Medical Data Spaces in Healthcare Data Ecosystems

Berlage, Thomas; Claussen, Carsten; Geisler, Sandra; Velasco, Carlos A.; Decker, Stefan

doi:10.1007/978-3-030-93975-5_18

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In the field of data spaces, this work relates to use-cases of data spaces in medical domain. In medical data spaces, health data is integrated for different purposes, such as enlarging a data set for increasing the statistical analysis outcome or to improve or enable precision medicine [2]. Even the patient may be integrated in the data space by e. g. wearables [23].…”

Section: B Related Workmentioning

confidence: 99%

Reconstruction of 1D Images with a Neural Network for Magnetic Particle Imaging

Gladiss¹,

Memmesheimer²,

Theisen³

et al. 2022

Informatik Aktuell

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Magnetic particle imaging (MPI) is an emerging medical imaging modality which offers a unique combination of high temporal and spatial resolution, sensitivity and biocompatibility. For system-matrix (SM) based image reconstruction in MPI, a huge amount of calibration data needs to be acquired prior to reconstruction in a time-consuming procedure. Conventionally, the data is recorded on-site inside the scanning device, which significantly limits the time that the scanning device is available for patient care in a clinical setting. Due to its size, handling the calibration data can be challenging. To solve these issues of recording and handling the data, data spaces could be used, as it has been shown that the calibration data can be measured in dedicated devices off-site. We propose a data space aimed at improving the efficiency of SM-based image reconstruction in MPI. The data space consists of imaging facilities, calibration data providers and reconstruction experts. Its specifications follow the reference architecture model of international data spaces (IDS). Use-cases of image reconstruction in MPI are formulated. The stakeholders and tasks are listed and mapped to the terminology of IDS. The signal chain in MPI is analysed to identify a minimum information model which is used by the data space.

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Section: B Related Workmentioning

confidence: 99%