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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.


Volume rendered illustrations of segmented medical data sets for testing compression algorithms with a aorta, b skeleton, c MR kidney, d CT kidney, and e skull
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Fig2: Volume rendered illustrations of segmented medical data sets for testing compression algorithms with a aorta, b skeleton, c MR kidney, d CT kidney, and e skull

Mentions: The data sets for testing compression methods were selected from various modalities and anatomical objects with diverse spatial structures so that the performance of the compression techniques can be evaluated for a wide range of applications. 10 data sets are used for each anatomical structure. The “Aorta” data sets were acquired using CT with a contrast medium. They have 288 slices on average with a slice thickness (ST) of 1.5 mm. They have a segmented volume of interest (VOI) of 139 × 322 × 288 voxels, which gives them the largest average dimensions and file size (1631 kB) (Fig. 2a). The “Kidney MR” data sets are coronal MRI image series with 72 slices and an ST of 1.4 mm (Fig. 2c). Their file size is 167 kB. Since the segmentation performance from MRI is limited due to noise, a second type of data set with smoothed versions of the “Kidney MR” series was also used (i.e., Kidney MR-2). The selected VOI has 121 × 52 × 205 voxels. The file size is also 167 kB. The CT kidney data sets consist of 238 slices with an ST of 1 mm and a VOI of 114 × 101 × 112 voxels (Fig. 2d). The file size is 166 kB. The skull CT data sets consist of 361 slices with an ST of 0.7 mm and a VOI of 175 × 214 × 302 voxels (Fig. 2e). The file size is 1389 kB. Finally, “Skeleton” includes ribs and hips from CT (288 slices, ST: 1.5 mm). Its VOI is 239 × 146 × 288 voxels and file size is 1232 kB (Fig. 2b).Fig. 2


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

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

Volume rendered illustrations of segmented medical data sets for testing compression algorithms with a aorta, b skeleton, c MR kidney, d CT kidney, and e skull
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Volume rendered illustrations of segmented medical data sets for testing compression algorithms with a aorta, b skeleton, c MR kidney, d CT kidney, and e skull
Mentions: The data sets for testing compression methods were selected from various modalities and anatomical objects with diverse spatial structures so that the performance of the compression techniques can be evaluated for a wide range of applications. 10 data sets are used for each anatomical structure. The “Aorta” data sets were acquired using CT with a contrast medium. They have 288 slices on average with a slice thickness (ST) of 1.5 mm. They have a segmented volume of interest (VOI) of 139 × 322 × 288 voxels, which gives them the largest average dimensions and file size (1631 kB) (Fig. 2a). The “Kidney MR” data sets are coronal MRI image series with 72 slices and an ST of 1.4 mm (Fig. 2c). Their file size is 167 kB. Since the segmentation performance from MRI is limited due to noise, a second type of data set with smoothed versions of the “Kidney MR” series was also used (i.e., Kidney MR-2). The selected VOI has 121 × 52 × 205 voxels. The file size is also 167 kB. The CT kidney data sets consist of 238 slices with an ST of 1 mm and a VOI of 114 × 101 × 112 voxels (Fig. 2d). The file size is 166 kB. The skull CT data sets consist of 361 slices with an ST of 0.7 mm and a VOI of 175 × 214 × 302 voxels (Fig. 2e). The file size is 1389 kB. Finally, “Skeleton” includes ribs and hips from CT (288 slices, ST: 1.5 mm). Its VOI is 239 × 146 × 288 voxels and file size is 1232 kB (Fig. 2b).Fig. 2

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.