Anatomic Surface Reconstruc1on from Sampled Point Cloud Data and Prior Models

Transcription

1 Anatomic Surface Reconstruc1on from Sampled Point Cloud Data and Prior Models Deyu Sun, Maryam E. Rettmann, David R. Holmes III, Cristian Linte, Bruce Cameron, Jiquan Liu, Douglas Packer, Richard A. Robb

2 Catheter- Based Cardiac Abla1on Used to treat le> atrial fibrilla1on Ectopic electrical foci o>en originate in pulmonary veins Typical treatment strategy electrical isola1on of pulmonary veins Anatomical guidance and targe1ng is key component of procedure

3 Standard Visualiza1ons Standard visualization tools include fluoroscopy, ultrasound, EAM Registration of preoperative surface models (Dickfeld et. al. Circulation 2003, Reddy et. al. JACC 2004, Sun et. al. SPIE MI 2005) Catheter in fluoroscopy image Electroanatomic map constructed from Biosense CARTO system Real- 1me intracardiac echocardiography (ICE)

4 Introduc1on Goals of study Intra- opera1ve surface reconstruc1on from tracked, ECG- gated ICE images and pre- op model built from CT scan Assump1ons ICE and pre- op model are acquired in the same ECG phase and have been registered to pa1ent Anatomy contour has been segmented from ICE images

5 Introduc1on Challenges Freehand ICE images are acquired in irregular posi1ons and cannot be reconstructed by conven1onal methods such as marching algorithms Point cloud generated from intra- opera1ve scanning are sparse, thus difficult to be reconstructed by conven1onal methods developed in computer graphics area for dense laser scanning data

6 Introduc1on Screened Poisson Surface Reconstruc1on A high resolu1on and quality surface reconstruc1on method, robust to noise In addi1on to point posi1on informa1on, also integra1ng normal direc1on, thus working for sparse point cloud M. Kazhdan, M. Bolitho, H. Hoppe, Poisson Surface Reconstruc1on, Eurographics Symposium on Geometry Processing, (2006),

7 Introduc1on Challenge Consistent es1ma1on of normal vector for complex structure from sparse point cloud Pre- op model of le> atrium Normal es1ma1on by Hoppe et al. method

8 Proposed Normal Vector Es1ma1on Method K- NN based consistent es1ma1on of normal vector Using the average normal vectors of k nearest neighbor ver1ces in the pre- op model as an es1ma1on for each point of the point cloud

9 Workflow CT/MR scan Pre- op model Pre- op model registered to patient Inter- op images Point cloud Point cloud with estimated normals Reconstructed model

10 Simula1on Experiment Goal Es1mate reconstruc1on error (RMS error) due to different factors Single factor simula1on experiment Possible factors: point cloud density, ICE image noise, registra1on error, k value in k- NN normal vector es1ma1on Combined factors simula1on experiment Combine all factors together and evaluate reconstruc1on error

11 Single Factor Experiment Results

12 Single Factor Experiment Results

13 Single Factor Experiment Results

14 Single Factor Experiment Results

15 Combined Factors Experiment Results 35% subsampled point set from original model Add zero mean Gaussian noise to X,Y,Z respectively; σ=1.8mm Translate along X, Y, and Z axis respectively with random distances, mean=0, σ=2.0mm Rotate around X, Y, Z axis respectively with random angles, mean=0, σ=3.0 degree Repeate 20 times Estimate normal vectors with K=1,4,8,12 respectively Reconstruction & Measure error

16 Combined Factors Experiment Results #10 Visualization of reconstruction error of 18th experiment on the surface of the prior model. Different colors indicate different reconstruction errors; the color map was scaled from 0 to 3.6mm reconstruction error; if the error is larger than 3.6mm, then it was set to the same color as 3.6mm. The reconstructed models were reconstruction results of 18th experiment. The reconstruction parameters for a-d are K=1 & RMS=0.88mm, K=4 & RMS=1.02mm, K=8 & RMS=1.05mm, K=12 & RMS=1.06mm, respectively.

17 Conclusion & Future Development Summary Proposed an approach to es1mate consistent normal vectors which helps adapt SPSR in modeling le> atrium from intra- op point cloud and prior models Es1mated reconstruc1on error caused by prac1cal factors by simula1on experiments Sebng factors to normal values, the surface reconstruc1on error is 0.88±0.03mm Future development Further valida1on by collec1ng data via ICE transducer from real le> atrium phantom and animals

18 Acknowledgement Thanks to my partners from Biomedical Imaging Resource lab Thank you for your afen1on