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Improving the accuracy of Image co-registration between magnetic resonance imaging and optoacoustic imaging modalities
The objective of this study is to develop an algorithm for image registration between MRI and optoacoustic imaging, to streamline and automatize the registration processes for imaging in animal models.
Keywords: magnetic resonance imaging, optoacoustic imaging, image registration, data analysis, brain,
We have used multimodal imaging approaches with high-resolution (micrometer) magnetic resonance imaging (MRI) at 7T and 9.4T combined with optoacoustic imaging to understand the molecular events in animal models of Alzheimer’s disease. Optoacoustic imaging provides in vivo visualization of deep-tissue targets (mm range) with intrinsic photonic absorption contrasts (e.g. hemoglobin) or optically active probes in e.g. the brains of mice. MRI provides read-outs for alternations in structural connectivity in brain of animal model and brain atrophy with voxel-based morphometry as well as physiological read-outs (e.g. perfusion). Accurate registering of images between both modalities provides a tool to combine structural and functional with molecular information. It is key to quantify the topographic development of pathologies, as well as to monitor the effects of putative treatments. This will greatly enhance our understanding of brain diseases with a complex pathophysiology such as Alzheimer`s disease. For co-registration between both modalities we currently use a Landmark based co-registering algorithm which has certain limitations. In addition, the errors of different registration methods have not been quantified to allow for optimization.
We have used multimodal imaging approaches with high-resolution (micrometer) magnetic resonance imaging (MRI) at 7T and 9.4T combined with optoacoustic imaging to understand the molecular events in animal models of Alzheimer’s disease. Optoacoustic imaging provides in vivo visualization of deep-tissue targets (mm range) with intrinsic photonic absorption contrasts (e.g. hemoglobin) or optically active probes in e.g. the brains of mice. MRI provides read-outs for alternations in structural connectivity in brain of animal model and brain atrophy with voxel-based morphometry as well as physiological read-outs (e.g. perfusion). Accurate registering of images between both modalities provides a tool to combine structural and functional with molecular information. It is key to quantify the topographic development of pathologies, as well as to monitor the effects of putative treatments. This will greatly enhance our understanding of brain diseases with a complex pathophysiology such as Alzheimer`s disease. For co-registration between both modalities we currently use a Landmark based co-registering algorithm which has certain limitations. In addition, the errors of different registration methods have not been quantified to allow for optimization.
The objective of this study is to develop an algorithm for image registration between MRI and optoacoustic imaging, to streamline and automatize the registration processes for imaging in animal models.
The specific aims include:
1) Test fiducial marker registration (and/or deformable registration) for MRI - optoacoustic images.
2) Implement measures for quality analysis for different registration methods.
3) Develop an automatize registration pipeline for processing MRI-optoacoustic images acquired in mouse models.
Students with interest and experience in biomedical engineering, electrical engineering, computational sciences, and image processing are encouraged to apply. The student will develop algorithms (Matlab or python based) that can register MRI and optoacoustic images in 3D.
Tasks: 60% computational, 30% image processing, experimental 10%
The objective of this study is to develop an algorithm for image registration between MRI and optoacoustic imaging, to streamline and automatize the registration processes for imaging in animal models.
The specific aims include: 1) Test fiducial marker registration (and/or deformable registration) for MRI - optoacoustic images. 2) Implement measures for quality analysis for different registration methods. 3) Develop an automatize registration pipeline for processing MRI-optoacoustic images acquired in mouse models.
Students with interest and experience in biomedical engineering, electrical engineering, computational sciences, and image processing are encouraged to apply. The student will develop algorithms (Matlab or python based) that can register MRI and optoacoustic images in 3D.
Each year the IDEA League offers the students of its partner universities over 180 monthly grants for a short-term research exchange. In general, these grants are awarded based on academic merit. For more information visit http://idealeague.org/student-grant/