(Max 30 lines of text) Image registration is a fundamentally important capability in modern neuroscience and clinical medicine. Normalization in functional imaging studies, studies of shape changes in growth, aging, and disease, overlaying surgical plans on intraoperative images, and geometric distortion correction are examples of important applications of image registration. There are dozens of needs for registration in both intrasubject and intersubject applications as well. Therefore, any improvement in image registration performance will have an immediate impact on the scientific and clinical communities. Despite numerous advances in image reconstruction algorithms, the use of multiple modalities (or tissue contrasts) to carry out image registration is a virtually untapped area. The vastly dominant framework is to register a single image of the subject to another single image of the target, and if multiple images are available of either subject or target, they are registered by using the transformation derived from the single image registration. The proposed research will develop, evaluate, and validate a very simply explained but quite radical idea for multi-modal registration. The basic idea is to synthesize a """"""""proxy"""""""" image from the subject image that has the same tissue contrast and intensity range as the target image and then use a conventional metric such as sum-of-square difference to carry out the registration between the subject proxy and target. Preliminary results demonstrate significant benefits in this approach. In the grant we will: 1) Optimize """"""""proxy"""""""" multimodal image registration by exploring its theoretical justification as well as key parameters of the overall approach;2) Apply """"""""proxy"""""""" multimodal image registration to three key applications in neuroscience in order to validate the method and develop principles of best practice;and 3) Write open source software to carry out image synthesis, similarity computation, and rigid and deformable registration using the """"""""proxy"""""""" image concept. Both the software to synthesize images for use in a user's favorite image registration method as well as software to carry out the entire """"""""proxy"""""""" registration process in an optimized way will be made publicly available as open source computer code. The results of this research will lead to a new era in image registration by changing the way researchers and practitioners acquire and use data for neuroscientific studies and clinical medicine.

Public Health Relevance

Medical image registration is a method used throughout clinical medicine and medical research and it is vital to the success of many treatments and therapies and for answering a myriad of important scientific questions. This research will permit better alignment by devising and testing a new similarity criterion for multimodal images using the first significantly new approach in over a decade. The result will be better alignment of these images, which will enable better clinical diagnosis and prognosis and more significant research discoveries.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB017743-01
Application #
8614480
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Pai, Vinay Manjunath
Project Start
2013-09-30
Project End
2016-08-31
Budget Start
2013-09-30
Budget End
2014-08-31
Support Year
1
Fiscal Year
2013
Total Cost
$343,798
Indirect Cost
$118,798
Name
Johns Hopkins University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Zhao, Can; Carass, Aaron; Jog, Amod et al. (2016) Effects of Spatial Resolution on Image Registration. Proc SPIE Int Soc Opt Eng 9784:
Chen, Min; Carass, Aaron; Jog, Amod et al. (2016) Cross contrast multi-channel image registration using image synthesis for MR brain images. Med Image Anal 36:2-14
Chen, Min; Jog, Amod; Carass, Aaron et al. (2015) Using image synthesis for multi-channel registration of different image modalities. Proc SPIE Int Soc Opt Eng 9413:
Jog, Amod; Carass, Aaron; Pham, Dzung L et al. (2015) Tree-Encoded Conditional Random Fields for Image Synthesis. Inf Process Med Imaging 24:733-45
Roy, Snehashis; He, Qing; Sweeney, Elizabeth et al. (2015) Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation. IEEE J Biomed Health Inform 19:1598-609
Jog, Amod; Carass, Aaron; Pham, Dzung L et al. (2015) Multi-Output Decision Trees for Lesion Segmentation in Multiple Sclerosis. Proc SPIE Int Soc Opt Eng 9413:
Jog, Amod; Carass, Aaron; Roy, Snehashis et al. (2015) MR image synthesis by contrast learning on neighborhood ensembles. Med Image Anal 24:63-76
Jog, Amod; Carass, Aaron; Prince, Jerry L (2014) IMPROVING MAGNETIC RESONANCE RESOLUTION WITH SUPERVISED LEARNING. Proc IEEE Int Symp Biomed Imaging 2014:987-990
Roy, Snehashis; Wang, Wen-Tung; Carass, Aaron et al. (2014) PET attenuation correction using synthetic CT from ultrashort echo-time MR imaging. J Nucl Med 55:2071-7
Roy, Snehashis; Carass, Aaron; Jog, Amod et al. (2014) MR to CT Registration of Brains using Image Synthesis. Proc SPIE Int Soc Opt Eng 9034:

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