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[Preprint]. 2024 May 6:arXiv:2405.03147v1.

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI

Ernesto Diaz  1 , Renuka Sriram  1 , Jeremy W Gordon  1 , Avantika Sinha  1 , Xiaoxi Liu  1 , Sule Sahin  1 , Jason Crane  1 , Marram P Olson  1 , Hsin-Yu Chen  1 , Jenna Bernard  1 , Daniel B Vigneron  1   2 , Zhen Jane Wang  1 , Duan Xu  1   2 , Peder E Z Larson  1   2
Affiliations

Data Format Standardization and DICOM Integration for Hyperpolarized 13C MRI

Ernesto Diaz et al. ArXiv. .

Update in

Abstract

Hyperpolarized (HP) 13C MRI has shown promise as a valuable modality for in vivo measurements of metabolism and is currently in human trials at 15 research sites worldwide. With this growth it is important to adopt standardized data storage practices as it will allow sites to meaningfully compare data. In this paper we (1) describe data that we believe should be stored and (2) demonstrate pipelines and methods that utilize the Digital Imaging and Communications in Medicine (DICOM) standard. This includes proposing a set of minimum set of information that is specific to HP 13C MRI studies. We then show where the majority of these can be fit into existing DICOM Attributes, primarily via the "Contrast/Bolus" module. We also demonstrate pipelines for utilizing DICOM for HP 13C MRI. DICOM is the most common standard for clinical medical image storage and provides the flexibility to accommodate the unique aspects of HP 13C MRI, including the HP agent information but also spectroscopic and metabolite dimensions. The pipelines shown include creating DICOM objects for studies on human and animal imaging systems with various pulse sequences. We also show a python-based method to efficiently modify DICOM objects to incorporate the unique HP 13C MRI information that is not captured by existing pipelines. Moreover, we propose best practices for HP 13C MRI data storage that will support future multi-site trials, research studies and technical developments of this imaging technique.

Keywords: DICOM format; Experiment metadata; Hyperpolarized 13C MRI; metabolic imaging.

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Conflict of interest statement

Financial interests: Authors ED, RS, AS, XL, JB, ZJW, and XU declare they have no financial interests. Authors PEZ, DBV, and JWG received research funding from GE Healthcare

Figures

Figure 1:
Figure 1:
Example of how HP 13C MRI can be used in a workflow for improved targeting of biopsies in prostate cancer. Using DICOM has been critical in the success of this workflow, as this allows for integration into PACS as well as the fusion biopsy platform, with radiologists and urologists easily able to review the images and resulting annotations outlining the biopsy targets. Figure reproduced with permission from.
Figure 2:
Figure 2:
This is an example of the PyDicom functions used. It reads a file, then adds an attribute and saves the file.
Figure 4:
Figure 4:
The prompts questions that gives option for default values or user input. As well, showing the attributes that has been modified.
See this image and copyright information in PMC

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References

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