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Background: Recent studies have shown a clear relationship between absorbed dose and tumor response to treatment after hepatic radioembolization. These findings help to create more personalized treatment planning and dosimetry. However, crucial to this goal is the ability to predict the dose distribution prior to treatment. The microsphere distribution is ultimately determined by (i) the hepatic vasculature and the resulting blood flow dynamics and (ii) the catheter position. Purpose: To show that pretreatment, intra‐procedural imaging of blood flow patterns, as quantified by catheter‐directed intra‐arterial contrast enhancement, correlate with posttreatment microsphere accumulation and, consequently, absorbed dose. Materials and methods: Patients who participated in a clinical trial (NCT01177007) and for whom both a pretreatment dual‐phase contrast‐enhanced cone‐beam CT (CBCT) and a posttreatment 90Y PET/CT scan were available were included in this retrospective study. Tumors and perfused volumes were manually delineated on the CBCT by an experienced radiologist. The mean, sum, and standard deviation of the voxels in each volume were recorded. The delineations were transferred to the PET‐based absorbed dose maps by coregistration of the corresponding CTs. Linear multiple regression was used to correlate pretreatment CBCT enhancement to posttreatment 90Y PET/CT‐based absorbed dose in each region. Leave‐one‐out cross‐validation and Bland–Altman analyses were performed on the predicted versus measured absorbed doses. Results: Nine patients, with a total of 23 tumors were included. All presented with hepatocellular carcinoma (HCC). Visually, all patients had a clear correspondence between CBCT enhancement and absorbed dose. The correlation between CBCT enhancement and posttherapy absorbed tumor dose based was strong (R2 = 0.91), and moderate for the non‐tumor liver tissue (R2 = 0.61). Limits of agreement were approximately ±55 Gray for tumor tissue. Conclusion: There is a linear relationship between pretreatment blood dynamics in HCC tumors and posttreatment absorbed dose, which, if shown to be generalizable, allows for pretreatment tumor absorbed dose prediction. [ABSTRACT FROM AUTHOR]

Background: Pediatric molecular imaging requires a balance between administering an activity that will yield sufficient diagnostic image quality while maintaining patient radiation exposure at acceptable levels. In current clinical practice, this balance is arrived at by the current North American Consensus Guidelines in which patient weight is used to recommend the administered activity (AA). Purpose: We have previously demonstrated that girth (waist circumference at the level of the kidneys) is better at equalizing image quality than patient weight for pediatric Tc‐99m DMSA renal function imaging. However, the correlation between image quality (IQ), AA, and patient girth has not been rigorously and systematically developed. In this work, we generate a series of curves showing the tradeoff between AA and IQ as a function of patient girth, providing the data for standards bodies to develop the next generation of dosing guideline for pediatric DMSA SPECT. Methods: An anthropomorphic phantom series that included variations in age (5, 10, and 15 years), gender (M, F), local body morphometry (5, 10, 50, 90, and 95th girth percentiles), and kidney size (±15% standard size), was used to generate realistic SPECT projections. A fixed and clinically challenging defect‐to‐organ volume percentage (0.49% of renal cortex value) was used to model a focal defect with zero uptake (i.e., full local loss of renal function). Task‐based IQ assessment methods were used to rigorously measure IQ in terms of renal perfusion defect detectability. This assessment was performed at multiple count levels (corresponding to various AAs) for groups of patients that had similar girths and defect sizes. Receiver‐operating characteristics (ROC) analysis was applied; the area under the ROC curve (AUC) was used as a figure‐of‐merit for task performance. Curves showing the tradeoff between AUC and AA were generated for these groups of phantoms. Results: Overall, the girth‐based dosing method suggested different amounts of AA compared to weight‐based dosing for the phantoms that had a relatively large body weight but a small girth or phantoms with relatively small bodyweight but large girth. Reductions of AA to 62.9% compared to weight‐based dosing guidelines can potentially be realized while maintaining a baseline (AUC = 0.80) IQ for certain 15‐year‐olds who have a relatively small girth and large defect size. Note that the task‐based IQ results are heavily dependent on the simulated defect size for the defect detection task and the appropriate AUC value must be decided by the physicians for this diagnostic task. These results are based purely on simulation and are subject to future clinical validation. Conclusions: The study provides simulation‐based IQ‐AA data for a girth‐based dosing method for pediatric renal SPECT, suggesting that patient waist circumference at the level of kidneys should be considered in selecting the AA needed to achieve an acceptable IQ. This data may be useful for standards bodies to develop girth‐based dosing guidelines. [ABSTRACT FROM AUTHOR]

Purpose: 90Y selective internal radiation therapy (SIRT) has become a safe and effective treatment option for liver cancer. However, segmentation of target and organ‐at‐risks is labor‐intensive and time‐consuming in 90Y SIRT planning. In this study, we developed a convolutional neural network (CNN)‐based method for automated lungs, liver, and tumor segmentation on 99mTc‐MAA SPECT/CT images for 90Y SIRT planning. Methods: 99mTc‐MAA SPECT/CT images and corresponding clinical segmentations were retrospectively collected from 56 patients who underwent 90Y SIRT. The collected data were used to train three CNN‐based segmentation algorithms for lungs, liver, and tumor segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), surface DSC, and average symmetric surface distance (ASSD). Dosimetric parameters (volume, counts, and lung shunt fraction) were measured from the segmentation results and were compared with clinical reference segmentations. Results: The evaluation results show that the method can accurately segment lungs, liver, and tumor with median [interquartile range] DSCs of 0.98 [0.97–0.98], 0.91 [0.83–0.93], and 0.85 [0.71–0.88]; surface DSCs of 0.99 [0.97–0.99], 0.86 [0.77–0.93], and 0.85 [0.62–0.93], and ASSDs of 0.91 [0.69–1.5], 4.8 [2.6‐8.4], and 4.7 [3.5–9.2] mm, respectively. Dosimetric parameters from the three segmentation networks show relationship with those from the reference segmentations. The overall segmentation took about 1 min per patient on an NVIDIA RTX‐2080Ti GPU. Conclusion: This work presents CNN‐based algorithms to segment lungs, liver, and tumor from 99mTc‐MAA SPECT/CT images. The results demonstrated the potential of the proposed CNN‐based segmentation method for assisting 90Y SIRT planning while drastically reducing operator time. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative bone single‐photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy due to its ability to better quantify activity in overlapping structures. An important element of assessing the response of bone metastasis is accurate image segmentation. However, limited by the properties of QBSPECT images, the segmentation of anatomical regions‐of‐interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partitioning a QBSPECT image into lesion, bone, and background. Methods: We present a new unsupervised segmentation loss function and its semi‐ and supervised variants for training a convolutional neural network (ConvNet). The loss functions were developed based on the objective function of the classical Fuzzy C‐means (FCM) algorithm. The first proposed loss function can be computed within the input image itself without any ground truth labels, and is thus unsupervised; the proposed supervised loss function follows the traditional paradigm of the deep learning‐based segmentation methods and leverages ground truth labels during training. The last loss function is a combination of the first and the second and includes a weighting parameter, which enables semi‐supervised segmentation using deep learning neural network. Experiments and results: We conducted a comprehensive study to compare our proposed methods with ConvNets trained using supervised, cross‐entropy and Dice loss functions, and conventional clustering methods. The Dice similarity coefficient (DSC) and several other metrics were used as figures of merit as applied to the task of delineating lesion and bone in both simulated and clinical SPECT/CT images. We experimentally demonstrated that the proposed methods yielded good segmentation results on a clinical dataset even though the training was done using realistic simulated images. On simulated SPECT/CT, the proposed unsupervised model's accuracy was greater than the conventional clustering methods while reducing computation time by 200‐fold. For the clinical QBSPECT/CT, the proposed semi‐supervised ConvNet model, trained using simulated images, produced DSCs of 0.75 and 0.74 for lesion and bone segmentation in SPECT, and a DSC of 0.79 bone segmentation of CT images. These DSCs were larger than that for standard segmentation loss functions by >0.4 for SPECT segmentation, and >0.07 for CT segmentation with P‐values <0.001 from a paired t‐test. Conclusions: A ConvNet‐based image segmentation method that uses novel loss functions was developed and evaluated. The method can operate in unsupervised, semi‐supervised, or fully‐supervised modes depending on the availability of annotated training data. The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT. The method can potentially be applied to other medical image segmentation applications. [ABSTRACT FROM AUTHOR]

Purpose: Computerized phantoms have been widely used in nuclear medicine imaging for imaging system optimization and validation. Although the existing computerized phantoms can model anatomical variations through organ and phantom scaling, they do not provide a way to fully reproduce the anatomical variations and details seen in humans. In this work, we present a novel registration‐based method for creating highly anatomically detailed computerized phantoms. We experimentally show substantially improved image similarity of the generated phantom to a patient image. Methods: We propose a deep‐learning‐based unsupervised registration method to generate a highly anatomically detailed computerized phantom by warping an XCAT phantom to a patient computed tomography (CT) scan. We implemented and evaluated the proposed method using the NURBS‐based XCAT phantom and a publicly available low‐dose CT dataset from TCIA. A rigorous tradeoff analysis between image similarity and deformation regularization was conducted to select the loss function and regularization term for the proposed method. A novel SSIM‐based unsupervised objective function was proposed. Finally, ablation studies were conducted to evaluate the performance of the proposed method (using the optimal regularization and loss function) and the current state‐of‐the‐art unsupervised registration methods. Results: The proposed method outperformed the state‐of‐the‐art registration methods, such as SyN and VoxelMorph, by more than 8%, measured by the SSIM and less than 30%, by the MSE. The phantom generated by the proposed method was highly detailed and was almost identical in appearance to a patient image. Conclusions: A deep‐learning‐based unsupervised registration method was developed to create anthropomorphic phantoms with anatomies labels that can be used as the basis for modeling organ properties. Experimental results demonstrate the effectiveness of the proposed method. The resulting anthropomorphic phantom is highly realistic. Combined with realistic simulations of the image formation process, the generated phantoms could serve in many applications of medical imaging research. [ABSTRACT FROM AUTHOR]

Purpose: Many interventional procedures aim at changing soft tissue perfusion or blood flow. One problem at present is that soft tissue perfusion and its changes cannot be assessed in an interventional suite because cone‐beam computed tomography is too slow (it takes 4–10 s per volume scan). In order to address the problem, we propose a novel method called IPEN for Intra‐operative four‐dimensional soft tissue PErfusion using a standard x‐ray system with No gantry rotation. Methods: IPEN uses two input datasets: (a) the contours and locations of three‐dimensional regions‐of‐interest (ROIs) such as arteries and sub‐sections of cancerous lesions, and (b) a series of x‐ray projection data obtained from an intra‐arterial contrast injection to contrast enhancement to wash‐out. IPEN then estimates a time‐enhancement curve (TEC) for each ROI directly from projections without reconstructing cross‐sectional images by maximizing the agreement between synthesized and measured projections with a temporal roughness penalty. When path lengths through ROIs are known for each x‐ray beam, the ROI‐specific enhancement can be accurately estimated from projections. Computer simulations are performed to assess the performance of the IPEN algorithm. Intra‐arterial contrast‐enhanced liver scans over 25 s were simulated using XCAT phantom version 2.0 with heterogeneous tissue textures and cancerous lesions. The following four sub‐studies were performed: (a) The accuracy of the estimated TECs with overlapped lesions was evaluated at various noise (dose) levels with either homogeneous or heterogeneous lesion enhancement patterns; (b) the accuracy of IPEN with inaccurate ROI contours was assessed; (c) we investigated how overlapping ROIs and noise in projections affected the accuracy of the IPEN algorithm; and (d) the accuracy of the perfusion indices was assessed. Results: The TECs estimated by IPEN were sufficiently accurate at a reference dose level with the root‐mean‐square deviation (RMSD) of 0.0027 ± 0.0001 cm−1 or 13 ± 1 Hounsfield unit (mean ± standard deviation) for the homogeneous lesion enhancement and 0.0032 ± 0.0005 cm−1 for the heterogeneous enhancement (N = 20 each). The accuracy was degraded with decreasing doses: The RMSD with homogeneous enhancement was 0.0220 ± 0.0003 cm−1 for 20% of the reference dose level. Performing 3 × 3 pixel averaging on projection data improved the RMSDs to 0.0051 ± 0.0002 cm−1 for 20% dose. When the ROI contours were inaccurate, smaller ROI contours resulted in positive biases in TECs, whereas larger ROI contours produced negative biases. The bias remained small, within ± 0.0070 cm−1, when the Sorenson–Dice coefficients (SDCs) were larger than 0.81. The RMSD of the TEC estimation was strongly associated with the condition of the problem, which can be empirically quantified using the condition number of a matrix Az that maps a vector of ROI enhancement values z to projection data and a weighted variance of projection data: a linear correlation coefficient (R) was 0.794 (P < 0.001). The perfusion index values computed from the estimated TECs agreed well with the true values (R ≥ 0.985, P < 0.0001). Conclusion: The IPEN algorithm can estimate ROI‐specific TECs with high accuracy especially when 3 × 3 pixel averaging is applied, even when lesion enhancement is heterogeneous, or ROI contours are inaccurate but the SDC is at least 0.81. [ABSTRACT FROM AUTHOR]

Introduction: The ability to determine the microscopic distribution of glass microspheres in 90Y radioembolization has important applications in post‐treatment microdosimetry and cluster analysis. Current methods are time‐intensive and labor‐intensive and thus are typically only applied to small samples. Materials and methods: A high‐resolution micro‐CT image with a voxel size of 8.74 µm was acquired of phantoms containing ~25 µm‐diameter glass microspheres embedded in tissue‐equivalent materials that were optically transparent, which allowed true microsphere locations to be determined using transmission light microscopy. A 3‐stage algorithm was developed to estimate the number and locations of microspheres in tissue regions. The stages are thresholding the CT image and discarding regions with insufficient voxels, estimating the number of microspheres in each region using the values of the detected and neighboring region voxels and estimating locations for each microsphere using the outputs of the previous two stages. Two different methods for estimating the number of microspheres in each region were derived, as were five methods for localizing microspheres. Metrics for each stage were computed, and the mean absolute error (MAE) between the dose to 72 µm voxels of the true and estimated dose maps created from the microsphere locations was used as the figure of merit for overall algorithm performance. Microsphere locations identified in the optical micrograph were used as the gold standard for the metrics of all stages. The method's utility was then demonstrated using a specimen from a human neuroendocrine tumor (NET) treated with glass 90Y microspheres. Results: The stage detecting regions containing microspheres found 100% of microspheres inside regions. The number of incorrectly detected regions without microspheres was 1.5% of the total number of regions. In stage 2, with these parameters, nearly 94% of the actual number of spheres in each region was correctly counted, and only 5% of the estimated sphere quantities in each region were false positives. The MAE between the true dose maps and dose maps estimated using the full algorithm with optimal parameter and method choices was 4.2%. A total of 5,713 glass microspheres were identified as being distributed heterogeneously in the NET specimen with a maximum tumor dose of >2500 Gy and 46% of the specimen receiving <20 Gy. Conclusions: This work developed and evaluated a method to detect and estimate the three‐dimensional locations of glass microspheres in whole tissue samples that require less manual effort than traditional methods. This method could be used to gain important insights into the heterogeneity of microsphere distributions that would be useful for improving radioembolization treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: In the current clinical practice, administered activity (AA) for pediatric molecular imaging is often based on the North American expert consensus guidelines or the European Association of Nuclear Medicine dosage card, both of which were developed based on the best clinical practice. These guidelines were not formulated using a rigorous evaluation of diagnostic image quality (IQ) relative to AA. In the guidelines, AA is determined by a weight‐based scaling of the adult AA, along with minimum and maximum AA constraints. In this study, we use task‐based IQ assessment methods to rigorously evaluate the efficacy of weight‐based scaling in equalizing IQ using a population of pediatric patients of different ages and body weights. Methods: A previously developed projection image database was used. We measured task‐based IQ, with respect to the detection of a renal functional defect at six different AA levels (AA relative to the AA obtained from the guidelines). IQ was assessed using an anthropomorphic model observer. Receiver‐operating characteristics (ROC) analysis was applied; the area under the ROC curve (AUC) served as a figure‐of‐merit for task performance. In addition, we investigated patient girth (circumference) as a potential improved predictor of the IQ. Results: The data demonstrate a monotonic and modestly saturating increase in AUC with increasing AA, indicating that defect detectability was limited by quantum noise and the effects of object variability were modest over the range of AA levels studied. The AA for a given value of the AUC increased with increasing age. The AUC vs AA plots for all the patient ages indicate that, for the current guidelines, the newborn and 10‐ and 15‐yr phantoms had similar IQ for the same AA suggested by the North American expert consensus guidelines, but the 5‐ and 1‐yr phantoms had lower IQ. The results also showed that girth has a stronger correlation with the needed AA to provide a constant AUC for 99mTc‐DMSA renal SPECT. Conclusions: The results suggest that (a) weight‐based scaling is not sufficient to equalize task‐based IQ for patients of different weights in pediatric 99mTc‐DMSA renal SPECT; and (b) patient girth should be considered instead of weight in developing new administration guidelines for pediatric patients. [ABSTRACT FROM AUTHOR]

Purpose We investigated the feasibility of using simpler methods than manual whole-organ volume-of-interest (VOI) definition to estimate the organ activity concentration in single photon emission computed tomography (SPECT) in cases where the activity in the organ can be assumed to be uniformly distributed on the scale of the voxel size. In particular, we investigated an anatomic region-of-interest (ROI) defined in a single transaxial slice, and a single sphere placed inside the organ boundaries. Methods The evaluation was carried out using Monte Carlo simulations based on patient indium 111In pentetreotide SPECT and computed tomography (CT) images. We modeled constant activity concentrations in each organ, validating this assumption by comparing the distribution of voxel values inside the organ VOIs of the simulated data with the patient data. We simulated projection data corresponding to 100, 50, and 25% of the clinical count level to study the effects of noise level due to shortened acquisition time. Images were reconstructed using a previously validated quantitative SPECT reconstruction method. The evaluation was performed in terms of the accuracy and precision of the activity concentration estimates. Results The results demonstrated that the non-uniform image intensity observed in the reconstructed images in the organs with normal uptake was consistent with uniform activity concentration in the organs on the scale of the voxel size; observed non-uniformities in image intensity were due to a combination of partial-volume effects at the boundaries of the organ, artifacts in the reconstructed image due to collimator-detector response compensation, and noise. Using an ROI defined in a single transaxial slice produced similar biases compared to the three-dimensional (3D) whole-organ VOIs, provided that the transaxial slice was near the central plane of the organ and that the pixels from the organ boundaries were not included in the ROI. Although this slice method was sensitive to noise, biases were less than 10% for all the noise levels studied. The use of spherical VOIs was more sensitive to noise. The method was more accurate for larger spheres and larger organs such as the liver in comparison to the kidneys. Biases lower than 7% were found in the liver when using large enough spheres (radius ≥ 28 mm), regardless of the position, of the VOI inside the organ even with shortened acquisition times. The biases were more position-dependent for smaller organs. Conclusions Both of the simpler methods provided suitable surrogates in terms of accuracy and precision. The results suggested that a spherical VOI was more appropriate for estimating the activity concentration in larger organs such as the liver, regardless of the position of the sphere inside the organ. Larger spheres resulted in better estimates. A single-slice ROI was more suitable for activity estimation in smaller organs such as the kidneys, providing that the transaxial slice selected was near the central plane of the organ and that voxels from the organ boundaries were excluded. Under those conditions, activity concentrations with biases lower than 5% were observed for all the studied count levels and coefficients of variation were less than 9% and 5% for the 25% and 100% count levels, respectively. [ABSTRACT FROM AUTHOR]

Purpose: Radioembolization with yttrium-90 microspheres may be optimized with patient-specific pretherapy treatment planning. Dose verification and validation of treatment planning methods require quantitative imaging of the post-therapy distribution of yttrium-90 (Y-90). Methods for quantitative imaging of Y-90 using both bremsstrahlung SPECT and PET have previously been described. The purpose of this study was to compare the two modalities quantitatively in humans. Methods: Calibration correction factors for both quantitative Y-90 bremsstrahlung SPECT and a non-time-of-flight PET system without compensation for prompt coincidences were developed by imaging three phantoms. The consistency of these calibration correction factors for the different phantoms was evaluated. Post-therapy images from both modalities were obtained from 15 patients with hepatocellular carcinoma who underwent hepatic radioembolization using Y-90 glass microspheres. Quantitative SPECT and PET images were rigidly registered and the total liver activities and activity distributions estimated for each modality were compared. The activity distributions were compared using profiles, voxel-by-voxel correlation and Bland-Altman analyses, and activity-volume histograms. Results: The mean ± standard deviation of difference in the total activity in the liver between the two modalities was 0% ± 9% (range -21%-18%). Voxel-by-voxel comparisons showed a good agreement in regions corresponding roughly to treated tumor and treated normal liver; the agreement was poorer in regions with low or no expected activity, where PET appeared to overestimate the activity. The correlation coefficients between intrahepatic voxel pairs for the two modalities ranged from 0.86 to 0.94. Cumulative activity volume histograms were in good agreement. Conclusions: These data indicate that, with appropriate reconstruction methods and measured calibration correction factors, either Y-90 SPECT/CT or Y-90 PET/CT can be used for quantitative post-therapy monitoring of Y-90 activity distribution following hepatic radioembolization. [ABSTRACT FROM AUTHOR]

Purpose: Three-dimensional (3D) dosimetry has the potential to provide better prediction of response of normal tissues and tumors and is based on 3D estimates of the activity distribution in the patient obtained from emission tomography. Dose-volume histograms (DVHs) are an important summary measure of 3D dosimetry and a widely used tool for treatment planning in radiation therapy. Accurate estimates of the radioactivity distribution in space and time are desirable for accurate 3D dosimetry. The purpose of this work was to develop and demonstrate the potential of penalized SPECT image reconstruction methods to improve DVHs estimates obtained from 3D dosimetry methods. Methods: The authors developed penalized image reconstruction methods, using maximum a posteriori (MAP) formalism, which intrinsically incorporate regularization in order to control noise and, unlike linear filters, are designed to retain sharp edges. Two priors were studied: one is a 3D hyperbolic prior, termed single-time MAP (STMAP), and the second is a 4D hyperbolic prior, termed cross-time MAP (CTMAP), using both the spatial and temporal information to control noise. The CTMAP method assumed perfect registration between the estimated activity distributions and projection datasets from the different time points. Accelerated and convergent algorithms were derived and implemented. A modified NURBS-based cardiac-torso phantom with a multicompartment kidney model and organ activities and parameters derived from clinical studies were used in a Monte Carlo simulation study to evaluate the methods. Cumulative dose-rate volume histograms (CDRVHs) and cumulative DVHs (CDVHs) obtained from the phantom and from SPECT images reconstructed with both the penalized algorithms and OS-EM were calculated and compared both qualitatively and quantitatively. The STMAP method was applied to patient data and CDRVHs obtained with STMAP and OS-EM were compared qualitatively. Results: The results showed that the penalized algorithms substantially improved the CDRVH and CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM. For example, the mean squared errors (MSEs) of the CDRVHs for the liver at 5 h postinjection obtained with CTMAP and STMAP were about 15% and 17%, respectively, of the MSEs obtained with optimally filtered OS-EM. For the CDVH estimates, the MSEs obtained with CTMAP and STMAP were about 16% and 19%, respectively, of the MSEs from OS-EM. For the kidneys and renal cortices, larger residual errors were observed for all algorithms, likely due to partial volume effects. The STMAP method showed promising qualitative results when applied to patient data. Conclusions: Penalized image reconstruction methods were developed and evaluated through a simulation study. The study showed that the MAP algorithms substantially improved CDVH estimates for large organs such as the liver compared to optimally postfiltered OS-EM reconstructions. For small organs with fine structural detail such as the kidneys, a large residual error was observed for both MAP algorithms and OS-EM. While CTMAP provided marginally better MSEs than STMAP, given the extra effort needed to handle misregistration of images at different time points in the algorithm and the potential impact of residual misregistration, 3D regularization methods, such as that used in STMAP, appear to be a more practical choice. [ABSTRACT FROM AUTHOR]

Purpose: Post-therapy quantitative 90Y bremsstrahlung single photon emission computed tomography (SPECT) has shown great potential to provide reliable activity estimates, which are essential for dose verification. Typically 90Y imaging is performed with high- or medium-energy collimators. However, the energy spectrum of 90Y bremsstrahlung photons is substantially different than typical for these collimators. In addition, dosimetry requires quantitative images, and collimators are not typically optimized for such tasks. Optimizing a collimator for 90Y imaging is both novel and potentially important. Conventional optimization methods are not appropriate for 90Y bremsstrahlung photons, which have a continuous and broad energy distribution. In this work, the authors developed a parallel-hole collimator optimization method for quantitative tasks that is particularly applicable to radionuclides with complex emission energy spectra. The authors applied the proposed method to develop an optimal collimator for quantitative 90Y bremsstrahlung SPECT in the context of microsphere radioembolization. Methods: To account for the effects of the collimator on both the bias and the variance of the activity estimates, the authors used the root mean squared error (RMSE) of the volume of interest activity estimates as the figure of merit (FOM). In the FOM, the bias due to the null space of the image formation process was taken in account. The RMSE was weighted by the inverse mass to reflect the application to dosimetry; for a different application, more relevant weighting could easily be adopted. The authors proposed a parameterization for the collimator that facilitates the incorporation of the important factors (geometric sensitivity, geometric resolution, and septal penetration fraction) determining collimator performance, while keeping the number of free parameters describing the collimator small (i.e., two parameters). To make the optimization results for quantitative 90Y bremsstrahlung SPECT more general, the authors simulated multiple tumors of various sizes in the liver. The authors realistically simulated human anatomy using a digital phantom and the image formation process using a previously validated and computationally efficient method for modeling the image-degrading effects including object scatter, attenuation, and the full collimator-detector response (CDR). The scatter kernels and CDR function tables used in the modeling method were generated using a previously validated Monte Carlo simulation code. Results: The hole length, hole diameter, and septal thickness of the obtained optimal collimator were 84, 3.5, and 1.4 mm, respectively. Compared to a commercial high-energy general-purpose collimator, the optimal collimator improved the resolution and FOM by 27% and 18%, respectively. Conclusions: The proposed collimator optimization method may be useful for improving quantitative SPECT imaging for radionuclides with complex energy spectra. The obtained optimal collimator provided a substantial improvement in quantitative performance for the microsphere radioembolization task considered. [ABSTRACT FROM AUTHOR]

Purpose: In yttrium-90 (90Y) microsphere brachytherapy (radioembolization) of unresectable liver cancer, posttherapy 90Y bremsstrahlung single photon emission computed tomography (SPECT) has been used to document the distribution of microspheres in the patient and to help predict potential side effects. The energy window used during projection acquisition can have a significant effect on image quality. Thus, using an optimal energy window is desirable. However, there has been great variability in the choice of energy window due to the continuous and broad energy distribution of 90Y bremsstrahlung photons. The area under the receiver operating characteristic curve (AUC) for the ideal observer (IO) is a widely used figure of merit (FOM) for optimizing the imaging system for detection tasks. The IO implicitly assumes a perfect model of the image formation process. However, for 90Y bremsstrahlung SPECT there can be substantial model-mismatch (i.e., difference between the actual image formation process and the model of it assumed in reconstruction), and the amount of the model-mismatch depends on the energy window. It is thus important to account for the degradation of the observer performance due to model-mismatch in the optimization of the energy window. The purpose of this paper is to optimize the energy window for 90Y bremsstrahlung SPECT for a detection task while taking into account the effects of the model-mismatch. Methods: An observer, termed the ideal observer with model-mismatch (IO-MM), has been proposed previously to account for the effects of the model-mismatch on IO performance. In this work, the AUC for the IO-MM was used as the FOM for the optimization. To provide a clinically realistic object model and imaging simulation, the authors used a background-known-statistically and signal-known-statistically task. The background was modeled as multiple compartments in the liver with activity parameters independently following a Gaussian distribution; the signal was modeled as a tumor with a Gaussian-distributed activity parameter located randomly with equal probability at one of three positions. The IO test statistics (i.e., likelihood ratios) were estimated using Markov-chain Monte Carlo methods. The authors realistically modeled human anatomy using a digital phantom code, and realistically simulated 90Y bremsstrahlung SPECT imaging with a clinical SPECT system and typical imaging parameters using a previously validated Monte Carlo bremsstrahlung simulation method. Model-mismatch was included by modeling image formation process in the calculation of IO test statistics using an analytic modeling method previously developed for quantitative 90Y bremsstrahlung SPECT. To demonstrate the effects of the model-mismatch on the detection task, the authors optimized the energy window both with and without model-mismatch included in the IO. Results: For all the energy windows, the AUC values for the IO-MM were smaller than that for the IO. The optimal windows for the IO-MM and the IO were 80-180 and 60-400 keV, respectively. Conclusions: The authors have demonstrated the degradation of the ideal performance due to model-mismatch and optimized the energy window for 90Y bremsstrahlung SPECT for detection tasks by accounting for the effects of the model-mismatch. The obtained optimal window was much narrower when taking into account the model-mismatch and similar to that obtained previously for estimation tasks. [ABSTRACT FROM AUTHOR]

Purpose: Peptide receptor radionuclide therapy (PRRT) delivers high absorbed doses to kidneys and may lead to permanent nephropathy. Reliable dosimetry of kidneys is thus critical for safe and effective PRRT. The aim of this work was to assess the feasibility of planning PRRT based on 3D radiobiological dosimetry (3D-RD) in order to optimize both the amount of activity to administer and the fractionation scheme, while limiting the absorbed dose and the biological effective dose (BED) to the renal cortex. Methods: Planar and SPECT data were available for a patient examined with 111In-DTPA-octreotide at 0.5 (planar only), 4, 24, and 48 h post-injection. Absorbed dose and BED distributions were calculated for common therapeutic radionuclides, i.e., 111In, 90Y and 177Lu, using the 3D-RD methodology. Dose-volume histograms were computed and mean absorbed doses to kidneys, renal cortices, and medullae were compared with results obtained using the MIRD schema (S-values) with the multiregion kidney dosimetry model. Two different treatment planning approaches based on (1) the fixed absorbed dose to the cortex and (2) the fixed BED to the cortex were then considered to optimize the activity to administer by varying the number of fractions. Results: Mean absorbed doses calculated with 3D-RD were in good agreement with those obtained with S-value-based SPECT dosimetry for 90Y and 177Lu. Nevertheless, for 111In, differences of 14% and 22% were found for the whole kidneys and the cortex, respectively. Moreover, the authors found that planar-based dosimetry systematically underestimates the absorbed dose in comparison with SPECT-based methods, up to 32%. Regarding the 3D-RD-based treatment planning using a fixed BED constraint to the renal cortex, the optimal number of fractions was found to be 3 or 4, depending on the radionuclide administered and the value of the fixed BED. Cumulative activities obtained using the proposed simulated treatment planning are compatible with real activities administered to patients in PRRT. Conclusions: The 3D-RD treatment planning approach based on the fixed BED was found to be the method of choice for clinical implementation in PRRT by providing realistic activity to administer and number of cycles. While dividing the activity in several cycles is important to reduce renal toxicity, the clinical outcome of fractionated PRRT should be investigated in the future. [ABSTRACT FROM AUTHOR]

Purpose: Yttrium-90 (90Y) is one of the most commonly used radionuclides in targeted radionuclide therapy (TRT). Since it decays with essentially no gamma photon emissions, surrogate radionuclides (e.g., 111In) or imaging agents (e.g., 99mTc MAA) are typically used for treatment planning. It would, however, be useful to image 90Y directly in order to confirm that the distributions measured with these other radionuclides or agents are the same as for the 90Y labeled agents. As a result, there has been a great deal of interest in quantitative imaging of 90Y bremsstrahlung photons using single photon emission computed tomography (SPECT) imaging. The continuous and broad energy distribution of bremsstrahlung photons, however, imposes substantial challenges on accurate quantification of the activity distribution. The aim of this work was to develop and evaluate an improved quantitative 90Y bremsstrahlung SPECT reconstruction method appropriate for these imaging applications. Methods: Accurate modeling of image degrading factors such as object attenuation and scatter and the collimator-detector response is essential to obtain quantitatively accurate images. All of the image degrading factors are energy dependent. Thus, the authors separated the modeling of the bremsstrahlung photons into multiple categories and energy ranges. To improve the accuracy, the authors used a bremsstrahlung energy spectrum previously estimated from experimental measurements and incorporated a model of the distance between 90Y decay location and bremsstrahlung emission location into the SIMIND code used to generate the response functions and kernels used in the model. This improved Monte Carlo bremsstrahlung simulation was validated by comparison to experimentally measured projection data of a 90Y line source. The authors validated the accuracy of the forward projection model for photons in the various categories and energy ranges using the validated Monte Carlo (MC) simulation method. The forward projection model was incorporated into an iterative ordered subsets-expectation maximization (OS-EM) reconstruction code to allow for quantitative SPECT reconstruction. The resulting code was validated using both a physical phantom experiment with spherical objects in a warm background and a realistic anatomical phantom simulation. In the physical phantom study, the authors evaluated the method in terms of quantitative accuracy of activity estimates in the spheres; in the simulation study, the authors evaluated the accuracy and precision of activity estimates from various organs and compared them to results from a previously proposed method. Results: The authors demonstrated excellent agreement between the experimental measurement and Monte Carlo simulation. In the XCAT phantom simulation, the proposed method achieved much better accuracy in the modeling (error in photon counts was -1.1 %) compared to a previously proposed method (errors were more than 20 %); the quantitative accuracy of activity estimates was excellent for all organs (errors were from -1.6 % to 11.9 %) and comparable to previously published results for 131I using the same collimator. Conclusions: The proposed 90Y bremsstrahlung SPECT reconstruction method provided very accurate estimates of organ activities, with accuracies approaching those previously observed for 131I. The method may be useful in verifying organ doses for targeted radionuclide therapy using 90Y. [ABSTRACT FROM AUTHOR]

Purpose: The radionuclide 131I has found widespread use in targeted radionuclide therapy (TRT), partly due to the fact that it emits photons that can be imaged to perform treatment planning or posttherapy dose verification as well as beta rays that are suitable for therapy. In both the treatment planning and dose verification applications, it is necessary to estimate the activity distribution in organs or tumors at several time points. In vivo estimates of the 131I activity distribution at each time point can be obtained from quantitative single-photon emission computed tomography (QSPECT) images and organ activity estimates can be obtained either from QSPECT images or quantification of planar projection data. However, in addition to the photon used for imaging, 131I decay results in emission of a number of other higher-energy photons with significant abundances. These higher-energy photons can scatter in the body, collimator, or detector and be counted in the 364 keV photopeak energy window, resulting in reduced image contrast and degraded quantitative accuracy; these photons are referred to as downscatter. The goal of this study was to develop and evaluate a model-based downscatter compensation method specifically designed for the compensation of high-energy photons emitted by 131I and detected in the imaging energy window. Methods: In the evaluation study, we used a Monte Carlo simulation (MCS) code that had previously been validated for other radionuclides. Thus, in preparation for the evaluation study, we first validated the code for 131I imaging simulation by comparison with experimental data. Next, we assessed the accuracy of the downscatter model by comparing downscatter estimates with MCS results. Finally, we combined the downscatter model with iterative reconstruction-based compensation for attenuation (A) and scatter (S) and the full (D) collimator-detector response of the 364 keV photons to form a comprehensive compensation method. We evaluated this combined method in terms of quantitative accuracy using the realistic 3D NCAT phantom and an activity distribution obtained from patient studies. We compared the accuracy of organ activity estimates in images reconstructed with and without addition of downscatter compensation from projections with and without downscatter contamination. Results: We observed that the proposed method provided substantial improvements in accuracy compared to no downscatter compensation and had accuracies comparable to reconstructions from projections without downscatter contamination. Conclusions: The results demonstrate that the proposed model-based downscatter compensation method is effective and may have a role in quantitative 131I imaging. [ABSTRACT FROM AUTHOR]

Purpose: The objective of the study was to demonstrate that, in x-ray computed tomography (CT), more than two types of materials can be effectively separated with the use of an energy resolved photon-counting detector and classification methodology. Specifically, this applies to the case when contrast agents that contain K-absorption edges in the energy range of interest are present in the object. This separation is enabled via the use of recently developed energy resolved photon-counting detectors with multiple thresholds, which allow simultaneous measurements of the x-ray attenuation at multiple energies. Methods: To demonstrate this capability, we performed simulations and physical experiments using a six-threshold energy resolved photon-counting detector. We imaged mouse-sized cylindrical phantoms filled with several soft-tissue-like and bone-like materials and with iodine-based and gadolinium-based contrast agents. The linear attenuation coefficients were reconstructed for each material in each energy window and were visualized as scatter plots between pairs of energy windows. For comparison, a dual-kVp CT was also simulated using the same phantom materials. In this case, the linear attenuation coefficients at the lower kVp were plotted against those at the higher kVp. Results: In both the simulations and the physical experiments, the contrast agents were easily separable from other soft-tissue-like and bone-like materials, thanks to the availability of the attenuation coefficient measurements at more than two energies provided by the energy resolved photon-counting detector. In the simulations, the amount of separation was observed to be proportional to the concentration of the contrast agents; however, this was not observed in the physical experiments due to limitations of the real detector system. We used the angle between pairs of attenuation coefficient vectors in either the 5-D space (for non-contrast-agent materials using energy resolved photon-counting acquisition) or a 2-D space (for contrast agents using energy resolved photon-counting acquisition and all materials using dual-kVp acquisition) as a measure of the degree of separation. Compared to dual-kVp techniques, an energy resolved detector provided a larger separation and the ability to separate different target materials using measurements acquired in different energy window pairs with a single x-ray exposure. Conclusions: We concluded that x-ray CT with an energy resolved photon-counting detector with more than two energy windows allows the separation of more than two types of materials, e.g., soft-tissue-like, bone-like, and one or more materials with K-edges in the energy range of interest. Separating material types using energy resolved photon-counting detectors has a number of advantages over dual-kVp CT in terms of the degree of separation and the number of materials that can be separated simultaneously. [ABSTRACT FROM AUTHOR]

Purpose: Recently, photon counting x-ray detectors (PCXDs) with energy discrimination capabilities have been developed for potential use in clinical computed tomography (CT) scanners. These PCXDs have great potential to improve the quality of CT images due to the absence of electronic noise and weights applied to the counts and the additional spectral information. With high count rates encountered in clinical CT, however, coincident photons are recorded as one event with a higher or lower energy due to the finite speed of the PCXD. This phenomenon is called a 'pulse pileup event' and results in both a loss of counts (called 'deadtime losses') and distortion of the recorded energy spectrum. Even though the performance of PCXDs is being improved, it is essential to develop algorithmic methods based on accurate models of the properties of detectors to compensate for these effects. To date, only one PCXD (model DXMCT-1, DxRay, Inc., Northridge, CA) has been used for clinical CT studies. The aim of that study was to evaluate the agreement between data measured by DXMCT-1 and those predicted by analytical models for the energy response, the deadtime losses, and the distorted recorded spectrum caused by pulse pileup effects. Methods: An energy calibration was performed using 99mTc (140 keV), 57Co (122 keV), and an x-ray beam obtained with four x-ray tube voltages (35, 50, 65, and 80 kVp). The DXMCT-1 was placed 150 mm from the x-ray focal spot; the count rates and the spectra were recorded at various tube current values from 10 to 500 μA for a tube voltage of 80 kVp. Using these measurements, for each pulse height comparator we estimated three parameters describing the photon energy-pulse height curve, the detector deadtime τ, a coefficient k that relates the x-ray tube current I to an incident count rate a by a=k×I, and the incident spectrum. The mean pulse shape of all comparators was acquired in a separate study and was used in the model to estimate the distorted recorded spectrum. The agreement between data measured by the DXMCT-1 and those predicted by the models was quantified by the coefficient of variation (COV), i.e., the root mean square difference divided by the mean of the measurement. Results: Photon energy versus pulse height curves calculated with an analytical model and those measured using the DXMCT-1 were in agreement within 0.2% in terms of the COV. The COV between the output count rates measured and those predicted by analytical models was 2.5% for deadtime losses of up to 60%. The COVs between spectra measured and those predicted by the detector model were within 3.7%-7.2% with deadtime losses of 19%-46%. Conclusions: It has been demonstrated that the performance of the DXMCT-1 agreed exceptionally well with the analytical models regarding the energy response, the count rate, and the recorded spectrum with pulse pileup effects. These models will be useful in developing methods to compensate for these effects in PCXD-based clinical CT systems. [ABSTRACT FROM AUTHOR]

Purpose: Recently, novel CdTe photon counting x-ray detectors (PCXDs) with energy discrimination capabilities have been developed. When such detectors are operated under a high x-ray flux, however, coincident pulses distort the recorded energy spectrum. These distortions are called pulse pileup effects. It is essential to compensate for these effects on the recorded energy spectrum in order to take full advantage of spectral information PCXDs provide. Such compensation can be achieved by incorporating a pileup model into the image reconstruction process for computed tomography, that is, as a part of the forward imaging process, and iteratively estimating either the imaged object or the line integrals using, e.g., a maximum likelihood approach. The aim of this study was to develop a new analytical pulse pileup model for both peak and tail pileup effects for nonparalyzable detectors. Methods: The model takes into account the following factors: The bipolar shape of the pulse, the distribution function of time intervals between random events, and the input probability density function of photon energies. The authors used Monte Carlo simulations to evaluate the model. Results: The recorded spectra estimated by the model were in an excellent agreement with those obtained by Monte Carlo simulations for various levels of pulse pileup effects. The coefficients of variation (i.e., the root mean square difference divided by the mean of measurements) were 5.3%–10.0% for deadtime losses of 1%–50% with a polychromatic incident x-ray spectrum. Conclusions: The proposed pulse pileup model can predict recorded spectrum with relatively good accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Quantitative estimation of in vivo organ uptake is an essential part of treatment planning for targeted radionuclide therapy. This usually involves the use of planar or SPECT scans with acquisition times chosen based more on image quality considerations rather than the minimum needed for precise quantification. In previous simulation studies at clinical count levels (185 MBq 111In), the authors observed larger variations in accuracy of organ activity estimates resulting from anatomical and uptake differences than statistical noise. This suggests that it is possible to reduce the acquisition time without substantially increasing the variation in accuracy. Methods: To test this hypothesis, the authors compared the accuracy and variation in accuracy of organ activity estimates obtained from planar and SPECT scans at various count levels. A simulated phantom population with realistic variations in anatomy and biodistribution was used to model variability in a patient population. Planar and SPECT projections were simulated using previously validated Monte Carlo simulation tools. The authors simulated the projections at count levels approximately corresponding to 1.5–30 min of total acquisition time. The projections were processed using previously described quantitative SPECT (QSPECT) and planar (QPlanar) methods. The QSPECT method was based on the OS-EM algorithm with compensations for attenuation, scatter, and collimator-detector response. The QPlanar method is based on the ML-EM algorithm using the same model-based compensation for all the image degrading effects as the QSPECT method. The volumes of interests (VOIs) were defined based on the true organ configuration in the phantoms. The errors in organ activity estimates from different count levels and processing methods were compared in terms of mean and standard deviation over the simulated phantom population. Results: There was little degradation in quantitative reliability when the acquisition time was reduced by half for the QSPECT method (the mean error changed by <1%, e.g., 0.9%–0.3%=0.6% for the spleen). The magnitude of the errors and variations in errors for large organ with high uptake were still acceptable for 1.5 min scans, even though the errors were slightly larger than those for the 30 min scans (i.e., <2% for liver, <3% for heart). The errors over the ranges of scan times studied for the QPlanar method were all within 0.3% for all organs. Conclusions: These data indicate that, for the purposes of organ activity estimation, acquisition times could be reduced at least by a factor of 2 for the QSPECT and QPlanar methods with little effect on the errors in organ activity estimates. The acquisition time can be further reduced for the QPlanar method, assuming well-registered VOIs are available and the activity distribution in organs can be treated as uniform. Although the differences in accuracy and precision were statistically significant for all the acquisition times shorter than 30 min, the magnitude of the changes in accuracy and precision were small and likely not clinically important. The reduction in SPECT acquisition time justified by this study makes the use of SPECT a more clinically practical alternative to conventional planar scanning for targeted radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

A model-based method has been previously developed to estimate and compensate for the crosstalk and downscatter contamination in simultaneous 123I/99mTc dual-isotope SPECT imaging. In this method, photon scatter in the object is modeled using the effective source scatter estimate technique. Photon interactions with the collimator-detector are estimated using precalculated Monte Carlo simulated point response functions. Two different approaches, simultaneous and alternating model-based compensations, have been proposed for iterative reconstruction-based crosstalk and downscatter contamination compensation. In this work, both model-based approaches were evaluated in the context of quantitative accuracy when imaging the dopaminergic system using both Monte Carlo simulated and experimentally acquired data. Results indicate that model-based estimates of the crosstalk and downscatter contamination in both energy windows were in good agreement with the truth for the simulated data. The effects of the contamination reduced image contrast and overestimated absolute activity in all structures by up to 66%. Compensation using both model-based approaches improved image contrast. Errors in absolute activity quantitation were also reduced to less than ±5% for most brain structures. The accuracy of striatal specific binding potentials, calculated as the ratio of activity in various striatal structures to the background, was also greatly improved after model-based compensation. In conclusion, model-based compensation of simultaneously acquired images of 99mTc and 123I labeled brain imaging agents provided image quality and quantitative accuracy that were comparable to the image without crosstalk. Both proposed compensation approaches can potentially be applied clinically, but when reconstruction time is a limiting factor, the alternating model-based compensation may be preferable. [ABSTRACT FROM AUTHOR]

The estimation of organ residence time is essential for high-dose myeloablative regimens in radioimmunotherapy (RIT). Frequently, this estimation is based on a series of simple planar scans and planar processing. The authors previously performed a simulation study which demonstrated that the accuracy of this methodology is limited compared to a hybrid planar/SPECT residence time estimation method. In this work the authors applied this hybrid method to data from a clinical trial of high-dose myeloablative yttrium-90 ibritumomab tiuxetan therapy. Image data acquired from 18 patients were comprised of planar scans at five time points ranging from 1 to 144 h postinjection and abdominal and thoracic SPECT/CT scans obtained at 24 h postinjection. The simple planar processing method used in this work was based on the geometric mean method with energy window based scatter compensation. No explicit background subtraction nor object or source thickness corrections were performed. The SPECT projections were reconstructed using iterative reconstruction with compensations for attenuation, scatter, and full collimator-detector response. Large differences were observed when residence times were estimated using the simple planar method compared to the hybrid method. The differences were not constant but varied in magnitude and sign. For the dose-limiting organ (liver), the average difference was -18% and variation in the difference was 19%, similar to the differences observed in a previously reported simulation study. The authors also looked at the relationship between the weight of the patient and the liver residence time and found that there was no meaningful correlation for either method. This indicates that weight would not be an adequate proxy for an experimental estimate of residence time when choosing the activity to administer for therapy. The authors conclude that methods such as the simple planar method used here are inadequate for RIT treatment planning. More sophisticated methods, such as the hybrid SPECT/planar method investigated here, are likely to be better predictors of organ dose and, as a result, organ toxicities. [ABSTRACT FROM AUTHOR]

Estimating organ residence times is an essential part of patient-specific dosimetry for radioimmunotherapy (RIT). Quantitative imaging methods for RIT are often evaluated using a single physical or simulated phantom but are intended to be applied clinically where there is variability in patient anatomy, biodistribution, and biokinetics. To provide a more relevant evaluation, the authors have thus developed a population of phantoms with realistic variations in these factors and applied it to the evaluation of quantitative imaging methods both to find the best method and to demonstrate the effects of these variations. Using whole body scans and SPECT/CT images, organ shapes and time-activity curves of 111In ibritumomab tiuxetan were measured in dosimetrically important organs in seven patients undergoing a high dose therapy regimen. Based on these measurements, we created a 3D NURBS-based cardiac-torso (NCAT)-based phantom population. SPECT and planar data at realistic count levels were then simulated using previously validated Monte Carlo simulation tools. The projections from the population were used to evaluate the accuracy and variation in accuracy of residence time estimation methods that used a time series of SPECT and planar scans. Quantitative SPECT (QSPECT) reconstruction methods were used that compensated for attenuation, scatter, and the collimator-detector response. Planar images were processed with a conventional (CPlanar) method that used geometric mean attenuation and triple-energy window scatter compensation and a quantitative planar (QPlanar) processing method that used model-based compensation for image degrading effects. Residence times were estimated from activity estimates made at each of five time points. The authors also evaluated hybrid methods that used CPlanar or QPlanar time-activity curves rescaled to the activity estimated from a single QSPECT image. The methods were evaluated in terms of mean relative error and standard deviation of the relative error in the residence time estimates taken over the phantom population. The mean errors in the residence time estimates over all the organs were <9.9% (pure QSPECT), <13.2% (pure QPLanar), <7.2% (hybrid QPlanar/QSPECT), <19.2% (hybrid CPlanar/QSPECT), and 7%–159% (pure CPlanar). The standard deviations of the errors for all the organs over all the phantoms were <9.9%, <11.9%, <10.8%, <22.0%, and <107.9% for the same methods, respectively. The processing methods differed both in terms of their average accuracy and the variation of the accuracy over the population of phantoms, thus demonstrating the importance of using a phantom population in evaluating quantitative imaging methods. Hybrid CPlanar/QSPECT provided improved accuracy compared to pure CPlanar and required the addition of only a single SPECT acquisition. The QPlanar or hybrid QPlanar/QSPECT methods had mean errors and standard deviations of errors that approached those of pure QSPECT while providing simplified image acquisition protocols, and thus may be more clinically practical. [ABSTRACT FROM AUTHOR]

In this work, we developed a model-based method to estimate and compensate for the crosstalk contamination in simultaneous 123I and 99mTc dual isotope brain single photo emission computed tomography imaging. The model-based crosstalk compensation (MBCC) includes detailed modeling of photon interactions inside both the object and the detector system. In the method, scatter in the object is modeled using the effective source scatter estimation technique, including contributions from all the photon emissions. The effects of the collimator-detector response, including the penetration and scatter components due to high-energy 123I photons, are modeled using pre-calculated tables of Monte Carlo simulated point-source response functions obtained from sources in air at various distances from the face of the collimator. The model-based crosstalk estimation method was combined with iterative reconstruction based compensation to reduce contamination due to crosstalk. The MBCC method was evaluated using Monte Carlo simulated and physical phantom experimentally acquired simultaneous dual-isotope data. Results showed that, for both experimental and simulation studies, the model-based method provided crosstalk estimates that were in good agreement with the true crosstalk. Compensation using MBCC improved image contrast and removed the artifacts for both Monte Carlo simulated and experimentally acquired data. The results were in good agreement with images acquired without any crosstalk contamination. [ABSTRACT FROM AUTHOR]