Virtual Histology Through Statistical Descriptors: Enhancing MRI for Cancer Diagnosis

Accurate cancer diagnosis currently relies on invasive biopsies to obtain microscopic histological data, a process that poses risks of false negatives and significant patient discomfort. Recent advancements in magnetic resonance imaging (MRI) offer a promising alternative due to its non-invasive nature and its ability to capture certain cellular properties like size and density. However, state-of-the-art MRI methods cannot visualise the complex tissue morphology essential for accurate diagnosis, as it depends on simplifying assumptions about cell shape and arrangement. These assumptions introduce unwanted errors and degeneracy that limit diagnostic reliability and restrict the scope of non-invasive cancer imaging.

To address these limitations, the supervisory team is pioneering a disruptive methodology inspired by materials science and condensed matter physics. This approach involves using statistical descriptors (SDs) to characterise tissue microstructure via MRI signals, enabling histology-like reconstructions of tissue architecture without the need for shape or arrangement assumptions. By capturing statistical information of different tissue compartments, SDs provide a high-accuracy, in vivo representation of its microarchitecture at different scales. Preliminary results indicate that critical cellular characteristics—shape, size, and spatial organisation—can be accurately reconstructed, paving the way for non-invasive “virtual histology” of live tissues.

In this project, the PhD candidate will pioneer the use of SDs in cancer imaging, with a focus on prostate and brain tumours. In a first stage, the student will validate the SD approach against available histological data, first demonstrating the technique’s value for differentiating cancer stages. In the second phase, they will adapt the SD framework to enhance MRI sensitivity to these descriptors for accurate tissue reconstruction. In vivo experiments in purposedly-designed phantoms and participants will allow to validate the technique. This high-risk, high-reward project has the potential to transform MRI-based cancer diagnosis, equipping clinicians with a non-invasive, precise tool to better detect, monitor, and treat cancer.

Project timeline

·        Year 0-1: Training (histology processing, SDs framework). Determination of SDs from data.

·        Year 1-2: Training (MRI acquisition and processing, “virtual histology”). Cancerous tissues reconstructions based on MRI data.

·        Year 2-3.5 Automatic cancer scoring based on SDs. Clinical benchmarking. Thesis writing.

The PhD project will be based at the Cardiff University Brain Research Imaging Centre (CUBRIC), a leader in brain imaging research. CUBRIC is home to over 200 researchers from various disciplines, making it a dynamic, multidisciplinary research hub. The centre boasts cutting-edge MRI facilities, including two 3T clinical MRI scanners and the ultra-high-gradient Connectom MRI scanner. The student will have access to these state-of-the-art resources for data collection and analysis throughout the project.

The successful candidate will receive unique training opportunities, including specialised tuition in MRI operation and sequence design. Additionally, the student will engage with industrial partners, such as MRI vendors and pharmaceutical companies, who are already collaborating with the supervisory team on ongoing projects. Training will also encompass relevant courses in MRI physics and machine learning, expanding the candidate’s technical expertise in these areas.

The student will be embedded in CUBRIC’s MicroTeam group, which consists of over 30 experts in microstructural MRI, ranging from undergraduate project students to established academics. This collaborative environment will provide ample opportunities for discussion, feedback, and preparation for international conferences, fostering the student’s academic and professional development. The student will also be part of a cohort of five current PhD students working on different aspects of microstructural cancerous tissue characterisation, further enhancing the synergy within the research group.

How to apply 

Applicants should submit an application for postgraduate study via the Cardiff University webpages (https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy) including: 

• your academic CV 

• Your degree certificates and transcripts to date including certified translations if these are not in English 

• a personal statement/covering letter 

• two references, at least one of which should be academic. Your references can be emailed by the referee to physics-admissions@cardiff.ac.uk  

Please note: We are do not contact referees directly for references for each applicant due to the volume of applications we receive.     

Cardiff University and the School of Physics and Astronomy are committed to supporting and promoting equality and diversity. Our inclusive environment welcomes applications from talented people from diverse backgrounds. We strongly welcome female applicants and those from any ethnic minority group, as they are underrepresented in our School. The School of Physics & Astronomy has a Juno Practitioner accreditation that recognises good employment practice and a commitment to develop the careers of women working in science. The University is committed to ensuring that we sustain a positive working environment for all staff to flourish and achieve. As part of this commitment, the University has developed a flexible and responsive framework of procedures to support staff in managing their work and personal commitments wherever possible. Applications are welcome from individuals who wish to work part-time or full time. 

Cardiff University is a signatory to the San Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions we will evaluate applicants on the quality of their research, not publication metrics or the identity of the journal in which the research is published.

Applications may be submitted in Welsh, and an application submitted in Welsh will not be treated less favourably than an application submitted in English. We very much welcome applications in Welsh.