Three-dimensional photoacoustic imaging systems in breast cancer are few and far between, [1] but hold tremendous potential in carrying information that can help in diagnosing the disease. [2,3] Blood-vessel rich images are being developed with a highly sophisticated system with unprecedented contrast and resolution. [4,5] However, interpretation of images is challenging due to the complexity and shortage of the data sets and the fact that there is little experience in the clinics with the modality. There is thus a need for developing software tools able to extract clinically significant and quantitative information from the images using image processing and machine learning techniques. Developing such tools include the design of algorithms for the registration between conventional images such as Magnetic Resonance images and Ultrasound images with the photoacoustic images, the generation of digitally synthesized photoacoustic images, the automated segmentation of structures-of-interests in the image and the extraction of quantitative radiomics features. The means for achieving the above will be based on the most innovative image analysis approaches with a particular focus on deep learning methods [6].
YOUR PROFILE
- Do you want to contribute to a disruptive technology for breast cancer imaging?
- Are you interested in image analysis methods using Machine Learning on clinical data from a highly sophisticated photoacoustic-ultrasound breast imaging system?
- Do you want to work shoulder-to-shoulder with highly talented researchers, engineers and clinicians in a consortium within a EU project?
The successful candidate is an applied physicist, applied mathematician or engineer with a PhD in image analysis or image reconstruction in general, but experience in photoacoustic or ultrasound modalities is desirable. Preference is given to those with experience in Machine Learning. Excellent research skills in these topics should have been demonstrated during the PhD. An appropriate number of high quality papers should have been published during the PhD. High creativity, excellent self-motivation and the ability to lead a small team are essential. You will work with 3-4 researchers at the University of Twente, and several in the company PA Imaging and Academic Hospital Radboud UMC. We expect you to have excellent command of the English language as well as professional communication and team working skills.
OUR OFFER
The University of Twente offers a stimulating work environment in an area of applied, forefront research. You will have a fulltime employment contract for the duration of 1 year and can participate in all employee benefits the UT offers. Salary and conditions will be in accordance with the Collective Labour Agreement (CAO) of the Dutch Universities. Gross monthly salary depends on experience and qualifications and ranges from € 3.557 to € 4.670. Additionally, the University of Twente provides excellent facilities for professional and personal development, a holiday allowance (amounts to 8%), an end-of-year bonus (amounts to 8,3%) and a number of additional benefits.
INFORMATION AND APPLICATION
For further information you can contact Prof. Dr. Srirang Manohar (s.manohar@utwente.nl). Candidates are invited to send their applications, including a short motivation letter, references (at least 3), and CV (including obtained degrees and publications).
Please note that this position will be available from 1 September 2022 at the latest.
References:
1) https://www.pammoth-2020.eu/objectives.html
2) Wang, L.V. and Hu, S., 2012. Photoacoustic tomography: in vivo imaging from organelles to organs. Science, 335(6075), pp.1458-1462.
3) Manohar, S. and Dantuma, M., 2019. Current and future trends in photoacoustic breast imaging. Photoacoustics, 16, p.100134.
4) Schoustra, S.M., Piras, D., Huijink, R., Op’t Root, T.J., Alink, L., Kobold, W.M.F., Steenbergen, W. and Manohar, S., 2019. Twente Photoacoustic Mammoscope 2: system overview and three-dimensional vascular network images in healthy breasts. Journal of Biomedical Optics, 24(12), p.121909.
5) Lin, L., Hu, P., Shi, J., Appleton, C.M., Maslov, K., Li, L., Zhang, R. and Wang, L.V., 2018. Single-breath-hold photoacoustic computed tomography of the breast. Nature Communications, 9(1), pp.1-9.
6) Hauptmann, A. and Cox, B.T., 2020. Deep Learning in Photoacoustic Tomography: Current approaches and future directions. Journal of Biomedical Optics, 25(11), p.112903.