The registration algorithm presented in this chapter has been used in other studies of the cartilage apart from the ones described in this and the next chapter. Since a detailed description of these studies would take much space and depart from the scope of this thesis, only the principal results are outlined here.
5.5.1 Reproducibility of cartilage thickness
In quantitative cartilage morphology the progression of OA is assessed by the changes in the cartilage volume and mean thickness measured in follow-up examinations (see Section 3.3.1). However, due
5.5 Further applications of the registration algorithm
to their global character these parameters are not very sensitive to detect focal lesions in follow-up examinations of the cartilage. Using the thickness at a voxel basis for the detection of focal lesions would be possible only if the reproducibility of thickness is known at a voxel basis. Therefore, aim of this study was to assess the voxel-based reproducibility of the thickness of the cartilage.
In close analogy to the study ofT2 reproducibility presented in this chapter, 6 healthy volunteers
were imaged at 7 different time points with a FLASH WE sequence with the imaging parameter described in section 5.2.3. Cartilage was segmented and the bone-cartilage interface and articular surface automatically separated for calculation of the thickness. Cartilage thickness was calculated for each voxel at the bone-cartilage interface with two methods: the minimal distance, which is the minimal distance from each voxel at the bone-cartilage interface to the articular surface, and the normal-based distance, which is the distance from each voxel at the bone-cartilage interface to the articular surface along the normal vector to the bone-cartilage interface at each voxel.
CVs for the thickness were calculated after registration, similarly as forT2(see Section 5.1.4). Me-
dian CV over all volunteers and all registrations was 12.80% with the minimal distance and 14.47% with the normal-based distance. To study regional variations all bone-cartilage interfaces were di- vided into an identical number of regions (6 in the CC direction and 4 in the LR direction). All voxels pertaining to the same region were pooled together, and their average CV used to characterize the region (Fig. ).
Figure 5.5:Regional differences in the reproducibility of thickness with the minimal distance (A) and the normal-based distance (B). All bone-cartilage interfaces have been divided into same number of regions (6(CC)×4(LR)=24). Regions were selected by equally dividing the number of voxels. The
median of the absolute value of the CV of all voxels belonging to the same region in all patellae was used to characterize errors in each region.
5.5.2 Cartilage deformation after exercise
Alterations in cartilage volume, maximal thickness and mean thickness have been reported as re- sponse of cartilage to mechanical load after different types of exercises [Eckstein00, Stammberger00]. A voxel-based analysis of the changes in cartilage thickness after exercise can help to identify the load areas of the cartilage and may be useful to evaluate the risk of certain exercises for OA.
10 healthy volunteers without any episode of knee pain in the last three years (n= 5 male andn= 5
different imaging sessions. In each image session sagittal high-resolution FLASH WE images of the volunteers (image resolution 0.31×0.31×1.5 mm3, the remaining MR parameters as indicated in
section 4.2.1), were acquired after 45 min at rest. Afterwards, volunteers were asked to perform one of the following tasks for 20 min: kneel, squat, bend and sitting on the calf. In each imaging session a different task was performed. Immediately after exercise, volunteers underwent imaging with the same FLASH WE sequence. To assess the degree of recovery of the cartilage, a third MRI with the FLASH sequence was performed after one hour at rest.
Femoral, patellar and tibial (medial and lateral) cartilages of all volunteers were segmented in all images. In each segmented cartilage the bone-cartilage interface and the articular surface were auto- matically identif ed and cartilage thickness calculated with the minimum distance method. The f rst image in each imaging session was considered as the baseline examination for comparison with the acquisitions after exercise and after one hour at rest. The bone-cartilage interface of the examinations direct after exercise and one hour after exercise were registered to the bone-cartilage interface of the baseline. After registration differences in thickness with respect to the baseline were calculated. Maps of the signif cant changes (changes larger than the reproducibility) were produced (Fig. 5.6).
Although a careful analysis of the results of this study must still be performed, f rst inspection of the data showed differentiated patterns of load for the different exercises and cartilages, thus indicating the feasibility and potential of the technique.
5.5.3 Interindividual model using clustering
Data illustrating interindividual variability in cartilageT2are of potential interest for the workup of cartilage disease in OA, e.g. by providing a base to differentiate any individual data set from a healthy reference. In this study we used a hierarchical clustering method for interindividual analysis of theT2
of healthy human patellar cartilage.
Anatomical images for measurement of patellar cartilage thickness were acquired in 10 healthy volunteers with aT1-weighted FLASH WE sequence. Images forT2calculation were acquired with
the MSME sequence with the same parameters given in section 4.2.1. After cartilage segmentation,T2
maps were calculated with the NCEXP method (see Section 4.1). All segmented cartilage of the same patient were registered together and divided into regions: eight regions in the CC direction, three in the AP and nine in the LR direction. Regions were def ned so that they contain approximately an equal number of voxels. The distribution of theT2values of all voxels in one region in all 10 volunteers was used to def ne a measurement of similarity, which varies between 0 (nonT2 value in common) and 1 (exactly the same distribution ofT2values). Regions in the patella were grouped according to
their similarity using a hierarchical clustering method. Quality of the clustering was assessed with the cophenetic correlation coeff cient (ρ), which is 0 for inconsistent clustering and 1 for perfectly consistent clustering.
Distribution ofT2 allowed a clustering of data (Fig. 1) with ρ of 0.85. Clusters include regions
which were neighbors and coincide with the expected anatomical regions of the patellar cartilage. This is noteworthy, since the similarity did not include any spatial information. Hierarchical clustering allows regional interindividual characterization of patellar cartilageT2. It may provide insight into