A Framework for Integration of Heterogeneous Medical Imaging Networks

Viana-Ferreira, Carlos; Ribeiro, Luís S.; Costa, Carlos

doi:10.2174/1874431101408010020

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Dicoogle is a comprehensive PACS developed for research purposes that enables distributed queries across multiple PACS sites. It creates indices of metadata for a fast query capability and allows for many plugins and applications on image-oriented studies [ 28 , 36 ]. It is not directly used with a CDW and its query is image-centric [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Kaspar

Liman²,

Morbach³

et al. 2022

J Digit Imaging

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This study aims to show the feasibility and benefit of single queries in a research data warehouse combining data from a hospital’s clinical and imaging systems. We used a comprehensive integration of a production picture archiving and communication system (PACS) with a clinical data warehouse (CDW) for research to create a system that allows data from both domains to be queried jointly with a single query. To achieve this, we mapped the DICOM information model to the extended entity–attribute–value (EAV) data model of a CDW, which allows data linkage and query constraints on multiple levels: the patient, the encounter, a document, and a group level. Accordingly, we have integrated DICOM metadata directly into CDW and linked it to existing clinical data. We included data collected in 2016 and 2017 from the Department of Internal Medicine in this analysis for two query inquiries from researchers targeting research about a disease and in radiology. We obtained quantitative information about the current availability of combinations of clinical and imaging data using a single multilevel query compiled for each query inquiry. We compared these multilevel query results to results that linked data at a single level, resulting in a quantitative representation of results that was up to 112% and 573% higher. An EAV data model can be extended to store data from clinical systems and PACS on multiple levels to enable combined querying with a single query to quickly display actual frequency data.

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Section: Discussionmentioning

confidence: 99%

Querying a Clinical Data Warehouse for Combinations of Clinical and Imaging Data

Kaspar

Liman²,

Morbach³

et al. 2022

J Digit Imaging

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“…Multi-center medical records are inherently heterogeneous due to the various hospital acquisition protocols, scanner types, data modalities, and patient outcomes 20,21 . With the growth of medical data volumes, integrative analysis of multi-center data is increasingly needed to accelerate clinical workflow efficiency.…”

Section: Introductionmentioning

confidence: 99%

Mining Multi-Center Heterogeneous Medical Data with Distributed Synthetic Learning

Chang

Yan²,

Zhou³

et al. 2022

Preprint

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Overcoming barriers of multi-center data analysis is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose Distributed Synthetic Learning (DSL) architecture to learn across multi-medical centers without leaking sensitive personal information. DSL emphasizes the building of a homogeneous data center with entirely synthetic medical images via a form of GAN-based synthetic learning. In particular, DSL architecture is extensible with three key variances: multi-modality learning, missing modality completion learning, and continuous learning over time. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider for various image tasks from an ideal synthetic image quality metric called Dist-FID. We show that our model can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%. The proposed DSL framework demonstrates its potential for integrating multi-center heterogeneous data to support downstream clinical decision making.

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