Periodic Reporting for period 2 - OncoViroMRI (Brain Cancer Therapy Monitoring using a Novel Quantitative and Rapid Magnetic Resonance Imaging-based Method)
Période du rapport: 2021-11-01 au 2022-10-31
1) A novel MRI-based method for quantitative and fast molecular imaging was developed and validated using numerical simulations and imaging of tissue-mimicking models.
2) The ability to detect reporter genes using molecular MR imaging was improved and validated using tumor-bearing mice.
3) A deep learning approach was designed and combined with biophysical MRI models.
4) The deep learning framework was initially validated using tissue-mimicking models (phantoms), and later using an extensive tumor-bearing mice imaging study, successfully allowing the early detection of tumor-treatment response to virus-based therapy. The resulting quantitative molecular images were in good agreement with histology.
5) The technological progress was translated to clinical MRI scanners and examined on healthy human volunteers and a small cohort of brain tumor patients. A good agreement was demonstrated between the quantitative biophysical parameter maps of the healthy brain obtained by the proposed method and the literature.
6) Two additional methods for accelerating the image acquisition and reconstruction time using machine-learning approaches were developed.
The results were disseminated in 8 scientific journal publications (6 original research and two review papers), 13 presentations/posters at scientific conferences, and presented at several scientific/general public gatherings/events.
Our work describes a new method for detecting tumor cell death non-invasively using MRI. The capacity to do this could be useful for non-invasive monitoring of cancer treatment, potentially improving patient care and tailoring the treatment to an individual patient. The same approach might also be beneficial for detecting and characterizing other medical conditions where elevated cell death occurs, such as stroke and liver disease. While the study was mainly validated using a mouse brain tumor model, we have also customized and further optimized the image-acquisition protocol and reconstruction method for clinical MR scanners. This allows for future examinations and validation of the developed approach in humans (beyond the proof-of-concept human studies performed in the project).