The use of magnetic resonance imaging to predict ACL graft structural properties

Abstract

Magnetic resonance imaging (MRI) could potentially be used to non-invasively predict the strength of an ACL graft after ACL reconstruction. We hypothesized that the volume and T2 relaxation parameters of the ACL graft measured with MRI will predict the graft structural properties and anteroposterior (AP) laxity of the reconstructed knee. Nine goats underwent ACL reconstruction using a patellar tendon autograft augmented with a collagen or collagen-platelet composite. After 6 weeks of healing, the animals were euthanized, and the reconstructed knees were retrieved and imaged on a 3T scanner. AP laxity was measured prior to dissecting out the femur-graft-tibia constructs which were then tested to tensile failure to determine the structural properties. Regression analysis indicated a statistically significant relationship between the graft volume and the failure load (r(2)=0.502; p=0.049). When graft volume was normalized to the T2 relaxation time, the relationship was even greater (r(2)=0.687; p=0.011). There was a significant correlation between the graft volume and the linear stiffness (r(2)=0.847; p<0.001), which remained significant with T2 normalization (r(2)=0.764; p=0.002). For AP laxity at 30° flexion, there was not a significant correlation with graft volume, but there was a significant correlation with volume normalized by the T2 relaxation time (r(2)=0.512; p=0.046). These results suggest that MRI volumetric measures combined with graft T2 properties may be useful in predicting the structural properties of ACL grafts.

Figure 1

Sagittal oblique images were oriented perpendicular to the distal femoral epiphyseal plate in both the coronal and sagittal planes. The intra-articular volume of the ACL graft (shown in green) was determined by segmenting the ACL within each slice and stacking the slices.

Figure 2

Seven MR relaxometry images of the same slice, acquired with a fixed repetition time (TR) of 2000 ms, and varying excitation time (TE) of 0, 22, 26, 30, 34, 38 and 42 ms, respectively, followed by the T2 map calculated from the first 7 images are shown. For each voxel in the slice, the T2 is calculated by fitting the signal intensity, S, as a function of TE, according to the equation: S(TE) ∝S₀exp(−TE/T2), where S₀ is the signal intensity at TE=0. A linear least-squares fit was used to calculate the T2 and S₀. The T2 map shown shows the image constructed from the pixel-wise T2 fitting. The region of interest (ROI) used to calculate the mean T2 of the graft is shown in yellow on the T2 map.

Figure 3

Graft failure load after 6 weeks of healing significantly correlated with graft volume when normalized by the T2 relaxation signal. Solid line represents the linear regression while dashed lines represent 95% confidence intervals.

Figure 4

Graft linear stiffness after 6-weeks of graft healing was proportional to the graft volume when normalized by the T2 relaxation signal. Solid line represents the linear regression while dashed lines represent 95% confidence intervals.

Figure 5

AP knee laxity at 30° flexion after 6-weeks of healing correlated with graft volume normalized by the T2 relaxation signal. Solid line represents the linear regression while dashed lines represent 95% confidence intervals.