Abstract
Respiratory motion is one major complicating factor in many image acquisition applications and image-guided interventions. Existing respiratory motion estimation and compensation methods typically rely on breathing motion models learned from certain training data, and therefore may not be able to effectively handle intra-subject and/or inter-subject variations of respiratory motion. In this paper, we propose a respiratory motion compensation framework that directly recovers motion fields from sparsely spaced and efficiently acquired dynamic 2-D MRIs without using a learned respiratory motion model. We present a scatter-to-volume deformable registration algorithm to register dynamic 2-D MRIs with a static 3-D MRI to recover dense deformation fields. Practical considerations and approximations are provided to solve the scatter-to-volume registration problem efficiently. The performance of the proposed method was investigated on both synthetic and real MRI datasets, and the results showed significant improvements over the state-of-art respiratory motion modeling methods. We also demonstrated a potential application of the proposed method on MRI-based motion corrected PET imaging using hybrid PET/MRI.
| Original language | English (US) |
|---|---|
| Journal | Computerized Medical Imaging and Graphics |
| DOIs | |
| State | Accepted/In press - Aug 25 2015 |
| Externally published | Yes |
Fingerprint
Keywords
- Image registration
- Motion estimation
- MRI
- PET
- Respiratory motion
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Health Informatics
- Radiological and Ultrasound Technology
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
Cite this
Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs. / Miao, S.; Wang, Z. J.; Pan, L.; Butler, J.; Moran, G.; Liao, R.
In: Computerized Medical Imaging and Graphics, 25.08.2015.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Scatter to volume registration for model-free respiratory motion estimation from dynamic MRIs
AU - Miao, S.
AU - Wang, Z. J.
AU - Pan, L.
AU - Butler, J.
AU - Moran, G.
AU - Liao, R.
PY - 2015/8/25
Y1 - 2015/8/25
N2 - Respiratory motion is one major complicating factor in many image acquisition applications and image-guided interventions. Existing respiratory motion estimation and compensation methods typically rely on breathing motion models learned from certain training data, and therefore may not be able to effectively handle intra-subject and/or inter-subject variations of respiratory motion. In this paper, we propose a respiratory motion compensation framework that directly recovers motion fields from sparsely spaced and efficiently acquired dynamic 2-D MRIs without using a learned respiratory motion model. We present a scatter-to-volume deformable registration algorithm to register dynamic 2-D MRIs with a static 3-D MRI to recover dense deformation fields. Practical considerations and approximations are provided to solve the scatter-to-volume registration problem efficiently. The performance of the proposed method was investigated on both synthetic and real MRI datasets, and the results showed significant improvements over the state-of-art respiratory motion modeling methods. We also demonstrated a potential application of the proposed method on MRI-based motion corrected PET imaging using hybrid PET/MRI.
AB - Respiratory motion is one major complicating factor in many image acquisition applications and image-guided interventions. Existing respiratory motion estimation and compensation methods typically rely on breathing motion models learned from certain training data, and therefore may not be able to effectively handle intra-subject and/or inter-subject variations of respiratory motion. In this paper, we propose a respiratory motion compensation framework that directly recovers motion fields from sparsely spaced and efficiently acquired dynamic 2-D MRIs without using a learned respiratory motion model. We present a scatter-to-volume deformable registration algorithm to register dynamic 2-D MRIs with a static 3-D MRI to recover dense deformation fields. Practical considerations and approximations are provided to solve the scatter-to-volume registration problem efficiently. The performance of the proposed method was investigated on both synthetic and real MRI datasets, and the results showed significant improvements over the state-of-art respiratory motion modeling methods. We also demonstrated a potential application of the proposed method on MRI-based motion corrected PET imaging using hybrid PET/MRI.
KW - Image registration
KW - Motion estimation
KW - MRI
KW - PET
KW - Respiratory motion
UR - http://www.scopus.com/inward/record.url?scp=84966495202&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966495202&partnerID=8YFLogxK
U2 - 10.1016/j.compmedimag.2016.03.002
DO - 10.1016/j.compmedimag.2016.03.002
M3 - Article
C2 - 27180910
AN - SCOPUS:84966495202
JO - Computerized Medical Imaging and Graphics
JF - Computerized Medical Imaging and Graphics
SN - 0895-6111
ER -