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Automated 3D registration between optoacoustic tomography and magnetic resonance imaging
The objective of this study is to further develop an algorithm for 3D image registration between MRI and optoacoustic imaging, to streamline and automatize the registration processes for imaging in animal models.
Keywords: magnetic resonance imaging, photoacoustic 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 stroke and 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. In our earlier and ongoing projects, we have developed an algorithm and user-friendly GUI for 2D image registration between MRI and optoacoustic imaging, to streamline and automatize the registration processes for imaging in animal 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%
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 stroke and 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. In our earlier and ongoing projects, we have developed an algorithm and user-friendly GUI for 2D image registration between MRI and optoacoustic imaging, to streamline and automatize the registration processes for imaging in animal 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%
Specific Aims
1. Develop 3D automated volumetric registration of optoacoustic and MRI datasets
2. Validate the code with previous acquire nanoprobe dynamic contrast enhanced dataset, quantitatively access the accuracy and registration error and optimize the quantification.
3. Apply the registration on imaging dataset acquired on disease models.
Specific Aims 1. Develop 3D automated volumetric registration of optoacoustic and MRI datasets 2. Validate the code with previous acquire nanoprobe dynamic contrast enhanced dataset, quantitatively access the accuracy and registration error and optimize the quantification. 3. Apply the registration on imaging dataset acquired on disease models.
Prof.Jan Klohs klohs@biomed.ee.ethz.ch
Dr.Ruiqing Ni ni@biomed.ee.ethz.ch
Wolfgang-Pauli-strasse 27 HIT E22.4, 8093 Zurich
Prof.Jan Klohs klohs@biomed.ee.ethz.ch Dr.Ruiqing Ni ni@biomed.ee.ethz.ch Wolfgang-Pauli-strasse 27 HIT E22.4, 8093 Zurich
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/