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Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

Fischer F, Selver MA, Gezer S, Dicle O, Hillen W - J Med Biol Eng (2015)

Bottom Line: To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data.The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists.The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

View Article: PubMed Central - PubMed

Affiliation: FH-Aachen, Juelich Division, Medical Informatics Laboratory, Aachen, Germany ; Nautavis GmbH, Linnich, Germany.

ABSTRACT

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

No MeSH data available.


a 2DPR IOD modules (excerpt). Complete overview of all modules of PR information object can be found elsewhere [4, 5]. b Excerpt from DICOM Data Dictionary. VR stands for “Value Representation” (data type), and VM stands for “Value Multiplicity” (frequency). c 3DPR IOD modules (extract). d, e Excerpts from the Data Dictionary for the 3DPR. “FD” and “OB” means “Floating Double” and “Other Byte”, respectively
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Fig5: a 2DPR IOD modules (excerpt). Complete overview of all modules of PR information object can be found elsewhere [4, 5]. b Excerpt from DICOM Data Dictionary. VR stands for “Value Representation” (data type), and VM stands for “Value Multiplicity” (frequency). c 3DPR IOD modules (extract). d, e Excerpts from the Data Dictionary for the 3DPR. “FD” and “OB” means “Floating Double” and “Other Byte”, respectively

Mentions: Each 2DPR corresponds to a DICOM information object in the construction of a composite IOD. The attributes of the individual modules define which operations are to be applied to a particular image (or series of images). There are 27 different modules for 2DPR, which are divided into five different units (Fig. 5a). There are modules that must be available in an information entity (M = Mandatory), modules that exist under certain conditions (C = Conditional), and modules that are optional (U = User Option). Patient information entities (such as Study, Series, and Equipment) are not discussed here since they include only demographic information about patients.Fig. 5


Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

Fischer F, Selver MA, Gezer S, Dicle O, Hillen W - J Med Biol Eng (2015)

a 2DPR IOD modules (excerpt). Complete overview of all modules of PR information object can be found elsewhere [4, 5]. b Excerpt from DICOM Data Dictionary. VR stands for “Value Representation” (data type), and VM stands for “Value Multiplicity” (frequency). c 3DPR IOD modules (extract). d, e Excerpts from the Data Dictionary for the 3DPR. “FD” and “OB” means “Floating Double” and “Other Byte”, respectively
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4666236&req=5

Fig5: a 2DPR IOD modules (excerpt). Complete overview of all modules of PR information object can be found elsewhere [4, 5]. b Excerpt from DICOM Data Dictionary. VR stands for “Value Representation” (data type), and VM stands for “Value Multiplicity” (frequency). c 3DPR IOD modules (extract). d, e Excerpts from the Data Dictionary for the 3DPR. “FD” and “OB” means “Floating Double” and “Other Byte”, respectively
Mentions: Each 2DPR corresponds to a DICOM information object in the construction of a composite IOD. The attributes of the individual modules define which operations are to be applied to a particular image (or series of images). There are 27 different modules for 2DPR, which are divided into five different units (Fig. 5a). There are modules that must be available in an information entity (M = Mandatory), modules that exist under certain conditions (C = Conditional), and modules that are optional (U = User Option). Patient information entities (such as Study, Series, and Equipment) are not discussed here since they include only demographic information about patients.Fig. 5

Bottom Line: To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data.The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists.The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

View Article: PubMed Central - PubMed

Affiliation: FH-Aachen, Juelich Division, Medical Informatics Laboratory, Aachen, Germany ; Nautavis GmbH, Linnich, Germany.

ABSTRACT

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

No MeSH data available.