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 a 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.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Pai, Vinay Manjunath
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Johns Hopkins University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
United States
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Frangi, Alejandro F; Tsaftaris, Sotirios A; Prince, Jerry L (2018) Simulation and Synthesis in Medical Imaging. IEEE Trans Med Imaging 37:673-679
Dewey, Blake E; Carass, Aaron; Blitz, Ari M et al. (2017) Efficient Multi-Atlas Registration using an Intermediate Template Image. Proc SPIE Int Soc Opt Eng 10137:
Zhao, Can; Carass, Aaron; Lee, Junghoon et al. (2017) A supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis. Simul Synth Med Imaging (2017) 10557:33-40
Lee, Junghoon; Carass, Aaron; Jog, Amod et al. (2017) Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning. Proc SPIE Int Soc Opt Eng 10133:
Jog, Amod; Carass, Aaron; Roy, Snehashis et al. (2017) Random forest regression for magnetic resonance image synthesis. Med Image Anal 35:475-488
Chen, Min; Carass, Aaron; Jog, Amod et al. (2017) Cross contrast multi-channel image registration using image synthesis for MR brain images. Med Image Anal 36:2-14
Jog, Amod; Carass, Aaron; Prince, Jerry L (2016) Self Super-resolution for Magnetic Resonance Images. Med Image Comput Comput Assist Interv 9902:553-560
Zhao, Can; Carass, Aaron; Jog, Amod et al. (2016) Effects of Spatial Resolution on Image Registration. Proc SPIE Int Soc Opt Eng 9784:
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

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