University of Oulu

Isosalo, A., Islam, J., Mustonen, H., Räinä, E., Inkinen, S. I., Brix, M., Kumar, T., Reponen, J., Nieminen, M. T., & Harjula, E. (2023). Local edge computing for radiological image reconstruction and computer-assisted detection: A feasibility study. Finnish Journal of EHealth and EWelfare, 15(1), 52–66.

Local edge computing for radiological image reconstruction and computer-assisted detection : a feasibility study

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Author: Isosalo, Antti1; Islam, Johirul2; Mustonen, Henrik1;
Organizations: 1Research Unit of Health Sciences and Technology, University of Oulu, Oulu
2Centre for Wireless Communications - Networks and Systems, University of Oulu, Oulu
3Research Unit of Health Sciences and Technology, University of Oulu, Oulu; Department of Diagnostic Radiology, Oulu University Hospital, Oulu
4Centre for Wireless Communications - Networks and Systems, University of Oulu
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Finnish Social and Health Informatics Association, 2023
Publish Date: 2023-04-17


Computational requirements for data processing at different stages of the radiology value chain are increasing. Cone beam computed tomography (CBCT) is a diagnostic imaging technique used in dental and extremity imaging, involving a highly demanding image reconstruction task. In turn, artificial intelligence (AI) assisted diagnostics are becoming increasingly popular, thus increasing the use of computation resources. Furthermore, the need for fully independent imaging units outside radiology departments and with remotely performed diagnostics emphasize the need for wireless connectivity between the imaging unit and hospital infrastructure. In this feasibility study, we propose an approach based on a distributed edge-cloud computing platform, consisting of small-scale local edge nodes, edge servers with traditional cloud resources to perform data processing tasks in radiology. We are interested in the use of local computing resources with Graphics Processing Units (GPUs), in our case Jetson Xavier NX, for hosting the algorithms for two use-cases, namely image reconstruction in cone beam computed tomography and AI-assisted cancer detection from mammographic images. Particularly, we wanted to determine the technical requirements for local edge computing platform for these two tasks and whether CBCT image reconstruction and breast cancer detection tasks are possible in a diagnostically acceptable time frame. We validated the use-cases and the proposed edge computing platform in two stages. First, the algorithms were validated use-case-wise by comparing the computing performance of the edge nodes against a reference setup (regular workstation). Second, we performed qualitative evaluation on the edge computing platform by running the algorithms as nanoservices. Our results, obtained through real-life prototyping, indicate that it is possible and technically feasible to run both reconstruction and AI-assisted image analysis functions in a diagnostically acceptable computing time. Furthermore, based on the qualitative evaluation, we confirmed that the local edge computing capacity can be scaled up and down during runtime by adding or removing edge devices without the need for manual reconfigurations. We also found all previously implemented software components to be transferable as such. Overall, the results are promising and help in developing future applications, e.g., in mobile imaging scenarios, where such a platform is beneficial.

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Series: Finnish Journal of eHealth and eWelfare
ISSN: 1798-0798
ISSN-E: 1798-0798
ISSN-L: 1798-0798
Volume: 15
Issue: 1
Pages: 52 - 66
DOI: 10.23996/fjhw.122647
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Funding: This research work was supported by Academy of Finland, under the DigiHealth (grant 326291) and 6G Flagship projects (346208). Antti Isosalo re-ceived funding from the Jenny and Antti Wihuri Foundation.
Academy of Finland Grant Number: 346208
Detailed Information: 346208 (Academy of Finland Funding decision)
Copyright information: © 2023 Finnish Journal of eHealth and eWelfare. This work is licensed under a Creative Commons Attribution 4.0 International License.