Explore the words cloud of the SIMULTANEOUS DBTMI project. It provides you a very rough idea of what is the project "SIMULTANEOUS DBTMI" about.
The following table provides information about the project.
|Coordinator Country||Sweden [SE]|
|Total cost||203˙852 €|
|EC max contribution||203˙852 € (100%)|
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
|Duration (year-month-day)||from 2019-08-15 to 2021-08-14|
Take a look of project's partnership.
This MSCA is designed to support Dr. Predrag Bakic in his professional development and reintegration into European research community, after he obtained Ph.D. and spent 12 years as a faculty in USA. The host institution, Lund University (LU), is one of the largest in Sweden and among the most prestigious in Europe. Dr. Bakic and his LU supervisors, Dr. Sophia Zackrisson and Dr. Anders Tingberg, share the research focus in breast imaging, with unique complementary expertise: Dr. Bakic in Virtual Clinical Trials (VCTs) based upon the simulation of breast anatomy and imaging systems, and LU team in Mechanical Imaging (MI) and conducting clinical trials of breast imaging. Our action is motivated by a persistent challenge of underdiagnosis and false positives in breast cancer healthcare. The four most exciting innovations in breast cancer imaging that have recently emerged include: Digital Breast Tomosynthesis (DBT), MI, VCTs, and artificial intelligence (AI). In this application we will utilize extensive experience of LU and Dr. Bakic to interconnect these innovations efficiently and flexibly, enabling significant benefits. Within the two-year timeline, we will design and build a simultaneous DBT and MI (termed DBTMI) prototype system, and develop image processing and DBT reconstruction to maximize image quality. We will evaluate the prototype, first preclinically by VCTs and physical phantoms, followed by a pilot co-clinical trial (which combines clinical and simulated data). We will also explore introducing modern AI methods, in the form of Deep Learning Networks (DLN) to improve DBTMI performance. DLN has demonstrated ability to discover complex correlations in clinical images, leading to superior detection and classification of clinical findings. Combined complementary experience, carefully designed knowledge-exchange activities, and LU excellent institutional resources, guarantee the success of this application, and Dr. Bakic's successful reintegration.
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The information about "SIMULTANEOUS DBTMI" are provided by the European Opendata Portal: CORDIS opendata.
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