An iterative technique to segment PET lesions using a Monte Carlo based mathematical model.

PURPOSE: The need for an accurate lesion segmentation tool in 18FDG PET is a prerequisite for the estimation of lesion response to therapy, for radionuclide dosimetry, and for the application of 18FDG PET to radiotherapy planning. In this work, the authors have developed an iterative method based on a mathematical fit deduced from Monte Carlo simulations to estimate tumor segmentation thresholds. METHODS: The GATE software, a GEANT4 based Monte Carlo tool, was used to model the GE Advance PET scanner geometry. Spheres ranging between 1 and 6 cm in diameters were simulated in a 10 cm high and 11 cm in diameter cylinder. The spheres were filled with water-equivalent density and simulated in both water and lung equivalent background. The simulations were performed with an infinite, 8/1, and 4/1 target-to-background ratio (T/B). A mathematical fit describing the correlation between the lesion volume and the corresponding optimum threshold value was then deduced through analysis of the reconstructed images. An iterative method, based on this mathematical fit, was developed to determine the optimum threshold value. The effects of the lesion volume and T/B on the threshold value were investigated. This method was evaluated experimentally using the NEMA NU2-2001 IEC phantom, the ACNP cardiac phantom, a randomly deformed aluminum can, and a spheroidal shape phantom implemented artificially in the lung, liver, and brain of patient PET images. Clinically, the algorithm was evaluated in six lesions from five patients. Clinical results were compared to CT volumes. RESULTS: This mathematical fit predicts an existing relationship between the PET lesion size and the percent of maximum activity concentration within the target volume (or threshold). It also showed a dependence of the threshold value on the T/B, which could be eliminated by background subtraction. In the phantom studies, the volumes of the segmented PET targets in the PET images were within 10% of the nominal ones. Clinically, the PET target volumes were also within 10% of those measured from CT images. CONCLUSIONS: This iterative algorithm enabled accurately segment PET lesions, independently of their contrast value.

Phase and amplitude binning for 4D-CT imaging.

We compare the consistency and accuracy of two image binning approaches used in 4D-CT imaging. One approach, phase binning (PB), assigns each breathing cycle 2pi rad, within which the images are grouped. In amplitude binning (AB), the images are assigned bins according to the breathing signal's full amplitude. To quantitate both approaches we used a NEMA NU2-2001 IEC phantom oscillating in the axial direction and at random frequencies and amplitudes, approximately simulating a patient's breathing. 4D-CT images were obtained using a four-slice GE Lightspeed CT scanner operating in cine mode. We define consistency error as a measure of ability to correctly bin over repeated cycles in the same field of view. Average consistency error mue+/-sigmae in PB ranged from 18%+/-20% to 30%+/-35%, while in AB the error ranged from 11%+/-14% to 20%+/-24%. In PB nearly all bins contained sphere slices. AB was more accurate, revealing empty bins where no sphere slices existed. As a proof of principle, we present examples of two non-small cell lung carcinoma patients' 4D-CT lung images binned by both approaches. While AB can lead to gaps in the coronal images, depending on the patient's breathing pattern, PB exhibits no gaps but suffers visible artifacts due to misbinning, yielding images that cover a relatively large amplitude range. AB was more consistent, though often resulted in gaps when no data existed due to patients' breathing pattern. We conclude AB is more accurate than PB. This has important consequences to treatment planning and diagnosis.

Evaluation of an automated deformable image matching method for quantifying lung motion in respiration-correlated CT images.

We have evaluated an automated registration procedure for predicting tumor and lung deformation based on CT images of the thorax obtained at different respiration phases. The method uses a viscous fluid model of tissue deformation to map voxels from one CT dataset to another. To validate the deformable matching algorithm we used a respiration-correlated CT protocol to acquire images at different phases of the respiratory cycle for six patients with nonsmall cell lung carcinoma. The position and shape of the deformable gross tumor volumes (GTV) at the end-inhale (EI) phase predicted by the algorithm was compared to those drawn by four observers. To minimize interobserver differences, all observers used the contours drawn by a single observer at end-exhale (EE) phase as a guideline to outline GTV contours at EI. The differences between model-predicted and observer-drawn GTV surfaces at EI, as well as differences between structures delineated by observers at EI (interobserver variations) were evaluated using a contour comparison algorithm written for this purpose, which determined the distance between the two surfaces along different directions. The mean and 90% confidence interval for model-predicted versus observer-drawn GTV surface differences over all patients and all directions were 2.6 and 5.1 mm, respectively, whereas the mean and 90% confidence interval for interobserver differences were 2.1 and 3.7 mm. We have also evaluated the algorithm's ability to predict normal tissue deformations by examining the three-dimensional (3-D) vector displacement of 41 landmarks placed by each observer at bronchial and vascular branch points in the lung between the EE and EI image sets (mean and 90% confidence interval displacements of 11.7 and 25.1 mm, respectively). The mean and 90% confidence interval discrepancy between model-predicted and observer-determined landmark displacements over all patients were 2.9 and 7.3 mm, whereas interobserver discrepancies were 2.8 and 6.0 mm. Paired t tests indicate no significant statistical differences between model predicted and observer drawn structures. We conclude that the accuracy of the algorithm to map lung anatomy in CT images at different respiratory phases is comparable to the variability in manual delineation. This method has therefore the potential for predicting and quantifying respiration-induced tumor motion in the lung.

Quantitation of respiratory motion during 4D-PET/CT acquisition.

We report on the variability of the respiratory motion during 4D-PET/CT acquisition. The respiratory motion for five lung cancer patients was monitored by tracking external markers placed on the abdomen. CT data were acquired over an entire respiratory cycle at each couch position. The x-ray tube status was recorded by the tracking system, for retrospective sorting of the CT data as a function of respiration phase. Each respiratory cycle was sampled in ten equal bins. 4D-PET data were acquired in gated mode, where each breathing cycle was divided into ten 500 ms bins. For both CT and PET acquisition, patients received audio prompting to regularize breathing. The 4D-CT and 4D-PET data were then correlated according to their respiratory phases. The respiratory periods, and average amplitude within each phase bin, acquired in both modality sessions were then analyzed. The average respiratory motion period during 4D-CT was within 18% from that in the 4D-PET sessions. This would reflect up to 1.8% fluctuation in the duration of each 4D-CT bin. This small uncertainty enabled good correlation between CT and PET data, on a phase-to-phase basis. Comparison of the average-amplitude within the respiration trace, between 4D-CT and 4D- PET, on a bin-by-bin basis show a maximum deviation of approximately 15%. This study has proved the feasibility of performing 4D-PET/CT acquisition. Respiratory motion was in most cases consistent between PET and CT sessions, thereby improving both the attenuation correction of PET images, and co-registration of PET and CT images. On the other hand, in two patients, there was an increased partial irregularity in their breathing motion, which would prevent accurately correlating the corresponding PET and CT images.

Four-dimensional (4D) PET/CT imaging of the thorax.

We have reported in our previous studies on the methodology, and feasibility of 4D-PET (Gated PET) acquisition, to reduce respiratory motion artifact in PET imaging of the thorax. In this study, we expand our investigation to address the problem of respiration motion in PET/CT imaging. The respiratory motion of four lung cancer patients were monitored by tracking external markers placed on the thorax. A 4D-CT acquisition was performed using a "step-and-shoot" technique, in which computed tomography (CT) projection data were acquired over a complete respiratory cycle at each couch position. The period of each CT acquisition segment was time stamped with an "x-ray ON" signal, which was recorded by the tracking system. 4D-CT data were then sorted into 10 groups, according to their corresponding phase of the breathing cycle. 4D-PET data were acquired in the gated mode, where each breathing cycle was divided into ten 0.5 s bins. For both CT and PET acquisitions, patients received audio prompting to regularize breathing. The 4D-CT and 4D-PET data were then correlated according to respiratory phase. The effect of 4D acquisition on improving the co-registration of PET and CT images, reducing motion smearing, and consequently increase the quantitation of the SUV, were investigated. Also, quantitation of the tumor motions in PET, and CT, were studied and compared. 4D-PET with matching phase 4D-CTAC showed an improved accuracy in PET-CT image co-registration of up to 41%, compared to measurements from 4D-PET with clinical-CTAC. Gating PET data in correlation with respiratory motion reduced motion-induced smearing, thereby decreasing the observed tumor volume, by as much as 43%. 4D-PET lesions volumes showed a maximum deviation of 19% between clinical CT and phase- matched 4D-CT attenuation corrected PET images. In CT, 4D acquisition resulted in increasing the tumor volume in two patients by up to 79%, and decreasing it in the other two by up to 35%. Consequently, these corrections have yielded an increase in the measured SUV by up to 16% over the clinical measured SUV, and 36% over SUV's measured in 4D-PET with clinical-CT Attenuation Correction (CTAC) SUV's. Quantitation of the maximum tumor motion amplitude, using 4D-PET and 4D-CT, showed up to 30% discrepancy between the two modalities. We have shown that 4D PET/CT is clinically a feasible method, to correct for respiratory motion artifacts in PET/CT imaging of the thorax. 4D PET/CT acquisition can reduce smearing, improve the accuracy in PET-CT co-registration, and increase the measured SUV. This should result in an improved tumor assessment for patients with lung malignancies.

Effect of respiratory gating on reducing lung motion artifacts in PET imaging of lung cancer.

Positron emission tomography (PET) has shown an increase in both sensitivity and specificity over computed tomography (CT) in lung cancer. However, motion artifacts in the 18F fluorodioxydoglucose (FDG) PET images caused by respiration persists to be an important factor in degrading PET image quality and quantification. Motion artifacts lead to two major effects: First, it affects the accuracy of quantitation, producing a reduction of the measured standard uptake value (SUV). Second, the apparent lesion volume is overestimated. Both impact upon the usage of PET images for radiation treatment planning. The first affects the visibility, or contrast, of the lesion. The second results in an increase in the planning target volume, and consequently a greater radiation dose to the normal tissues. One way to compensate for this effect is by applying a multiple-frame capture technique. The PET data are then acquired in synchronization with the respiratory motion. Reduction in smearing due to gating was investigated in both phantoms and patient studies. Phantom studies showed a dependence of the reduction in smearing on the lesion size, the motion amplitude, and the number of bins used for data acquisition. These studies also showed an improvement in the target-to-background ratio, and a more accurate measurement of the SUV. When applied to one patient, respiratory gating showed a 28% reduction in the total lesion volume, and a 56.5% increase in the SUV. This study was conducted as a proof of principle that a gating technique can effectively reduce motion artifacts in PET image acquisition.

Standardized uptake value in pediatric patients: an investigation to determine the optimum measurement parameter.

Although the standardized uptake value (SUV) is currently used in fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) imaging, concerns have been raised over its accuracy and clinical relevance. Dependence of the SUV on body weight has been observed in adults and this should be of concern in the pediatric population, since there are significant body changes during childhood. The aim of the present study was to compare SUV measurements based on body weight, body surface area and lean body mass in the pediatric population and to determine a more reliable parameter across all ages. Sixty-eight pediatric FDG-PET studies were evaluated. Age ranged from 2 to 17 years and weight from 11 to 77 kg. Regions of interest were drawn at the liver for physiologic comparison and at FDG-avid malignant lesions. SUV based on body weight (SUV(bw)) varied across different weights, a phenomenon less evident when body surface area (SUV(bsa)) normalization is applied. Lean body mass-based SUV (SUV(lbm)) also showed a positive correlation with weight, which again was less evident when normalized to bsa (SUV(bsa-lbm)). The measured liver SUV(bw) was 1.1+/-0.3, a much lower value than in our adult population (1.9+/-0.3). The liver SUV(bsa) was 7.3+/-1.3. The tumor sites had an SUV(bw) of 4.0+/-2.7 and an SUV(bsa) of 25.9+/-15.4 (65% of the patients had neuroblastoma). The bsa-based SUVs were more constant across the pediatric ages and were less dependent on body weight than the SUV(bw). These results indicate that SUV calculated on the basis of body surface area is a more uniform parameter than SUV based on body weight in pediatric patients and is probably the most appropriate approach for the follow-up of these patients.

FDG-PET standardized uptake values in normal anatomical structures using iterative reconstruction segmented attenuation correction and filtered back-projection.

Filtered back-projection (FBP) is the most commonly used reconstruction method for PET images, which are usually noisy. The iterative reconstruction segmented attenuation correction (IRSAC) algorithm improves image quality without reducing image resolution. The standardized uptake value (SUV) is the most clinically utilized quantitative parameter of [fluorine-18]fluoro-2-deoxy-D-glucose (FDG) accumulation. The objective of this study was to obtain a table of SUVs for several normal anatomical structures from both routinely used FBP and IRSAC reconstructed images and to compare the data obtained with both methods. Twenty whole-body PET scans performed in consecutive patients with proven or suspected non-small cell lung cancer were retrospectively analyzed. Images were processed using both IRSAC and FBP algorithms. Nonquantitative or gaussian filters were used to smooth the transmission scan when using FBP or IRSAC algorithms, respectively. A phantom study was performed to evaluate the effect of different filters on SUV. Maximum and average SUVs (SUVmax and SUVavg) were calculated in 28 normal anatomical structures and in one pathological site. The phantom study showed that the use of a nonquantitative smoothing filter in the transmission scan results in a less accurate quantification and in a 20% underestimation of the actual measurement. Most anatomical structures were identified in all patients using the IRSAC images. On average, SUVavg and SUVmax measured on IRSAC images using a gaussian filter in the transmission scan were respectively 20% and 8% higher than the SUVs calculated from conventional FBP images. Scatterplots of the data values showed an overall strong relationship between IRSAC and FBP SUVs. Individual scatterplots of each site demonstrated a weaker relationship for lower SUVs and for SUVmax than for higher SUVs and SUVavg. A set of reference values was obtained for SUVmax and SUVavg of normal anatomical structures, calculated with both IRSAC and FBP image reconstruction algorithms. The use of IRSAC and a gaussian filter for the transmission scan seems to give more accurate SUVs than are obtained from conventional FBP images using a nonquantitative filter for the transmission scan.

Use of PET to monitor the response of lung cancer to radiation treatment.

Approximately 170,000 people are diagnosed with lung cancer in the United States each year. Many of these patients receive external beam radiation for treatment. Fluorine-18 2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is increasingly being used in evaluating non-small cell lung cancer and may be of clinical utility in assessing response to treatment. In this report, we present FDG PET images and data from two patients who were followed with a total of eight and seven serial FDG PET scans, respectively, through the entire course of their radiation therapy. Changes in several potential response parameters are shown versus time, including lesion volume (V(FDG)) by PET, SUVav, SUVmax, and total lesion glycolysis (TLG) during the course of radiotherapy. The response parameters for patient 1 demonstrated a progressive decrease; however, the response parameters for patient 2 showed an initial decrease followed by an increase. The data presented here may suggest that the outcome of radiation therapy can be predicted by PET imaging, but this observation requires a study of additional patients.

Pharmacokinetics and dosimetry of an alpha-particle emitter labeled antibody: 213Bi-HuM195 (anti-CD33) in patients with leukemia.

UNLABELLED: Data from nine patients with leukemia participating in a phase I activity-escalation study of HuM195, labeled with the alpha-particle emitter 213Bi (half-life = 45.6 min), were used to estimate pharmacokinetics and dosimetry. This is the first trial using an alpha-particle emitter in humans. The linear energy transfer of alpha particles is several hundredfold greater than that of beta emissions. The range in tissue is approximately 60-90 microm. METHODS: The activity administered to patients ranged from 0.6 to 1.6 GBq. Patient imaging was initiated at the start of each injection. Thirty 1-min images followed by ten 3-min images were collected in dynamic mode; a 20% photopeak window centered at 440 keV was used. Blood samples were collected until 3 h postinjection and counted in a gamma counter. Contours around the liver and spleen were drawn on the anterior and posterior views and around a portion of the spine on the posterior views. No other organs were visualized. RESULTS: The percentage injected dose in the liver and spleen volumes increased rapidly over the first 10-15 min to a constant value for the remaining hour of imaging, yielding a very rapid uptake followed by a plateau in the antibody uptake curves. The kinetic curves were integrated to yield cumulated activity. The mean energy emitted per nuclear transition for 213Bi and its daughters, adjusted by a relative biologic effectiveness of 5 for alpha emissions, was multiplied by the cumulated activity to yield the absorbed dose equivalent. Photon dose to the total body was determined by calculating a photon-absorbed fraction. The absorbed dose equivalent to liver and spleen volumes ranged from 2.4 to 11.2 and 2.9 to 21.9 Sv, respectively. Marrow (or leukemia) mean dose ranged from 6.6 to 12.2 Sv. The total-body dose (photons only) ranged from 2.2 x 10(-4) to 5.8 x 10(-4) Gy. CONCLUSION: This study shows that patient imaging of 213Bi, an alpha-particle emitter, labeled to HuM195 is possible and may be used to derive pharmacokinetics and dosimetry. The absorbed dose ratio between marrow, liver and spleen volumes and the whole body for 213Bi-HuM195 is 1000-fold greater than that commonly observed with beta-emitting radionuclides used for radioimmunotherapy.

Use of the fast Hartley transform for three-dimensional dose calculation in radionuclide therapy.

Effective radioimmunotherapy may depend on a priori knowledge of the radiation absorbed dose distribution obtained by trace imaging activities administered to a patient before treatment. A new, fast, and effective treatment planning approach is developed to deal with a heterogeneous activity distribution. Calculation of the three-dimensional absorbed dose distribution requires convolution of a cumulated activity distribution matrix with a point-source kernel; both are represented by large matrices (64 x 64 x 64). To reduce the computation time required for these calculations, an implementation of convolution using three-dimensional (3-D) fast Hartley transform (FHT) is realized. Using the 3-D FHT convolution, absorbed dose calculation time was reduced over 1000 times. With this system, fast and accurate absorbed dose calculations are possible in radioimmunotherapy. This approach was validated in simple geometries and then was used to calculate the absorbed dose distribution for a patient's tumor and a bone marrow sample.

Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding.

BACKGROUND: It is common protocol in radionuclide therapies to administer a tracer dose of a radiopharmaceutical, determine its lesion uptake and biodistribution by gamma imaging, and then use this information to determine the most effective therapeutic dose. This treatment planning approach can be used to quantitate accurately the activity and volume of lesions and organs with positron emission tomography (PET). In this article, the authors focus on the specification of appropriate volumes of interest (VoI) using PET in association with computed tomography (CT). METHODS: The authors have developed an automatic image segmentation schema to determine the VoI of metastases to the lung from PET images, under conditions of variable background activity. An elliptical Jaszczak phantom containing a set of spheres with volumes ranging from 0.4 to 5.5 mL was filled with F-18 activity (2-3 microCi/mL) corresponding to activities clinically observed in lung lesions. Images were acquired with a cold background and then with variable source-to-background (S/B) ratios of: 7.4, 5.5, 3.1, and 2.8. Lesion VoI analysis was performed on 10 patients with 17 primary or metastatic lung lesions, applying the optimum threshold values derived from the phantom experiments. Initial volume estimates for lung lesions were determined from CT images. Approximate S/B ratios were obtained for the corresponding lesions on F-18-fluoro-2-deoxy-D-glucose (18FDG)-PET images. From the CT estimate of the lesion size and the PET estimate of the S/B ratio, the appropriate optimum threshold could be chosen. The threshold was applied to the PET images to obtain lesion activity and a final estimate of the lesion volume. RESULTS: Phantom data analysis showed that image segmentation converged to a fixed threshold value (from 36% to 44%) for sphere volumes larger than 4 mL, with the exact value depending on the S/B ratios. For patients, the use of optimum threshold schema demonstrated a good correlation (r = 0.999) between the initial volume from CT and the final volume derived from the 18FDG-PET scan (P < 0.02). The mean difference for those volumes was 8.4%. CONCLUSIONS: The adaptive thresholding method applied to PET scans enables the definition of tumor VoI, which hopefully leads to accurate tumor dosimetry. This method can also be applied to small lesions (<4 mL). It should enable physicians to track objectively changes in disease status that could otherwise be obscured by the uncertainties in the region-of-interest drawing, even when the scans are delineated by the same physician.

Quantitative bone metastases analysis based on image segmentation.

UNLABELLED: Preliminary evidence indicates that the fraction of bone containing metastatic lesions is a strong prognostic indicator of survival longevity for prostate and breast cancer. Our current approach to quantify metastatic bone lesions, called the Bone Scan Index, is based on an inspection of the bone scan, estimating visually the fraction of each bone involved and then summing across all bones to determine the percentage of total skeletal involvement. This approach, however, is time consuming, subjective and dependent on individual interpretation. METHODS: To overcome these problems, a semiautomated image segmentation program was developed for the quantitation of metastases from planar whole-body bone scans. The user is required to insert a seed point into each metastatic region on the image. The algorithm then connects pixels to the seed pixel in all directions until a contrast-dependent threshold is reached. The optimal threshold for cessation of the region growing is determined from phantom studies. On the images, lesion delineation and size measurements were performed by the algorithm. Each delineated lesion is associated with a bone site using pull-down menus. The program then computes the fraction of lesion involvement in each bone based on look-up-tables containing the relationship of bone mass with race, sex, height and age. These look-up-tables were obtained by multiple regression of the skeletal mass measurements in humans. The total fraction of skeletal involvement is then obtained from the individual fractional masses. For individual fractional mass, values given in International Commission on Radiation Protection Publication No. 23 were used. RESULTS: The bone metastases analysis system has been used on 11 scans from 6 patients. The correlation was high (r = 0.83) between conventional (manually drawn region-of-interest) and this analysis system. Bone metastases analysis results in consistently lower estimates of fractional involvement in bone compared with the conventional region-of-interest drawing or visual estimation method. This is due to the apparent broadening of objects at and below the limits of resolution of the gamma camera. CONCLUSION: Image segmentation reduces the delineation and quantitation time of lesions by at least two compared with manual region-of-interest drawing. The objectivity of this technique allows the detection of small variations in follow-up patient scans for which the manual region-of-interest method may fail, due to performance variability of the user. This method preserves the diagnostic skills of the nuclear medicine physician to select which bony structures contain lesions, yet combines it with an objective delineation of the lesion.

Treatment planning for radio-immunotherapy.

To foster the success of clinical trials in radio-immunotherapy (RIT), one needs to determine (i) the quantity and spatial distribution of the administered radionuclide carrier in the patient over time, (ii) the absorbed dose in the tumour sites and critical organs based on this distribution and (iii) the volume of tumour mass(es) and normal organs from computerized tomography or magnetic resonance imaging and appropriately correlated with nuclear medicine imaging techniques (such as planar, single-photon emission computerized tomography or positron-emission tomography). Treatment planning for RIT has become an important tool in predicting the relative benefit of therapy based on individualized dosimetry as derived from diagnostic, pre-therapy administration of the radiolabelled antibody. This allows the investigator to pre-select those patients who have 'favorable' dosimetry characteristics (high time-averaged target: non-target ratios) so that the chances for treatment success may be more accurately quantified before placing the patient at risk for treatment-related organ toxicities. The future prospects for RIT treatment planning may yield a more accurate correlation of response and critical organ toxicity with computed absorbed dose, and the compilation of dose-volume histogram information for tumour(s) and normal organ(s) such that computing tumour control probabilities and normal tissue complication probabilities becomes possible for heterogeneous distributions of the radiolabelled antibody. Additionally, radiobiological consequences of depositing absorbed doses from exponentially decaying sources must be factored into the interpretation when trying to compute the effects of standard external beam isodose display patterns combined with those associated with RIT.

Threshold estimation in single photon emission computed tomography and planar imaging for clinical radioimmunotherapy.

Thresholding is the most widely used organ or tumor segmentation technique used in single photon emission computed tomography (SPECT) and planar imaging for monoclonal antibodies. Selecting the optimal threshold requires a priori knowledge (volumes from CT or magnetic resonance) for the size and contrast level of the organ in question. Failure to select an optimal threshold leads to overestimation or underestimation of the volume and, subsequently, the organ-absorbed dose value in radio-immunotherapy. To investigate this threshold selection problem, we performed a phantom experiment using six lucite spheres ranging from 1 to 117 ml and filled with a uniform activity of 1 microCi/ml Tc-99m. These spheres were placed at the center and off-center locations of a Jasczsak phantom and scanned with a three-headed gamma camera in SPECT and planar modes. Target-nontarget (T:NT) ratios were changed by adding the appropriate activity to the background. A threshold search algorithm with an interpolative background correction was applied to sphere images. This algorithm selects a threshold that minimizes the difference between the true and measured volumes (SPECT) or areas (planar). It was found that for spheres equal to or larger than 20 ml [diameter (D) > 38 mm] and T:NT ratios higher than 5:1, mean thresholds at 42% for SPECT and 38% for planar imaging yielded minimum image segmentation errors, which is in agreement with current literature. However, for small T:NT ratios (< 5:1), the threshold values as high as 71% for SPECT and 85% for planar imaging were substantially different than those fixed thresholds for large spheres (D > 38 mm). Hence, the use of fixed thresholds in low contrasts and with tumor and organ sizes of clinical interest (25 < or = D < or = 50 mm) may result in limited volume estimation accuracy. Therefore, we have provided the investigator a method to obtain the threshold values in which the proper threshold can be selected based on the organ and tumor size and image contrast. By measuring and calibrating the proper threshold value derived through machine-specific phantom measurements, a more accurate volume and activity quantitation can be performed. This, in turn, will provide tumor-absorbed dose optimization and greater accuracy in the measurement of potentially subacute, toxic absorbed doses to normal organs for patients undergoing radioimmunotherapy.

A new fiducial alignment system to overlay abdominal computed tomography or magnetic resonance anatomical images with radiolabeled antibody single-photon emission computed tomographic scans.

BACKGROUND: The use of computed tomography (CT) or magnetic resonance (MR) to overlay or register uptake patterns displayed by single-photon emission computed tomography (SPECT) with specific underlying anatomy has the potential to improve image interpretation and decrease diagnostic reading errors. The authors have developed a method that will allow the selection of a region of interest on MR or CT images that correlates with SPECT antibody images from the same patient. This method was validated first in phantom studies and subsequently was used on three patients with suspected colorectal carcinoma. METHODS: Two patients were injected with the technetium-99m-labeled 88BV59 immunoglobulin G human antibody, and the third patient was injected with the iodine-131-labeled 16.88 immunoglobulin M human antibody. CT or MR scans were obtained before antibody infusion, and subsequent SPECT scans were obtained on the first or fourth day after infusion. A customized body cast with landmarks was used for each patient during the CT, MR, and SPECT scans to match slice positions for all scanning modalities. Corresponding fiducial landmarks were identified on axial images. A computer graphics program was written to match and overlay corresponding landmarks for each imaging modality. The image registration accuracy was measured by comparing fiducial marker separations (center to center) on the registered scans. This separation uncertainty was 1-2 mm for CT-MR and 3-4 mm for CT-SPECT phantom studies. RESULTS: For patient studies, the fiducial alignment uncertainty was 3-4 mm for axial CT-SPECT and MR-SPECT images, and 6-8 mm for sagittal CT-SPECT and MR-SPECT images. The accuracy of the anatomic alignment of the patient and image registration system was +/- 1 cm in the medial-lateral axis and +/- 2 cm in the cranial-caudal direction. CONCLUSIONS: This type of image analysis may resolve uncertainties with the anatomic correlation of SPECT images that otherwise may be regarded as questionable when SPECT is used alone for radioimmunodiagnosis.

Tumor activity confirmation and isodose curve display for patients receiving iodine-131-labeled 16.88 human monoclonal antibody.

A study was performed to correlate activity quantitation derived from external imaging with surgical tumor specimens in patients who received radiolabeled monoclonal antibody. Patients were given I-131 labeled 16.88 human antibody and scanned 3-5 times by planar and/or single photon emission computed tomography imaging methods to acquire time-dependent activity data in tumor and normal tissues. A method also was developed to assess the heterogeneous activity distributions in tumor samples. Postsurgical tumor and normal tissue samples were subdivided into volume elements (voxels) of 0.5 cm x 0.5 cm x 0.05 cm thick, which were used to verify the activity quantitation computed by the conjugate view method and to appraise the heterogeneity of radiolabeled antibody uptake. Through the use of the measured voxel activities, along with the time-dependent activity curves available for the entire tumor specimen derived from imaging, the cumulated activity and absorbed dose for each voxel were uniquely determined. The calculated total absorbed dose values were color-coded as isodose curves and overlaid on a correlated computed tomographic image. In two patients, activity quantitation derived from external imaging correlated with surgical tumor resection specimens within +/- 11%. The tumor-absorbed dose heterogeneity ratio was found to be as high as 10:1, with an average tumor to whole body absorbed dose ratio of 4:1. The mapping of activity with a histologic overlay showed a good correlation among activity uptake, the presence of tumor, and antigen expression on a microscopic scale. The resultant isodose curves overlaid on correlative computed tomographic scans represent the first images obtained with actual radiolabeled antibody biodistribution data in patients.