# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 5 Conclusions  
Automatic registration of pre-operative MRI with intra-operative US enables real-time guidance during the surgery and can enhance the quality and safety of image-guided knee arthroscopy. In this paper, we have introduced a fully automated registration algorithm consisting of a segmentation based pre-initialization, followed by a distance transform step to achieve global initialization. This is followed by registration with an LC\({}^{2}\) similarity metric which uses the total amount of available image information from both the modalities by locally matching US intensities to both MRI intensity and gradient magnitude. Our method is evaluated on 11 MRI-US pairs from six volunteers at different knee flexion angles and resulted in a mean registration error of 3.45 mm and 7.76\({}^{\circ}\) in translation and rotation, respectively. In addition, an inter-observer variability study is performed, comparing the registrations performed by two experienced surgeons and resulted in a mean difference between the registrations of 4.41 mm and 7.77\({}^{\circ}\). Although the registration error of our algorithm increased with the angle of flexion of the knee, mean registration error of the proposed method is smaller than inter-observer variability (\(p\)-value \(<\) 0.002). We have shown that the proposed automated 3D multi-modal registration of US and MRI is globally convergent, robust and efficient. For the US-MRI volumetric registration, the proposed method can form the starting point for developing more complex transforms, including deformable, and can be explored as part of the future work.  
Conceptualization, K.R.; Data curation, F.S. and T.Y.; Formal analysis, G.K.; Funding acquisition, A.K.P. and M.S.; Investigation, M.A.; Methodology, G.K.; Project administration, A.K.P. and M.S.; Resources, M.A.; Supervision, K.R. and D.F.; Validation, J.S.; Visualization, G.K.; Writing--original draft, G.K.; Writing--review and editing, M.A. and K.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Indo-Australian Biotechnology Fund through Department of Biotechnology, Government of India (Intelligent Robotic Imaging System for Keyhole Surgeries) under Grant No. AISRF53820.  
The Queensland University of Technology Ethics Committee (No. 1700001110) granted the ethics approval for the Ultrasound and MRI data collection.  
Figure 9: Output of proposed registration method for subject 6 overlaid on its respective ground truth for all four of the flexion angles. The output of the proposed algorithm is shown in red and ground truth for the study is shown in blue.  
**Informed Consent Statement:** The study objective and measurement procedure were explained to each participant and their written informed consent was procured.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author, D.F.  
**Acknowledgments:** The authors would like to thank [PERSON], diagnostic radiologist and senior consultant at Sri Ramachandra Medical Centre, and professor in Radiology at Sri Ramachandra Institute for Higher Education and Research, Chennai, India, for assisting in annotation of the US volumes. [PERSON] acknowledges her fellowship provided by the Centre for Biomedical Technologies, Queensland University of Technology, Australia.  
**Conflicts of Interest:** The authors declare no conflict of interest.