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  Robust Deep Learning for Medical Image Reconstruction

   School of Science and Engineering

   Wednesday, February 28, 2024  Self-Funded PhD Students Only

About the Project

The Department of Biomedical Engineering at the University of Dundee is inviting applications for a self-funded PhD research project under the supervision of Dr Alessandro Perelli. This position is an opportunity to conduct cutting-edge research within computational medical imaging, machine learning and medicine, with the possibility of collaboration with clinicians at the Ninewells Hospital in Dundee.

This research project focuses on the development of robust deep learning algorithms for image reconstruction in medical imaging with applications to X-ray Computed Tomography (CT) and Magnetic Resonance Imaging (MRI).

Image reconstruction is an inverse problem that deals with the estimation of an image from measurement data obtained from physics-based acquisition processes. As instance, modern techniques used in oncology such as CT and MRI require fast and accurate image reconstruction algorithms to deal with incomplete data to speed-up the acquisition, noise and artefacts [1-3].

Deep learning methods, that use for training past datasets of successfully reconstructed images together with the measurement that produced them, have been shown to produce some impressive results in image reconstruction. However, the problem with such data-driven imaging is currently the reliability of the results and robustness to model acquisition mismatch, i.e. different scanners used in training and testing which hinders the use for clinical assessment. Indeed, even small deviations in the data can result in large differences in the outcome which has devastating implications for many applications [4].

Furthermore, the collection of training ground truth clinical data is sometimes impossible to obtain under indirect acquisition of the specimen through medical scanners.

Therefore, an important question is to understand whether it is possible to deploy deep learning algorithms which do not require accurate ground truth data rather noisy ones or can learn from the testing image or data itself without training dataset.

In this project you will answer these questions by developing new algorithms, based on deep learning and optimization, and analysis techniques towards robust computational image reconstruction for CT and MRI. You are expected to further advance current developments of supervised (or self-supervised) model-based deep learning with convolutional network and deep generative prior [5-7]. The expected project’s outcomes will involve writing research papers with the developed methods together with the creation of the software to reproduce the results and clinical validation.

There will be opportunities for the student to collaborate with clinicians at NHS Tayside and to exploit the facilities in the Division for Imaging and Technology, University of Dundee.

For further information, please see

For informal enquiries about the project, contact Dr Alessandro Perelli ().

For general enquiries about the University of Dundee, contact


Applicants should hold, or expect to receive, a master’s level qualification and/or First/Upper Second-Class Honours degree in a relevant subject. A strong background in mathematics and programming experience in Python is essential. Previous experience in deep learning (PyTorch/TensorFlow), signal/image processing (Python/MATLAB) and computer vision is highly desirable.

English language requirement: IELTS (Academic) overall score must be at least 6.5 (with not less than 6.0 in writing or 5.5 in reading, listening or speaking). The University of Dundee accepts a variety of equivalent qualifications and alternative ways to demonstrate language proficiency; please see full details of the University’s English language requirements here:

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research. We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.


Step 1: Email Dr Alessandro Perelli () to (1) send a copy of your CV and (2) discuss your potential application and any practicalities (e.g. suitable start date, length of degree).

Step 2: After discussion with Dr Perelli, formal applications can be made via our direct application system. When applying, please follow the instructions below:

Candidates must apply for the Doctor of Philosophy (PhD) degree in Biomedical Engineering (3 Year or 4 Year route) using our direct application system: apply for Biomedical Engineering.

Please select the study mode (full-time/part-time) and start date agreed with the lead supervisor.

In the Research Proposal section, please:

-       Enter the lead supervisor’s name in the ‘proposed supervisor’ box

-       Enter the project title listed at the top of this page in the ‘proposed project title’ box

In the ‘personal statement’ section, please outline your suitability for the project selected.

Computer Science (8) Engineering (12) Mathematics (25) Medicine (26)

Funding Notes

There is no funding attached to this project. The successful applicant will be expected to provide the funding for tuition fees and living expenses, via external sponsorship or self-funding.


[1] C. M. Sandino, J. Y. Cheng, F. Chen, M. Mardani, J. M. Pauly and S. S. Vasanawala, "Compressed Sensing: From Research to Clinical Practice With Deep Neural Networks: Shortening Scan Times for Magnetic Resonance Imaging," in IEEE Signal Processing Magazine, vol. 37, no. 1, pp. 117-127, 2020, doi: 10.1109/MSP.2019.2950433.
[2] V. Monga, Y. Li and Y. C. Eldar, "Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing," in IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 18-44, 2021, doi: 10.1109/MSP.2020.3016905.
[3] G. Wang, M. Jacob, X. Mou, Y. Shi and Y. C. Eldar, "Deep Tomographic Image Reconstruction: Yesterday, Today, and Tomorrow - Editorial for the 2nd Special Issue “Machine Learning for Image Reconstruction”," in IEEE Transactions on Medical Imaging, vol. 40, no. 11, pp. 2956-2964, 2021, doi: 10.1109/TMI.2021.3115547.
[4] D. Gilton, G. Ongie and R. Willett, "Model Adaptation for Inverse Problems in Imaging," in IEEE Transactions on Computational Imaging, vol. 7, pp. 661-674, 2021, doi: 10.1109/TCI.2021.3094714.
[5] A. Jalal, M. Arvinte, G. Daras, E. Price, A.G. Dimakis, J. Tamir, “Robust compressed sensing MRI with deep generative priors” in Advances in Neural Information Processing Systems, 34, 2021.
[6] A. A. Hendriksen, D. M. Pelt and K. J. Batenburg, "Noise2Inverse: Self-Supervised Deep Convolutional Denoising for Tomography," in IEEE Transactions on Computational Imaging, vol. 6, pp. 1320-1335, 2020, doi: 10.1109/TCI.2020.3019647.
[7] A. Perelli, M. Lexa, A. Can, M. E. Davies, “Compressive computed tomography reconstruction through denoising approximate message passing”, in SIAM Journal on Imaging Sciences, 13(4), 1860-1897, 2020, doi: 10.1137/19M1310013.

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