MRI Without Boundaries: intelligent medical imaging for the playful child

PRoject no.: 2026_001

Supervisor 1: Jo Hajnal
Supervisor 2: Shaihan Malik
Supervisor 3: Tomoki Arrichi

RESEARCH DOMAINS: Breakthrough Technologies; Affordable and Accessible Imaging

INDUSTRY PARTNER: Siemens Healthineers

This PhD project aims to transform how Magnetic Resonance Imaging (MRI) is performed in young children by developing a completely new free-pose MRI capability. Conventional paediatric MRI relies on sedation because children struggle to remain still in the confined, noisy scanner, causing motion artefacts that degrade image quality. This project seeks to break that paradigm by building on four cutting-edge technologies:
1) A new ultra-wide-bore, low-field MRI scanner with light flexible receiver coils provides a spacious and safe environment where a child can adopt any comfortable posture.
2) In scanner interactive immersive technology creates a virtual world that engages the child and promotes natural stillness using eye-gaze control.
3) Real-time artificial intelligence (AI) to detect movement, estimate body pose, and dynamically update imaging geometry to maintain anatomical consistency.
4) Adaptive reconstruction algorithms to ensure high-resolution images despite motion or changing positions.

The project combines advances in biomedical engineering, AI, and human-machine interaction to deliver a technically transformative and socially impactful solution for paediatric MRI. By developing new software and methods you will create a system that continuously learns and adapts during scanning, enabling diagnostic-quality brain images without anaesthesia.

Agentic AI for Personalised and Clinical Task-Based PET Image Reconstruction

PRoject no.: 2026_002

Supervisor 1: Andrew Reader
Supervisor 2: Alexander Hammers

RESEARCH DOMAINS: Breakthrough Technologies; Precise Diagnosis and Prognosis

INDUSTRY PARTNER: Siemens Healthineers

Positron emission tomography (PET) is a powerful tool for studying the brain, but conventional image reconstruction methods use fixed algorithms and assumptions that cannot adapt to each patient’s unique data or clinical question.

This project introduces a new generation of intelligent PET image reconstruction using agentic artificial intelligence (AI): AI systems that can reason, adapt, and interact like human experts. Instead of applying one-size-fits-all settings, the AI will adjust the reconstruction process in real time, optimising parameters and priors based on the specific diagnostic task (for example, epilepsy or dementia) and even for different regions of the brain.

In the first year, the project will benchmark state-of-the-art methods, including advanced diffusion-based priors, to establish the best achievable image quality. In the second year, multiple cooperating AI agents will be developed to guide and refine reconstruction based on clinical intent, image content, and diagnostic feedback. By the third year, the system will evaluate its own reliability and uncertainty, producing confidence maps that show how trustworthy each part of the image is. This combination of agentic and generative AI aims to deliver clearer, more informative PET images tailored to each patient, supporting more accurate diagnoses and better clinical outcomes.

Self-driving fetal MRI

PRoject no.: 2026_003

Supervisor 1: Shaihan Malik
Supervisor 2: Jana Hutter
Supervisor 3: Lisa Story

RESEARCH DOMAINS: Breakthrough Technologies; Affordable and Accessible Imaging

INDUSTRY PARTNER: Siemens Healthineers

Magnetic Resonance Imaging (MRI) provides exceptional insight into anatomy and development without using radiation. Each MRI examination consists of several sequences, each designed to capture different tissue contrasts or orientations. However, when the subject moves during acquisition, image quality degrades and sequences must often be repeated, extending scan time. This problem is particularly severe in fetal MRI, where fetal motion and maternal breathing cause frequent image artefacts, and where the unpredictable position of the fetus requires careful, manual planning by experienced operators.

Previous work demonstrated that artificial intelligence can enable the scanner to automatically track and follow the fetus in real time during MRI. This PhD project will extend that capability toward a self-driving fetal MRI examination, capable of autonomously planning and adapting the scan as it runs. The project will focus on developing methods for dynamic slice prescription, adaptive sequence control, and real-time adjustment of imaging parameters in response to motion and anatomy. By integrating these approaches into the scanner workflow, the system aims to acquire complete, high-quality data efficiently and reliably minimising operator input and improving both patient comfort and throughput.

New 99mTc and 188Re theranostic radiotracers for cancer treatment

PRoject no.: 2026_005

Supervisor 1: Michelle Ma
Supervisor 2: Gary Cook
Supervisor 3: Lefteris Livieratos

RESEARCH DOMAINS: Molecular Probes and Theranostics; Affordable and Accessible Imaging

INDUSTRY PARTNER: Serac Healthcare

This project aims to develop dual diagnostic imaging and injectable radiotherapeutic drugs for “hard-to-treat” cancers. It will apply synthetic, inorganic and radio chemistry, alongside in vitro and in vivo experiments including SPECT imaging, to identify new and efficacious radiotracers.

We have recently developed a versatile chelator platform to incorporate either diagnostic technetium-99m (Tc-99m) or radiotherapeutic rhenium-188 (Re-188) into cancer-targeting peptides or molecules (1), and demonstrated proof-of-principle by preparing a pair of Tc-99m and Re-188 radiotracers that target prostate cancer (2). This enables preparation of receptor-targeted, “theranostic” radiopharmaceuticals, for dual diagnostic imaging and systemic radiotherapy.

In this project, the student will select a biomolecule that targets a specific cell-surface receptor of a hard-to-treat cancer (pancreatic cancer, glioblastoma, ovarian cancer). The biomolecule will then be attached to our diphosphine chelator, and the resulting disphophine-biomolecule will be radiolabelled with Tc-99m and Re-188. The student will undertake in vitro and in vivo studies of the theranostic radiotracers, including biodistribution, SPECT imaging and therapeutic efficacy studies in a relevant mouse model of cancer.

This translational project would suit a student with a chemistry, pharmaceutical sciences or biochemistry background. The student will be supported by supervisors with expertise in chemistry, clinical cancer imaging, dosimetry and clinical translation of new radiotracers.
(1) Nuttall et al., Chem. Sci., 2025, 16, 17112. DOI: 10.1039/D5SC02110C.
(2) Pham et al., J. Nucl. Med., 2024, 65, 1087. DOI: 10.2967/jnumed.124.267450.

Development of Scandium-47 Theranostics from production to bedside

PRoject no.: 2026_006

Supervisor 1: Graeme Stasiuk
Supervisor 2: Steve Archibald
Supervisor 3: Lefteris Livieratos
Supervisor 4: Antonios Pouliopoulos

RESEARCH DOMAINS: Molecular Probes and Theranostics

INDUSTRY PARTNER: Astral Systems

Current clinical care of cancers relies on imaging of the tumour for diagnosis and therapeutic interventions (surgery, chemotherapy, radiotherapy) performed separately, sometimes several weeks apart. Critically, during this time, the tumour has the opportunity to grow and spread. In this project, we seek to combine both imaging and therapy into a match-paired theranostic treatment. This has the potential to inhibit tumour growth directly following diagnosis, by using the same intervention, i.e. with two different isotopes of the same element, one for imaging and one for therapy.

This project will utilise a “theranostic pair” in which two radionuclides of the same element are used (44/47Sc). This approach has the advantage of providing identical biological distribution between the imaging and therapy ensuring accurate diagnosis, dosimetry, and monitoring of disease progression. To do this we must improve the supply of the radionuclides, with this PhD opportunity focusing on the development of 47Sc for therapy and 44Sc for PET imaging, alongside the development of the targeting agent for brain tumours.
This project aims to develop 44/47Sc theranostics for brain tumours:
1) Developing methodology for production and purification of 47Sc
2) Developing 44/47Sc bioconjugate for glioma
3) Validate the theranostic of 44/47Sc in glioma models

A rational radiochemistry basis for iodine-124 and astatine-211 theranostics

PRoject no.: 2026_007

Supervisor 1: Steve Archibald
Supervisor 2: Sam Terry
Supervisor 3: Sheila Singh
Supervisor 4: Alexander Hammers

RESEARCH DOMAINS: Molecular Probes and Theranostics

EXTERNAL ADVISOR: Phil Blower

CIodine radioisotopes have, since the mid-20th century, been foundational radionuclides for both diagnostic and therapeutic nuclear medicine; beta-emitting iodine-131 remains the standard treatment for thyroid cancer, supported by imaging with gamma-emitting iodine-123. Recently these radionuclides have been augmented by positron-emitting iodine-124 for PET imaging. Moreover, an alpha-emitting therapeutic equivalent, astatine-211, far more potent therapeutically than iodine-131, is now emerging; its (putative) chemical analogy to iodine makes it an “honorary” theranostic partner.

However, although astatine-211 has been incorporated into a few radiopharmaceuticals, its chemistry is largely mysterious; the supposed analogy with iodine is based on expectations based on its periodic relationships rather than actual chemical evidence. Even the chemical form of cyclotron-produced astatine-211 is not fully characterised.

This project will investigate its chemistry experimentally in relation to iodine, providing a rational basis for design and synthesis of iodine-astatine theranostics. New ways to bind iodine and astatine to molecules to deliver them to tumour cells will be explored, exploiting not only conventional bonds to carbon but also to other elements including metals, nanoparticles, and supramolecular hosts. This understanding will lead to improvement in both production of the radionuclide and its incorporation into targeting vector molecules, for imaging and treatment of cancer.

Opportunistic Screening and Characterisation of Aortic Valve Disease from routine CT scans

PRoject no.: 2026_008

Supervisor 1: Pablo Lamata
Supervisor 2: Richard Jones
Supervisor 3: Adelaide de Vecchi

RESEARCH DOMAINS: Precise Diagnosis and Prognosis

FUNDING PARTNER: Mid and South Essex NHS Foundation Trust

Aortic stenosis (AS), a narrowing of the aortic valve predominantly due to calcification, is the most common form of valvular heart disease in the UK. Once symptoms develop in patients with severe AS prognosis is poor, with average survival of only 2–3 years without intervention. Current treatment is surgical or transcatheter aortic valve replacement (TAVI). However, many patients remain undiagnosed until late in the disease process, and optimal patient selection for intervention can be challenging.

The aim of this project is to improve early detection and better characterisation of disease mechanisms to enable personalised care. The first specific objective is to extract imaging biomarkers of the left ventricular anatomy and of the aortic valve calcification from lung cancer screening CTs – these scans represent is an opportunistic cardiovascular screening and risk stratification. The second specific objective is to recapitulate in a model the low-flow, low-gradient (LFLG) aortic stenosis phenotype, and use a sensitivity study to better understand the mechanical and physiological drivers of such phenotype – the goal is to guide patient selection for intervention.

From Molecular Markers to Population Health: Nanoparticle-Enabled Portable Imaging for AI-driven Decentralised Blood Diagnostics

PRoject no.: 2026_009

Supervisor 1: Hector Zenil
Supervisor 2: Mark Green
Supervisor 3: Kourosh Saeb-Parsy

RESEARCH DOMAINS: Molecular Probes and Theranostics; Affordable and Accessible Imaging

INDUSTRY PARTNER: Oxford Immune Algorithmics Ltd

This project aims to expand the capabilities of a portable haematology device already in clinical use and commercialised by a King’s College London spin-out, Immune Algorithmics Ltd (OIA). The existing instrument combines bright-field microscopy and spectroscopy to analyse blood samples at the point of care. Building on this foundation, the project introduces immunofluorescence imaging using non-toxic nanoparticles that bind specifically to immune cell markers, enabling advanced, low-cost immune profiling.

Early results show successful fluorescent labelling of white blood cells, confirming the feasibility of integrating this technology into the portable device. The goal is to create a safe and affordable home-use system capable of identifying 8–10 immune cell types and detecting antibodies and cytokines. Combined with AI-driven image analysis, the platform will support continuous, remote monitoring of the immune system, reducing healthcare inequities and expanding access to precision diagnostics.

The supervisory team includes world-leading experts in artificial intelligence, immunology, and nanotechnology, ensuring that this project is scientifically robust and translationally viable. This project will appeal to candidates with experience or strong interest in any of the following: nanotechnology, biomedical imaging, immunology, optical instrumentation, AI-based image analysis.
A full skills audit will be conducted for the appointed candidate at the start of the PhD, and tailored training will be provided, supported by the Programme’s structured training and multidisciplinary supervisory team.

Epicardial Fat Deposition as a Multiscale Biomarker of Heart Failure

PRoject no.: 2026_010

Supervisor 1: Joaquim Nunes Vieira
Supervisor 2: Pier-Giorgio Masci
Supervisor 3: Kerrin Small

RESEARCH DOMAINS: Precise Diagnosis and Prognosis; Imaging at Scale

FUNDING PARTNER: King’s BHF-Centre of Research Excellence

Epicardial adipose tissue (EAT), the fat surrounding the heart, is increasingly recognised as a key driver of cardiovascular disease and heart failure. This project brings together large scale imaging, genetic analysis, and laboratory modelling to uncover how EAT contributes to heart disease and to identify new ways of predicting risk.

Using UK Biobank imaging data, we will measure EAT over time and link these findings to heart health outcomes. Genetic studies (TwinsUK and GWAS) will help pinpoint biological markers associated with EAT deposition. In parallel, laboratory models using human stem cell derived epicardial cells will explore how the heart’s outer layer can transform into fat cells, revealing mechanisms that drive disease progression.

By integrating imaging, genetics, and mechanistic insights, the project aims to deliver interpretable biomarkers that enable precision risk stratification in heart failure. The outcomes will include a scalable imaging pipeline, discovery of genetic loci and biomarkers, and new mechanistic understanding of epicardial biology. Ultimately, this work will support more accurate diagnosis, prognosis, and personalised management of heart disease, aligning with national priorities in imaging innovation and cardiovascular research

Integrating molecular imaging with new therapeutics to diagnose and prevent the development of heart failure

PRoject no.: 2026_011

Supervisor 1: Alkystis Phinikaridou
Supervisor 2: Mauro Giacca

RESEARCH DOMAINS: Molecular Probes and Theranostics; Precise Diagnosis and Prognosis

FUNDING PARTNER: King’s BHF-Centre of Research Excellence

Heart failure (HF) affects over 1 million people in the UK and 64 million worldwide, representing a major driver of morbidity, mortality, and healthcare costs. Following myocardial infarction, the primary complications are cardiomyocyte loss, limited proliferation of the remaining cardiomyocytes, and adverse remodelling of the extracellular matrix (ECM). Cardiac fibroblast differentiation into myofibroblasts triggers excessive ECM deposition, particularly collagens, which causes cardiac function decline leading to hypertrophy and eventual heart failure. Currently, no effective therapeutic approaches exist to stimulate cardiac regeneration or limit post-infarction fibrosis. Moreover, imaging methods capable of detecting cardiac fibrosis at the molecular level essential for enabling precision diagnosis and guiding treatment remain lacking.

This project addresses this critical clinical need by integrating molecular imaging with newly developed gene- and RNA-based therapies. Our aim is to detect at-risk hearts before failure occurs, guide therapeutic delivery, monitor treatment efficacy, and ultimately develop novel therapeutics to prevent heart failure progression.

Diffusion Models for Time-of-Flight Total-Body PET Image Reconstruction and Dual-Tracer Separation

PRoject no.: 2026_A01

PLEASE NOTE: This is an aligned project (not directly managed by this particular Doctoral Programme). However, any student appointed to this project would still be fully integrated into the Doctoral Programme cohort.

Supervisor 1: Andrew J. Reader
Supervisor 2: TBC

RESEARCH DOMAINS: Breakthrough Technologies; Affordable and Accessible Imaging

INDUSTRY PARTNER: Siemens Healthineers

This PhD project will explore the intersection of medical imaging physics and generative AI by developing diffusion-model–based methods for time-of-flight (TOF) total-body PET image reconstruction and dual-tracer imaging. Diffusion models have recently shown strong promise for enhancing PET image quality [1], and this project will extend their use to the challenging and clinically important setting of dual-tracer total-body PET.

The research will begin by developing two foundation diffusion models for total-body PET, one for FDG (imaging glucose metabolism) and one for PSMA (imaging of prostate cancer). These models will be trained on standard-quality patient data acquired at St Thomas’ Hospital using Siemens Quadra total-body PET scanners. These models will act as robust tracer-specific image priors. Building on these, the project will investigate patient-specific diffusion modelling strategies, in which multi-subject training data are spatially co-registered to a target patient’s CT, enabling the aggregation of aligned datasets into higher signal-to-noise image representations for training richer quality diffusion models. Next, the diffusion models will be integrated directly into TOF PET reconstruction by embedding a physical forward model of the PET acquisition process within the generative pipeline. Data consistency will be enforced through gradient-based updates using measured TOF data at each diffusion step.

Finally, the framework will be extended to the exciting potential of dual-tracer PET by combining diffusion-based TOF PET reconstruction with deep separation networks for FDG and PSMA. Overall, this project will deliver a physics-informed generative AI framework that improves image quality and quantitative accuracy in single-tracer TOF PET while also advancing the practical feasibility of dual-tracer total-body PET imaging.

[1] G Webber and A J Reader, “Diffusion models for medical image reconstruction”, BJR|Artificial Intelligence, Volume 1, Issue 1, January 2024, ubae013, https://doi.org/10.1093/bjrai/ubae013

Imaging New Classes of Antibody Therapies for Targeting Folate Receptor-alpha in Cancer

PRoject no.: 2026_A02

PLEASE NOTE: This is an aligned project (not directly managed by this particular Doctoral Programme). However, any student appointed to this project would still be fully integrated into the Doctoral Programme cohort.

Supervisor 1: Michelle Ma
Supervisor 2: Sophia Karagiannis

RESEARCH DOMAINS: Molecular Probes and Theranostics

FUNDING PARTNER: : CRUK

Antibody therapies have revolutionised cancer outcomes, and advances in antibody engineering could increase cancer patient survival. The use of Positron Emission Tomography (PET) in combination with radiolabelled antibodies can enable important insights into inter-tumour and intra-tumour heterogeneity of the target receptor expression, the behaviour of antibodies in vivo, including antibody infiltration, antibody pharmacokinetics and dose/receptor occupancy patterns. Antibodies are also being used to deliver cytotoxic payloads, including radioactive isotopes for systemic radiotherapy, to specifically target diseased tissue. Furthermore, the human immune system exerts immune protective effects against pathogens by generating nine different antibody isotypes; however, only three isotypes, IgG1, IgG2 and IgG4, are used clinically for therapeutic purpose.
Folate receptor-alpha is expressed in several solid tumours that are hard-to-treat, including ovarian, triple negative breast, and non-small cell lung, cancers. Intensive research efforts have focused on developing folate receptor-alpha targeted anti-cancer therapeutics.

The PhD objectives are to:
(1) Apply radionuclide imaging, primarily PET imaging, to interrogate the distribution of different classes of therapeutic antibodies targeting the cancer-associated antigen folate receptor-alpha, now a clinical target for antibody therapy, and the role of the antibody Fc domains, the part of the antibody that recognises Fc receptors on immune cells, and how these influence antibody distribution at the whole body level;
(2) Develop new folate receptor-alpha-targeted radiotherapeutic antibody conjugates.
(3) There is scope for a student with an appropriate background to utilise PET cell tracking methods to quantify the migration and tumour accumulation of immune effector cells that interact with different classes of therapeutic antibodies to exert a cytotoxic tumour response.

This PhD project is part of a CRUK-funded Discovery Programme Foundation Award, and the successful candidate will be part of two multidisciplinary teams (Ma and Karagiannis groups). This project would suit a candidate with a background in biomedical sciences, pharmaceutical sciences, cancer biology, biochemistry, immunology, molecular biology or related disciplines. Students will be trained in antibody production, radiochemistry methods, in vitro methods to characterise new immunoconjugates, in vivo models of cancer and related experimental methods, and preclinical PET/CT imaging.

This project is eligible only to students who qualify for “home fee status” in the United Kingdom.
Please contact Michelle Ma directly for more details on this project: michelle.ma@kcl.ac.uk

References
(1) Scaranti, M.; Cojocaru, E.; Banerjee, S.; Banerji, U. Exploiting the Folate Receptor α in Oncology. Nat. Rev. Clin. Oncol. 2020 176 2020, 17, 349–359. https://doi.org/10.1038/s41571-020-0339-5.
(2) Spicer, J.; Basu, B.; Montes, A.; Banerji, U.; Kristeleit, R.; Miller, R.; Veal, G. J.; Corrigan, C. J.; Till, S. J.; Figini, M.; et al. Safety and Anti-Tumour Activity of the IgE Antibody MOv18 in Patients with Advanced Solid Tumours Expressing Folate Receptor-Alpha: A Phase I Trial. Nat. Commun. 2023, 14, 4180. https://doi.org/10.1038/S41467-023-39679-9.
(3) Pham, T. T.; Chenoweth, A.; Patel, N.; Banu, A.; Osborn, G.; Blower, P. J.; Karagiannis, S. N.; Michelle T Ma. In Vivo PET Imaging of 89Zr-Labeled Natural Killer Cells and the Modulating Effects of a Therapeutic Antibody. J. Nucl. Med. 2024, 65, 1035–1042. https://doi.org/10.2967/jnumed.124.267876.