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Review
. 2019 Mar 6;8(3):316.
doi: 10.3390/jcm8030316.

The Challenges of Diagnostic Imaging in the Era of Big Data

Marco Aiello  1 , Carlo Cavaliere  2 , Antonio D'Albore  3 , Marco Salvatore  4
Affiliations
Review

The Challenges of Diagnostic Imaging in the Era of Big Data

Marco Aiello et al. J Clin Med. .

Abstract

The diagnostic imaging field has undergone considerable growth both in terms of technological development and market expansion; with the following increasing production of a considerable amount of data that potentially fully poses diagnostic imaging in the Big data in the context of healthcare. Nevertheless, the mere production of a large amount of data does not automatically permit the real exploitation of their intrinsic value. Therefore, it is necessary to develop digital platforms and applications that favor the correct and advantageous management of diagnostic images such as Big data. This work aims to frame the role of diagnostic imaging in this new scenario, emphasizing the open challenges in exploiting such intense data generation for decision making with Big data analytics.

Keywords: Anthropometry; Big data; Brain Connectivity; Diagnostic Imaging; Human Connectome; Radiology; Radiomics; Simulation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Diagnostic Imaging Workflow. Diagnostic images are acquired through an imaging device, stored in a standard picture archiving and communication (PACS) and radiological information system (RIS) and therefore visually inspected on a digital imaging and communications in medicine (DICOM)/PACS viewer by a specialist (usually radiologists or nuclear physicians), who produces a structured or unstructured report representative of the clinical outcome of the examination.
Figure 2
Figure 2
Radiomics workflow. The image shows the three main steps involved in the estimation of radiomic features: magnetic resonance (MR) image acquisition, definition of regions of interest (ROIs), and extraction of numerical descriptors (radiomic features).
Figure 3
Figure 3
Human connectome estimation. The image shows an example of connectome generation from MR images: (a–c) MR anatomical images can be segmented into several meaningful regions, diffusion imaging provides an estimation of axonal connections linking each pair of parcels (red tracts) for the construction of the structural connectome matrix (d).
Figure 4
Figure 4
Workflow for decision making in Big data science.
See this image and copyright information in PMC

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