Section of Nuclear Medicine Department of Radiology University of Illinois Hospital, Chicago
Whole body, planar, and SPECT imaging with the indium-111 labeled monoclonal antibody capromab pendetide (indium-111 MoAb CYT-356) has been shown to dramatically increase the detection of early spread of disease in prostate cancer patients. However, at the time of delayed imaging, In-111 MoAb-CYT-356 can be seen in tumor tissue (including perivascular nodes) as well as in the vascular system. This can make metastatic tumor detection difficult especially if involved nodes are located in close proximity to blood vessels. We have developed an alternate imaging approach to the detection of metastatic prostate tumors, based on a single simultaneous In-111MoAb and Tc-99m RBCs SPECT acquisition. In this article we describe the "first" version of this method with preliminary results from twenty patients. This new method detected 59% more suspicious lesions than the routine evaluation of the In-111MoAb images alone, and led to the reversal of the negative diagnoses in three cases. Thirty one percent of these new lesions have been confirmed. Future versions of this method will include a statistical analysis of the differences of In-111 and Tc-99m images and a unique Compton scatter correction program for Tc-99m images (see APPENDIX II).
Metastatic prostate cancer in pelvic lymph nodes is a common finding. Spread of prostate cancer occurs by both hematogenous and lymphatic routes, especially along the pelvic and abdominal great vessels. It is estimated that lymphatic or extraprostatic extension of disease occurs in 40%-60% of patients with clinically localized prostate cancer (1,2). Early metastatic nodal tumors from prostate cancer are usually small (< 1 cm) and can frequently be missed with high-resolution, anatomically based imaging procedures (eg, computed tomography [CT] and magnetic resonance [MR] imaging). Nevertheless, it is important to distinguish patients with confined prostate cancer (stages A and B) from those with lymph node and/or distant spread (stages C and D), because the corresponding treatment approaches can be dramatically different. It is also important to distinguish local residual or recurrent disease in the prostatic fossa from nodal or distant metastases in treated patients with rising serum prostate-specific antigen (PSA) values.
Staging newly discovered prostate cancer or restaging patients with suspected recurrent or residual disease has been limited in the past. The ability to accurately determine the presence of extracapsular lesions in newly discovered disease by means of serum PSA levels is limited by specificity (3). Ultrasound imaging by itself is similarly limited because 50% of patients with presumed localized disease are found to have capsular penetration on pathologic review (4). Digital rectal examination, although specific, is not sensitive (5). Current high-resolution anatomic imaging procedures such as CT have poor sensitivity for detecting capsular penetration, seminal vesicle involvement, and lymph node extension. Sensitivity for CT has been reported to be in the range of 6%-30% (6). MR imaging, with an endorectal coil has been shown to slightly improve the accuracy of local staging (7,8). Therefore, in most patients lymph node status can be accurately assessed only by performing bilateral pelvic lymphadenectomy, which does not accurately assess the presence of distant disease (9). Many of the same staging limitations noted in the patient with newly discovered disease also exist for patients with suspected recurrent prostate cancer.
Recently the Food and Drug Administration (FDA) approved the In-111-labeled monoclonal antibody (MoAb) capromab pendetide (7E11-C5.3, CYT-356, or ProstaScint; Cytogen, Princeton, NJ) for staging prostate cancer. Initial clinical trials have demonstrated that this new staging procedure is particularly sensitive for detecting soft-tissue metastases often found in pelvic and extrapelvic nodes (10-12). One phase III clinical trial reported that in 152 patients with newly discovered prostate cancer who underwent surgery, capromab pendetide radioimmunoscintigraphy showed a sensitivity of 62%, specificity of 72%, and an overall accuracy of 68% (12). Another study reported that in 181 patients with suspected residual or recurrent prostate cancer following radical prostatectomy, the antibody localized in the prostatic fossa in 32 patients, in fossa and extrafossa sites in 30 patients, and in extrafossa sites in 42 patients. Thus, 72 of 181 patients had MoAb scan evidence of extrafossa disease, including 42 patients with localization to abdominal lymph nodes when all other tests had negative or equivocal results (11). In another study, capromab pendetide sensitivity, specificity, and positive and negative prognostic values for the detection of pelvic lymph node metastases were reported to be 59%, 85%, 72%, and 76%, respectively. In contrast, the overall sensitivity of CT in this population was 6%. The specificity of CT was high (100%; 108 of 108) because of the strong tendency toward negative scan interpretations (13).
The performance and interpretation of capromab pendetide radioimmunoscintigraphy can be difficult and labor intensive. The manufacturer's recommended and FDA-approved imaging procedure involves obtaining single-photon emission CT (SPECT) images of the pelvis 30 minutes after infusion of In-111 MoAb. These day 0 images display the vascular anatomy and bladder activity, which can be used when interpreting delayed images on days 4, 5, or 6. Activity on delayed images can be seen in the bone marrow, vascular structures, and tumor tissue (if present). Nonspecific accumulation of activity can also be seen in the liver, small and large bowel, and kidneys. Reconstructed 1-cm-thick SPECT sections of the pelvis obtained on days 0 and 5 are compared for the presence of activity (disease) in the prostate, prostatic fossa, and nodes. Accurate alignment of sections from these two days can be problematic. This can make metastatic tumor detection difficult, especially if involved nodes are located close to blood vessels.
In this report, we describe the imaging results from 20 patients who underwent the manufacturer's recommended imaging procedure plus an additional SPECT acquisition of the pelvis with a second isotope on day 5 (delayed imaging). We also describe the implementation and results of the first version of our computer blood pool subtraction method for detecting malignant lesions, including the mathematical equations on which the method is based. Furthermore, we illustrate the application of the method on several cases from our database, with images produced by our computer subtraction program.
All 20 patients who underwent capromab pendetide radioimmunoscintigraphy examination were part of a multicenter phase III study sponsored by Cytogen Corporation. Patients suspected of having primary (n = 6) or recurrent (n = 14) prostate cancer were studied (age range, 57-81 years; mean, 67.8 years). All patients had histologically confirmed adenocarcinoma of the prostate and elevated serum PSA values. Every patient had a negative bone scan and a negative CT or MR study of the pelvis. Patients with a second active primary malignancy or serious illnesses involving the cardiac, respiratory, renal, or hepatic organ systems or central nervous system were excluded from the study. Previous administration of a murine antibody (other than capromab pendetide) was also an exclusion criterion. Written informed consent was obtained from each patient in accordance with the guidelines established by the Institutional Research Board of the University of Illinois. Patients were asked to undergo the standard recommended imaging procedure (manufacturer's protocol) and an additional SPECT acquisition on day 5 with a second isotope. During the second SPECT acquisition on day 5, we used Tc-99m-labeled red blood cell (RBC) activity (in addition to In-111 MoAb activity) to define the vascular system and to suppress the vascular component in In-111-MoAb-based images. Accurate removal of the vascular structures requires flawless spatial coregistration of the In-111 and Tc-99m images, which is achieved during simultaneous SPECT acquisition of two isotopes (see a related analysis [14] of the propagation of the registration errors in brain SPECT imaging).
SPECT images of the pelvis were obtained at 30 minutes and 5 days after injection of 5-6 mCi (1.85-2.22 × 108 Bq) of In-111 CYT-356 MoAb. Five days after infusion was selected as the ideal time for delayed imaging on the basis of a visual evaluation of image target-to-background ratios observed on days 4, 5, and 6 in five patient studies performed before this investigation (data not included). Patients were required to take a cathartic (4 L of Colyte) on the afternoon and evening of day 4 after infusion. If significant colonic activity was seen on planar images on day 5, the imaging was discontinued and the patient was asked to take another dose of cathartic and return for imaging on day 6. On day 5 (or day of delayed imaging) a 3-mL sample of whole blood was initially collected from the patient for RBC labeling with the Ultra-Tag Kit (Mallinckrodt Medical, St Louis, Mo) procedure. During the RBC labeling procedure, whole-body images and planar images of the chest, abdomen, and pelvis were obtained. SPECT images of the pelvis (and abdomen when indicated) were then obtained with care to estimate the exact position that was used on day 0. After all recommended planar and SPECT images were obtained, the patient was infused with 15 to 20 mCi (5.55-7.4 × 108 Bq) of Tc-99m RBC to ensure high counts in the Tc-99m window for our CT-SPECT registration program (not described here). Thirty minutes after infusion of Tc-99m RBCs the patient underwent simultaneous dual-isotope SPECT imaging of the pelvis (and abdomen if indicated).
Whole-body and planar images were obtained with a large-field-of-view, single-rectangular-headed gamma camera (Sophy DSX; SMV America, Twinsburg, Ohio) interfaced with a dedicated computer. A medium-energy collimator was used, and both photopeaks of In-111 were used with 15% energy windows. Whole-body images were acquired on a 1,024 × 2,084 matrix and with a table imaging speed of 8 cm/min. SPECT pelvic images were obtained with a dedicated triple-headed camera system (Prism 3000XP; Picker International, Cleveland, Ohio) with an attached UNIX-based computer workstation (Odyssey VP; Picker International). Each camera head was equipped with a medium-energy collimator. Fifteen percent windows were used for the two In-111 photons and a 10% window for the Tc-99m photon. A narrow window of 10% was chosen for Tc-99m to decrease the contribution of Compton scatter from the In-111 photons. Visual inspection suggested that the narrower window increased the signal-to-background ratio for the Tc-99m blood pool images. (It was estimated from a Tc-99m and Tc-99m-In-111 source study that a loss of 5.5% in the total counts occurred when the window was narrowed from 15% to 10%.) On average, less than 8% of the total counts in the Tc-99m window originated from In-111 downscatter. The raw projection images were acquired on a 128 × 128 matrix with 40 steps per head and 20 seconds per stop on day 0 and 50 seconds per stop on day 5. First-order Chang attenuation correction was then applied to the filtered data set. Orthogonal images were displayed for visualization purposes.
Two sets of images were obtained for purposes of this study. Image Set 1 consisted of SPECT images of the pelvis obtained on days 0 and 5. Whole-body and planar images obtained on day 5 were also included in this data set. Only In-111-based images were collected in this imaging set. Image Set 2 consisted of dual-isotope SPECT images of the pelvis (and abdomen when indicated) obtained on day 5 and whole-body and planar images obtained on day 5 before the injection of Tc-99m-labeled RBCs. In-111- and In-111/Tc-99m-based images were collected in Image Set 2.
The basic idea of the computer processing of reconstructed SPECT In-111 and Tc-99m images seems to be quite simple: just subtract the activity in the Tc-99m image from the activity in the In-111 image. However, a number of problems emerge when one analyzes the actual data:
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1. | Can hidden metastatic tumors be visualized with a simple subtraction technique? |
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2. | If yes, is a single subtraction enough to show all suspected lesions? |
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3. | How much of the Tc-99m image should be subtracted to obtain a meaningful difference image? |
Although we deal with these problems only briefly here, we address them and the mathematical aspects of the proposed solution in detail in Appendix A. For simplicity, let us assume that the vascular activity components in both images are mutually proportional; in other words, that the vascular In-111 component is a constant fraction of the corresponding Tc-99m component. If one could find a region that exhibits only vascular uptake, this fraction would be known and a simple corresponding subtraction could be performed over all image pairs of the study. However, based on our experience from 20 cases, not only is it difficult to find a vascular region consistently, the value of the fraction for optimal separation of different tumor sites and the vascular component in the same study is not constant. To avoid these problems, the question of elusive background, and the problem of optimal normalization of intensities, we have developed a more general approach to the detection of the domains with disproportionately higher uptake of In-111 than Tc-99m. Our approach has resulted in several mutually complementary methods that are quite insensitive to errors and inconsistencies in intensity normalization. Consequently, while we normalized both In-111 and Tc-99m voxel intensities by scaling their 99.9 or 99.5 percentiles of the cumulative histogram of intensities to the same level (1,000), any reasonable linear intensity scaling that maintains the viablility of both images would yield acceptable results.
The first method, the method of dynamic subtraction, presents the results in the cine mode: a sequence of difference images is calculated by continuously increasing the amount of the subtracted Tc-99m image. This image sequence can then displayed as a forward-backward image loop, or the operator can interactively display individual difference images. Examples of such a cine display together with the original In-111 and Tc-99m images are presented in Figure 1. One can observe the disappearance of the vascular component and reappearance of the previously masked In-111 structures. By visual analysis of this cine difference image sequence, one can search for lesions that are not apparent in the original In-111 image. The operator is free to set the number and size of the subtraction steps (see the parameter A(t) in Appendix A).
The histogram blob method is based on the realization that in the two-dimensional (2D) histogram scatter plot of In-111 and Tc-99m voxel intensities, a protuberance (blob) to the right (ie, toward high In-111 intensities) of the main body represents the disproportionately high uptake of In-111 and may consequently be a tumor signature (Fig 2). The voxels generating this tumor signature can be mapped back on the original In-111 image to point out the location of the tumor (Fig 3). Not all disproportionately high uptakes of In-111 must be associated with such a protuberance; theoretically, some may be represented by "latent blobs" inside of the histogram scatter plot.
The 2D histogram projection method is an attempt for a compromise with the original intention to find the best single difference image. The constant fraction is determined by the direction of the straight line that separates the protuberance tumor signature from the rest of the histogram. As there may be more than one suspicious histogram blob, each of them may require a separation line with a different direction and consequently more than one difference image may be needed.
It must be stressed that a disproportionately high uptake of In-111 is not necessarily associated with a metastatic tumor: it may be related, for example, to bone marrow activity, and it is up to the nuclear medicine physician to interpret it correctly. The anatomic interpretation can be further aided by registration and fusion of CT/MR image data with the SPECT images. Details of this procedure are being prepared for publication.
Image Sets 1 and 2 were obtained as described in Materials and Methods. Single- and dual-isotope SPECT images from the two sets of imaging data were separated in time by 30-40 minutes. At the end of the study, single SPECT images obtained on day 0 (from Image Set 1) and Tc-99m window images from the dual-isotope SPECT images obtained on day 5 (from Image Set 2) were visually inspected and found to be essentially identical.
Data from Image Set 1 were presented as the official results of the clinical trial site to the sponsor. At the end of the study, all 20 dual-isotope SPECT imaging studies (Image Set 2) were processed on a Microsoft Windows-based personal computer with the image subtraction software (Appendix A). Image Set 2 was read by the same nuclear medicine physician (M.J.B.) 8 months after the evaluation of Image Set 1. Image Set 2 studies were read without referring to the films obtained from the manufacturer's recommended protocol. Readings based on Image Set 2 were then compared with the official results of the clinical trial (Image Set 1).
The results of the routine planar and SPECT film readings (Image Set 1) showed abnormal uptake in 14 patients and normal uptake in six patients (no evidence of disease). Two patients demonstrated evidence of prostatic fossa disease only, eight of fossa and/or pelvic nodal disease, and four of fossa, pelvic, and extrapelvic nodal disease. The results of the dual-isotope SPECT film readings (Image Set 2) showed abnormal uptake in 17 patients and normal uptake in three patients (no evidence of disease). One patient demonstrated evidence of prostatic fossa disease only, 11 of fossa and/or pelvic nodal disease, and five of fossa, pelvic, and extrapelvic disease. Lesions outside the pelvis and abdomen were detected on whole-body images included in Image Set 2. These results are presented in Table 1. Table 1 Number Of Patients and Location of Nodal Disease Detected in Image Set 1 and Image Set 2
| Location of Nodal Disease | Image Set 1 | Image Set 2 |
| No evidence of nodal disease | 6 | 3 |
| Prostatic fossa disease only | 2 | 1 |
| Fossa and pelvic regions | 8 | 11 |
| Fossa, pelvic, and extrapelvic regions | 4 | 5 |
Image Set 1 readings were compared with the results obtained from the planar and dual-isotope SPECT dynamic subtraction images (Image Set 2) and from the histogram blob analysis. The results of the comparison are as follows: seven pelvic node lesions were detected on computer-generated images obtained in three patients whose original images were visually read as normal or as showing no evidence of disease. In 14 patients, 32 lesions were found visually and 44 with the computer subtraction program. Both imaging protocols did not reveal any lesions in three patients. Table 2 lists the 51 lesions detected in 17 patients with computer-based images (Image Set 2). Table 2 Number of Patients and Location of Nodal Lesions Detected in Image Set 2
| Location | No. of Patients | Total No. of Lesions |
| Prostatic fossa only | 1 | 1 |
| Right internal iliac | 2 | 4 |
| Left internal iliac | 4 | 24 |
| Right external iliac | 2 | 4 |
| Right obturator | 1 | 1 |
| Right common iliac | 2 | 4 |
| Left common iliac | 1 | 1 |
| Periaortic region | 2 | 6 |
| Mesenteric region | 2 | 6 |
| No disease detected | 3 | 0 |
The data from Image Set 2, after processing by the computer subtraction program, increased the lesion detection rate by 62%; 31% of these lesions are presently confirmed. Clinical confirmation was accomplished with other imaging modalities, physical examination, and/or biopsy results. Two head and neck lesions and one lung lesion were confirmed at biopsy. Multiple abdominal and pelvic lesions were confirmed with CT or MR imaging findings during follow-up examinations. Abnormal uptake was detected in 17 patients, and no evidence of disease was noted in three patients.
The overall effect of the new dual-isotope SPECT imaging protocol in the 20 study patients was to reveal disease in three patients previously thought to be disease free. Three patients were found to be disease free with both imaging protocols. The same number of lesions were found with both imaging protocols in two patients. The number of pelvic and abdominal lesions detected with the dual-isotope procedure increased in 12 patients. Although the increase in the number of lesions did not change the clinical stage of these 12 patients, a better indication of the amount of metastatic disease present was obtained.
All patients are being followed up for verification of assumed positive lesions. Most unsuspected computer-detected lesions were found in the pelvic lymph nodes. Bone marrow and small and large bowel activity continued to present scan interpretation problems with both protocols. Whole-body planar imaging was found to be essential, as a left supraclavicular node in one patient and single lung lesion in two patients would not have been found with SPECT imaging of the abdomen and pelvis alone. The supraclavicular node and one lung lesion have been confirmed.
On the basis of our limited experience in 20 patients of comparing the manufacturer's recommended imaging protocol with the dual-isotope SPECT imaging and blood pool subtraction protocol, we found the latter method easier to read and interpret, and probably more sensitive. The overall clinical outcome of the new dual-isotope SPECT imaging protocol was the finding of disease in three patients previously thought to be disease free. One patient was found to have extrapelvic disease (abdominal SPECT) with the dual-isotope method that was not detected with the original protocol, and this influenced the treatment approach. Three patients were found to be disease free with both imaging protocols. The same number of suspicious pelvic nodes were found in two patients with both protocols, and the number of suspicious extraprostatic fossa nodes detected with the dual-isotope procedure increased in 12 patients. Although the increase in the number of lesions did not change the clinical stage of these 12 patients, it provided a better estimate of the amount of metastatic disease present. The improved sensitivity of this new alternative procedure is presumed, since only 31% of the "new" lesions have been confirmed. Consequently, further follow-up and a larger patient database are needed to confirm this increased sensitivity and to describe the interaction between sensitivity and specificity with this new technique.
The visual comparison of the day 0 SPECT images with the Tc-99m window images from the dual-isotope SPECT images on day 5 assured us that both sets of data were essentially the same in terms of their ability to represent the blood pool. We found in this and a previous study that it is practically impossible to reposition patients for a subsequent scan on day 5 within an accuracy of one-quarter or one-eighth of a voxel (14). It is also difficult to achieve accurately aligned images with registration processing software after data acquisition (14). Perfect registration of blood pool and delayed MoAb images can be obtained with a single simultaneous dual-isotope SPECT acquisition, with the following advantages immediately realized:
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1. | Registration problems are eliminated. |
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2. | Time required for a patient to lie still in a SPECT camera can be decreased (initial blood pool SPECT on day 0 can be eliminated). |
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3. | System throughput is doubled as day 0 imaging is eliminated. |
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4. | Patient scheduling is simplified. |
A number of problems still exist with this this new dual-isotope procedure. For example, receiver operating characteristic (ROC) analysis was not performed during this pilot study because of lack of sufficient numbers of patients, data, and comparably skilled readers/operators (ROC analysis will be applied in the later stages of this investigation). Consequently, we do not have enough information to evaluate specificity reliably. However, based on the limited data set available (we are not aware of any false-positive findings), we believe that the lost specificity is more than offset by the gained sensitivity.
A related problem is the verification of presumed lesions detected with our computer subtraction images that are not seen with the classic imaging protocol or with any other method. We found that some of the lesions detected with dual-isotope SPECT imaging of the pelvis with blood pool subtraction can be invisible to the naked eye in the original In-111 MoAb images. Complete subtraction of normal and well-defined structures such as blood vessels and urinary bladder can be dynamically observed on the computer screen. Both In-111 and Tc-99m activity can be seen in the urinary bladder, and this structure can be localized and removed from our final display by using our software. Only nodes and bone marrow that have excess In-111 counts (compared with Tc-99m) cannot be effectively subtracted. Long-term follow-up of all patients is needed and is in progress to validate presumed positive lesions.
In summary, we report the establishment of a protocol for simultaneous dual-isotope SPECT imaging of the pelvis in men with prostate cancer, utilizing In -111 MoAb CYT-356 and Tc-99m RBC on day 5 after infusion. We have also developed three methods for visualizing disproportional counts of In-111 MoAb CYT-356 (relative to Tc-99m RBCs) to aid in the detection of metastatic prostate tumor sites. Each of these methods alone provided superior results compared with routine film reading of the pelvis. While the dynamic subtraction method may be more demanding of the operator's attention, we prefer it to the single subtraction approach, as it provides more information about the consistency of the In-111 uptake and is more helpful in the detection of lesions associated with different values of the subtraction coefficient. We have found that the histogram blob method is useful for confirmation of the results established with the subtraction methods and for investigation of the importance of the disproportional In-111 MoAb uptake. Attempts to use regions of interest (ROIs), drawn to delineate vascular domains for defining a suitable subtraction coefficient, yielded inconsistent results, inferior to both the histogram blob method and the histogram projection method.
As previously mentioned, the generated images should be interpreted by an experienced nuclear medicine physician. Capromab pendetide image interpretation can be difficult. Our data suggest that correctly identified presence of focal In-111 MoAb accumulation in lymph nodes by computer analysis of SPECT data can be helpful in the diagnosis of metastatic tumors. As we have demonstrated (15), the extraction of tumor information from the simultaneously obtained images is plagued by Compton scatter spillover from In-111 into the Tc-99m window. This can be corrected by calculating the Compton image in the Tc-99m energy window and subtracting it from the acquired Tc-99m image . While we have not implemented this correction in the first software version, the planned Compton scatter correction method (16,17) is briefly described in Appendix B, and the corresponding results will be presented in future communications. Further, practically every tumor signature is contained in a cluster of voxels. Some tumors may have single-voxel intensities too low to be detected as tumor voxels individually with single-voxel statistical analysis (see no. 3 below).
On the basis of the experience obtained with the preliminary version of the techniques described above, we are working on improvements in the method. Our current developmental work is concerned mainly with the following problems:
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1. | A deeper analysis of the tumor signature (detection of latent blobs in the 2D histogram) |
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2. | The Compton scatter correction (see Appendix B) |
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3. | An analysis of clusters in dynamic difference images to estimate the statistical significance of the corresponding In-111 uptake |
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4. | Automatic and optimized search for tumor signatures |
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5. | Fusion of CT/MR pelvic image data with SPECT images |
We thank Helene Levi, RN, and Bhupendra Patel, B.S. for their conscientious and efficient management of patients and of image data acquisition. The following authors contributed to the presented work in the following way: Michael Blend, PhD, DO, initialized the project by recognizing the need for suppression of the vascular component, proposed to search for a solution by dual energy acquisition and subtraction, and while supervising the project has been responsible for the clinical part of the project, including the clinical evaluation of results. Karen Qing Lin, M.S. processed the raw SPECT data, extracted reconstructed images for PC analysis and transformed the text of the paper into HTML format. Jerry Sychra, PhD, developed the presented solutions based on the simultaneous acquisition approach, developed the corresponding algorithms and software, proposed the Compton scatter correction method, analyzed the SPECT data of 20 cases, and obtained the resulting images.
Let P and T be three-dimensional (3D) image blocks resulting from the tomographic reconstruction of SPECT image data obtained by simultaneous dual energy (In-111 and Tc-99m) pelvic scans, respectively, and let P(x) and T(x) be the corresponding voxel intensities (counts) at the location x = [x1,x2,x3].
One may propose a simple model by assuming that T(x) is proportional approximately to the vascular component of P(x); in other words, the sought-after nonvascular component Po(x) of P(x) is given by
Po(x) 
P(x) - A T(x). |
(A1) |
The coefficient A is not easy to estimate, however. First, it is difficult to reliably find a location x where Po(x) = 0 and the remaining components of Equation (A1) are large enough to calculate A directly from Equation (A1). Not even the use of an ROI drawn over a "purely vascular" part of the set {x | Po(x)=0} yields consistent estimates of A. This can be explained by A being not a constant but a function of the location x, which is also confirmed by 2D histograms H(P,T) (see below). To overcome these difficulties, we have avoided the model Equation (A1) with constant A and developed mutually complementing methods for the detection of the nonvascular component (more precisely, methods for detection of domains with disproportionately higher uptake of In-111 relative to that of Tc-99m).
The first approach is based on a visualization of a progressive dynamic subtraction of the Tc-99m image from the In-111 image. In this case the parameter A in Equation (A1) becomes a function of time, and the resulting difference image D is displayed in a cine mode:
| D(x,t) P(x)
- A (t) T(x). |
(A2) |
The method has been implemented on an IBM PC-compatible computer, under MS Windows, using the fourth generation language IDL (Interactive Data Language, Research Systems Inc.), and its execution consists of the following steps:
At start, the "raw" reconstructed image blocks P and T are read into the computer memory. Currently, two sizes of image cubes are supported: 643 or 1283 voxel cubes. Next, sagittal, coronal, and transverse integral views of the In-111 image data are displayed to enable the operator to select a tomographic slab for further analysis. For example, the integral view R in the (x,y) plane is calculated by summing voxel intensities along the z axis:
R(x,y) = 
P(x,y,z). |
(A3) |
During the following step, the operator selects the active view (sagittal, coronal, or transverse). Subsequently, the range <a,b> of the tomographic slices (the main tomographic slab) to be analyzed is determined interactively by moving ranging bars on any of the other integral views. (Optionally, this initial slab may be automatically split into four slabs of approximately the same thickness to be analyzed independently later.) In the next step the range <A1,A2> of the parameter A is requested, as well as its incremental step s; in other words, at the instance t the used value of A is
| A(t) = A1 + t s, | (A4) |
where t = 0,1, ..., 
(A2 - A1)/s. Further, to optimize
the image displays, one may select a display intensity clipping (ranges)
and a mixture of histogram modifying (including histogram equalization)
and image filtering transforms (high/low pass filters). Any of these parameters
may be selectively modified any time later, and the corresponding analyses
can be executed without reloading the input image data.
One may either step manually through the display of the 2D version of the differential image D,
| D(x,y,A) P(x,y,z)
- A T(x,y,z)
. |
(A5) |
by interactively changing A, or the dynamic differential image is displayed in an "infinite" cine loop, with A changing forward and backward on the interval <A1,A2> in agreement with Equation (A4). Examples of such a cine display, together with the original In-111 and Tc-99m images are presented in Figure 1, below. As each displayed image of the series is independently normalized intensity wise, one can observe, during the transition from pure In-111 image to a difference image with the progressively more subtracted Tc-99m component, disappearance of the vascular component and reappearance of the previously masked In-111 structures. By visual analysis of this cine image sequence one can search for lesions that are not apparent on the original In-111 image.
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Figure 1. The dynamic difference method. The first column (left column) shows Tc-99m images, the second column In-111 images, the third column dynamic (cine) difference images (In - ATc) with 10 different values of the subtraction parameter A, the fourth column the location of the slab. Each row represents a different case. The first two cases are easier to diagnose from In-111 images than the last two.
Let us assume as an instance that Equation (A1) is valid and that both In-111 and Tc-99m images contain only vascular components. In the absence of noise, the graph of the 2D histogram H(P,T) of In-111 (horizontal axis) and Tc-99m (vertical axis) intensities would be composed of segments of a straight line passing through the origin [0,0]. When a tumor or other tissues that take up only In-111 are present, they are represented as horizontal histogram extensions to the right of the line
| P - A T = 0 . | (A6) |
However, because of noise, uneven uptake of In-111 and Tc-99m in other tissues, and the fact that A is not a constant, the actual histogram graph has a "smeared" form. Further, the actual histograms suggest that the idealization (Eq [A1]) may be replaced by a piecewise "broken-upward" straight line (or by two lines):
| P - Ai T = Bi, I
= 1,2 , |
(A7) |
where the constants B1 = 0 for P < P1, and B2 = P1 (A1 - A2)/A1. P1 is the idealized uptake level of In-111 above which the true and greater vascular Tc-99m versus In-111 uptake ratio,
A2-1 =  T/P, |
(A8) |
is superimposed over the uptake ratio A1-1 of other tissues.
The tumor signature then often takes the form of a "protuberance" (blob) to the right of the main body of the histogram scatter plot (Fig 2). Once the voxels generating this signature are identified, they can be mapped back onto the original In-111 image to suggest the location of the tumor (Fig 3). The distance of the signature from the main body of the histogram is obviously associated with the magnitude of the disproportional uptake of In-111 relative to that of Tc-99m. (It is possible that a tumor may be represented by a "latent blob" that is "hidden" in the main body of the H(P,T) histogram. The development of a method for detection of these blobs is a subject of our current investigation).
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| Figure 2. Two-dimensional histogram of In-111 (blue axis) and Tc-99m (red axis) intensities (counts) for two cases. Each dot represents a voxel in the intensity space. The flashing red "protuberances" to the right of the histogram obviously represent disproportionately high uptakes of In-111 versus Tc-99m. These potential tumor signatures may be mapped back into the original images (Fig 3). | |
One must stress that a disproportionately high uptake of In-111 is not necessarily associated with a metastatic tumor site. It may be related, for example, to bone marrow, and it is up to the nuclear medicine physician to interpret the result correctly. The anatomic interpretation can be further aided by registration and fusion of CT/MR image data with the SPECT images.
Dynamic subtraction and the 2D histogram blob analysis complement and strengthen each other. However, one may insist on the use of Equation (A2) with a restriction to a single value of A (ie, to a single difference image) only. As we mentioned above, the use of ROIs drawn over purely vascular domains has yielded inconsistent estimates of A. A more consistent estimate can be obtained by fitting the straight line (Eq [A7], I = 2) to the upper left edge of the graph H(P,T). Obviously,
| A2 = - sin r/cos r ,
-90° < r < 0° ,
|
(A9) |
where r is the projection angle. The calculation (Eq [A2]) of the difference image D is then equivalent to the orthogonal projection of the histogram H(P,T) on the vector e and to the subsequent reconstruction of the difference image D from these new synthetic voxel intensities. The factor e = [cos r, sin r] is the unit vector perpendicular to the line (Eq [A7]) and aiming toward high P and low T. In other words, if a voxel x has intensity value h(x) = [P,T], the difference image D has voxel value
| D(x) = e h(x). | (A10) |
(To avoid negative pixel values, a constant shift and/or clipping of the negative values is usually employed before the actual image is calculated and displayed.)
To further increase the separation of the disproportional uptake of In-111 in the difference image from the rest, the definition of the projection vector e can be modified by taking into consideration the corresponding protuberances of the 2D histogram H(P,T). This may be simply achieved by drawing a straight line in the graph of the histogram H(P,T) to separate important portions of the protuberances from the rest of the histogram. As above, the vector e is then the unit vector perpendicular to this new line (see the red line in Fig 4).
Existing Compton scatter corrections do not address the nonstationary nature of Compton scattering in a satisfactory way. Many are based on unproven or incorrect assumptions(19) and yield results with unknown errors. An excellent review and critique of recent attempts to solve the Compton scatter correction problem was written by Buvat et al (19), with a slight bias toward the factor analysis approach (20,21).
This appendix contains a brief, nonmathematical description of a new method of Compton scatter correction. It is based on the approaches described in a more rigorous and mathematical manner in (16,17). The proposed Compton scatter correction is designed to avoid the pitfalls previously mentioned (19), especially those associated with nonstationary scatter. It will provide an estimate of the Compton image (the image that would be obtained in the Tc-99m window when In-111 is present and Tc-99m is absent from the patient's body) from data obtained in the In-111 window and optionally from a window between the In-111 and Tc-99m windows during the simultaneous acquisition. The corrected Tc-99m image will be obtained by subtraction of the Compton image from the image calculated from data obtained in the Tc-99m window during the simultaneous acquisition. The proposed method also yields an estimate of the probable error of the calculated Compton scatter correction. With improved tumor detection and delineation, the diagnosis and grading of tumors will be improved as well.
The amount of "Compton-spilled-over" photons depends not only on the imaged object but also on the acquisition geometry (collimator, crystal, light guides and photo multiplier tubes, and the distance between the object and the collimator). However, we will not be concerned with acquisition geometry and will assume that it is kept constant. Consequently, the portability of the Compton scatter correction will also not be addressed. It is also assumed that after scaling and normalization by means of a nonlinear spatial coregistration of cases (see below), the errors of Compton scatter correction caused by differences in the 3D distribution of the scatter cross section between individual patients can be neglected (such an assumption is routinely made in current attenuation correction methods).
It can be shown (16,17) that if the primary image (In-111) is a linear combination of 3D subimages, the corresponding Compton image may be approximated by the same linear combination of the corresponding Compton subimages; in other words, the Compton function (mapping of the In-111 image on the Compton image) may be viewed as a metric operator satisfying the additive property. Consequently, if the geometry of all cases in the database is the same and a studied primary image can be approximated by a linear combination of primary images of the database, then the Compton image is the same combination of the corresponding Compton images of the database. However, the pelvic geometries of cases in the real database differ. Nevertheless, after proper modifications, the idea of linear-combination image prototypes and of the corresponding Compton images may still be used to derive the Compton scatter correction.
| 1. | Let us assume that the available image database consists of a representative sample of n cases -- ie, of the set of n In-111 images, {Ii}i=1...n and of n corresponding Compton images, {Ci}i=1...n. To make studies comparable intensity wise, normalize the i-th In-111 image Ii to the same, preselected mean voxel intensity m by the factor si, i = 1...n. Scale the corresponding Compton images by the same factor si, i= 1...n. | ||||
| 2. | Select a case and coregister the rest of the 3D In-111 images of the database cases on this model image. The coregistration transform is assumed to be a nonlinear function f(ai,x), i= 1...n, where x is the location and ai is the parameter vector defining this function for the i-th case. Select the general form of the coregistration function f (e.g., a multinomial of the second order). | ||||
| 3. | Perform two separate principal component analyses (16,18)
on the image set {Ii} and image set {Ci}i=1...n,
to obtain the corresponding sets {Pi}i=1...n
and {Ri}i=1...n, respectively,
of principal component images. Each In-111 image I of the database
can then be approximated by m significant
principal component images P,
and each Compton image by h significant images R,
where vk and wk are first m and h components of the "loading" principal component eigenvectors, respectively. (The number of significant components can be determined by methods described in 18). |
||||
| 4. | The search for the Compton function can obviously be replaced
by a search for the mapping function G,
where W = [w1,w2,...,wh] and V = [v1,v2,...,vm]. The function G can be found by a back-propagation neural network trained on the available database (16). |
||||
| 5. | Once the back-propagation neural network is trained, it can be used for Compton scatter correction on image data acquisition described above (ie, when In-111 and Tc-99m image data are acquired simultaneously) as follows: |
| a) |
Normalize the In-111 image I by following steps (1 and 2) above (obtain s and a). |
| b) |
Obtain coefficient vector V of the principal component expansion of the normalized image I. |
| c) |
Feed the neural network by V,a and s to obtain W. |
| d) |
Calculate the Compton image estimate by Equation (B2). |
| e) |
Renormalize (reverse steps 1 and 2) the Compton image. |
| f) |
Subtract the Compton image from the uncorrected Tc-99m image. |
On the basis of experience with principal component analysis of other types of medical images (functional MR images, SPECT brain and cardiac planar radionuclide image sets), we expect that n = 50 is a sufficient size of the training database and that the number of significant principal components is h < m < 10. However, a larger database will increase the accuracy of the derived Compton scatter correction.
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vk Pk,
wk
Rk ,