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Image Co-registration from CT Blood Vessels to MR Brain Tissues
This is a clinical image registration and visualization project that tries to map a zoomed-in CT view with a zoomed-out MR modality. The CT view can see very detailed blood vessels and bones, while the MR view sees the soft brain tissues but without vessels. The clinicians want to register them together automatically, as they are currently aligning the two views by hand manually and takes them a lot of time. The outcome of this project is an automated, fast, and accurate image co-registration software that can be deployed in the hospital to improve clinical care.
Keywords: image co-registration, medical image analysis, medical image visualization, medical software
In this Co-Registration and Image visualization Project, you will work closely with neuro-radiologists in the University Hospital of Zurich (USZ) to co-register a zoomed-in view of cone-beam CT angiography on T1 brains of the same patients. The technical challenge of the co-registration is from the different fields of view and anatomical information from the joint-modalities. In one modality, the angiography of the blood vessels are detailed and in very high contrast, while in the other modality, the blood vessels are not seen and the view is much more zoomed out. The manual registration process is currently done by clinical experts specializing in neuro-radiology and is very time consuming.
In this Co-Registration and Image visualization Project, you will work closely with neuro-radiologists in the University Hospital of Zurich (USZ) to co-register a zoomed-in view of cone-beam CT angiography on T1 brains of the same patients. The technical challenge of the co-registration is from the different fields of view and anatomical information from the joint-modalities. In one modality, the angiography of the blood vessels are detailed and in very high contrast, while in the other modality, the blood vessels are not seen and the view is much more zoomed out. The manual registration process is currently done by clinical experts specializing in neuro-radiology and is very time consuming.
The desired outcome of this project is an automated, fast, and accurate image co-registration software that can be deployed in the hospital to improve clinical care. Semi-automated methods are also possible, and you can be creative in the human-computer-interaction aspects.
The desired outcome of this project is an automated, fast, and accurate image co-registration software that can be deployed in the hospital to improve clinical care. Semi-automated methods are also possible, and you can be creative in the human-computer-interaction aspects.
Please send your CV and application to Kaiyuan Yang (**kaiyuan.yang@uzh.ch**).
Please send your CV and application to Kaiyuan Yang (**kaiyuan.yang@uzh.ch**).