (depending on the specific noise level), in order to limit the influence of image noise on the outcome variables. ad 2: The purpose of this step is to provide the user with a visual impression of the signal intensity distribution throughout the joint surface, either for the total thickness of the cartilage layer or for specific layers. A common problem in reconstructing 3D objects from planar sections is the anisotropic character of the data. This includes the ‘branching’ and ‘corresponding’ problem [45]. The 3D modeling of articular cartilage, however, does not require a special approach for these problems, since cartilage forms a continuous surface in the physiological or early disease case. Therefore a direct approach for the 3D reconstruction was chosen, by triangulating the cartilage boundaries. This included: 앫 Arranging the points of all slice contours ‘N’ that form the cartilage surface in an anti-clockwise orientation. 앫 Separating the parts of the contours that form the cartilage surface from the parts that form the bonecartilage-interface. The following procedures were performed on both contour sets (articular surface, bone- cartilage- interface):

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1. Loop through all contours ‘N’ (0 ⱕ i ⬍ N⫺1) 2. Loop in all contours i through all ‘M ⫹ 1’ contour points Pj,i (0ⱕj(i) ⱕM(i)) with a user defined step width ‘k’ 3. For each point Pj,i met in step 2: 4. Calculation of the closest (Euclidian distance) contour point Pclose,i+1 of the following contour (i+1) or special conditions: If it is the first contour point P0,i also the first contour point P0,i+1 of the following contour is chosen and, according to ‘k’, there is created a triangle ‘fan’ between Pclose,i+1, P0,i+1 and P0,i.If it is the last contour point PM(i),i but the last contour point of the following contour PM(i+1),i+1 is not reached yet, create a triangle ‘fan’, according to ‘k’, between PM(i),i, PM(i+1),i+1 and Pclose,i+1. 5. If the last contour point Pj-k,i exists, create a triangle between Pj,i, Pclose,i+1 and Pj-k,i. 6. If a previous closest contour point PlastClose,i+1 of the contour i+1 exists, create a triangle between Pj-k,i, Pclose,i+1 and Plastclose,i+1. 7. PlastClose,i ⫹ 1 ⫽ Pclose,i ⫹ 1 8. The algorithm stops, if: i ⫽ N ⫺ 2, j(i) ⫽ M(i) and j(i ⫹ 1) ⫽ M(i ⫹ 1) Please note that the algorithm was applied to each surface (articular surface and bone interface), facilitating the treatment of sharp edges of the cartilage plate. ad 3: The 3D model was calculated from the same contour points that served as a basis for the calculation of sub-regions. This permitted to project the mean signal intensities of arbitrary sub-regions to the corresponding area on the cartilage surface. By using suitable visualization tools (OpenGL [46]), the signal intensities were visualized encoded in different grayscale values or colors (Fig. 2). A minimum threshold of voxels per subregion was also defined at this step. If a sufficient number of voxels was not reached for a particular region, no color was attributed in the 3D reconstruction.

3. Results Fig. 2 shows the result of a regional analysis of PD signal intensity plot of one volunteer on the surface of the 3D model of the patella. In Fig. 2a, the averaged signal intensity throughout the depth of the tissue is projected onto the surface, whereas Fig. 2 b-d show the results for the deep, middle, and surface layer, respectively. The WE and PD signal intensity values tended to be higher in the specimens than in the volunteers, but also the regional distribution showed some differences: In the specimens, the regional SI values in the WE sequence ranged from 1.61 to 1.86. The layer-specific analysis revealed higher values in the superficial and middle

layer, and lower ones toward the bone interface. The signal intensities of eight sections (medial to lateral) tended to attain the highest values in the central areas (Fig. 3). The PD analysis showed SI values ranging from 0.84 to 1.36, with increasing SI values from the surface of to the deep layer (Table 1). The sectional analysis exhibited a trend towards higher values in lateral patellar facet (Fig. 4). In the volunteers, the regional SI values ranged from 1.39 to 1.65 for the WE sequence. The highest SI was observed in the superficial and middle layer. As in the specimens, the volunteers showed the highest SI values in the more central sections with the WE sequence (Fig. 5). The analysis of the PD images exhibited a SI range between 0.52 and 0.78, the values decreasing from the cartilage surface to the deep layer (Table 1). There also was a trend toward higher values in lateral patellar surface (Fig. 6). The results of the precision analysis are given in Table 2. The CV% of the repeated measurement ranged from 0.2% (entire patellar cartilage plate, WE, volunteer 2) to 9.1% (medial patellar cartilage plate, PD, volunteer 2). The CV% for the regional analysis was in the same range as that for the total patellar cartilage plate.

4. Discussion Previous efforts in digital image processing of MR images of articular cartilage have focused on semi-automated segmentation and 3D analysis of cartilage macromorphology, in particular volume and thickness [7– 8,47]. This next step involves the development of a postprocessing technique for a detailed characterization of the signal intensity variations throughout the cartilage, as these may reflect variations in cartilage microstructure and biochemistry, and may be used as potential surrogate markers of structural or biochemical alterations in early osteoarthritis. This current work specifically presents a computational method for analyzing the regional signal intensity in articular cartilage throughout the depth of the tissue (layers) and throughout the joint surface (sections). After automated partitioning into sub-regions, the calculated signal intensity can be visualized as a grayscale of color values onto a 3D model of the joint surface. As an example, we have applied the method to WE and PD images of articular cartilage, the latter being calculated from three primary images with different echo times and flip angles. The computational technique presented is, however, not confined to these specific MR protocols and can be readily applied to any other MR image sequence. When computing secondary data sets from several primary images, such as for the PD [21.39.48] it is important to keep motion artifacts minimal. The volun-

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Fig. 2. Signal intensity values of the patellar cartilage (proton density) are mapped to the articular surface and encoded in different grey values. (a) Signal intensity averaged throughout the depth of the tissue; (b–d) signal intensities of the deep (b), middle (c), and surface (d) layer, respectively.

teers were therefore fixed into a collar and were instructed to remain motionless. No motion artifacts were observed during imaging, but a displacement of up to 3 voxels (0.45 mm) was observed in between different data sets. These displacements could be successfully reduced by the semi-automated matching algorithm described previously [39]. The automated calculation of the sub-regions throughout the 3D cartilage plate allows for fast and reproducible measurements, since the regions are calculated based on an equidistant subdivision of the segmentation area. The partitioning technique is specifically adapted to the flat form of articular cartilage, but is not necessarily confined to the analysis of this tissue. The 3D reconstruction of the cartilage plate represents a model of the contours of the segmented areas and permits to directly assign the signal intensity values to corresponding surface vertices. The user is free to choose the number of desired subregions, depending on the specific question of interest. The method therefore represents a flexible tool for a variety of applications. When analyzing smaller regions of interest, however, the noise of the MR images must be considered. Larger acquisition times can reduce image

noise, but have the disadvantage that they are clinically problematic and increase motion artifacts. Due to this image noise, the primary 3D data sets displayed SI uncertainties between 15% and 20% per voxel. This SI uncertainty is reduced by the square-root of the number of voxels per sub-region, and a minimum of 100 voxels per region reduces the SI uncertainty to less than 2%. This should permit the detection of relatively small SI variations. However, a very detailed SI analysis of sections, layers and slices can reduce the number of voxels at the peripheral sub-regions to less than 100 (approx. 10 mm2 at the given resolution) within an MR image. Because the SI variation can exceed the expected SI differences between the sub-regions, image quality is an important factor in how small the sub-regions can be chosen. An additional problem is that the calculation of secondary data sets from several primary data sets increases the noise. Our estimates show that the SI uncertainty can rise to up to 40% per voxel in the secondary data sets, increasing the maximum uncertainty of a region of 100 voxels to 4%. In our sample, the sections and layers included a minimum voxel number of between 1500 and

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Fig. 3. Analysis of signal intensity for the WE-sequence in the three specimens. Results are given for different layers (a) and sections (b).

Fig. 4. Analysis of the proton density in the three specimens. Results are given for different layers (a) and sections (b).

Table 1 Cartilage signal intensity values (proton density) in the specimens and volunteers as a function of depth

higher resolution. Furthermore, we separated the cartilage into eight different sections from medial to lateral. These are exposed to different load during knee flexion [49] and may therefore be expected to demonstrate differences in ultrastructural composition [43]. This work focusses on the presentation of a post-processing method for regional signal intensity analysis, and therefore the number of (exemplary) data sets examined has been limited. In the volunteers, the PD decreased from superficial to the deeper cartilage layers, as described in the literature [50], but this was not the case in the specimens. However, the analysis throughout the surface revealed similarities between the volunteers and the specimens, with the highest PD values in the lateral patellar facet. An interesting observation may be that the signal intensity values of the specimens exceeded those of the volunteers, both in the WE and in the PD data sets. These differences may be due to freezing, as changes in the ultrastructure of articular cartilage and of the meniscus have also been reported in histological and biochemical studies [51–54]. This lends some evidence that this or comparable techniques are indeed able to reveal structural changes of the cartilage. In conclusion, we present a post-processing technique

Superficial layer Middle layer Specimen 1 2 3 Volunteer 1 2 3

Deep layer

0.88 1.14 1.02

0.90 1.24 1.03

0.92 1.36 1.11

0.78 0.64 0.64

0.66 0.60 0.63

0.61 0.59 0.58

11,000, the SI uncertainty being approximately 0.2% to 0.5% for the primary WE sequence, and 0.4–1.0% for secondary (PD) data sets. In the current study we analyzed 3 equidistant layers of the patellar cartilage, because the biochemical structure of the cartilage has been shown to vary throughout the tissue depth. Since we used layers of equal thickness, these do not correspond precisely with the width of the histologic zones of the cartilage [43]. Although the software principally allows one to adjust the regions of interest to these zones, this would require images with

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Fig. 5. Analysis of signal intensity for the WE-sequence in three healthy volunteers. Results are given for different layers (a) and sections (b).

Table 2 Coefficient of variation (CV%) for repeated analyses (3) of regional signal intensity values (layers, sections, and combinations) in two volunteers at 4 week intervals. The precision of the regional values is compared to that of the total patella MR protocol

Entire patella Surface layer Middle layer Deep layer Lateral section Middle section Medial section Surface/lateral Middle/middle Deep/medial

Volunteer 1

Volunteer 2

WE

PD

WE

5.2 2.2 3.4 3.7 2.8 3.1 3.9 4.2 3.4 1.6

1.3 2.4 0.3 4.8 1.6 3.8 1.7 0.8 4.6 2.2

0.3 7.5 6.0 5.5 4.8 6.1 6.8 4.1 5.6 8.3

PD 1.5 3.6 7.1 9.2 0.4 5.0 9.1 1.5 6.3 4.2

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Fig. 6. Analysis of the proton density in three healthy volunteers. Results are given for different layers (a) and sections (b).

ers onto the cartilage, a comprehensive three-dimensional analysis of anatomically or physiologically interesting regions becomes feasible, overcoming the need to subjectively grade series of 2D images. In conjunction with suitable MR imaging protocols, the technique may allow one to detect early changes in cartilage biochemistry and structure. This is of particular interest for screening groups at high risk of osteoarthritis (e.g. patients with ligament rupture), and for objectively monitoring drug effects on the progression of cartilage damage.

Acknowledgements The study has been supported by the Deutsche Forschungsgemeinschaft (DFG).

References that is capable of analyzing and visualizing the MR signal intensity of different regions of the cartilage (layers, sections). These signal intensity values can be stored for statistical analysis, or can be visualized on the cartilage surface. By projecting signal intensities of different lay-

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