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
read more

If you have questions concerning a specific publication please use this form with subject 'information about publications' and giving the full citation in the message body.

Collaborate Research Projects
Home > Publications > Abstract >

Blade DCE MRI - Towards Renal Perfusion Measurements with reduced Motion Artifacts

F. Lietzmann, F. Zöllner and L. Schad

Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Honolulu,, p.4137

The major problems in renal DCE MRI are, like in the entire field of abdominal imaging, motion artifacts that primarily arise from the patient´s respiration. Here, we present an approach utilizing Blade for DCE-MRI of the human kidney to suppress motion while keeping critical parameters like temporal and spatial resolution within acceptable limits. A saturation recovery pre-pulse was added to a conventional FLASH sequence to gain T1 contrast. We showed that the reduction of blade width, leading to a higher number of blades recorded for each image, correlates with a noticeable correction of the respiratory movement and hence will lead to less distorted signal time courses.

Contact: Prof. Dr. Frank Zöllner last modified: 30.11.2020
to top of page