Field of Interest:cs, math, nucl-ex, physics.ins-det, physics-other
Deadline: 2017-12-01
Region: Europe
Job description:
Job description
The research group in mathematical imaging within the department of mathematics is offering a two-year postdoctoral fellowship based on a grant from the applied mathematics programme at the Swedish Foundation for Strategic Research.
The position is part of a larger medical imaging project where the overall goal is to develop theory and algorithms for image reconstruction applicable to x-ray based medical imaging with under-sampled and/or highly noisy data. Overall clinical goals are to significantly reduce the total dose of x-rays and/or acquisition time while maintaining a clinically useful image quality, alternatively to significantly improve image quality given a fixed total dose/acquisition time.
The position includes research & development of algorithms for PET and SPECT image reconstruction. The position is closely tied to on-going mathematical research initiatives related to image reconstruction and clinical applications in nuclear medical imaging. Some examples of these are multi-channel regularization in PET/CT and SPECT/CT, joint reconstruction and image matching for spatiotemporal pulmonary PET/CT and cardiac SPECT/CT imaging, and task-based reconstruction by iterative deep neural networks. The main development task is to integrate routines for forward and backprojection from reconstruction packages like STIR and EMrecon for PET and NiftyRec for SPECT with ODL, our Python based framework for reconstruction. Research related tasks are to devise computationally feasible models for incorporating detector response and scatter in PET and SPECT imaging. This can be approached using traditional physics based models, but one may also try out methods that rely on deep learning, the latter utilizing the existing integration between ODL and TensorFlow. A final task is to develop a framework for task-based evaluation of reconstruction algorithms that is relevant for the clinical imaging problems that focus on PET/CT for pulmonary cancer diagnosis and staging and SPECT/CT for cardiac perfusion imaging.
Qualifications
A PhD degree in physics, signal processing or computational engineering that has been awarded (or planned to be awarded) before the commencement of the position is a requirement. The candidate should have a strong background from medical imaging, preferably in the context of PET and SPECT physics and image reconstruction. The candidate must also have experience from software development in scientific computing, preferably using Python and/or C/C++. Experience from the software packages STIR, EMrecon and/or Nifyrec is highly beneficial. The position is placed close to the nuclear medical imaging clinic at the Karolinska University Hospital in Stockholm, so a successful candidate also needs to be able to conduct collaborative research in PET/SPECT imaging with clinicians.
Application
Follow the instructions in the formal announcement at https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:158920/type:job/where:4/apply:1
More Information:https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:158920/type:job/where:4/apply:1
Deadline: 2017-12-01
Region: Europe
Job description:
Job description
The research group in mathematical imaging within the department of mathematics is offering a two-year postdoctoral fellowship based on a grant from the applied mathematics programme at the Swedish Foundation for Strategic Research.
The position is part of a larger medical imaging project where the overall goal is to develop theory and algorithms for image reconstruction applicable to x-ray based medical imaging with under-sampled and/or highly noisy data. Overall clinical goals are to significantly reduce the total dose of x-rays and/or acquisition time while maintaining a clinically useful image quality, alternatively to significantly improve image quality given a fixed total dose/acquisition time.
The position includes research & development of algorithms for PET and SPECT image reconstruction. The position is closely tied to on-going mathematical research initiatives related to image reconstruction and clinical applications in nuclear medical imaging. Some examples of these are multi-channel regularization in PET/CT and SPECT/CT, joint reconstruction and image matching for spatiotemporal pulmonary PET/CT and cardiac SPECT/CT imaging, and task-based reconstruction by iterative deep neural networks. The main development task is to integrate routines for forward and backprojection from reconstruction packages like STIR and EMrecon for PET and NiftyRec for SPECT with ODL, our Python based framework for reconstruction. Research related tasks are to devise computationally feasible models for incorporating detector response and scatter in PET and SPECT imaging. This can be approached using traditional physics based models, but one may also try out methods that rely on deep learning, the latter utilizing the existing integration between ODL and TensorFlow. A final task is to develop a framework for task-based evaluation of reconstruction algorithms that is relevant for the clinical imaging problems that focus on PET/CT for pulmonary cancer diagnosis and staging and SPECT/CT for cardiac perfusion imaging.
Qualifications
A PhD degree in physics, signal processing or computational engineering that has been awarded (or planned to be awarded) before the commencement of the position is a requirement. The candidate should have a strong background from medical imaging, preferably in the context of PET and SPECT physics and image reconstruction. The candidate must also have experience from software development in scientific computing, preferably using Python and/or C/C++. Experience from the software packages STIR, EMrecon and/or Nifyrec is highly beneficial. The position is placed close to the nuclear medical imaging clinic at the Karolinska University Hospital in Stockholm, so a successful candidate also needs to be able to conduct collaborative research in PET/SPECT imaging with clinicians.
Application
Follow the instructions in the formal announcement at https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:158920/type:job/where:4/apply:1
More Information:https://www.kth.se/en/om/work-at-kth/lediga-jobb/what:job/jobID:158920/type:job/where:4/apply:1