title: Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures.
abstract: OBJECTIVE
  In cardiac ablation therapy, accurate anatomic guidance is necessary to create effective tissue lesions for elimination of left atrial fibrillation. While fluoroscopy, ultrasound, and electroanatomic maps are important guidance tools, they lack information regarding detailed patient anatomy which can be obtained from high resolution imaging techniques. For this reason, there has been significant effort in incorporating detailed, patient-specific models generated from preoperative imaging datasets into the procedure. Both clinical and animal studies have investigated registration and targeting accuracy when using preoperative models; however, the effect of various error sources on registration accuracy has not been quantitatively evaluated.
  
  
  METHODS
  Data from phantom, canine, and patient studies are used to model and evaluate registration accuracy. In the phantom studies, data are collected using a magnetically tracked catheter on a static phantom model. Monte Carlo simulation studies were run to evaluate both baseline errors as well as the effect of different sources of error that would be present in a dynamic in vivo setting. Error is simulated by varying the variance parameters on the landmark fiducial, physical target, and surface point locations in the phantom simulation studies. In vivo validation studies were undertaken in six canines in which metal clips were placed in the left atrium to serve as ground truth points. A small clinical evaluation was completed in three patients. Landmark-based and combined landmark and surface-based registration algorithms were evaluated in all studies. In the phantom and canine studies, both target registration error and point-to-surface error are used to assess accuracy. In the patient studies, no ground truth is available and registration accuracy is quantified using point-to-surface error only.
  
  
  RESULTS
  The phantom simulation studies demonstrated that combined landmark and surface-based registration improved landmark-only registration provided the noise in the surface points is not excessively high. Increased variability on the landmark fiducials resulted in increased registration errors; however, refinement of the initial landmark registration by the surface-based algorithm can compensate for small initial misalignments. The surface-based registration algorithm is quite robust to noise on the surface points and continues to improve landmark registration even at high levels of noise on the surface points. Both the canine and patient studies also demonstrate that combined landmark and surface registration has lower errors than landmark registration alone.
  
  
  CONCLUSIONS
  In this work, we describe a model for evaluating the impact of noise variability on the input parameters of a registration algorithm in the context of cardiac ablation therapy. The model can be used to predict both registration error as well as assess which inputs have the largest effect on registration accuracy.
authors: Biomedical Imaging Resource, Mayo Clinic College of Medicine, Rochester, Minnesota 55905.
authors: Maryam E
authors: ME
authors: Rettmann
authors: Biomedical Imaging Resource, Mayo Clinic College of Medicine, Rochester, Minnesota 55905.
authors: David R
authors: DR
authors: Holmes
authors: Department of Bioengineering, Clemson University, Clemson, South Carolina 29634.
authors: David M
authors: DM
authors: Kwartowitz
authors: Department of Biochemistry and Molecular and Cellular Biology, Georgetown University, Washington D.C. 20057.
authors: Mia
authors: M
authors: Gunawan
authors: Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905.
authors: Susan B
authors: SB
authors: Johnson
authors: Biomedical Imaging Resource, Mayo Clinic College of Medicine, Rochester, Minnesota 55905.
authors: Jon J
authors: JJ
authors: Camp
authors: Biomedical Imaging Resource, Mayo Clinic College of Medicine, Rochester, Minnesota 55905.
authors: Bruce M
authors: BM
authors: Cameron
authors: Clinical Cardiac Electrophysiology, Cardiology Division Hospital Sao Paulo, Federal University of Sao Paulo, 04024-002 Brazil.
authors: Charles
authors: C
authors: Dalegrave
authors: David Grant Medical Center, Fairfield, California 94535.
authors: Mark W
authors: MW
authors: Kolasa
authors: Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905.
authors: Douglas L
authors: DL
authors: Packer
authors: Biomedical Imaging Resource, Mayo Clinic College of Medicine, Rochester, Minnesota 55905.
authors: Richard A
authors: RA
authors: Robb
mesh_terms: D000465:Algorithms; D000818:Animals; D017115:Catheter Ablation / Q000379:methods*; D004285:Dogs; D006325:Heart Atria / Q000033:anatomy & histology* / Q000601:surgery*; D006801:Humans; D008953:Models, Anatomic; D009010:Monte Carlo Method; D019047:Phantoms, Imaging; D057285:Precision Medicine / Q000379:methods*; D057234:Preoperative Period