Quantification of the accuracy of MRI generated 3D models of long bones compared to CT generated 3D models

Abstract

Orthopaedic fracture fixation implants are increasingly being designed using accurate 3D models of long bones based on computer tomography (CT). Unlike CT, magnetic resonance imaging (MRI) does not involve ionising radiation and is therefore a desirable alternative to CT. This study aims to quantify the accuracy of MRI-based 3D models compared to CT-based 3D models of long bones. The femora of five intact cadaver ovine limbs were scanned using a 1.5 T MRI and a CT scanner. Image segmentation of CT and MRI data was performed using a multi-threshold segmentation method. Reference models were generated by digitising the bone surfaces free of soft tissue with a mechanical contact scanner. The MRI- and CT-derived models were validated against the reference models. The results demonstrated that the CT-based models contained an average error of 0.15 mm while the MRI-based models contained an average error of 0.23 mm. Statistical validation shows that there are no significant differences between 3D models based on CT and MRI data. These results indicate that the geometric accuracy of MRI based 3D models was comparable to that of CT-based models and therefore MRI is a potential alternative to CT for generation of 3D models with high geometric accuracy.

Low-field magnetic resonance imaging for 3D volume rendering of canine stifle joint pathologies: a case report

“…The obtained bone model was based on the contour of the bone marrow, because the cortical bone is not visible on the MRI. Rathnayaka et al (2012) determined that long bone models obtained from MRI-based data did not differ statistically from those obtained with CT.…”

“…All manual adjustments must be performed by a person capable of verifying the segmentation area to minimise the loss of digital image integrity and to avoid minor discontinuities during segmentation. In order to obtain a 3D model with a high geometric accuracy, the person performing the modelling should be familiar with anatomy in order that errors in bone boundary determination are kept to a minimum (Lee et al 2008;Anastasi et al 2009;Rathnayaka et al 2012). Similar observations were made during segmentation of CT data (Rathnayaka et al 2010;Rathnayaka et al 2012).…”

“…In order to obtain a 3D model with a high geometric accuracy, the person performing the modelling should be familiar with anatomy in order that errors in bone boundary determination are kept to a minimum (Lee et al 2008;Anastasi et al 2009;Rathnayaka et al 2012). Similar observations were made during segmentation of CT data (Rathnayaka et al 2010;Rathnayaka et al 2012). In our case, approximately 70% of the surgical procedures were performed automatically.…”

“…The data acquired during the MRI examination included noise and other artefacts; therefore, segmentation could not be fully automated. This applies particularly to the proximal and distal end of the bone due to the overlap of signal intensities between the bones and the adjacent tissues such as ligaments, tendons, muscles, fat and cartilage (Rathnayaka et al 2012). Moreover, small, ill-defined structures could be difficult to visualise due to the loss of information during the process, potentially necessitating manual correction (Anastasi et al 2007).…”

Can MRI accurately detect pilon articular malreduction? A quantitative comparison between CT and 3T MRI bone models

“…Rathnayaka et al (17) and Lee et al (18) have used 3D bone models reconstructed from CT and MRI images to then quantified their surface differences. They reported that the mean errors between the CT and MRI models were as low as 0.23 and 0.5 mm for the human and ovine femora, respectively (17,18). These results suggested that MRI models can be reconstructed with good accuracy as compared to the CT models.…”