TheF.A.S.T. Cloud SDKsupports
a variety of render modes. These render modes are used to visualize the
dataset in different ways. Render modes operate using either brute
force or adaptive algorithms, and either parallel or perspective
projections.
Brute Force vs. Adaptive Rendering Modes
In
brute force renderings, the dataset is evaluated at sampling points
along the ray path through the volume. The density of sampling points
depends upon the zoom level for the dataset. At high zoom levels there
will be at least one sample point per voxel, resulting in a highly
detailed image. The lower the zoom level, the further apart the sample
points will be. If the zoom level is low enough, some small anatomical
structures may be missed because they are smaller than the sample point
separation. Therefore, brute force modes are ideal for viewing at high
zoom levels. Brute force modes are more computationally intensive
because they sample more individual data points than adaptive rendering
modes do, but they yield more detail. This is in part because the
rendering algorithm, while computing a final high-detail image, uses
tricubic interpolation to calculate the voxel values at the sample
points during ray traversal. Brute force modes use trilinear
interpolation when calculating interactive quality images. Because of
the additional computational load of brute force renderings they should
be used only with slab thicknesses of < 50 mm. All brute force
render modes use a parallel projection.
Adaptive
rendering modes make use of an octree data structure to accelerate
rendering. Use of the octree structure allows the rendering algorithm
to incorporate data from all voxels along the ray's traversal path
through the dataset, without the brute force method of actually
collecting sample values from every individual voxel. This enables
adaptive rendering modes to render faster than brute force modes.
Adaptive rendering modes use trilinear interpolation when calculating
voxel values during ray traversal, which is less accurate than the
tricubic interpolation used by brute force modes for final quality
images. Because brute force modes are computationally intensive,
adaptive modes should be used on datasets with a slab thickness of >
50 mm. Transfer function based render modes are only available when
using the adaptive algorithm.
Parallel vs. Perspective Projection Rendering
Render
modes using parallel projection utilize rays that are all parallel to
each other. Therefore, all rendered objects appear to be the same
distance from the current viewpoint regardless of their actual
distance. Parallel rendering modes are useful for taking measurement
points across the dataset, because there is a one-to-one relationship
between pixels and length. That is, each pixel on the screen has the
same absolute size relative to the dataset.
Render
modes that use perspective projection utilize rays that are emitted
from the center of the current viewpoint, and more closely simulate how
the human visual systems works. Objects in a perspective
projection appear smaller the farther they are from the camera, whereas
objects appear larger the closer they are to the camera. Perspective
rendering allows the viewer to explore complex regions close to or
inside the dataset, and is useful for fly-through or auto-navigation of
the colon, vessels, airway and nerve canals.
.
Parallel Projection
Perspective Projection
Render modes are selected by setting theRenderParams.renderTypemember to a value from theRenderTypeenumeration. Multiple values fromFovia.RenderType may map to the same render mode. For more information on use of theRenderParamsstructure, see theRender Parameterssection below. The appropriateRenderTypevalue for each mode is shown below.g
Transfer Function
Transfer
Function based rendering modes produce colored 3D volume renderings
with translucent, transparent and opaque effects. The transfer function
maps a voxel's Hounsfield value (or other dataset unit) to three
components: color, lighting and opacity values. When rays are cast
through the volume, they are differentially absorbed and colored by the
voxels through which they pass, based on these three components. The
result is a colored 3D volume rendering. TheF.A.S.T. Cloud SDKsupports multipletransfer functions,
allowing groups of voxels to be assigned to different transfer
functions. These voxel groups can be visualized separately through the
use ofsegmentationtechniques.
Transfer Function rendering is available in the following modes.
A
MIP rendering is one that displays only the voxel with the highest
Hounsfield value (or other dataset unit) along the ray's direction of
travel. The voxel is visualized as a grayscale pixel ranging from black
(minimum density) to white (maximum density). This mode is useful for
identifying regions of high density, regardless of the structure's
position within the dataset. This mode is best suited to visualize
bone, contrast-enhanced or other materials with a high attenuation
value.
MIP rendering is available in the following modes.
A
Faded MIP rendering is similar to MIP. However, in Faded MIP voxels
become darker as they increase in distance from the camera. In a
conventional MIP rendering, the voxel with the highest Hounsfield value
(or other dataset unit) along the ray path is always displayed. This
can cause foreground structures of lower density to become obscured by
background structures of high density. Use of Faded MIP rendering
allows better visualization of foreground structures in these cases.
Faded MIP rendering is available in the following modes.
Parallel brute force:Fovia.RenderType.thinFMIP
Parallel adaptive: Fovia.RenderType.fmip
Minimum Intensity Projection (MinIP)
A
MinIP rendering is a form of MPR rendering in which only the voxels of
lowest Hounsfield value (or other dataset unit) are visualized. A MinIP
image is calculated in the same manner as an MPR rendering, but the
visualized image shows only voxels of minimum attenuation. MinIP images
are useful for identifying regions of low relative density.
MinIP rendering is available in the following modes.
An
MPR rendering is a grayscale image consisting of rendered pixels that
represent the average Hounsfield value (or other dataset unit) of the
voxels through which the ray passes. An MPR rendering is generated by
casting rays through the volume, accumulating the value of the voxels,
calculating the average value of the voxels and projecting the image
onto a 2D plane. Brighter pixels correspond to material of high
density, and darker pixels correspond to material of low density.
MPR-Average modes work best on datasets with slab thicknesses of <
50 mm. As slab thickness increases, the image will blur more due to the
averaging that takes place along the ray's traversal through the
dataset.
MPR rendering is available in the following mode.
Parallel brute force: Fovia.RenderType.thin
Curved Multiplanar Reformatting (Curved MPR)
Curved MPRis
a special render mode that can only be used in conjunction with the
Curved MPR feature. This mode must be set prior to using Curved MPR.
The Curved MPR technique is used to apply a transformation to the
visualization of the dataset, generally for straightening the
visualization of a curved structure to improve diagnostic imaging.
Trace of a curved vessel
Vessel straightened with CurvedMPR
Curved MPR rendering is available in the following mode.
One of the most important structures in theF.A.S.T. Cloud SDKis theRenderParamsstructure.
This defines the type of render engine used and the complete state of
the rendering engine. All engine parameters, including viewpoint
position and orientation, lighting, clipping planes, transfer function,
image quality, and other parameters, are contained in this structure
that determines how the rendering engine renders the dataset.
Once defined, aRenderParamsstructure is applied using theRenderEngineContext.setRenderParams()method. Seethe Rendering Modessection above for additional information about the different render engine types that are available.
The F.A.S.T. Cloud SDK supports a variety of render modes. These render modes are used to visualize the dataset in different ways. Render modes operate using either brute force or adaptive algorithms, and either parallel or perspective projections.
Brute Force vs. Adaptive Rendering Modes
In brute force renderings, the dataset is evaluated at sampling points along the ray path through the volume. The density of sampling points depends upon the zoom level for the dataset. At high zoom levels there will be at least one sample point per voxel, resulting in a highly detailed image. The lower the zoom level, the further apart the sample points will be. If the zoom level is low enough, some small anatomical structures may be missed because they are smaller than the sample point separation. Therefore, brute force modes are ideal for viewing at high zoom levels. Brute force modes are more computationally intensive because they sample more individual data points than adaptive rendering modes do, but they yield more detail. This is in part because the rendering algorithm, while computing a final high-detail image, uses tricubic interpolation to calculate the voxel values at the sample points during ray traversal. Brute force modes use trilinear interpolation when calculating interactive quality images. Because of the additional computational load of brute force renderings they should be used only with slab thicknesses of < 50 mm. All brute force render modes use a parallel projection.
Adaptive rendering modes make use of an octree data structure to accelerate rendering. Use of the octree structure allows the rendering algorithm to incorporate data from all voxels along the ray's traversal path through the dataset, without the brute force method of actually collecting sample values from every individual voxel. This enables adaptive rendering modes to render faster than brute force modes. Adaptive rendering modes use trilinear interpolation when calculating voxel values during ray traversal, which is less accurate than the tricubic interpolation used by brute force modes for final quality images. Because brute force modes are computationally intensive, adaptive modes should be used on datasets with a slab thickness of > 50 mm. Transfer function based render modes are only available when using the adaptive algorithm.
Parallel vs. Perspective Projection Rendering
Render modes using parallel projection utilize rays that are all parallel to each other. Therefore, all rendered objects appear to be the same distance from the current viewpoint regardless of their actual distance. Parallel rendering modes are useful for taking measurement points across the dataset, because there is a one-to-one relationship between pixels and length. That is, each pixel on the screen has the same absolute size relative to the dataset.
Render modes that use perspective projection utilize rays that are emitted from the center of the current viewpoint, and more closely simulate how the human visual systems works. Objects in a perspective projection appear smaller the farther they are from the camera, whereas objects appear larger the closer they are to the camera. Perspective rendering allows the viewer to explore complex regions close to or inside the dataset, and is useful for fly-through or auto-navigation of the colon, vessels, airway and nerve canals.
.
Parallel Projection
Perspective Projection
Render modes are selected by setting the RenderParams.renderType member to a value from the RenderType enumeration. Multiple values from Fovia.RenderType may map to the same render mode. For more information on use of the RenderParams structure, see the Render Parameters section below. The appropriate RenderType value for each mode is shown below.g
Transfer Function
Transfer Function based rendering modes produce colored 3D volume renderings with translucent, transparent and opaque effects. The transfer function maps a voxel's Hounsfield value (or other dataset unit) to three components: color, lighting and opacity values. When rays are cast through the volume, they are differentially absorbed and colored by the voxels through which they pass, based on these three components. The result is a colored 3D volume rendering. The F.A.S.T. Cloud SDK supports multiple transfer functions, allowing groups of voxels to be assigned to different transfer functions. These voxel groups can be visualized separately through the use of segmentation techniques.
Transfer Function rendering is available in the following modes.
Parallel adaptive: Fovia.RenderType.parallel
Perspective adaptive: Fovia.RenderType.perspective
Maximum Intensity Projection (MIP)
A MIP rendering is one that displays only the voxel with the highest Hounsfield value (or other dataset unit) along the ray's direction of travel. The voxel is visualized as a grayscale pixel ranging from black (minimum density) to white (maximum density). This mode is useful for identifying regions of high density, regardless of the structure's position within the dataset. This mode is best suited to visualize bone, contrast-enhanced or other materials with a high attenuation value.
MIP rendering is available in the following modes.
Parallel brute force: Fovia.RenderType.thinMIP
Parallel adaptive: Fovia.RenderType.mip
Perspective adaptive: Fovia.RenderType.thickMIPPerspective
Faded MIP
A Faded MIP rendering is similar to MIP. However, in Faded MIP voxels become darker as they increase in distance from the camera. In a conventional MIP rendering, the voxel with the highest Hounsfield value (or other dataset unit) along the ray path is always displayed. This can cause foreground structures of lower density to become obscured by background structures of high density. Use of Faded MIP rendering allows better visualization of foreground structures in these cases.
Faded MIP rendering is available in the following modes.
Parallel brute force:Fovia.RenderType.thinFMIP
Parallel adaptive: Fovia.RenderType.fmip
Minimum Intensity Projection (MinIP)
A MinIP rendering is a form of MPR rendering in which only the voxels of lowest Hounsfield value (or other dataset unit) are visualized. A MinIP image is calculated in the same manner as an MPR rendering, but the visualized image shows only voxels of minimum attenuation. MinIP images are useful for identifying regions of low relative density.
MinIP rendering is available in the following modes.
Parallel brute force: Fovia.RenderType.thinMinip
Parallel adaptive: Fovia.RenderType.thickMinIP
Perspective adaptive: Fovia.RenderType.thickMinIPPerspective
Multiplanar Reconstruction (MPR-Average)
An MPR rendering is a grayscale image consisting of rendered pixels that represent the average Hounsfield value (or other dataset unit) of the voxels through which the ray passes. An MPR rendering is generated by casting rays through the volume, accumulating the value of the voxels, calculating the average value of the voxels and projecting the image onto a 2D plane. Brighter pixels correspond to material of high density, and darker pixels correspond to material of low density. MPR-Average modes work best on datasets with slab thicknesses of < 50 mm. As slab thickness increases, the image will blur more due to the averaging that takes place along the ray's traversal through the dataset.
MPR rendering is available in the following mode.
Parallel brute force: Fovia.RenderType.thin
Curved Multiplanar Reformatting (Curved MPR)
Curved MPR is a special render mode that can only be used in conjunction with the Curved MPR feature. This mode must be set prior to using Curved MPR. The Curved MPR technique is used to apply a transformation to the visualization of the dataset, generally for straightening the visualization of a curved structure to improve diagnostic imaging.
Trace of a curved vessel
Vessel straightened with CurvedMPR
Curved MPR rendering is available in the following mode.
Non-linear brute force: Fovia.RenderType.thinCurveMPR
Render Paramaters
One of the most important structures in the F.A.S.T. Cloud SDK is the RenderParams structure. This defines the type of render engine used and the complete state of the rendering engine. All engine parameters, including viewpoint position and orientation, lighting, clipping planes, transfer function, image quality, and other parameters, are contained in this structure that determines how the rendering engine renders the dataset.
Once defined, a RenderParams structure is applied using the RenderEngineContext.setRenderParams() method. See the Rendering Modes section above for additional information about the different render engine types that are available.