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Developing Computational Methods for Bone Tissue Analysis from Photon-Counting Detector Computed Tomography Images
The goal of this project is to develop new algorithms to characterize cortical and trabecular bone structures from PCD-CT. To this end, we aim to (1) combine novel ideas with established methods [1,2], and (2) to define the basics for a new open-source workflow to process bone images from PCD-CT.
Keywords: Bone measurement, medical image analysis, open science
Background:
Photon-Counting Detector Computed Tomography (PCD-CT) is a new technology that allows ex-vivo scanning of large volumes at a very high resolution (up to 100µm isotropic). It provides exceptional visibility of anatomical details and opens doors to the development of novel algorithms for tissue characterization, which will ultimately lead to a better understanding of physiological and mechanical bone behavior.
Aims:
The goal of this project is to develop new algorithms to characterize cortical and trabecular bone structures from PCD-CT. To this end, we aim to (1) combine novel ideas with established methods [1,2], and (2) to define the basics for a new open-source workflow to process bone images from PCD-CT. Your qualifications / What we are looking for
- Experience in medical image processing and analysis
- Good programming skills in Python
- Genuine interest in open and reproducible research
- Good English communication skills
What we offer:
- A project with potential real-world medical impact
- The possibility to bring in your own ideas to the project
- Close supervision by an international team of experts
References:
[1] Laib et al. 1998. www.doi.org/10.3233/THC-1998-65-606
[2] Burghardt et al. 2010. www.doi.org/10.1016/j.bone.2010.05.034
Background: Photon-Counting Detector Computed Tomography (PCD-CT) is a new technology that allows ex-vivo scanning of large volumes at a very high resolution (up to 100µm isotropic). It provides exceptional visibility of anatomical details and opens doors to the development of novel algorithms for tissue characterization, which will ultimately lead to a better understanding of physiological and mechanical bone behavior.
Aims: The goal of this project is to develop new algorithms to characterize cortical and trabecular bone structures from PCD-CT. To this end, we aim to (1) combine novel ideas with established methods [1,2], and (2) to define the basics for a new open-source workflow to process bone images from PCD-CT. Your qualifications / What we are looking for
- Experience in medical image processing and analysis - Good programming skills in Python - Genuine interest in open and reproducible research - Good English communication skills
What we offer:
- A project with potential real-world medical impact - The possibility to bring in your own ideas to the project - Close supervision by an international team of experts
References: [1] Laib et al. 1998. www.doi.org/10.3233/THC-1998-65-606 [2] Burghardt et al. 2010. www.doi.org/10.1016/j.bone.2010.05.034
The goal of this project is to develop new algorithms to characterize cortical and trabecular bone structures from PCD-CT. To this end, we aim to (1) combine novel ideas with established methods [1,2], and (2) to define the basics for a new open-source workflow to process bone images from PCD-CT.
The goal of this project is to develop new algorithms to characterize cortical and trabecular bone structures from PCD-CT. To this end, we aim to (1) combine novel ideas with established methods [1,2], and (2) to define the basics for a new open-source workflow to process bone images from PCD-CT.
Please, send your CV to Serena Bonaretti (serena.bonaretti@balgristcampus.ch) and Andrew Burghardt (andrew.burghardt@ucsf.edu). Links to previous work (e.g., your GitHub profile) are highly appreciated.
Please, send your CV to Serena Bonaretti (serena.bonaretti@balgristcampus.ch) and Andrew Burghardt (andrew.burghardt@ucsf.edu). Links to previous work (e.g., your GitHub profile) are highly appreciated.