Abstract
Cardiac health is a big concern, worldwide, with cardiovascular disease (CVD) being the most predominant cause of death, making early diagnosis a top priority in public health. The technology supporting the diagnosis of CVD, e.g., cardiac computerized tomography angiography (CTA), has been evolving at a fast pace providing a wide range of data on the anatomy and function of the heart. When developing novel processing and analysis methods to tackle this data, one important challenge concerns how to make them available for clinicians to test. The aim would be to enable full exploration of these methods in the clinical workflow, i.e., supporting all the standard image visualization and analysis features provided by clinical workstations, from early on, to foster insight over their features and usefulness to inform development. Additionally, with the advances of technology, mobile devices, beyond the traditional workstations, have gained importance, e.g., during consultation. However, they have limited processing resources. In this regard, this work explores a modular multiplatform solution to support the early deployment of cardiac analysis methods to the clinician’s office. To this effect, we define the requirements for such a platform and present a first instance of its development exploring the Open Health Imaging Foundation resources. At its current stage, we demonstrate the feasibility of the approach and the integration of simple image processing modules in the pipeline.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
OpenCV: https://opencv.org/.
- 3.
ITK: https://itk.org/.
- 4.
MevisLab: https://www.mevislab.de/.
- 5.
PostDICOM: https://www.postdicom.com/.
- 6.
Horos: https://horosproject.org/.
- 7.
Mango: https://ric.uthscsa.edu/mango/.
- 8.
Dicom Web Viewer: https://ivmartel.github.io/dwv/.
- 9.
Cornerstone.js: https://www.cornerstonejs.org/.
- 10.
References
Chang, H.J., et al.: Selective referral using CCTA versus direct referral for individuals referred to invasive coronary angiography for suspected CAD: a randomized, controlled, open-label trial. JACC: Cardiovasc. Imaging 12(7 Part 2), 1303–1312 (2019)
Danad, I., Szymonifka, J., Schulman-Marcus, J., Min, J.K.: Static and dynamic assessment of myocardial perfusion by computed tomography. Eur. Heart J. - Cardiovasc. Imaging 17(8), 836–844 (2016). https://doi.org/10.1093/ehjci/jew044
Erik Z., et al.: Open health imaging foundation viewer: an extensible open-source framework for building web-based imaging applications to support cancer research. JCO Clin. Cancer Inf. 4, 336-345. https://doi.org/10.1200/CCI.19.00131. https://github.com/OHIF/Viewers
Hoffmann, U., Ferencik, M., Cury, R.C., Pena, A.J.: Coronary CT angiography
Ince, D.C., Hatton, L., Graham-Cumming, J.: The case for open computer programs. Nature 482(7386), 485–488 (2012)
Nieman, K., Balla, S.: Dynamic CT myocardial perfusion imaging
Marano, R., et al.: CCTA in the diagnosis of coronary artery disease. Radiol. Med. (Torino) 125(11), 1102–1113 (2020)
Mu, D., et al.: Calcium scoring at coronary CT angiography using deep learning. Radiology 302(2), 309–316 (2022)
Noack, P., Jang, K.H., Moore, J.A., Goldberg, R., Poon, M., et al.: Computer-aided analysis of 64-and 320-slice coronary computed tomography angiography: a comparison with expert human interpretation. Int. J. Cardiovasc. Imaging 34(9), 1473–1483 (2018)
Nolden, M., et al.: The medical imaging interaction toolkit: challenges and advances. Int. J. Comput. Assist. Radiol. Surg. 8(4), 607–620 (2013)
Nowbar, A.N., Gitto, M., Howard, J.P., Francis, D.P., Al-Lamee, R.: Mortality from ischemic heart disease: analysis of data from the world health organization and coronary artery disease risk factors from NCD risk factor collaboration. Circ.: Cardiovasc. Qual. Outcomes 12(6), e005375 (2019)
Rankin, S.: CT angiography. Eur. Radiol. 9(2), 297–310 (1999)
Roduit, N.: Weasis medical viewer : Weasis documentation (2021). https://nroduit.github.io/en/
Roth, G.A., Mensah, G.A., et al.: Global burden of cardiovascular diseases and risk factors, 1990–2013;2019. J. Am. Coll. Cardiol. 76(25), 2982–3021 (2020). https://doi.org/10.1016/j.jacc.2020.11.010
Sun, W., Zhang, P., Wang, Z., Li, D.: Prediction of cardiovascular diseases based on machine learning. ASP Trans. Internet Things 1(1), 30–35 (2021)
Tzoulaki, I., Elliott, P., Kontis, V., Ezzati, M.: Worldwide exposures to cardiovascular risk factors and associated health effects: current knowledge and data gaps. Circulation 133(23), 2314–2333 (2016)
Van Assen, M., et al.: Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease. J. Cardiovasc. Comput. Tomogr. 13(3), 26–33 (2019)
Varga-Szemes, A., Meinel, F.G., Cecco, C.N.D., Fuller, S.R., Bayer, R.R., Schoepf, U.J.: CT myocardial perfusion imaging. Am. J. Roentgenol. 204(3), 487–497 (2015). https://doi.org/10.2214/ajr.14.13546
World-Health-Organization: Cardiovascular diseases (CVDs) (2021). www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Acknowledgements
This research is supported by National Funds, through FCT, in the scope of project CAD-FACTS (PTDC/EMD-EMD/0980/2020) and by IEETA - Institute of Electronics and Informatics Engineering of Aveiro Research Unit funding (UIDB/00127/2020).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Abrantes, J., Almeida, N., Silva, S. (2023). Exploring a Modular Approach for Deploying and Testing Cardiac Image Processing and Analysis Methods in the Clinical Workflow. In: Cunha, A., M. Garcia, N., Marx Gómez, J., Pereira, S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 484. Springer, Cham. https://doi.org/10.1007/978-3-031-32029-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-32029-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-32028-6
Online ISBN: 978-3-031-32029-3
eBook Packages: Computer ScienceComputer Science (R0)