Evaluation of CT‐MR image registration methodologies for 3D preoperative planning of forearm surgeries

Abstract

Computerised surgical planning for forearm procedures that considers both soft and bony tissue, requires alignment of preoperatively acquired computed tomography (CT) and magnetic resonance (MR) images by image registration. Normalised mutual information (NMI) registration techniques have been researched to improve efficiency and to eliminate the user dependency associated with manual alignment. While successfully applied in various medical fields, application of NMI registration to images of the forearm, for which the relative pose of the radius and ulna likely differs between CT and MR acquisitions, is yet to be described. To enable the alignment of CT and MR forearm data, we propose an NMI-based registration pipeline, which allows manual steering of the registration algorithm to the desired image subregion and is, thus, applicable to the forearm. Successive automated registration is proposed to enable planning incorporating multiple target anatomical structures such as the radius and ulna. With respect to gold-standard manual registration, the proposed registration methodology achieved mean accuracies of 0.08 ± 0.09 mm (0.01 -0.41 mm range) in comparison to 0.28 ± 0.23 mm (0.03 -0.99 mm range) associated with a landmark-based registration when tested on 40 patient datasets. Application of the proposed registration pipeline required less than 10 minutes on average compared to 20 minutes required by the landmark-based registration. The clinical feasibility and relevance of the method were tested on two different clinical applications, a forearm tumour resection and radioulnar joint instability analysis, obtaining accurate and robust CT-MR image alignment for both cases.

Interaction of loading and ligament injuries in subtalar joint instability quantified by 3D weightbearing computed tomography

“… 30,31 This method minimizes the point‐to‐point distances in the least square sense 32 and has a reported registration error of less than 0.4 mm 33,34 . The algorithm employed an end condition that is set to a surface misalignment of 0.001 mm RMS or lesser or a maximum of 100 iterations 35 …”

“…33,34 The algorithm employed an end condition that is set to a surface misalignment of 0.001 mm RMS or lesser or a maximum of 100 iterations. 35 The obtained translations were defined based on the displacement of the centroid of the talus relative to the frontal, axial, and sagittal planes. Measurements were performed in millimeters; medial, anterior, and dorsal displacements were defined by positive values.…”

Methodological Research on Image Registration Based on Human Brain Tissue In Vivo

“…Different from MSD and NCC, NMI measures the registration depending on the statistical correlation between two images. It is the ratio of edge entropy to joint entropy [34,35], as shown in Equation (7). The more relevant the two images, the smaller the value of joint entropy between them, and vice versa.…”