title: A new markerless patient-to-image registration method using a portable 3D scanner.
abstract: OBJECTIVE
  Patient-to-image registration is critical to providing surgeons with reliable guidance information in the application of image-guided neurosurgery systems. The conventional point-matching registration method, which is based on skin markers, requires expensive and time-consuming logistic support. Surface-matching registration with facial surface scans is an alternative method, but the registration accuracy is unstable and the error in the more posterior parts of the head is usually large because the scan range is limited. This study proposes a new surface-matching method using a portable 3D scanner to acquire a point cloud of the entire head to perform the patient-to-image registration.
  
  
  METHODS
  A new method for transforming the scan points from the device space into the patient space without calibration and tracking was developed. Five positioning targets were attached on a reference star, and their coordinates in the patient space were measured prior. During registration, the authors moved the scanner around the head to scan its entire surface as well as the positioning targets, and the scanner generated a unique point cloud in the device space. The coordinates of the positioning targets in the device space were automatically detected by the scanner, and a spatial transformation from the device space to the patient space could be calculated by registering them to their coordinates in the patient space that had been measured prior. A three-step registration algorithm was then used to register the patient space to the image space. The authors evaluated their method on a rigid head phantom and an elastic head phantom to verify its practicality and to calculate the target registration error (TRE) in different regions of the head phantoms. The authors also conducted an experiment with a real patient's data to test the feasibility of their method in the clinical environment.
  
  
  RESULTS
  In the phantom experiments, the mean fiducial registration error between the device space and the patient space, the mean surface registration error, and the mean TRE of 15 targets on the surface of each phantom were 0.34 ± 0.01 mm and 0.33 ± 0.02 mm, 1.17 ± 0.02 mm and 1.34 ± 0.10 mm, and 1.06 ± 0.11 mm and 1.48 ± 0.21 mm, respectively. When grouping the targets according to their positions on the head, high accuracy was achieved in all parts of the head, and the TREs were similar across different regions. The authors compared their method with the current surface registration methods that use only a part of the facial surface on the elastic phantom, and the mean TRE of 15 targets was 1.48 ± 0.21 mm and 1.98 ± 0.53 mm, respectively. In a clinical experiment, the mean TRE of seven targets on the patient's head surface was 1.92 ± 0.18 mm, which was sufficient to meet clinical requirements.
  
  
  CONCLUSIONS
  The proposed surface-matching registration method provides sufficient registration accuracy even in the posterior area of the head. The 3D point cloud of the entire head, including the facial surface and the back of the head, can be easily acquired using a portable 3D scanner. The scanner does not need to be calibrated prior or tracked by the optical tracking system during scanning.
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, China.
authors.forename: Yifeng
authors.initials: Y
authors.lastname: Fan
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, China.
authors.forename: Dongsheng
authors.initials: D
authors.lastname: Jiang
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, China.
authors.forename: Manning
authors.initials: M
authors.lastname: Wang
authors.affiliation: Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, and Shanghai Key Laboratory of Medical Imaging Computing and Computer-Assisted Intervention, Shanghai, 200032, China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Song
mesh_terms: D000465:Algorithms; D002138:Calibration; D004548:Elasticity; D004867:Equipment Design; D005240:Feasibility Studies; D006257:Head / Q000033:anatomy & histology; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000295:instrumentation* / Q000379:methods*; D008954:Models, Biological; D019635:Neurosurgical Procedures / Q000295:instrumentation / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods