1
|
Chang HH. Multimodal Image Registration Using a Viscous Fluid Model with the Bhattacharyya Distance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083278 DOI: 10.1109/embc40787.2023.10340615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Image registration is an elementary task in medical image processing and analysis, which can be divided into monomodal and multimodal. Direct 3D multimodal registration in volumetric medical images can provide more insight into the interpretation of subsequent image processing applications than 2D methods. This paper is dedicated to the development of a 3D multimodal image registration algorithm based on a viscous fluid model associated with the Bhattacharyya distance. In our approach, a modified Navier-Stoke's equation is exploited as the foundation of the multimodal image registration framework. The hopscotch method is numerically implemented to solve the velocity field, whose values at the explicit locations are first computed and the values at the implicit positions are solved by transposition. The differential of the Bhattacharyya distance is incorporated into the body force function, which is the main driving force for deformation, to enable multimodal registration. A variety of simulated and real brain MR images were utilized to assess the proposed 3D multimodal image registration system. Preliminary experimental results indicated that our algorithm produced high registration accuracy in various registration scenarios and outperformed other competing methods in many multimodal image registration tasks.Clinical Relevance- This facilitates the disease diagnosis and treatment planning that requires accurate 3D multimodal image registration without massive image data and extensive training regardless of the imaging modality.
Collapse
|
2
|
Wang B, Prastawa M, Irimia A, Saha A, Liu W, Goh SM, Vespa PM, Van Horn JD, Gerig G. Modeling 4D Pathological Changes by Leveraging Normative Models. COMPUTER VISION AND IMAGE UNDERSTANDING : CVIU 2016; 151:3-13. [PMID: 27818606 PMCID: PMC5094466 DOI: 10.1016/j.cviu.2016.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
With the increasing use of efficient multimodal 3D imaging, clinicians are able to access longitudinal imaging to stage pathological diseases, to monitor the efficacy of therapeutic interventions, or to assess and quantify rehabilitation efforts. Analysis of such four-dimensional (4D) image data presenting pathologies, including disappearing and newly appearing lesions, represents a significant challenge due to the presence of complex spatio-temporal changes. Image analysis methods for such 4D image data have to include not only a concept for joint segmentation of 3D datasets to account for inherent correlations of subject-specific repeated scans but also a mechanism to account for large deformations and the destruction and formation of lesions (e.g., edema, bleeding) due to underlying physiological processes associated with damage, intervention, and recovery. In this paper, we propose a novel framework that provides a joint segmentation-registration framework to tackle the inherent problem of image registration in the presence of objects not present in all images of the time series. Our methodology models 4D changes in pathological anatomy across time and and also provides an explicit mapping of a healthy normative template to a subject's image data with pathologies. Since atlas-moderated segmentation methods cannot explain appearance and locality pathological structures that are not represented in the template atlas, the new framework provides different options for initialization via a supervised learning approach, iterative semisupervised active learning, and also transfer learning, which results in a fully automatic 4D segmentation method. We demonstrate the effectiveness of our novel approach with synthetic experiments and a 4D multimodal MRI dataset of severe traumatic brain injury (TBI), including validation via comparison to expert segmentations. However, the proposed methodology is generic in regard to different clinical applications requiring quantitative analysis of 4D imaging representing spatio-temporal changes of pathologies.
Collapse
Affiliation(s)
- Bo Wang
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Drive, Salt Lake City, UT 84112 USA
- School of Computing, University of Utah, 50 S., Central Campus Drive, Salt Lake City, UT 84112 USA
| | - Marcel Prastawa
- Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY 10029 USA
| | - Andrei Irimia
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles CA 90089 USA
| | - Avishek Saha
- Yahoo Labs, 701 1st Ave, Sunnyvale, CA 94089 USA
| | - Wei Liu
- Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Drive, Salt Lake City, UT 84112 USA
- School of Computing, University of Utah, 50 S., Central Campus Drive, Salt Lake City, UT 84112 USA
| | - S.Y. Matthew Goh
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles CA 90089 USA
| | - Paul M. Vespa
- Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095 USA
| | - John D. Van Horn
- The Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Los Angeles CA 90089 USA
| | - Guido Gerig
- Tandon School of Engineering, Department of Computer Science and Engineering, NYU, USA
| |
Collapse
|