Andrada Ianus

 Research Fellow in Biomedical Imaging

Contact

Research

• In cancer the tissue architecture is altered as the tumour becomes more aggressive, which is the main source of contrast for diffusion MRI. Through appropriate computational models, diffusion data can be used to obtain parameter maps, such as ADC, pore size, shape and volume fraction, which reflect the properties of underlying tissue. The aim of my project is to develop a new generation of diffusion MRI pulse sequences which are more accurate for measuring these features compared to the standard pulsed gradient spin echo sequence (PGSE). Our novel computational methods for optimising the diffusion gradients allow maximum flexibility producing waveforms otherwise impossible to predict.
• One class of sequences I have been interested in is oscillating gradients, especially square waveforms, as they yield higher diffusion-weighting and SNR [2]. Another class I am currently investigating consists of gradients with time varying orientation, such as double PGSE or square waveforms in multiple directions. The aim of these advanced sequences is to give more information about the tissue organisation at the microscopic level. Thus, novel biomarkers such as microscopic anisotropy could become a useful diagnostic tool, better discriminating between different tissue types.

Publications

Software

• MISST - Microstructure Imaging Sequence Simulation ToolBox

Outreach and public engagement

• During the past years I have been involved and organized many outreach events as a committee member of the UCL Women Engineers society and as a STEM ambassador. You can see some of the events on the Public Engagement page.