The primary role of the lung is to deliver ambient air (ventilation) and the body blood supply (perfusion) to an interface (the alveolar walls) for the exchange of carbon dioxide and oxygen. In the best case, any regional differences in ventilation or perfusion are matched to the other such that the ventilation-perfusion ratio (V/Q) is uniform everywhere.
Attempts at quantifying V/Q relationships along with investigations into the physiologic determinants of regional ventilation and perfusion distribution have spawned numerous methods over the years. Nitrogen washout1, multiple inert gas elimination2, and radioactive xenon gas inhalation3 techniques were developed for ventilation measurements, while radiolabeled microaggregated albumin or radioactive microspheres injection in conjunction with external gamma counters4 or lung excision5 have provided perfusion data. More recently, the development of powerful imaging methods such as positron emission tomography (PET), single photon emission computed tomography (SPECT), magnetic resonance imaging (MRI) and x-ray computed tomography (CT) have provided new approaches to the study of pulmonary ventilation and perfusion.
Early measurements of lung structure and function were achieved from a unique CT scanner known as the Dynamic Spatial Reconstructor (DSR)6. Capable of acquiring up to 240 contiguous 0.9mm cross sections in one-sixtieth of a second and repeating this acquisition 60 times per second, the DSR required development of specialized software for the visualization and analysis of 3-d data sets. Display techniques such as shaded surface display and maximum intensity projections (ray casting)7 were used for the 3-d visualization of the lungs and vascular tree. Oblique sectioning of the volumetric data sets, by extracting slices perpendicular to the airway or vessel long axis, allowed for accurate measurements of cross sectional areas and branch angles of the pulmonary vascular tree8,9 and airways. Hoffman and colleagues demonstrated the capabilities of in vivo imaging of lung, by using the DSR to show that lung volume could be accurately measured to within 3% of the excised lung water displacement10 and that lung air content could be measured to within 7% with relative (region by region) accuracy reaching 3%11.
More recently, conventional CT scanners have been used to acquire highly collimated, thin slice (1mm) cross sections of the lung. Reconstructing the raw data with a high spatial frequency algorithm yields enhanced edge depiction, thereby generating high resolution (HRCT) slices for visualizing anatomic detail12. The advent of spiral CT13 has allowed for the acquisition of high resolution volumetric image data sets, and techniques originally developed for the DSR are being applied to the display and description of the pulmonary arteries14. While providing impressive anatomic information, this approach is restricted to the area conventional radiology has resided in ever since Roentgen's original application in the late 1800's, that is, depiction of structure.
Functional depiction of the lungs has traditionally been largely found in the radio-nuclear branch of radiology. Here, some research into relating blood flow to anatomy, when structures cannot truly be visualized (i.e. PET scanning or post mortem dicing), has taken the form of fractal analysis, whereby fractal dimensions are calculated to indirectly infer the underlying anatomical characteristics15. Some investigators have attempted matching data sets derived from multiple imaging modalities16 such as MRI (anatomy) and PET (function), but in large part such matching has been limited to the study of the brain17. Kugelmass and colleagues have taken this approach to provide methods of matching various data sets of the heart (this conference), a more challenging endeavor because of the greater fluidity of this organ's structure.
When it comes to the lung, not only is this organ's cyclic motion less repeatable than that of the heart, its air content causes increased problems for MR imaging due to magnetic susceptibility effects at air-water interfaces. Ultrafast CT18, however, has provided the ability to generate both high temporal and high spatial resolution detail of pulmonary function and anatomy. Since both structure and function can be imaged on the same scanner, we simply take care to monitor important physiologic parameters such as air flow, airway pressure, and the ECG so that the lung is always at the same volume during slice acquisition and that the heart is at the same location
2. Image Acquisition and Analysis
The Imatron Fastrac C-100 cine x-ray CT scanner provides a unique platform for simultaneously assessing pulmonary anatomy and function. The C-100 offers both high resolution and dynamic scanning modes while eliminating the need for any target or detector motion by magnetically steering a focused electron beam along a hemi-cylindrical target around a subject. An attached gun produces the electron beam which is electromagnetically deflected towards and swept around one of four tungsten target rings to provide multiple views. Opposite the four target rings are two stationary detectors each encompassing a 210o arc around the subject. Crystal photodiodes are used for recording the transmitted x-ray intensity with one detector ring being double populated for HRCT scanning [Figure 1].
Figure 1. Cross sectional view of the Imatron scanner. Note that the electron beams deflected from each target ring converge on the same detector pair, resulting in slightly fanned, rather than parallel slices in the dynamic mode. (With permission of Imatron Inc.)
High spatial resolution images are produced by moving a collimator into the field of view, thereby focusing the deflected electron beam on the double populated detector ring. In this thin slice mode, the current scanner configuration produces one 3 mm slice by a100 msec double sweep of the target ring. (Imatron is, at the time of this writing, now shipping scanners with 1.5 mm thin slice capability.) Advancing the patient table after each 100 msec sweep generates up to 40 3mm contiguous, parallel slices, providing a volume image from the apex to base of the lung in 52 seconds. Optional continuous slow infusion of contrast material (1 cc/sec) during the scan period allows enhanced high detail viualization of the pulmonary vascular tree.
Imatron provides several options when reconstructing images with a high spatial frequency algorithm for enhanced edge depiction. Choosing a transverse slice to have 256 by 256 elements covering a 15 cm field of view gives each picture element (pixel) a dimension of 0.59 mm on a side. Pixel dimensions can be reduced by either decreasing the field of view or increasing the size of the reconstruction matrix to 384 by 384 or 512 by 512. Adding the slice thickness of 3 mm to the pixel dimension, each element in the 3-d image (voxel) then represents 0.003 cm3 in the 256 by 256 matrix. Making measurements directly on the high resolution volume images with knowledge of the voxel dimension, provides a method for calculating such information as vessel diameter, cross sectional area, segment length, etc.
High temporal resolution images are generated by sequentially sweeping each of the four target rings at selected points in time (gated to the subject's ECG). Each sweep of a target ring requires 50 msec followed by a 8 msec reset time, requiring a total of only 224 msec for all four targets to be swept. A collimator is used so that the x-ray beam from each target sweep spans 16 mm of the body and focuses on the two detector rings, thereby generating a pair of 8mm thick cross sections. Due to cost limitations, scanner geometry is such that x-ray beams from each of the four parallel target rings are focused on the same detector pair, resulting in slightly fanned cross sections with a 4 mm gap between each of the four slice pairs. Sweeping all four targets in conjunction with bolus contrast injection generates eight 8mm slices, providing functional data spanning 7.6 cm of the subject's body per time point.
If contrast is injected into the right side of the heart, opacified blood is pumped through the pulmonary artery into the lung parenchyma. The flow of contrast media enhanced blood into and out of the parenchyma causes a change in the tissue radiodensity (Hounsfield number) over time. Scans are taken at multiple time points to sample the "time-intensity" curves, which are then used to calculate functional data (see below). Due to the 224 msec scan time, all eight slices are not acquired at an identical point in the cardiac cycle. Since this delay remains constant, gating scans to the ECG insures that each slice is always sampled at the same location in the cardiac cycle over multiple heart beats (i.e slice pair 3 and 4 will always be at peak QRS plus 58 msec over multiple cardiac cycles). Scans at multiple time points are acquired during a single breath hold, insuring that lung volume remains constant during the scan period.
The current scanner configuration, however, has a memory limitation of 80 slices before scanning must be halted to transfer images to backup media. Incorporating this limitation into our scanning protocol, to adequately sample contrast arrival and outflow, scans are taken every heart beat for the first five or six beats and every second or third heartbeat thereafter. If all eight spatially distributed slices are desired, up to 10 time points may be sampled. In order to provide at least one baseline point for blood flow calculations (see below), bolus injection must be timed in a manner such that contrast arrives at least one heart beat after initiation of the scan sequence. Acquiring six slices at a time reduces the apex to base span of functional data but allows sampling at thirteen time points (78 slices).
(1)
where
By definition, the accumulated amount of indicator in a sample is simply the volume of the sample multiplied by the concentration of indicator in the feeding vessel (A = Ci * V). Since CT scans exhibit a linear relationship between image brightness and amount of contrast material, the amount of contrast present in a region can simply be found from the magnitude of the regional "time-intensity" curve. Thus, in terms of CT scanning:
(2)
Blood flow measurements from dynamic CT images can, therefore, be calculated by relating a regional "time-intensity" curve to the curve found in the artery supplying the tissue (i.e. pulmonary artery).
To express blood flow per gram of parenchyma (Vp), we assume the region of interest (ROI) to be composed of air and "water" (i.e. blood + parenchyma)11. Because of the disparate densities of these two components, we can use the CT gray scale values (Hounsfield number) of the images to calculate the fraction of each component in the ROI.
The water fraction, for example, can be calculated by subtracting the CT value of pure air from the mean CT value of a ROI to give a number relating to the amount (density) of parenchyma + blood present in the selected region. Because of the linear relationship between Hounsfield number and density, comparing this value to the continuum ranging from pure water (Hounsfield number = 0) to pure air (Hounsfield number = -1024) yields the percentage of the ROI which is water (parenchyma + blood).
(3)
The air fraction of an ROI, then, is simply 1.0 - "water" fraction.
Furthermore, the amount of blood present within the ROI can be computed by comparing the "time-intensity" curve of the ROI to the curve in the feeding vessel.
(4)
Subtracting this result from the water fraction leaves the percentage of the ROI which is lung parenchyma. Multiplying the percentage of parenchyma, the number of voxels in the ROI, and the voxel dimension finally yields the volume of parenchyma (Vp) in mm3 (grams).
Tools for the analysis and display of multidimensional image data sets are provided by an in house developed image analysis package dubbed VIDA21. VIDA runs on UNIX workstations and is written in the X windows environment to provide an easy to use graphical interface to the various modules comprising it.
The normal sections, oblique sections, and volume render modules of VIDA are particularly useful for the visualization and display of pulmonary structure. The "Normal Sections" module can be used to quickly display sagittal, coronal and transverse slices from a 3-d volume of the lungs, while the "Oblique Sections" module allows the user to generate slices at any angle or orientation through the lung. We use the "Volume Render" module for the 3-d display of data from the HRCT scan. Brightest voxel projections through the high resolution images allows for detailed visualization of the pulmonary vascular tree from any orientation [Figure 2].
Figure 2. Multiple views of canine pulmonary vasculature demonstrating structural detail from the HSRCCT. Top row show the lung rotating about the right to left body axis while the bottom row shows the lung rotating about the head to foot axis. The data set represents an inverted brightest voxel display following interactive editing whereby a region growing algorithm was used to isolate the non pulmonary structures and multiply them by 15%.
A module which we originally developed for evaluating the upper airway is applicable to the quantitation of vascular anatomy21. Here, we find the centroid of non-branching vessel segments through an iterative process. The user pre-processes the image such that the voxels comprising the vessel segment of interest are of a unique gray scale relative to the rest of the image. Next, a voxel within the vessel segment at the beginning and end points are identified. The algorithm which identifies the centroids of the vessel strikes a line between these two points, calculates the plane perpendicular to this line, and identifies (based on gray scale) all voxels in the plane comprising the vessel and then calculates the average vessel voxel location. This voxel location coupled with the original two end point locations are used to generate two new line segments and the process is repeated to generate four line segments, etc., up to the number of iterations specified by the user. In the end, all of the points identified through this process become the list of tube centroids. Oblique sections are calculated along the full extent of the vessel so as to always be perpendicular to the local long axis of the vessel and vessel cross sectional area is graphed. The user can point to any location along this graph to obtain a numerical output of the cross sectional area along with the full gray scale oblique cutting the vessel at that level along with an image cutting through the length of the vessel, showing the location identified in the graph. We are also currently developing an approach to following branching structures, keeping track of segment lengths, branch angles, segment cross sectional areas, and associated nomenclature which will allow for a full description of the branching structure geometry. (See S. Wood et al. this conference).
Calculating relevant physiologic data from dynamic CT scans, however, has, in the past, been limited to the tedious and time consuming task of manually sampling regions within the images and then computing the desired information. We have, therefore, developed an additional VIDA module to automate the analysis of cine x-ray CT images, thereby rapidly providing such physiologic data as regional blood flow, regional tissue, blood and air contents, mean transit times, etc. [Figure 3] This module allows for the highly flexible application of essentially any blood flow model which utilizes a single input and single output function.
Figure 3. Composite image of the blood flow module of VIDA. See text for details.
The upper left window of figure 3 shows the main panel of our program. Using this panel, dynamic CT image sets are loaded into computer memory and displayed in the bottom half of the window. Controls on this panel allow the user to view any transverse slice of the image set at any time point in the scan. An additional control allows the user to select to the size of a region to be sampled, from a one pixel by one pixel box to a twelve by twelve pixel box. Shown in the figure is a loaded cine CT data set with the sample size chosen to be a five pixel by five pixel box.
"Pointing and clicking" on an area of the displayed image produces a graph of the time-intensity curve of that region (upper middle in figure 3). Note that areas of high blood flow (i.e. pulmonary artery) show large changes in CT value (upper curve) while regions of lower flow (i.e. parenchyma) show smaller changes over time (lower curve). After designating a curve for the pulmonary artery, all subsequent "clicks" use this curve to calculate blood flow, air content, blood content, tissue content, mean transit time, arrival time, etc. for that region as described above. These calculated values are displayed in a text window for the user to view (center bottom, figure 3).
The real power of this approach, however, is enabled by selecting the "Batch" button on the main panel. Choosing this option once the pulmonary arterial curve has been designated puts the program into an automated processing mode, needing no additional user interaction. This mode works by scanning a box, of the chosen sample size, over successive, non-overlapping regions of an entire slice and calculating physiologic data for each sample, then repeating this for all slices of the data set. Samples not meeting user defined criteria (middle panel, figure 3) are rejected. Selection criteria under user control include the minimum and maximum arrival and mean transit times, minimum and maximum air, blood, and tissue fractional contents, and maximum chi squared. Criteria can easily be changed by selecting a parameter from the scrolling list and simply typing in the new value. Choosing a set of default criteria unique to the lung field limits user input to identifying the pulmonary artery, for input to the blood flow model, and selecting the "Batch" button.
The batch mode samples the image set for data collection only. The criteria are applied and the model is solved for only when the user requests the computation of a functional image. This approach allows the program to respond quickly to changes in the region selection criteria without having to gather the data all over again.
Another feature of the program is that it allows the functional image to represent ANY parameter of the model, not just flow. So it is possible to construct mean-transit-time images, peak-opacification images, regional tissue, blood, or air content images, etc.
Blood flow calculations are made from a model of the user's choosing (upper right, figure 3). Our program currently provides myocardial blood flow models of Ritman22 and Weiss23 in addition to the Wolfkeil20 model (shown selected in the panel), which we use for pulmonary perfusion calculations. Again, any blood flow model requiring a single input function and producing one output can be easily integrated into our program and added to a list of models for the user to choose. Bad time points can be ommitted from the blood flow calculations by simply "clicking" on one of the time points listed in the "Use Phases" subhding on this panel.
A text file containing the three dimensional coordinates of all accepted samples along with their calculated corresponding physiologic parameters is generated and can be saved for later analysis by any
We have used the scanning protocol and analysis described above to characterize and visualize pulmonary blood flow distribution under various conditions, including alterations in body posture, lung volume, and with the imposition of artificially created pulmonary emboli.
Figure 4. Color coded pulmonary blood flow image combining the HTRCCT and HSRCCT scans.
Figure 4 shows transverse, sagittal, and coronal slices of imbedded blood flow expressed as ml/gram parenchyma/min. Transverse images are shown looking along the feet to head axis with the spine down. The sagittal image sequence is shown with the spine to the left in each cross section, and moving from right to left in the subject. The coronal image sequence represents moving from dorsal to ventral regions of the lung, with the right side of the animal shown on the viewer's left in each coronal slice. Images were acquired from a dog in the supine position, and demonstrate a significant ventral to dorsal blood flow gradient (low blood flow is displayed in the blue end of the spectrum while the pink/white end of the scale delineates regions of highest tissue flow). Higher perfusion is seen in the gravity dependent regions of the supine lung. However, when the animal is flipped to the prone posture this gradient does not reverse, but disappears, demonstrating important non-gravitational determinants of perfusion.
In a separate experiment, we simulated a pulmonary embolus by inflating a balloon catheter in the pulmonary vasculature [Figure 5].
Figure 5. HTRCCT and HSRCCT scans used in evaluating simulated pulmonary embolus.
High spatial resolution cine CT (HSRCCT) images of the lungs were used to visually locate the position of the balloon in the lung field and identify the vessel segment distal to the inflated balloon. After locating the vessel feeding the region of flow decrement (lower right panel), we extracted an oblique section perpendicular to the vessel cross section, yielding the image shown in the lower left panel. Here one can see the balloon occlusion tip of the Swan-Ganz catheter (arrow). The balloon is also shown in cross section in the lower middle panel as identified via the plane's intersection with the oblique section in the lower left panel. A high temporal resolution cine CT (HTRCCT) slice containing the distal vessel segment was then sampled. Parenchymal time-intensity curves of the sampled regions are shown in the figure. Note that the sample encompassing the region supplied by the distal vessel segment (2) has a flat time-intensity profile as compared with region 1 (sampled at the same chest level in the contralateral lung), indicating blockage of flow in region 2. The color coded image of regional blood flow clearly shows an abnormal loss of flow, allowing for easy embolus localization by visual inspection of the color coded images. Note also the high detail visualization capabilities of HSRCCT as compared to the blurry HTRCCT image, due to partial volume effects24 from the thick slice scanning mode.
The automated, rapid calculation of physiologic data and generation of corresponding color coded images may prove useful for diagnostic radiology. A dynamic CT scan of a patient can be analyzed, quickly providing relevant functional information. Visual inspection of the corresponding color coded images provides a simple tool for rapidly detecting abnormal parameters (i.e. abnormally low or absence of blood flow in a region, uncharacteristically high local tissue or blood content, etc.) while the subject rests on the scanner bed. If a defect is suspected, a high spatial resolution scan can be focused on a particular region, to visualize the underlying anatomy, while limiting patient radiation exposure. Cine x-ray CT holds a unique position, to date, in being able to supply such a combination of structural and functional information of the lung from a single energy source.
In this paper we have described a method for using two image acquisition modes, unique to cine x-ray CT scanners, for acquiring images of pulmonary anatomy and function. By using an appropriate blood flow model, regional parenchymal time-intensity curves, sampled from dynamic CT scans of bolus contrast injection, can be used to calculate regional blood flow, regional air, blood and tissue percentages, in addition to regional contrast mean transit times and arrival times. These values are quickly provided by a customized software module we have developed to automate image analysis. User interaction with the software can be limited to designating the feeding vessel for blood flow calculations (i.e pulmonary artery). Color coded images representing any of the calculated functional data are generated at user request and can be automatically imbedded into a corresponding high resolution scan of pulmonary anatomy. Our approach to image acquisition and post-processing can be used for detailed study of pulmonary structure-function relationships under various conditions in both clinical and research environments.
Acknowledgements: This study was supported in part by NIH RO1-HL42672
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