Magnetic Resonance Imaging (MRI) is a primary tool for clinical investigation of the brain and fetal organs. High resolution imaging with volumetric coverage using stacks of slices or true three dimensional (3D) methods is widely available and provides rich data for image analysis. However such detailed volumetric data generally takes several minutes to acquire and requires the subject to remain still or move only small distances during acquisition. Fetal organ imaging introduces a number of additional challenges. Maternal breathing may move the fetus and the fetus itself can and does spontaneously move during imaging. These movements are unpredictable and may be large, particularly involving substantial head and body rotations. Motion correction methods have revolutionized MRI of the fetus by reconstructing a high-resolution 3D volume of fetal organs from such motion corrupted stacks of 2D slices. Such reconstructions are valuable for both clinical and research applications. However, reconstruction is computationally expensive and can only be performed off line. Information about the accuracy of the scan and potential uncertainties is unknown or not considered in the clinical practice.
In this talk I will discuss the fundamentals of fetal MRI reconstruction and it's parallelization and hardware acceleration for a future on-line application during the scan. Furthermore, I am looking forward to a discussion about potential application of novel visualization techniques to communicate varying uncertainties of the reconstruction to examining radiologists and scientists.
There is a wide range of clinical systems (e.g. HIS, EMR) on the market that are intended to assist healthcare professionals with their daily work in the creation of clinical documentation.
The increasing need of sharing patient data between disparate clinical systems created the demand of a comprehensive framework for clinical information exchange.
Furthermore there are a variety of clinical documents available, which provide a large amount of information to the healthcare professionals. The multitude of documents of the patient medical history poses a challenge for the treating physician to filter and identify all the necessary information needed to provide accurate treatment to a patient.
For this reason a technology with the capability to store and index clinical information as smallest possible and reasonable data unit including the ability to assemble new tailored documents in an on-demand manner based on specific query requests would highly support the physicians treatment process. This technology, also called Clinical Data Repository, shall be based on established standards and best practices in ehealth to ensure interoperability and interconnectivity between clinical systems.
15 + 10
Institute of Visual Computing & Human-Centered Technology
Favoritenstr. 9-11 / E193-02
Austria - Europe