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DEVELOPMENT OF A 3D SPATIAL COMPOUNDING (STITCHING) ALGORITHM FOR HANDHELD OPTOACOUSTIC TOMOGRAPHY OF LARGE VOLUMES IN HUMANS
There is a growing need for non-invasive clinical imaging modalities covering large volumes of the human body accurately and rapidly for disease diagnosis and treatment monitoring. Optoacoustic tomography (OAT) images human tissue, particularly important vasculature structures, in a non-invasive, real-time and volumetric (3D) manner [1].
Keywords: medical imaging; photoacoustic imaging; image stitching; image processing
Early detection of vascular diseases, such as atherosclerosis, is crucial in reducing disease progression and lowering risk of mortality. OAT enables rapid imaging of human vasculature allowing to capture a field of view (FOV) covering an area of approximately 2 cm x 2 cm x 2 cm in one single laser pulse (e.g. the carotid artery bifurcation).
Advanced spatial compounding or stitching algorithms can extend the 2 cm x 2 cm x 2 cm FOV and cover a more extensive area of the human body (Fig 1) [2]. Handheld scanning capabilities of OAT enables us to cover areas of the human body in multiple directions while reducing motion artifacts. However, currently implemented spatial compounding algorithms are not capable of compounding OAT scans covering large volumes and are instable for complex scan patterns. To further expand usability of OAT in clinics, sophisticated algorithms capable of spatial compounding larger volumes accurately and in 3D are required.
[1] Ivankovic, I., Merčep, E., Schmedt, C. G., Deán-Ben, X. L., & Razansky, D. Real-time volumetric assessment of the human carotid artery: handheld multispectral optoacoustic tomography. Radiology 2019 181325
[2] Nitkunanantharajah S, Hennersperger C, Dean-Ben XL, Razansky D, Navab N. Trackerless panoramic optoacoustic imaging: a first feasibility evaluation. Int J CARS 2018;13(5):703–711
Early detection of vascular diseases, such as atherosclerosis, is crucial in reducing disease progression and lowering risk of mortality. OAT enables rapid imaging of human vasculature allowing to capture a field of view (FOV) covering an area of approximately 2 cm x 2 cm x 2 cm in one single laser pulse (e.g. the carotid artery bifurcation). Advanced spatial compounding or stitching algorithms can extend the 2 cm x 2 cm x 2 cm FOV and cover a more extensive area of the human body (Fig 1) [2]. Handheld scanning capabilities of OAT enables us to cover areas of the human body in multiple directions while reducing motion artifacts. However, currently implemented spatial compounding algorithms are not capable of compounding OAT scans covering large volumes and are instable for complex scan patterns. To further expand usability of OAT in clinics, sophisticated algorithms capable of spatial compounding larger volumes accurately and in 3D are required.
[1] Ivankovic, I., Merčep, E., Schmedt, C. G., Deán-Ben, X. L., & Razansky, D. Real-time volumetric assessment of the human carotid artery: handheld multispectral optoacoustic tomography. Radiology 2019 181325
[2] Nitkunanantharajah S, Hennersperger C, Dean-Ben XL, Razansky D, Navab N. Trackerless panoramic optoacoustic imaging: a first feasibility evaluation. Int J CARS 2018;13(5):703–711
The student is expected to develop and improve existing spatial compounding algorithms that can accurately cover large OAT handheld scanned volumes. Position sensing techniques should be implemented in addition to image-based spatial compounding to strengthen accuracy and facilitate the acquisition of complex motion patterns. Advanced skills in programing (MATLAB, Python, C++) are essential for this project. After understanding the scope of the project, the student is expected to work independently. Experience in hardware interfacing and mechatronics are of advantage.
The student is expected to develop and improve existing spatial compounding algorithms that can accurately cover large OAT handheld scanned volumes. Position sensing techniques should be implemented in addition to image-based spatial compounding to strengthen accuracy and facilitate the acquisition of complex motion patterns. Advanced skills in programing (MATLAB, Python, C++) are essential for this project. After understanding the scope of the project, the student is expected to work independently. Experience in hardware interfacing and mechatronics are of advantage.
Please send a brief introduction, CV and transcripts of records from your current studies to Ivana Ivankovic: iivana@student.ethz.ch
Please send a brief introduction, CV and transcripts of records from your current studies to Ivana Ivankovic: iivana@student.ethz.ch