Development of an AI-based algorithm for contrast agent dose reduction in magnet...
Development of an AI-based algorithm for contrast agent dose reduction in magnetic resonance imaging
In cranial magnetic resonance tomography (cMRT), the administration of contrast agent facilitates diagnosis of various pathologies such as metastases due to contrast agent enhancement. Patients undergoing a medically indicated cMR...
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Información proyecto DAICAR
Duración del proyecto: 12 meses
Fecha Inicio: 2023-06-01
Fecha Fin: 2024-06-30
Líder del proyecto
RELIOS.VISION GMBH
No se ha especificado una descripción o un objeto social para esta compañía.
Presupuesto del proyecto
75K€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
In cranial magnetic resonance tomography (cMRT), the administration of contrast agent facilitates diagnosis of various pathologies such as metastases due to contrast agent enhancement. Patients undergoing a medically indicated cMRT receive 100% of the authorized contrast agent dose, i.e., 0.1 mmol of gadolinium per kg body weight. The main component of MRT contrast agent is gadolinium, which is a highly toxic rare-earth element, which has potential health side effects (inter alia allergic reactions and accumulation in the body) as well as environmental side effects (accumulation in the tap water).
Due to the aforementioned issues, the reduction of contrast agent dose in cMRT is highly desirable. To tackle this issue, Katerina Deike-Hofmann from the Clinic for Neuroradiology at the University Hospital Bonn together with colleagues and collaborators from the Institute of Mathematics and Life Sciences developed an artificial intelligence (AI)-based algorithm that allows for contrast agent reduction called SmartContrast. Until today, more than 1.000 prospective data sets were acquired at seven different clinics and SmartContrast showed excellent generalizibility. Thus, Katerina Deike-Hofmann founded the relios.vision GmbH as a spin-off of the University Bonn to commercialize SmartContrast. Her goal is to advance the clinical standard by combining knowledge from the areas of medicine, mathematical image processing, computer vision, and artificial intelligence.