Despite its widespread use, traditional MRI is qualitative, meaning that the assessment of the MR images is performed through the visual inspection of the contrast between anatomical areas that are (supposedly) healthy and abnormal. This approach does not quantify biomarkers, it does not provide direct information about the nature of the pathology and it is not fit to identify subtle changes involving a whole tissue. Moreover, the features of traditional MR images are affected by incidental factors (e.g. the acquisition sequence and the hardware adopted), which do not allow a direct comparison of results obtained at different times and examination conditions.
To address these issues, quantitative imaging approaches including EPT and MRF are being developed, with the aim of eliminating interobserver variability and reducing the need for invasive procedures (e.g. biopsies). In addition, EPT and MRF should enable new biomarkers to be identified for a plethora of pathologies and they should boost early disease detection.
These quantitative techniques could be used to monitor the course of a disease over time and to optimize the clinical path, improving the quality of life of patients and reducing the associated economic burden.
The overall target of the project is to promote the development and possible combination of EPT and MRF, two MR-based techniques able to produce objective, quantitative and traceable images, and their adoption in clinical practice through a systematic characterisation of their reliability.
The specific objectives of the project are:
- To develop, improve and implement numerical algorithms for use in EPT and MRF and to characterise their performance.
- To make EPT and MRF suitable for practical use in the analysis of “high impact” clinical conditions, e.g. brain and heart diseases.
- To evaluate the accuracy of EPT and MRF procedures in magnetic resonance experiments under controlled conditions, using reference tissue-mimicking materials and traceable phantoms.
- To characterise EPT and MRF as diagnostic tools under real-world conditions (accounting for physiological variability) and use artificial intelligence to spot pathologies.
- To facilitate the take up of the technology and measurement infrastructure developed in the project.