Automatic intra-subject registration and fusion of multimodal cochlea 3D clinical images

• Published in: PloS one Vol. 17; no. 3; p. e0264449
• Main Authors: Al-Dhamari, Ibraheem, Helal, Rania, Morozova, Olesia, Abdelaziz, Tougan, Jacob, Roland, Paulus, Dietrich, Waldeck, Stephan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.03.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Cochlea; Cochlea - diagnostic imaging; Cochlea - surgery; Cochlear Implantation; Cochlear implants; Computed tomography; Computer and Information Sciences; Cone-Beam Computed Tomography - methods; Diagnostic imaging; Electrodes; Engineering and Technology; Humans; Image registration; Image resolution; Imaging, Three-Dimensional - methods; Implants, Artificial; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine and Health Sciences; Neuroimaging; Patients; Physical Sciences; Prosthesis; Registration; Research and Analysis Methods; Stochasticity; Germany; Cairo Egypt; Egypt
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0264449

The postoperative imaging assessment of Cochlear Implant (CI) patients is imperative. The main obstacle is that Magnetic Resonance imaging (MR) is contraindicated or hindered by significant artefacts in most cases with CIs. This study describes an automatic cochlear image registration and fusion method that aims to help radiologists and surgeons to process pre-and postoperative 3D multimodal imaging studies in cochlear implant (CI) patients. We propose a new registration method, Automatic Cochlea Image Registration (ACIR-v3), which uses a stochastic quasi-Newton optimiser with a mutual information metric to find 3D rigid transform parameters for registration of preoperative and postoperative CI imaging. The method was tested against a clinical cochlear imaging dataset that contains 131 multimodal 3D imaging studies of 41 CI patients with preoperative and postoperative images. The preoperative images were MR, Multidetector Computed Tomography (MDCT) or Cone Beam Computed Tomography (CBCT) while the postoperative were CBCT. The average root mean squared error of ACIR-v3 method was 0.41 mm with a standard deviation of 0.39 mm. The results were evaluated quantitatively using the mean squared error of two 3D landmarks located manually by two neuroradiology experts in each image and compared to other previously known registration methods, e.g. Fast Preconditioner Stochastic Gradient Descent, in terms of accuracy and speed. Our method, ACIR-v3, produces high resolution images in the postoperative stage and allows for visualisation of the accurate anatomical details of the MRI with the absence of significant metallic artefacts. The method is implemented as an open-source plugin for 3D Slicer tool.