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Introduction to the Language of Three-dimensional Imaging with Multidetector CT¹

¹ From the Department of Radiology, University of Texas Health Science Center, 7703 Floyd Curl Dr, San Antonio, TX 78229-3900. Recipient of a Certificate of Merit award for an education exhibit at the 2004 RSNA Annual Meeting. Received March 7, 2005; revision requested May 23 and received June 22; accepted June 30. All authors have no financial relationships to disclose.

View larger version (66K): [in a new window]   Figure 1a.  Beam collimation in 16-section CT. B = beam, C = collimator, DAS = data acquisition system, DE = detector elements, T = tube. (a) Narrow collimation exposes only the small central detector elements. (b) Wide collimation exposes all of the detector elements. The small central elements are paired or "binned" so that each pair acts as one larger element.

 

View larger version (64K): [in a new window]   Figure 1b.  Beam collimation in 16-section CT. B = beam, C = collimator, DAS = data acquisition system, DE = detector elements, T = tube. (a) Narrow collimation exposes only the small central detector elements. (b) Wide collimation exposes all of the detector elements. The small central elements are paired or "binned" so that each pair acts as one larger element.

 

View larger version (28K): [in a new window]   Figure 2a.  Section collimation in multi–detector row CT. (a) Narrow collimation is coordinated with the data acquisition system (DAS) to allow use of the small central detector elements (DE) individually, resulting in 16 sections with a thickness of 0.6 mm each. This setting allows data reconstruction down to a section thickness of 0.6 mm. (b) Wide collimation is coordinated with the data acquisition system (DAS) to pair the 16 small central detector elements (DE) and use the eight peripheral elements individually, resulting in 16 sections with a thickness of 1.2 mm each. This setting allows data reconstruction down to a section thickness of 1.2 mm.

 

View larger version (31K): [in a new window]   Figure 2b.  Section collimation in multi–detector row CT. (a) Narrow collimation is coordinated with the data acquisition system (DAS) to allow use of the small central detector elements (DE) individually, resulting in 16 sections with a thickness of 0.6 mm each. This setting allows data reconstruction down to a section thickness of 0.6 mm. (b) Wide collimation is coordinated with the data acquisition system (DAS) to pair the 16 small central detector elements (DE) and use the eight peripheral elements individually, resulting in 16 sections with a thickness of 1.2 mm each. This setting allows data reconstruction down to a section thickness of 1.2 mm.

 

View larger version (38K): [in a new window]   Figure 3.  Reconstruction of axial images from projection data. Projection data are never viewed directly. Rather, they are used to generate axial images. In multi–detector row CT, images used for primary axial interpretation usually have a section thickness several times larger than the minimum thickness available and may be called "thick sections." However, axial images can also be generated with a smaller section thickness, as determined by the section collimation. These are usually called "thin sections" and are essential for creating multiplanar reformatted and 3D images.

 

View larger version (71K): [in a new window]   Figure 4a.  Effects of an overlapping reconstruction interval. (a) Contiguous data set reconstructed with a section thickness and interval of 2.5 mm. Coronal reformatted image shows a jagged cortical contour due to stair-step artifact. (b) Overlapping data set reconstructed with a section thickness of 2.5 mm but with the interval decreased to 1.25 mm, an overlap of 50%. Such overlapping minimizes stair-step artifact and improves demonstration of a fracture of the right superior pubic ramus (arrowhead).

 

View larger version (70K): [in a new window]   Figure 4b.  Effects of an overlapping reconstruction interval. (a) Contiguous data set reconstructed with a section thickness and interval of 2.5 mm. Coronal reformatted image shows a jagged cortical contour due to stair-step artifact. (b) Overlapping data set reconstructed with a section thickness of 2.5 mm but with the interval decreased to 1.25 mm, an overlap of 50%. Such overlapping minimizes stair-step artifact and improves demonstration of a fracture of the right superior pubic ramus (arrowhead).

 

View larger version (45K): [in a new window]   Figure 5a.  Anisotropic and isotropic data. (a) Single–detector row CT performed with a nominal section thickness of 5 mm and a 512 x 512 matrix results in reconstructed data that are anisotropic, consisting of voxels with a facing pixel size of approximately 0.625 mm but a depth of 5 mm. This data set provides satisfactory axial images but has limited potential for secondary data reconstruction. (b) Sixteen-channel CT performed with wide collimation results in reconstructed data that are anisotropic, with a z-axis dimension (1.25 mm) approximately twice the size of the facing pixel (0.625 mm). By overlapping the reconstruction interval (which is not limited by section collimation), this data set provides excellent reformatted and volume-rendered images for many applications. (c) Sixteen-channel CT performed with narrow collimation results in reconstructed data that are isotropic, consisting of voxels that are relatively symmetric in all dimensions (0.625 mm). This data set provides exquisite data for multiplanar and 3D applications.

 

View larger version (47K): [in a new window]   Figure 5b.  Anisotropic and isotropic data. (a) Single–detector row CT performed with a nominal section thickness of 5 mm and a 512 x 512 matrix results in reconstructed data that are anisotropic, consisting of voxels with a facing pixel size of approximately 0.625 mm but a depth of 5 mm. This data set provides satisfactory axial images but has limited potential for secondary data reconstruction. (b) Sixteen-channel CT performed with wide collimation results in reconstructed data that are anisotropic, with a z-axis dimension (1.25 mm) approximately twice the size of the facing pixel (0.625 mm). By overlapping the reconstruction interval (which is not limited by section collimation), this data set provides excellent reformatted and volume-rendered images for many applications. (c) Sixteen-channel CT performed with narrow collimation results in reconstructed data that are isotropic, consisting of voxels that are relatively symmetric in all dimensions (0.625 mm). This data set provides exquisite data for multiplanar and 3D applications.

 

View larger version (54K): [in a new window]   Figure 5c.  Anisotropic and isotropic data. (a) Single–detector row CT performed with a nominal section thickness of 5 mm and a 512 x 512 matrix results in reconstructed data that are anisotropic, consisting of voxels with a facing pixel size of approximately 0.625 mm but a depth of 5 mm. This data set provides satisfactory axial images but has limited potential for secondary data reconstruction. (b) Sixteen-channel CT performed with wide collimation results in reconstructed data that are anisotropic, with a z-axis dimension (1.25 mm) approximately twice the size of the facing pixel (0.625 mm). By overlapping the reconstruction interval (which is not limited by section collimation), this data set provides excellent reformatted and volume-rendered images for many applications. (c) Sixteen-channel CT performed with narrow collimation results in reconstructed data that are isotropic, consisting of voxels that are relatively symmetric in all dimensions (0.625 mm). This data set provides exquisite data for multiplanar and 3D applications.

 

View larger version (44K): [in a new window]   Figure 6.  Use of a volumetric data set. Projection data are typically used to reconstruct axial images of interpretive thickness for conventional review, which is performed by using printed film or with a picture archiving and communication system. Although it is occasionally useful to view thin axial images for osseous detail, axial viewing is usually performed with a section thickness of 3–5 mm. If necessary, a thin-section data set can be generated in addition to or in place of the traditional interpretive axial images. This may be called the volumetric data set because it is intended to be used not for primary axial interpretation but rather for generating high-quality multiplanar reformatted or volume-rendered images. This data set typically consists of axial images with a section thickness approaching 1 mm or even less, preferably with an overlapping interval.

 

View larger version (115K): [in a new window]   Figure 7a.  MPR. (a) Coronal reformatted image from routine abdominal-pelvic CT of a patient with bowel ischemia related to systemic lupus erythematosus vasculitis. Imaging in the coronal plane allowed visualization of bowel loop distribution throughout the abdomen and pelvis on a total of 28 images. Thickened distal loops of ileum are seen in the right lower quadrant with dilatation of more proximal small bowel loops. Arterial and venous patency was confirmed with this examination. (b) Sagittal reformatted image produced from CT data acquired with a trauma protocol. Examination of the chest, abdomen, and pelvis was performed with a detector configuration of 16 x 1.25 mm. Although a primary reconstruction thickness of 5 mm was used for axial interpretation, secondary data reconstruction to a section thickness of 1.25 mm at an interval of 0.625 mm allows a set of detailed full-spine sagittal images (approximately 20 1.5-mm-thick sections) to be created for every trauma case.

 

View larger version (67K): [in a new window]   Figure 7b.  MPR. (a) Coronal reformatted image from routine abdominal-pelvic CT of a patient with bowel ischemia related to systemic lupus erythematosus vasculitis. Imaging in the coronal plane allowed visualization of bowel loop distribution throughout the abdomen and pelvis on a total of 28 images. Thickened distal loops of ileum are seen in the right lower quadrant with dilatation of more proximal small bowel loops. Arterial and venous patency was confirmed with this examination. (b) Sagittal reformatted image produced from CT data acquired with a trauma protocol. Examination of the chest, abdomen, and pelvis was performed with a detector configuration of 16 x 1.25 mm. Although a primary reconstruction thickness of 5 mm was used for axial interpretation, secondary data reconstruction to a section thickness of 1.25 mm at an interval of 0.625 mm allows a set of detailed full-spine sagittal images (approximately 20 1.5-mm-thick sections) to be created for every trauma case.

 

View larger version (53K): [in a new window]   Figure 8.  Row of data encountered along a ray of projection. The data consist of attenuation information calculated in Hounsfield units. The value of the displayed two-dimensional pixel is determined by the amount of data included in the calculation (slab thickness) and the processing algorithm (maximum, minimum, or average intensity projection [AIP] or ray sum).

 

View larger version (150K): [in a new window]   Figure 9a.  Curved planar reformation. (a) Three-dimensional volume-rendered image shows the curved course of the right coronary artery. (b) Curved planar image of the right coronary artery shows a cross section of the vessel in its entirety. In this case, several points were selected along the course of the vessel on axial images; semiautomated software then defined an imaging plane that includes the entire length of the vessel. Because the imaging plane is defined by the vessel, other structures in the image are distorted.

 

View larger version (155K): [in a new window]   Figure 9b.  Curved planar reformation. (a) Three-dimensional volume-rendered image shows the curved course of the right coronary artery. (b) Curved planar image of the right coronary artery shows a cross section of the vessel in its entirety. In this case, several points were selected along the course of the vessel on axial images; semiautomated software then defined an imaging plane that includes the entire length of the vessel. Because the imaging plane is defined by the vessel, other structures in the image are distorted.

 

View larger version (95K): [in a new window]   Figure 10.  AIP of data encountered by a ray traced through the object of interest to the viewer. The included data contain attenuation information ranging from that of air (black) to that of contrast media and bone (white). AIP uses the mean attenuation of the data to calculate the projected value.

 

View larger version (169K): [in a new window]   Figure 11a.  Effects of AIP on an image of the liver. (a) Coronal reformatted image created with a default thickness of 1 pixel (approximately 0.8 mm). (b) Increasing the slab thickness to 4 mm by using AIP results in a smoother image with less noise and improved contrast resolution. The image quality is similar to that used in axial evaluation of the abdomen.

 

View larger version (160K): [in a new window]   Figure 11b.  Effects of AIP on an image of the liver. (a) Coronal reformatted image created with a default thickness of 1 pixel (approximately 0.8 mm). (b) Increasing the slab thickness to 4 mm by using AIP results in a smoother image with less noise and improved contrast resolution. The image quality is similar to that used in axial evaluation of the abdomen.

 

View larger version (96K): [in a new window]   Figure 12.  MIP of data encountered by a ray traced through the object of interest to the viewer. The included data contain attenuation information ranging from that of air (black) to that of contrast media and bone (white). MIP projects only the highest value encountered.

 

View larger version (125K): [in a new window]   Figure 13a.  Effects of MIP slab thickness on a coronal image of the abdomen. (a, b) Changing from the AIP technique (a) to the MIP technique (b) at a fixed slab thickness of 2.5 mm results in increased conspicuity of vessels. (c–f) More vessels are included per image as the section thickness increases to 5 mm (c), 10 mm (d), 15 mm (e), and 20 mm (f). However, use of thick slabs also results in obscuration of the vessels by other high-attenuation structures (bones, other vessels).

 

View larger version (123K): [in a new window]   Figure 13b.  Effects of MIP slab thickness on a coronal image of the abdomen. (a, b) Changing from the AIP technique (a) to the MIP technique (b) at a fixed slab thickness of 2.5 mm results in increased conspicuity of vessels. (c–f) More vessels are included per image as the section thickness increases to 5 mm (c), 10 mm (d), 15 mm (e), and 20 mm (f). However, use of thick slabs also results in obscuration of the vessels by other high-attenuation structures (bones, other vessels).

 

View larger version (131K): [in a new window]   Figure 13c.  Effects of MIP slab thickness on a coronal image of the abdomen. (a, b) Changing from the AIP technique (a) to the MIP technique (b) at a fixed slab thickness of 2.5 mm results in increased conspicuity of vessels. (c–f) More vessels are included per image as the section thickness increases to 5 mm (c), 10 mm (d), 15 mm (e), and 20 mm (f). However, use of thick slabs also results in obscuration of the vessels by other high-attenuation structures (bones, other vessels).

 

View larger version (132K): [in a new window]   Figure 13d.  Effects of MIP slab thickness on a coronal image of the abdomen. (a, b) Changing from the AIP technique (a) to the MIP technique (b) at a fixed slab thickness of 2.5 mm results in increased conspicuity of vessels. (c–f) More vessels are included per image as the section thickness increases to 5 mm (c), 10 mm (d), 15 mm (e), and 20 mm (f). However, use of thick slabs also results in obscuration of the vessels by other high-attenuation structures (bones, other vessels).

 

View larger version (130K): [in a new window]   Figure 13e.  Effects of MIP slab thickness on a coronal image of the abdomen. (a, b) Changing from the AIP technique (a) to the MIP technique (b) at a fixed slab thickness of 2.5 mm results in increased conspicuity of vessels. (c–f) More vessels are included per image as the section thickness increases to 5 mm (c), 10 mm (d), 15 mm (e), and 20 mm (f). However, use of thick slabs also results in obscuration of the vessels by other high-attenuation structures (bones, other vessels).

 

View larger version (130K): [in a new window]   Figure 13f.  Effects of MIP slab thickness on a coronal image of the abdomen. (a, b) Changing from the AIP technique (a) to the MIP technique (b) at a fixed slab thickness of 2.5 mm results in increased conspicuity of vessels. (c–f) More vessels are included per image as the section thickness increases to 5 mm (c), 10 mm (d), 15 mm (e), and 20 mm (f). However, use of thick slabs also results in obscuration of the vessels by other high-attenuation structures (bones, other vessels).

 

View larger version (96K): [in a new window]   Figure 14.  MinIP of data encountered by a ray traced through the object of interest to the viewer. The included data contain attenuation information ranging from that of air (black) to that of contrast media and bone (white). MinIP projects only the lowest value encountered.

 

View larger version (100K): [in a new window]   Figure 15a.  Coronal slab image of the thorax (slab thickness = 20 mm) created with MinIP, AIP, and MIP. (a) On the MinIP image, the central airways are clearly demonstrated. Asymmetric emphysematous changes are seen in the right upper lobe. (b) On the AIP image, the central airways are not seen as well; the emphysematous changes remain visible but are less apparent. Interstitial and vascular structures within the lungs are seen better than on the MinIP image. (c) On the MIP image, the airways and emphysematous changes are obscured by vascular and soft-tissue structures. Longer segments of the vessels are visible than on the AIP image.

 

View larger version (100K): [in a new window]   Figure 15b.  Coronal slab image of the thorax (slab thickness = 20 mm) created with MinIP, AIP, and MIP. (a) On the MinIP image, the central airways are clearly demonstrated. Asymmetric emphysematous changes are seen in the right upper lobe. (b) On the AIP image, the central airways are not seen as well; the emphysematous changes remain visible but are less apparent. Interstitial and vascular structures within the lungs are seen better than on the MinIP image. (c) On the MIP image, the airways and emphysematous changes are obscured by vascular and soft-tissue structures. Longer segments of the vessels are visible than on the AIP image.

 

View larger version (136K): [in a new window]   Figure 15c.  Coronal slab image of the thorax (slab thickness = 20 mm) created with MinIP, AIP, and MIP. (a) On the MinIP image, the central airways are clearly demonstrated. Asymmetric emphysematous changes are seen in the right upper lobe. (b) On the AIP image, the central airways are not seen as well; the emphysematous changes remain visible but are less apparent. Interstitial and vascular structures within the lungs are seen better than on the MinIP image. (c) On the MIP image, the airways and emphysematous changes are obscured by vascular and soft-tissue structures. Longer segments of the vessels are visible than on the AIP image.

 

View larger version (132K): [in a new window]   Figure 16a.  SSD and volume-rendered images of an inferior vena cava filter overlying the spine. (a) SSD creates an effective 3D model for looking at osseous structures in a more anatomic perspective than is achieved with axial images alone. It was used in this case to evaluate pelvic fractures not included on this image. (b) Volume rendering achieves a similar 3D appearance to allow inspection of the bone surfaces in a relatively natural anatomic perspective. In addition, the color assignment tissue classification possible with volume rendering allows improved differentiation of the inferior vena cava filter from the adjacent spine.

 

View larger version (117K): [in a new window]   Figure 16b.  SSD and volume-rendered images of an inferior vena cava filter overlying the spine. (a) SSD creates an effective 3D model for looking at osseous structures in a more anatomic perspective than is achieved with axial images alone. It was used in this case to evaluate pelvic fractures not included on this image. (b) Volume rendering achieves a similar 3D appearance to allow inspection of the bone surfaces in a relatively natural anatomic perspective. In addition, the color assignment tissue classification possible with volume rendering allows improved differentiation of the inferior vena cava filter from the adjacent spine.

 

View larger version (87K): [in a new window]   Figure 17.  Data limitations of SSD. Surface data are segmented from other data by means of manual selection or an attenuation threshold. The graph in the lower part of the figure represents an attenuation threshold selected to include the brightly contrast-enhanced renal cortex and renal vessels during CT angiography. The "virtual spotlight" in the upper left corner represents the gray-scale shading process, which in reality is derived by means of a series of calculations. To illustrate the "hollow" data set that results from discarding all but the surface rendering data, the illustration was actually created by using a volume-rendered image of the kidney with a cut plane transecting the renal parenchyma. Subsequent editing was required to remove the internal features of the object while preserving the surface features of the original image. HU = Hounsfield units.

 

View larger version (81K): [in a new window]   Figure 18.  Data-rich nature of volume rendering. The graph in the lower part of the figure shows how attenuation data are used to assign values to a histogram-based tissue classification consisting of deformable regions for each type of tissue included. In this case, only fat, soft tissue, vessels, and bone are assigned values, but additional classifications can be added as needed. Opacity and color assignment may vary within a given region, and the shape of the region can be manipulated to achieve different image effects. Because there is often overlap in attenuation values between different tissues, the classification regions may overlap. Thus, accurate tissue and border classification may require additional mathematical calculations that take into consideration the characteristics of neighboring data. HU = Hounsfield units.

 

View larger version (88K): [in a new window]   Figure 19.  Three-dimensional volume-rendered image of a duplicated inferior vena cava. The color range selected is such that the opacity values of the partially contrast-enhanced venous structures are blue, whereas the more highly enhanced arterial structures are red. Color ramp was selected to achieve almost binary color assignment to avoid a graded appearance of the vessels.

 

View larger version (28K): [in a new window]   Figure 20.  Orthographic volume rendering of the airways. Volume-rendered image of a patient with tracheal stenosis (arrow) includes the airway from the hypopharynx to just above the carina. The image is not distorted by proximity or angle of the viewpoint and provides an "external" view of anatomic relationships. Segmentation of the airways was achieved by assigning a spike in opacity at the interface between air and soft-tissue attenuation. Overlying lung tissue was removed with region-of-interest editing to avoid obscuring the trachea.

 

View larger version (145K): [in a new window]   Figure 21.  Perspective volume rendering of the airways. Axial (top right), coronal (bottom left), and sagittal (bottom right) chest CT scans show a left hilar mass, which is positioned between vascular structures. Virtual bronchoscopy (immersive rendering with a point of view within the tracheobronchial tree) (top left) was used to guide subsequent transbronchial biopsy, allowing six biopsy passes between central vascular structures without significant bleeding.

 

View larger version (119K): [in a new window]   Figure 22a.  Region-of-interest editing. (a) Three-dimensional volume-rendered image (posterior view) from chest CT performed in a trauma patient with a fracture of T10. A region including a portion of the left ribs is defined manually (green area). (b) The selected region is then removed from the image. (c) Rib removal allows visualization of the fracture on a lateral projection without interference from overlying ribs.

 

View larger version (162K): [in a new window]   Figure 22b.  Region-of-interest editing. (a) Three-dimensional volume-rendered image (posterior view) from chest CT performed in a trauma patient with a fracture of T10. A region including a portion of the left ribs is defined manually (green area). (b) The selected region is then removed from the image. (c) Rib removal allows visualization of the fracture on a lateral projection without interference from overlying ribs.

 

View larger version (96K): [in a new window]   Figure 22c.  Region-of-interest editing. (a) Three-dimensional volume-rendered image (posterior view) from chest CT performed in a trauma patient with a fracture of T10. A region including a portion of the left ribs is defined manually (green area). (b) The selected region is then removed from the image. (c) Rib removal allows visualization of the fracture on a lateral projection without interference from overlying ribs.

 

View larger version (155K): [in a new window]   Figure 23a.  Use of an opacity threshold for segmentation, as shown on a full field of view 3D volume-rendered image of the chest and abdomen. (a) A low opacity threshold allows the skin to obscure the abdominal contents. A vertical row of shirt buttons is seen in the midline. (b–d) Progressively increasing the opacity threshold excludes first low-opacity soft tissues (skin, fat) (b) then high-opacity soft tissues (muscle, bowel wall) (c) while contrast-enhanced organs and vessels remain (d). (e) Eventually only the most opaque objects (bone, calcium, excreted contrast material) remain.

 

View larger version (158K): [in a new window]   Figure 23b.  Use of an opacity threshold for segmentation, as shown on a full field of view 3D volume-rendered image of the chest and abdomen. (a) A low opacity threshold allows the skin to obscure the abdominal contents. A vertical row of shirt buttons is seen in the midline. (b–d) Progressively increasing the opacity threshold excludes first low-opacity soft tissues (skin, fat) (b) then high-opacity soft tissues (muscle, bowel wall) (c) while contrast-enhanced organs and vessels remain (d). (e) Eventually only the most opaque objects (bone, calcium, excreted contrast material) remain.

 

View larger version (139K): [in a new window]   Figure 23c.  Use of an opacity threshold for segmentation, as shown on a full field of view 3D volume-rendered image of the chest and abdomen. (a) A low opacity threshold allows the skin to obscure the abdominal contents. A vertical row of shirt buttons is seen in the midline. (b–d) Progressively increasing the opacity threshold excludes first low-opacity soft tissues (skin, fat) (b) then high-opacity soft tissues (muscle, bowel wall) (c) while contrast-enhanced organs and vessels remain (d). (e) Eventually only the most opaque objects (bone, calcium, excreted contrast material) remain.

 

View larger version (113K): [in a new window]   Figure 23d.  Use of an opacity threshold for segmentation, as shown on a full field of view 3D volume-rendered image of the chest and abdomen. (a) A low opacity threshold allows the skin to obscure the abdominal contents. A vertical row of shirt buttons is seen in the midline. (b–d) Progressively increasing the opacity threshold excludes first low-opacity soft tissues (skin, fat) (b) then high-opacity soft tissues (muscle, bowel wall) (c) while contrast-enhanced organs and vessels remain (d). (e) Eventually only the most opaque objects (bone, calcium, excreted contrast material) remain.

 

View larger version (107K): [in a new window]   Figure 23e.  Use of an opacity threshold for segmentation, as shown on a full field of view 3D volume-rendered image of the chest and abdomen. (a) A low opacity threshold allows the skin to obscure the abdominal contents. A vertical row of shirt buttons is seen in the midline. (b–d) Progressively increasing the opacity threshold excludes first low-opacity soft tissues (skin, fat) (b) then high-opacity soft tissues (muscle, bowel wall) (c) while contrast-enhanced organs and vessels remain (d). (e) Eventually only the most opaque objects (bone, calcium, excreted contrast material) remain.

 

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