Anatomically Guided Registration for Multimodal Images

With an increase in full-body scans and longitudinal acquisitions to track disease progression, it becomes significant to find correspondence between multiple images. One example would be the monitoring size/location of tumors using PET images during chemotherapy to determine treatment progression....

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Bibliographic Details
Main Authors: Datar, M., Gopalakrishnan, G., Ranjan, S., Mullick, R.
Format: Conference Proceeding
Language:English
Subjects:
Anatomy
Computed tomography
Diseases
Head
Image motion analysis
Joints
Mutual information
Neoplasms
Optical imaging
Pathology
Online Access:Request full text
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Description
Summary:With an increase in full-body scans and longitudinal acquisitions to track disease progression, it becomes significant to find correspondence between multiple images. One example would be the monitoring size/location of tumors using PET images during chemotherapy to determine treatment progression. While there is a need to go beyond a single parametric transform to recover misalignments, pure deformable solutions become complex, time-consuming and unnecessary at times. Simple anatomically guided approach for whole body image registration offers enhanced alignment of large coverage inter-scan studies. In this experiment, we provide anatomy specific transformations to capture their independent motions. This solution is characterized by an automatic segmentation of regions in the image, followed by a custom registration and volume stitching. We have tested this algorithm on phantom images as well as clinical longitudinal datasets. We were successful in proving that decoupling transformations improves the overall registration quality.
ISSN:1550-5219
2332-5615
DOI:10.1109/AIPR.2006.14