The image acquisition system records ECG signals simultaneously with the acquisition of the Enhanced XA multi-frame image. All the ECG signals are acquired at the same sampling rate.

The acquisition of both image and ECG data are not triggered by an external signal.

The information can be exchanged via Offline Media or Network.

Synchronization between the ECG Curve and the image frames allows synchronized navigation in each of the data sets.

The General ECG IOD is used to store the waveform data recorded in parallel to the image acquisition encoded as Enhanced XA IOD.

The Synchronization Module is used to specify a common time-base.

The option of encoding trigger information is not recommended by this case.

The solution assumes implementation on a single imaging modality and therefore the mutual UID references between the General ECG and Enhanced XA objects is recommended. This will allow faster access to the related object.

This section provides detailed recommendations of the key attributes to address this particular scenario.

In the two following examples, the Image Modality acquires a multi-frame image of the coronary arteries lasting 4 seconds, at 30 frames per second.

Simultaneously, the same modality acquires two channels of ECG from a 2-Lead ECG (the first channel on Lead I and the second on Lead II) starting one second before the image acquisition starts, and lasting 5 seconds, with a sampling frequency of 300 Hz on 16 bits signed encoding, making up a number of 1500 samples per channel. The first ECG sample is 10 ms after the nominal start time of the ECG acquisition. Both ECG channels are sampled simultaneously. The time skew of both channels is 0 ms.

This section provides information on the encoding of the movement of the X-Ray Positioner during the acquisition of a rotational angiography.

The related image presentation parameters of the rotational acquisition that are defined in the Enhanced XA SOP Class, such as the mask information of subtracted display, are described in further sections of this annex.

This section provides information on the encoding of the movement of the X-Ray Table during the acquisition of a stepping angiography.

The related image presentation parameters of the stepping acquisition that are defined in the Enhanced XA SOP Class, such as the mask information of subtracted display, are described in further sections of this annex.

This section provides information on the encoding of the "sensitive areas" used for regulation control of the X-Ray generation of an image that resulted from applying these X-Rays.

The user selects a given size of the field of view before starting the acquisition. This size can be smaller than the size of the Image Detector.

The position of the field of view in the detector area changes during the acquisition in order to focus on an object of interest.

Acquired image is networked or stored in offline media, then the image is:

The XA SOP Class does not encode some information to fully characterize the geometry of the conic projection acquisition, such as the position of the Positioner Isocenter on the FOV area. Indeed, the XA SOP Class assumes that the isocenter is projected in the middle of the FOV.

The Enhanced XA SOP Class encodes the position of the Isocenter on the detector, as well as specific FOV attributes (origin, rotation, flip) per-frame or shared. It encodes some existing attributes from DX to specify information of the Digital Detector and FOV. It also allows differentiating the image intensifier vs. the digital detector and then defines conditions on attributes depending on image intensifier or digital detector.

This section provides detailed recommendations of the key attributes to address this particular scenario.

The user performs image acquisition with injection of contrast agent during the X-Ray acquisition. Some frames are acquired without contrast, some others with contrast.

The type of contrast agent can be radio-opaque (e.g., iodine) or radio-transparent (e.g., CO2).

The information of the type of contrast and its presence or absence in the frames can be used by post-processing applications to set up e.g., vessel detection or image quality algorithms automatically.

The Enhanced XA SOP Class encodes the characteristics of the contrast agent(s) used during the acquisition of the image, including the type of absorption (radio-opaque or radio-transparent).

The Enhanced XA SOP Class also allows encoding the presence of contrast in a particular frame or set of frames, by encoding the Contrast/Bolus Usage per-frame.

This section provides detailed recommendations of the key attributes to address this particular scenario.

In this example, the user starts the X-Ray acquisition at 4 frames per second at 3:35pm. After one second the user starts the injection of 45 milliliters of contrast media Iodipamide (350 mg/ml Cholographin (Bracco) ) at a flow rate of 15 ml/sec during three seconds, in intra-arterial route. When the injection of contrast agent is finished, the user continues the X-Ray acquisition for two seconds until wash out of the contrast agent.

There could be two ways to determine the presence of contrast agent on the frames:

In this example, the image acquired contains 25 frames: From frames 5 to 17, the contrast is being injected. From frames 8 to 23, the contrast is visible on the pixel data.

The figure below shows the attributes of this example in a graphical representation of the multi-frame acquisition.

The encoded values of the key attributes of this example are shown in Figure FFF.2.1-30.

The user performs X-Ray acquisitions during the examination. Some of them are dynamic acquisitions where the positioner and/or the table have moved between frames of the multi-frame image, the acquisition parameters such as kVp, mA and pulse width may change per-frame to be adapted to the different anatomy characteristics.

Later quality assurance wants to assess the X-Ray generation techniques in order to understand possible degradation of image quality, or to estimate the level of irradiation at different skin areas and body parts examined.

The XA SOP Class encodes the attributes kVp, mA and pulse duration as a unique value for the whole multi-frame image. For systems that can provide only average values of these attributes, this SOP Class is more appropriate.

The Enhanced XA SOP Class encodes per-frame kVp, mA and pulse duration, thus the estimated dose per frame can be now correlated to the positioner angles and table position of each frame.

In order to accurately estimate the dose per body area, other attributes are needed such as positioner angles, table position, SID, ISO distances, Field of View, etc.

This section provides detailed recommendations of the key attributes to address this particular scenario.

For more details, refer to the Section FFF.1.4

The image acquisition system performs complex acquisition protocols with groups of frames to be displayed at different frame rates and others to be skipped.

Allow frame-rates in viewing applications to be different than acquired rates.

The XA IOD provides only one group of frames between start and stop trim.

The Enhanced XA/XRF IOD allows encoding of multiple groups of frames (frame collections) with dedicated display parameters.

The Enhanced XA IOD provides an exact acquisition time for each frame.

This section provides detailed recommendations of the key attributes to address this particular scenario.

An example of a 4 position peripheral stepping acquisition with different frame-rates is provided. One group is only 2 Frames (e.g., due to fast contrast bolus) and will be skipped for display purposes.

The whole image is reviewed in looping mode:

The encoded values of the key attributes of this example are shown in Figure FFF.2.2-1.

The image acquisition may be performed with a variety of settings on the detector image pre-processing component that modifies the way the gray levels are stored in the pixel data.

In particular, it may impact the relationship between the X-Ray intensity and the gray level stored (e.g., non-linear function), as well as the geometry of the X-Ray beam (e.g., pincushion distortion).

Based on the characteristics of the stored pixel data, the acquisition system determines automatically an optimal way to display the pixel data on a frame-by-frame basis, which is expected to be applied by the viewing applications.

The XA SOP Class encodes the VOI settings to be common to all the frames of the image. It also restricts the Photometric Interpretation (0028,0004) to MONOCHROME2.

The Enhanced XA SOP Class encodes per-frame VOI settings. Additionally it allows the Photometric Interpretation (0028,0004) to be MONOCHROME1 in order to display low pixel values in white while using window width and window center VOI. Other characteristics and settings can be defined, such as:

This section provides detailed recommendations of the key attributes to address this particular scenario.

In this example, two different systems perform an X-Ray Acquisition of the coronary arteries injected with radio-opaque contrast agent.

The system A is equipped with a digital detector, and stores the pixel data with the lower level corresponding to the lower X-Ray intensity. Then the user creates two instances: one to display the injected vessels as black, and other to display the injected vessels as white.

The system B is equipped with an image intensifier configured to store the pixel data with the lower level corresponding to the higher X-Ray intensity. Then the user creates two instances: one to display the injected vessels as black, and other to display the injected vessels as white.

The figure below illustrates, for the two systems, the gray levels of the injected vessels on both the stored pixel data and the displayed pixels, which depend on the value of the attributes Pixel Intensity Relationship Sign (0028,1041), Photometric Interpretation (0028,0004), and Presentation LUT Shape (2050,0020).

A straightforward DSA acquisition is performed. The first few frames do not contain contrast, then the rest of frames contain contrast. An "averaged mask" may be selected to average some of the first frames without contrast.

A peripheral stepping DSA acquisition is performed. The acquisition is running in N steps and is timed to perform a mask run (e.g., from feet to abdomen) and then perform contrast runs at the positions of each mask, as triggered by the user.

One or more ranges of contrast frames will be used for subtraction from the mask for loop display. During the display, some ranges are to be fully subtracted, some others may be partially subtracted allowing a certain degree of visibility of the anatomical background visible on the mask, and finally some ranges are to be displayed un-subtracted.

The Enhanced XA SOP Class allows the encoding of the mask attributes similar to what the XA SOP Class provides.

The Enhanced XA SOP Class allows defining of specific display settings to be applied to a subset of frames, for instance the recommended viewing mode and the degree of visibility of the mask.

This section provides detailed recommendations of the key attributes to address this particular scenario.

The user performs an X-Ray acquisition in three steps:

In the three steps, the system automatically identifies the mask frame(s) to be associated with the contrast frames.

The encoded values of the key attributes of this example are shown in Figure FFF.2.3-2.

When performing DSA acquisitions, the acquisition system may choose a default subtraction pixel-shift to allow review of the whole multi-frame, as acquired.

With advanced post-processing function the medical user may add further subtraction pixel-shifts to carve out certain details or improve contrast bolus visualization of a part of the anatomy that suffered from different movement during the acquisition.

The Mask Module is used to encode the various subtractions applicable to a multi-frame image.

The Enhanced XA IOD allows creating groups of mask-contrast pairs in the Mask Module, each group identified by a unique Subtraction Item ID within the multi-frame image.

The Enhanced XA IOD, with per frame macro encoding, supports multiple and different pixel-shift values per frame, each pixel-shift value is related to a given Subtraction Item ID.

It has to be assured that all the frames in the scope of a Subtraction Item ID have the pixel-shift values defined under that Subtraction Item ID.

In case a frame does not belong to any Subtraction Item ID, that frame does not necessarily have a pixel shift value encoded.

This section provides detailed recommendations of the key attributes to address this particular scenario. The usage of the "Frame Pixel Shift" macro in a 'per frame' context is recommended. Only the usage of Mask Module and the Frame Pixel Shift Macro is further detailed.

The user wants to measure the size of objects in the patient with a default system calibration based on the acquisition geometry and the default distance from the table to the object. In order to have more accurate measurements than this default calibration, the user may provide information of the distance from the table to the object to be measured.

The image is stored in an archive system and retrieved by a second user who wants to re-use the calibration and needs to know which object this calibration applies to.

This second user may need to re-calibrate based on another object at a different geometry.

In conic projection imaging, the pixel size in the patient is not constant. If a value of Pixel Spacing (0028,0030) is provided, it is best appropriate at a given distance from the X-Ray source to the object of interest in the patient (patient plane). It is less exact for other objects at other distances.

In addition, the distance from the X-Ray source to the object of interest may change per frame in case of gantry or table motion. In this case the Enhanced XA SOP Class allows the pixel size in the patient to be defined per-frame.

A macro provides a compound set of all relevant attributes.

The value "Table to Object Height" can be used for individual patient plane definition.

Automatic isocenter calibration method is supported.

Values of gantry and table positions are provided to complete all necessary attributes for a later re-calibration.

This section provides detailed recommendations of the key attributes to address this particular scenario. See Section C.8.19.6.9.1 in PS3.3 for detailed description of the attributes involved in the calculation of the calibration.

The user performs an X-Ray acquisition with movement of the positioner during the acquisition. The patient is in Head First Supine position. During the review of the multi-frame image, a measurement of the object of interest in the frame "i" needs to be performed, which requires the calculation of the pixel spacing at the object location for that frame.

For the frame "i", the Positioner Primary Angle is -30.0 degrees, and the Positioner Secondary Angle is 20.0 degrees. According to the definition of the positioner angles and given the patient position, the Beam Angle is calculated as follows:

Beam Angle = arcos( |cos(-30.0) | * |cos(20.0) | ) = 35.53 degrees

The value of the other attributes defining the geometry of the acquisition for the frame "i" are the following:

ISO = 750 mm SID = 983 mm TH = 187 mm

ΔPx (Imager Pixel Spacing) = 0.2 mm/pix

The user provides, via the application interface, an estimated value of the distance from the object of interest to the tabletop: TO = 180 mm. This value can be encoded in the attribute Distance Object to Table Top (0018,9403) of the Projection Pixel Calibration Sequence (0018,9401) for further usage.

This results in an SOD of 741.4 mm (according to the equation SOD = 750mm - [(187mm-180mm) / cos(35.53°) ] ), and in a magnification ratio of SID/SOD of 1.32587.

The resulting pixel spacing at the object location and related to the center of the X-Ray beam is calculated as ΔPx * SOD / SID = 0.150844 mm/pix. This value can be encoded in the attribute Object Pixel Spacing in Center of Beam (0018,9404) of the Projection Pixel Calibration Sequence (0018,9401) for further usage.

The encoded values of the key attributes of this example are shown in Figure FFF.2.4-1.

An acquisition system performs several processing steps on an original image, and then it creates a derived image with the processed pixel data.

A viewing application applies post-processing algorithms to that derived image, e.g., measurements, segmentation etc. This application needs to know what kind of post-processing can or cannot be applied depending on the characteristics of the derived image.

The XA SOP Class does not encode any specific attribute values to characterize the type of derivation.

The Enhanced XA SOP Class encodes defined terms for processing applied to the Pixel Data, and allows getting back to linear relationship between pixel values and X-Ray intensity. Viewing applications can consistently interpret the stored pixel data and enable/disable applications like edge detection algorithms, subtraction, filtering, etc.

This section provides detailed recommendations of the key attributes to address this particular scenario.

The operator identifies the position of an object of interest projected on the stored pixel data of an image A, and estimates the magnification of the conic projection by a calibration process.

The operator wants to know the position of the projection of such object of interest on a second image B acquired under different geometry, assuming that the patient does not move between image A and image B (i.e., the images share the same frame of reference).

The XA SOP Class encodes the information in a patient-related coordinate system.

The Enhanced XA SOP Class additionally encodes the geometry of the acquisition system with respect to a fixed reference system defined by the manufacturer, so-called Isocenter reference system. Therefore, it allows encoding the absolute position of an object of interest and to track the projection of such object across the different images acquired under different geometry.

This section provides detailed recommendations of the key attributes to address this particular scenario.

In this example, the operator identifies the position (i, j) of an object of interest projected on the stored pixel data of an image A, and estimates the magnification of the conic projection by a calibration process.

The operator wants to know the position of the projection of such object of interest on a second image B acquired under different geometry.

The attributes that define the geometry of both images A and B are described in the following figure:

The following steps describe the process to calculate the position (i, j)_B of the projection of the object of interest in the Pixel Data of the image B, assuming that (i, j)_A is known and is the offset of the projection of the object of interest from the TLHC of the Pixel Data of the image A, measured in pixels of the Pixel Data matrix as a column offset "i" followed by a row offset "j". TLHC is defined as (0,0).

Step 1: Calculate the point (i, j)_A in FOV coordinates of the image A.

Step 2: Calculate the point (i, j)_A in physical detector coordinates of the image A.

Step 3: Calculate the point (P_u, P_v)_A in positioner coordinates of the image A.

Step 4: Calculate the point (P_Xp, P_Yp, P_Zp)_A in positioner coordinates of the image A.

Step 5: Calculate the point (P_X, P_Y, P_Z)_A in Isocenter coordinates of the image A.

Step 6: Calculate the point (P_Xt, P_Yt, P_Zt)_A in Table coordinates of the image A.

Step 7: Calculate the point (P_Xt, P_Yt, P_Zt)_B in Table coordinates in mm of the image B.

Step 8: Calculate the point (P_X, P_Y, P_Z)_B in Isocenter coordinates in mm of the image B.

Step 9: Calculate the point (P_Xp, P_Yp, P_Zp)_B in positioner coordinates of the image B.

Step 10: Calculate the point (P_u, P_v)_B in positioner coordinates of the image B.

Step 11: Calculate the point (i, j)_B in physical detector coordinates of the image B.

Step 12: Calculate the point (i, j)_B in FOV coordinates of the image B.

Step 13: Calculate the point (i, j)_B in Pixel Data of the image B.

In this example let's assume:

(i, j)_A = (310,122) pixels

Magnification ratio = 1.3

Step 1 : Image A: Point (i, j)_A in FOV coordinates

In this step, the FOV coordinates are calculated by taking into account the FOV rotation and Horizontal Flip applied to the FOV matrix when the Pixel Data were created:

1.1: Horizontal Flip : YES

new i = (columns -1) - i = 850 - 1 - 310 = 539

new j = j = 122

1.2: Image Rotation : 90 (clockwise)

new i = j = 122

new j = (columns -1) - i = 850 - 1 - 539 = 310

(i, j)_A = (122, 310) in stored pixel data.

Step 2: Image A: Point (i, j)_A in physical detector coordinates

In this step, the physical detector coordinates are calculated by taking into account the FOV origin and the ratio between Imager Pixel Spacing and Detector Element Spacing:

D_i = Imager Pixel Spacing (column) = 0.2 mm

D_j = Imager Pixel Spacing (row) = 0.2 mm

D_idet = Detector Element Spacing between two adjacent columns = 0.2 mm

D_jdet = Detector Element Spacing between two adjacent rows = 0.2 mm

Zoom Factor (column) = D_i / D_idet = 1.0

Zoom Factor (row) = D_j / D_jdet = 1.0

FOV Origin (column) = FOV_idet = 600.0

FOV Origin (row) = FOV_jdet = 600.0

new i = FOV_idet + (i + (1 - D_idet / D_i) / 2) * D_j / D_jdet = 600 + 122 * 1.0 = 722

new j = FOV_jdet + (j + (1 - D_jdet / D_j) / 2) * D_i / D_idet = 600 + 310 * 1.0 = 910

(i, j)_A = (722, 910) in detector elements.

Step 3: Image A: Point ( P_u, P_v)_A in positioner coordinates

In this step, the (P_u, P_v)_A coordinates in mm are calculated from (i, j)_A by taking into account the projection of the Isocenter in physical detector coordinates, and the Detector Element Spacing:

ISO_P_idet = Position of Isocenter Projection (column) = 1024.5

ISO_P_jdet = Position of Isocenter Projection (row) = 1024.5

D_idet = Detector Element Spacing between two adjacent columns = 0.2 mm

D_jdet = Detector Element Spacing between two adjacent rows = 0.2 mm

Pu = (i - ISO_P_idet) * D_idet = (722 - 1024.5) * 0.2 = -60.5 mm

Pv = (ISO_P_jdet - j) * D_jdet = (1024.5 - 910) * 0.2 = 22.9 mm

( P_u, P_v)_A = (-60.5, 22.9) in mm.

Step 4: Image A: Point (PXp, PYp, PZp)A in positioner coordinates

In this step, the positioner coordinates (P_Xp, P_Yp, P_Zp)_A are calculated from (P_u, P_v)_A by taking into account the magnification ratio, the Distance Source to Detector and the Distance Source to Isocenter:

SID = Distance Source to Detector = 1300 mm

ISO = Distance Source to Isocenter = 780 mm

Magnification ratio = SID / (ISO - P _Yp ) = 1.3

P _Yp = ISO - SID / 1.3 = 780 - 1300/1.3 = -220 mm

P _Xp = Pu / Magnification ratio = -60.5 / 1.3 = -46.54 mm

P _Zp = Pv / Magnification ratio = 22.9 / 1.3 = 17.62 mm

(P_Xp, P_Yp, P_Zp)_A = (-46.54, -220, 17.62) in mm.

Step 5: Image A: Point (P_X, P_Y, P_Z)_A in Isocenter coordinates

In this step, the isocenter coordinates (P_X, P_Y, P_Z)_A are calculated from the positioner coordinates (P_Xp, P_Yp, P_Zp)_A by taking into account the positioner angles of the image A in the Isocenter coordinate system:

Ap₁ = Positioner Isocenter Primary Angle = 60.0 deg

Ap₂ = Positioner Isocenter Secondary Angle = 20.0 deg

Ap₃ = Positioner Isocenter Detector Rotation Angle = 0.0 deg

(P_X, P_Y, P_Z)^T= (R₂ ·R₁)^T·(R₃ ^T·(P_Xp, P_Yp, P_Zp)^T)

(P_X, P_Y, P_Z)_A = (150.55, -65.41, 91.80) in mm.

Step 6: Image A: Point (P_Xt, P_Yt, P_Zt)_A in Table coordinates

In this step, the table coordinates (P_Xt, P_Yt, P_Zt)_A are calculated from the isocenter coordinates (P_X, P_Y, P_Z)_A by taking into account the table position and angles of the image A in the Isocenter coordinate system:

Tx =Table X Position to Isocenter = 10.0 mm

Ty =Table Y Position to Isocenter = 30.0 mm

Tz =Table Z Position to Isocenter = 100.0 mm

At₁ = Table Horizontal Rotation Angle = -10.0 deg

At₂ = Table Head Tilt Angle = 0.0 deg

At₃ = Table Cradle Tilt Angle = 0.0 deg

(P_Xt, P_Yt, P_Zt)^T= (R₃ · R₂ · R₁) · ((P_X, P_Y, P_Z)^T- (T_X, T_Y, T_Z)^T)

(P_Xt, P_Yt, P_Zt)_A = (136.99, -95.41, -32.48) in mm.

Step 7: Image B: Point (P_Xt, P_Yt, P_Zt)_B in Table coordinates

In this step, the table has moved from image A to image B. The table coordinates of the object of interest are the same on image A and image B because it is assumed that the patient is fixed on the table.

(P_Xt, P_Yt, P_Zt)_B = (136.99, -95.41, -32.48) in mm.

Step 8: Image B: Point (P_X, P_Y, P_Z)_B in Isocenter coordinates

In this step, the isocenter coordinates (P_X, P_Y, P_Z)_B are calculated from the table coordinates (P_Xt, P_Yt, P_Zt)_B by taking into account the table position and angles of the image B in the Isocenter coordinate system:

Tx =Table X Position to Isocenter = 20.0 mm

Ty =Table Y Position to Isocenter = 100.0 mm

Tz =Table Z Position to Isocenter = 0.0 mm

At₁ = Table Horizontal Rotation Angle = 0.0 deg

At₂ = Table Head Tilt Angle = 10.0 deg

At₃ = Table Cradle Tilt Angle = 0.0 deg

(P_X, P_Y, P_Z)^T= (R₃ · R₂ · R₁)^T· (P_Xt, P_Yt, P_Zt)^T+ (T_X, T_Y, T_Z)^T

(P_X, P_Y, P_Z)_B = (156.99, -12.11, -48.55) in mm.

Step 9: Image B: Point (P_Xp, P_Yp, P_Zp)_B in positioner coordinates

In this step, the positioner coordinates (P_Xp, P_Yp, P_Zp)_B are calculated from the isocenter coordinates (P_X, P_Y, P_Z)_B by taking into account the positioner angles of the image B in the Isocenter coordinate system:

Ap₁ = Positioner Isocenter Primary Angle = -30.0 deg

Ap₂ = Positioner Isocenter Secondary Angle = 0.0 deg

Ap₃ = Positioner Isocenter Detector Rotation Angle = 0.0 deg

(P_Xp, P_Yp, P_Zp)^T= R₃ · ((R₂ · R₁) · (P_X, P_Y, P_Z)^T)

(P_Xp, P_Yp, P_Zp)_B = (142.01, 68.00, -48.55) in mm.

Step 10: Image B: Point ( P_u, P_v)_B in positioner coordinates

In this step, the (P_u, P_v)_B coordinates in mm are calculated from the positioner coordinates (P_Xp, P_Yp, P_Zp)_B by taking into account the Distance Source to Detector and the Distance Source to Isocenter of the image B:

SID = Distance Source to Detector = 1000 mm

ISO = Distance Source to Isocenter = 800 mm

Magnification ratio = SID / (ISO - P _Yp ) = 1200/(800-68) = 1.366

Pu = P _Xp * Magnification ratio = 142.01 * 1.64 = 194.00 mm

Pv = P _Z p * Magnification ratio = -48.55 * 1.64 = -66.33 mm

( P_u, P_v)_B = (194.00, -66.33) in mm.

Step 11: Image B: Point (i, j)_B in physical detector coordinates

In this step, the physical detector coordinates (i, j)_B are calculated from the positioner coordinates ( P_u, P_v)_B by taking into account the projection of the Isocenter in physical detector coordinates, and the Detector Element Spacing of the image B:

ISO_P_idet = Position of Isocenter Projection (column) = 1024.5

ISO_P_jdet = Position of Isocenter Projection (row) = 1024.5

D_idet =Detector Element Spacing between two adjacent columns = 0.2

D_jdet =Detector Element Spacing between two adjacent rows = 0.2

i = ISO_P_idet + Pu / D_idet = 1024.5 + 194.00 / 0.2 = 1994.5

j = ISO_P_idet - Pv / D_idet = 1024.5 - (-66.33) / 0.2 = 1356.2

(i, j)_B = (1994.5, 1356.2) in detector elements.

Step 12 : Image B: Point (i, j)_B in FOV coordinates

In this step, the FOV coordinates are calculated from the physical detector coordinates by taking into account the FOV origin and the ratio between Imager Pixel Spacing and Detector Element Spacing of the image B:

D_i = Imager Pixel Spacing (column) = 0.4 mm

D_j = Imager Pixel Spacing (row) = 0.4 mm

D_idet = Detector Element Spacing between two adjacent columns = 0.2 mm

D_jdet = Detector Element Spacing between two adjacent rows = 0.2 mm

Zoom Factor (column) = D_i / D_idet = 2.0

Zoom Factor (row) = D_j / D_jdet = 2.0

FOV Origin (column) = FOV_idet = 25.0

FOV Origin (row) = FOV_jdet = 25.0

new i = (i - FOV_idet).D_idet / D_i - (1 - D_idet / D_i) / 2 = (1994.5 - 25.0) / 2.0 - 0.25 = 984.5

new j = (j - FOV_jdet).D_jdet / D_j - (1 - D_jdet / D_j) / 2 = (1356.2 - 25.0) / 2.0 - 0.25 = 665.35

(i, j)_B = (984.50, 665.35) in stored pixel data.

Step 13 : Image B: Point (i, j)_B in Pixel Data

In this step, the position (i, j)_B of the projection of the object of interest in the Pixel Data of the image B is calculated from the FOV coordinates by taking into account the FOV rotation and Horizontal Flip applied to the FOV matrix when the Pixel Data were created:

1.1: Horizontal Flip : NO

new i = i = 984.50

new j = j = 665.35

1.2: Image Rotation : 180 (clockwise)

new i = (columns -1) - i = 1000 - 1 - 984.50 = 14.50

new j = (rows -1) - j = 1000 - 1 - 665.35 = 333.65

(i, j)_B = (14.50, 333.65) in stored pixel data.