Spatio-Temporal Free-Form Registration of Cardiac MR Image Sequences

1. Spatio-Temporal Free-Form Registration of Cardiac MR Image Sequences Antonios Perperidis s0094336 10/02/2006

2. MR-Imaging • Magnetic Resonance (MR) imaging allows the acquisition of: • 3D images which describe the cardiac anatomy. • 4D cardiac image sequences which describe the cardiac anatomy and function. • Advances in cardiac MR imaging are making it an important clinical tool: • The improvement of the spatial and temporal resolution of the image sequences enabling the imaging of small cardiac structures. • The development of tagged MR imaging which allow the study of cardiac motion.

3. Cardiac Image Registration • Currently increased need for cardiac registration methods. • Cardiac image registration is a very complex problem due to 4D nature of the cardiac data: • Complicated non-rigid motion of the heart and the thorax. • Low resolution with which cardiac images are usually acquired. • Recently, cardiac image registration has emerged as an important tool for a large number of applications such as: • The construction of anatomical and functional atlases of the heart. • The analysis of the myocardial motion. • The segmentation of cardiac images. • The fusion of information from different modalities such as CT, MR, PET, and SPECT. • The comparison of images of the same subject. • The comparisons between the cardiac anatomy and function of different subjects.

4. Background • There are currently many registration techniques for cardiac imaging. • Most techniques focus on 3D images ignoring any temporal misalignment between two image sequences. • One exception: an approach for the spatial and temporal registration of cardiac SPECT and MR images. • Uses linear interpolation for the temporal mapping between the end-systolic and end-diastolic frames. • The heart has a complicated motion pattern. • This method ignores all the spatial information contained in the images between the end-systolic and end-diastolic frames.

5. Spatio-Temporal Registration Introduction • The heart is undergoing a spatially and temporally varying degree motion during the cardiac cycle. • Spatial alignment of corresponding frames of the image sequences not enough since frames may not correspond to the same position in the cardiac cycle of the hearts. • This is due to differences in: • The acquisition parameters. • The length of cardiac cycles. • The dynamic properties of the hearts .

6. Spatio-Temporal Registration Introduction • Spatio-temporal alignment enables to find the temporal relationship between the 2 image sequences. • We present 2 spatio-temporal alignment methods using image information only. • The 4-D mapping can be described by the transformation: • Mapping can be resolved into decoupled spatial and temporal components: • Spatial: • Temporal:

7. Spatial Alignment • Aim: to relate each spatial point of an image to a point of the reference image. • Tspatial can be written: • Tspatial/global: 3D affine transformation: • Coefficients  parameterize the 12 degrees of freedom. • Tspatial/local: Free-Form deformation (FFD) model based on B-Splines.

8. Temporal Alignment • Ttemporal can be written: • Ttemporal/global: affine transformation: • a accounts for scaling differences. • b accounts for translation differences. • Ttemporal/local: Free-Form deformation (FFD) using 1-D B-Splines.

9. Combined Optimization of the Spatial and Temporal Components • 2 registration algorithms for finding the optimal transformation T: • Optimizes the spatial and temporal transformation components simultaneously using image information only • Optimizes the temporal transformation component before optimizing the spatial component. • In the 1st algorithm: Optimal transformation T is found by maximizing a voxel based similarity measure. • Normalized Mutual Information (NMI): a measure of spatio-temporal alignment. • NMI is optimized as a function of Tspatial/global and Ttemporal/global using: • an iterative downhill descent algorithm. • a simple iterative gradient descent method.

10. Separate Optimization of the Spatial and Temporal Components 1 • Computational complexity of the previous method is very high. • Reduced by optimizing each transformation component separately. • Ttemporal/global: aligns the temporal ends of the image sequences • Ttemporal/local: aligns a limited temporal positions of the cardiac cycles. • Temporal positions detected by calculating the normalized cross-correlation coefficient between each frame of the sequence with the first frame.

11. Separate Optimization of the Spatial and Temporal Components 2 • The idea behind this approach: • during the contraction phase of the cardiac cycle each consecutive image will be less similar to the first image. • during the relaxation phase of the cardiac cycle each consecutive image will be more similar to the first image

12. Results • Algorithm evaluation: 15 cardiac MR images from healthy volunteers. • Reference subject: 32 different time frames acquired. • 14 4D cardiac MR images were registered to the reference subject. • Cardiac Cycle length: 300 to 800msec. • Qualitative evaluation through visual inspection. • Quality of registration in spatial domain measured by Calculating volume overlap for: • The left and right ventricles. • The myocardium.

13. Results – Separate optimization of the transformation components • Maximum contraction & and-diastole positions determined manually. • Positions compared with corresponding positions identified by the algorithm: • Mean error in the detection of maximum contraction: 1.2 frames. • Mean error of the end diastole detection: 0.93 frames.

14. Combined optimization of the transformation component - Qualitative Evaluation • Deformable temporal & spatial registration improves alignment of the image sequences in spatial and temporal domain

15. Combined optimization of the transformation component - Quantitative Evaluation • Optimising transformation components simultaneously provides better overlap measures the separate optimisation method.

16. Results – Using cross correlation based method to calculate temporal alignment. • Approach achieves very good spatio temporal registration of the images. • Computational complexity is reduced by 25% (approximately)

17. Large number of applications for this Spatio-Temporal registration method: Comparison between image sequences from the same subject. Comparison between image sequences from different subjects, Building probabilistic and statistical atlases of the cardiac anatomy and function. Applications of the Spatio-Temporal Registration Method.

18. Questions?