PET-MR image fusion in soft tissue sarcoma: accuracy, reliability and practicality of interactive point-based and automated mutual information techniques

Abstract

The fusion of functional positron emission tomography (PET) data with anatomical magnetic resonance (MR) or computed tomography images, using a variety of interactive and automated techniques, is becoming commonplace, with the technique of choice dependent on the specific application. The case of PET-MR image fusion in soft tissue is complicated by a lack of conspicuous anatomical features and deviation from the rigid-body model. Here we compare a point-based external marker technique with an automated mutual information algorithm and discuss the practicality, reliability and accuracy of each when applied to the study of soft tissue sarcoma. Ten subjects with suspected sarcoma in the knee, thigh, groin, flank or back underwent MR and PET scanning after the attachment of nine external fiducial markers. In the assessment of the point-based technique, three error measures were considered: fiducial localisation error (FLE), fiducial registration error (FRE) and target registration error (TRE). FLE, which represents the accuracy with which the fiducial points can be located, is related to the FRE minimised by the registration algorithm. The registration accuracy is best characterised by the TRE, which is the distance between corresponding points in each image space after registration. In the absence of salient features within the target volume, the TRE can be measured at fiducials excluded from the registration process. To assess the mutual information technique, PET data, acquired after physically removing the markers, were reconstructed in a variety of ways and registered with MR. Having applied the transform suggested by the algorithm to the PET scan acquired before the markers were removed, the residual distance between PET and MR marker-pairs could be measured. The manual point-based technique yielded the best results (RMS TRE =8.3 mm, max =22.4 mm, min =1.7 mm), performing better than the automated algorithm (RMS TRE =20.0 mm, max =30.5 mm, min =7.7 mm) when registering filtered back-projection PET images to MR. Image reconstruction with an iterative algorithm or registration of a composite emission-transmission image did not improve the overall accuracy of the registration process. We have demonstrated that, in this application, point-based PET-MR registration using external markers is practical, reliable and accurate to within approximately 5 mm towards the fiducial centroid. The automated algorithm did not perform as reliably or as accurately.