Deformable 3D-2D registration for high-precision guidance and verification of neuroelectrode placement. (1st November 2021)

• Record Type: Journal Article
• Authors: Uneri, A; Wu, P; Jones, C K; Vagdargi, P; Han, R; Helm, P A; Luciano, M G; Anderson, W S; Siewerdsen, J H
• Is Part Of: Physics in medicine & biology. Volume 66:Number 21(2021)
• Publication Date: 2021-11-01
• Subjects: intraoperative imaging -- 3D-2D registration -- deep learning -- object detection -- deformable registration -- metal artifact reduction; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac2f89
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Purpose. Accurate neuroelectrode placement is essential to effective monitoring or stimulation of neurosurgery targets. This work presents and evaluates a method that combines deep learning and model-based deformable 3D-2D registration to guide and verify neuroelectrode placement using intraoperative imaging. Methods. The registration method consists of three stages: (1) detection of neuroelectrodes in a pair of fluoroscopy images using a deep learning approach; (2) determination of correspondence and initial 3D localization among neuroelectrode detections in the two projection images; and (3) deformable 3D-2D registration of neuroelectrodes according to a physical device model. The method was evaluated in phantom, cadaver, and clinical studies in terms of (a) the accuracy of neuroelectrode registration and (b) the quality of metal artifact reduction (MAR) in cone-beam CT (CBCT) in which the deformably registered neuroelectrode models are taken as input to the MAR. Results. The combined deep learning and model-based deformable 3D-2D registration approach achieved 0.2 ± 0.1 mm accuracy in cadaver studies and 0.6 ± 0.3 mm accuracy in clinical studies. The detection network and 3D correspondence provided initialization of 3D-2D registration within 2 mm, which facilitated end-to-end registration runtime within 10 s. Metal artifacts, quantified as the standard deviation in voxel values in tissue adjacent to neuroelectrodes, were reduced by 72% in phantom studies and by 60% in first clinical studies. Conclusions. The method combines the speed and generalizability of deep learning (for initialization) with the precision and reliability of physical model-based registration to achieve accurate deformable 3D-2D registration and MAR in functional neurosurgery. Accurate 3D-2D guidance from fluoroscopy could overcome limitations associated with deformation in conventional navigation, and improved MAR could improve CBCT verification of neuroelectrode placement.