Stray Field NMR
Osteoporosis is a disease in which the density and quality of bone are reduced, affecting mainly post-menopausal women. For now, there is no easy method to monitor early changes in bone mineral density to assess hormonal treatment efficacy in osteoporotic patients. The goal of our project is to develop a small, simple, affordable, MR-based table-top scanner to detect cellular changes in the bone marrow as an early indicator of treatment efficacy.
The scientific basis of our project is that bone marrow adiposity in postmenopausal women increases with osteoporosis. Hormonal changes associated with the postmenopausal phase trigger cellular pathways that alter the differentiation of mesenchymal stem cells into adipocytes, disrupting the equilibrium between adipocyte and osteoblast differentiation. This process starts long before BMD changes are obvious and responds fast to hormonal treatment.
Experiments performed in our group demonstrated that changes in the bone marrow that are related to the onset of osteoporosis and to its treatment, can be detected by the use of a mobile stray field NMR. Based on these findings, we are developing a dedicated mobile stray-field NMR device that will allow monitoring bone marrow adiposity.
An axial MRI (Dixon) scan of an arm, showing the Radius (top) and the Ulna (bottom) bones
A custom made NMR table-top scanner
Magnetic resonance imaging (MRI) is mostly performed on protons associated with freely moving water molecules, as they are highly abundant in biological tissues yielding high signals. Nonetheless, much signal information originates from the non-aqueous species in the tissue, such as proteins and lipids. We aim to implement a novel MRI technique that allows the acquisition of signal generated by the protons adjacent to the semi-solid components of the tissue and combine it with quantitation analysis to extract the volumetric fraction of proteins or lipids. The proposed MRI sequence, MEX (magnetization exchange), can provide an insight into the content of species with restricted motion. The MEX sequence is illustrated in the figure.
Brain Tumor response assessment
Volumetric measurements of brain tumors are important for accurate diagnosis and follow-up. However in clinical setup manual segmentation of high grade gliomas (HGG) is usually inapplicable as it is highly challenging, time consuming and user dependent. The aim of this study was to implement a deep learning (DL) approach to automatic longitudinal assessment of brain tumor response based on the clinical response assessment in neuro-oncology (RANO) criteria.