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

Gerber, Nicolas; Carrillo, Fabio; Abegg, Daniel; Sutter, Reto; Zheng, Guoyan; Fürnstahl, Philipp (2020). Evaluation of CT-MR image registration methodologies for 3D preoperative planning of forearm surgeries. Journal of orthopaedic research, 38(9), pp. 1920-1930. Wiley 10.1002/jor.24641

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Computerized 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. Normalized 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, the 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 with 0.28 ± 0.23 mm (0.03-0.99 mm range) associated with a landmark-based registration when tested on 40 patient data sets. Application of the proposed registration pipeline required less than 10 minutes on average compared with 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 tumor resection and radioulnar joint instability analysis, obtaining accurate and robust CT-MR image alignment for both cases.

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