Материал: part17

AAAA.3.2 Acme​

The first part of this example is shown above in Table AAAA.3-2.​

The anatomical extent is defined in the reconstruction to represent the dataset of interest to the clinical trial. The extent was not​ defined in the localizer or acquisition. Sites are welcome to reflect their local practice in the localizer and acquisition extent as long​ as they permit production of the reconstruction as specified here.​

Table AAAA.3-2. CT Tumor Volumetric Measurement - Details - Acme​

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>>See Table AAAA.3-2c “First Reconstruction Protocol Element Specification”​

The following tables reflect the semantic contents of constraint sequences but not the actual structure of the IOD. The centered rows​ in italics clarify the context of the constrained Attributes that follow by indicating which sequence in the performed module contains​ the constrained Attribute (as specified in the Selector Sequence Pointer).​

Table AAAA.3-2a. First Acquisition Protocol Element Specification​

Table AAAA.3-2b. Second Acquisition Protocol Element Specification​

>CT X-Ray Details Sequence (0018,9325) - First Beam​

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BBBBColorinformationforParametricObject​ (Informative)​

BBBB.1 Introduction​

Functional imaging can create Parametric Maps showing a functional relation between the anatomical region and the specific func-​ tional activity. For display purposes it is useful to show this functional activity with the use of a color LUT on the related anatomical​ image. To be able to do this it is necessary to include a Palette Color Lookup Table for the Parametric Map and define how to map​ the (floating point) values to a specific RGB value.​

For a correct mapping it is important to know what range of continuous values needs to be mapped to the discrete range of RGB​ values of the LUT. For this the Minimum Stored Value Mapped (0028,1231) and the Maximum Stored Value Mapped (0028,1232)​ are defined. All values between the minimum and maximum will be distributed in a linear manner to the Palette Color Lookup Table​ that is supplied.​

The usage of floating point values for the stored values removes the need for a Real World Value transformation other than the​ identity transformation.​

This example illustrates BOLD fMRI activation data for a bipolar motor paradigm stored as a floating point parametric map encoding​ ‘t’ (statistical) Real World Values. Each voxel’s value represents how well the BOLD time series information at that location of the​ brain fits the general linear model (GLM) of the fMRI block paradigm pattern (right or left versus control, no movement). Right and​ left have been encoded as positive and negative t values, respectively.​

TheDoubleFloatMinimumStoredValueMappedandMaximumStoredValueMappedinthiscaseare-16.739and21.434,respectively.​ This range will be mapped to the low and high ends of the LUT applied to this activation map. In this case the Minimum Stored Value​ Mapped and Maximum Stored Value Mapped are equal to the RWV Minimum and Maximum, respectively, in the activation map data.​ Note several compelling reasons for the range to be different from the RWV Minimum and RWV Maximum:​

1.​Centering the RWV zero value on some desired index of the LUT; e.g., choosing -21.434 to +21.434 to properly center RWV​ zero on the middle of the LUT (presumably to match the LUT design).​

2.​ChoosinganarrowerrangeofMinimumStoredValueMappedand/orMaximumStoredValueMapped(negativeand/orpositive),​ i.e., windowing, to maximize the dynamic range of the LUT for critical RWV range(s).​

3.​Specifying a predetermined Minimum Stored Value Mapped and Maximum Stored Value Mapped regardless of the actual RWV​ data, in order to have key RWV transitions match LUT color effects, e.g., generally accepted hyperperfusion and hypoperfusion​ transition points in cerebral blood flow (CBF) maps.​

For the purpose of this example, the full RWV range of the activation map is appropriate to display with the full range of the Spring​ Color Palette.​

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Figure BBBB.1-1. Color Parametric Map on top of an anatomical image​

Astheactivationmapwithoutthresholdsuggests,areasoutsidethebrainhavebeenmaskedoff.ThesewouldbecodedwithPadding​ values in the parametric map.​

Thresholding (not part of the parametric map) will be applied for positive and/or negative ranges. Note that this operation does not​ change the color mapping (i.e., RWV x corresponds to LUT entry j) but only the opacity of voxels outside the range (forcing A=0 or​ transparent).​

Figure BBBB.1-2. Color Parametric Map with threshold applied on top of an anatomical image​

Other visualization methods such as smoothing and overall opacity may be applied to the colored, thresholded activation map.​

BBBB.2 Encoding Example​

This section illustrates the usage of the Color LUT in the context of a Parametric Map IOD with the Palette Color Lookup Table for​ the example described.​

Table BBBB.2-1. Example data for the Floating Point Image Pixel Module​

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