567-03-001 ISMRM Abstract

A plea for objective DICOM based MRI decoding

Primary: Cross-Organ

Secondary: Analysis Methods - Software Tools

567-03-001 · Standardizing MRI Through Data Formats and Phantoms · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row H

Keywords: Radiologic Practice Radiology Report Generation PACS DICOM BIDS

Accepted

Pim Pullens ^1,2,3

¹IBiTech – Medisip, Ghent University, Ghent, Belgium

²Department of Radiology and Nuclear Medicine, Ghent University Hospital, Gent, Belgium

³Ghent Institute of Functional and Metabolic Imaging (GIFMI), Ghent University, Ghent, Belgium

Presenting Author: Pim Pullens

Synopsis

Motivation:

Goals:

Approach:

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References

1. https://radsource.us/streamlining-radiology-the-role-of-hanging-protocols/

2. Zwiers MP, Moia S, Oostenveld R: BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure. Front Neuroinform 2022; 15. doi:10.3389/fninf.2021.770608 [doi]

3. Gauriau R, Bridge C, Chen L, et al.: Using DICOM Metadata for Radiological Image Series Categorization: a Feasibility Study on Large Clinical Brain MRI Datasets. J Digit Imaging 2020; 33:747–762. doi:10.1007/s10278-019-00308-x [doi]

4. Liang S, Beaton D, Arnott SR, et al.: Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach. Front Neuroinform 2021; 15. doi: 10.3389/fninf.2021.622951 [doi]

5. Kasmanoff N, Lee MD, Razavian N, Lui YW: Deep multi-task learning and random forest for series classification by pulse sequence type and orientation. Neuroradiology 2023; 65:77–87. doi:10.1007/s00234-022-03023-7 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/567-03-001