Data Curation for Preclinical and Clinical Multimodal Imaging Studies

Yamoah, Grace Gyamfuah; Cao, Liji; Wu, Chao; Beekman, Freek J.; Vandeghinste, Bert; Mannheim, Julia G.; Rosenhain, Stefanie; Leonardic, Kevin; Kießling, Fabian; Gremse, Felix

doi:10.1007/s11307-019-01339-0

Cited by 5 publications

(5 citation statements)

References 32 publications

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“…This standardised and open-file format includes a cryptographic hash to ensure data integrity in a provable and legally accepted way, provides fast and efficient image compression, and was developed in cooperation with several small-animal imaging hardware manufacturers and image-analysis software companies. 10…”

Section: Discussionmentioning

confidence: 99%

Design of a joint research data platform: A use case for severity assessment

et al. 2019

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Severity assessment in animal models is a data-driven process. We therefore present a use case for building a repository for interlaboratory collaboration with the potential of uploading specific content, making group announcements and internal prepublication discussions. We clearly show that it is possible to offer such a structure with minimal effort and a basic understanding of web-based services, also taking into account the human factor in individual data collection. The FOR2591 Online Repository serves as a blueprint for other groups, so that one day not only will data sharing among consortium members be improved but the transition from the private to the persistent domain will also be easier.

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Section: Discussionmentioning

confidence: 99%

Design of a joint research data platform: A use case for severity assessment

et al. 2019

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“…The volume can be in DICOM, NIfTI, Analyze, or GFF file format. For an overview of various file formats, see Yamoah et al 12 .…”

Section: Methods and Requirementsmentioning

confidence: 99%

“…Any series of such transformations can be described as a single transformation consisting of a rotation followed by a translation. The formula used by the software to move from the data coordinate to the metric target coordinate is shown in Figure 1C, where R is an orthonormal rotation matrix, d and v are voxel indices and sizes, and t is a 3 x 1 translation vector 12 . The rotation is detailed in Figure 1D.…”

Section: Transformation and Differential Transformationmentioning

confidence: 99%

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Thamm,

Jeffery,

Zhang

et al. 2023

JoVE

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Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic resonance imaging (MRI) combined with PET, are widely available and typically provide robustly co-registered volumes. However, separate devices are often needed to combine a standalone MRI with an existing PET-CT or to incorporate additional data from optical tomography or high-resolution X-ray microtomography. This necessitates image co-registration, which involves complex aspects such as multimodal mouse bed design, fiducial marker inclusion, image reconstruction, and software-based image fusion. Fiducial markers often pose problems for in vivo data due to dynamic range issues, limitations on the imaging field of view, difficulties in marker placement, or marker signal loss over time (e.g., from drying or decay). These challenges must be understood and addressed by each research group requiring image co-registration, resulting in repeated efforts, as the relevant details are rarely described in existing publications.This protocol outlines a general workflow that overcomes these issues. Although a differential transformation is initially created using fiducial markers or visual structures, such markers are not required in production scans. The requirements for the volume data and the metadata generated by the reconstruction software are detailed. The discussion covers achieving and verifying requirements separately for each modality.A phantom-based approach is described to generate a differential transformation between the coordinate systems of two imaging modalities. This method showcases how to co-register production scans without fiducial markers. Each step is illustrated using available software, with recommendations for commercially available phantoms.

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“…Although small datasets may suffice for the training of AI algorithms, large, well-curated datasets with associated annotations are deemed necessary for AI algorithms in the clinical setting [109]. To this direction the preparation and organization of data from various sources through their lifecycle, rendering them available for processing for research and/ or educational purposes is fundamental and is called data curation [110]. Data curation includes several steps like Ethical Approval, Deidentification, appropriate labeling and pre-processing, as well as specific dataset types.…”

Section: Data Curationmentioning

confidence: 99%

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

et al. 2021

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Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.

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Data Curation for Preclinical and Clinical Multimodal Imaging Studies

Cited by 5 publications

References 32 publications

Design of a joint research data platform: A use case for severity assessment

Design of a joint research data platform: A use case for severity assessment

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

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