University of Heidelberg
Faculty of Medicine Mannheim
University Hospital Mannheim
Genauere MRT-Auswertungen für MS-Patienten durch Künstliche Intelligenz
Modellprojekt mit CKM Beteiligung im Rahmen des Innovationswettbewerbs "KI für KMU" soll die Behandlung von MS-Patienten verbessern more
CKM receives DFG funding for Project "Prediktion von Therapieansprechen und Outcome beim lokal fortgeschrittenen Rektum-Karzinom mittels Radiomics und Deep Learning: eine beispielhafte Anwendung für eine allgemein verwendbare, Deep Learning basierte Prozessierungs-Pipeline für die Bild-Klassifikation" read more.
Alena-Kathrin Schnurr presented on our research on AI in medical imaging @ Research Plus forum
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Theoretical prediction of parameter stability in quantitative BOLD MRI: dependence on SNR and sequence parameters

M. Sohlin and L. Schad

Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Honolulu,, p.1623

A static dephasing model that analytically connects BOLD signal to hemodynamic parameters can be used to map the blood oxygenation level (Y) and venous cerebral blood volume (vCBV) in the brain. In this work, the accuracy of the method is tested by means of simulations and measurements. The result shows that accurate fitting can only be performed at high SNR (>500). A separate quantization of the vCBV would allow a stable method to quantify blood oxygenation even at low SNR (<200).

Contact: Prof. Dr. Frank Zöllner last modified: 30.11.2020
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