Diagnosis of Parkinsonian disorders using a channelized Hotelling observer model: proof of principle

Imaging dopamine transporters using PET and SPECT probes is a powerful technique for the early diagnosis of Parkinsonian disorders. In order to perform automated accurate diagnosis of these diseases, a channelized Hotelling observer (CHO) based model was developed and evaluated using the SPECT tracer [Tc-99m]TRODAT-1. Computer simulations were performed using a digitized striatal phantom to characterize early stages of the disease (20 lesion-present cases with varying lesion size and contrast). Projection data, modeling the effects of attenuation and geometric response function, were obtained for each case. Statistical noise levels corresponding to those observed clinically were added to the projection data to obtain 100 noise realizations for each case. All the projection data were reconstructed, and a subset of the transaxial slices containing the striatum was summed and used for further analysis. CHO models, using the Laguerre-Gaussian functions as channels, were designed for two cases: (1) By training the model using individual lesion-present samples and (2) by training the model using pooled lesion-present samples. A decision threshold obtained for each CHO model was used to classify the study population (n = 40). It was observed that individual lesion trained CHO models gave high diagnostic accuracy for lesions that were larger than those used to train the model and vice-versa. On the other hand, the pooled CHO model was found to give a high diagnostic accuracy for all the lesion cases (average diagnostic accuracy = 0.95 +/- 0.07; p < 0.0001 Fisher's exact test). Based on our results, we conclude that a CHO model has the potential to provide early and accurate diagnosis of Parkinsonian disorders, thereby improving patient management.     

Detection performance analysis for time-of-flight PET

In this paper, we investigate the performance of time-of-flight (TOF) positron emission tomography (PET) in improving lesion detectability. We present a theoretical approach to compare lesion detectability of TOF versus non-TOF systems and perform computer simulations to validate the theoretical prediction. A single-ring TOF PET tomograph is simulated using SimSET software, and images are reconstructed in 2D from list-mode data using a maximum a posteriori method. We use a channelized Hotelling observer to assess the detection performance. Both the receiver operating characteristic (ROC) and localization ROC curves are compared for the TOF and non-TOF PET systems. We first studied the SNR gains for TOF PET with different scatter and random fractions, system timing resolutions and object sizes. We found that the TOF information improves the lesion detectability and the improvement is greater with larger fractions of randoms, better timing resolution and bigger objects. The scatters by themselves have little impact on the SNR gain after correction. Since the true system timing resolution may not be known precisely in practice, we investigated the effect of mismatched timing kernels and showed that using a mismatched kernel during reconstruction always degrades the detection performance, no matter whether it is narrower or wider than the real value. Using the proposed theoretical framework, we also studied the effect of lumpy backgrounds on the detection performance. Our results indicated that with lumpy backgrounds, the TOF PET still outperforms the non-TOF PET, but the improvement is smaller compared with the uniform background case. More specifically, with the same correlation length, the SNR gain reduces with bigger number of lumpy patches and greater lumpy amplitudes. With the same variance, the SNR gain reaches the minimum when the width of the Gaussian lumps is close to the size of the tumor.     

Signal detectability in digital radiography: spatial domain figures of merit

The usefulness of Fourier-based measures of imaging performance has come into question for the evaluation of digital imaging systems. Figures of merit such as detective quantum efficiency (DQE) based on Fourier domain parameters are relevant for linear, shift-invariant systems with stationary noise. However, no digital imaging system is shift invariant, and realistic images do not satisfy the stationarity condition. Our methods for the task-based evaluation of imaging systems, based on signal detectability in the spatial domain, do not require such assumptions. We have computed the performance of ideal and quasi-ideal observers for the task of signal detection in digital radiography. Signal detectability in terms of an observer signal-to-noise-ratio (SNR) has been compared to results obtained from a Monte Carlo simulation of the digital image-acquisition process. The simulation incorporates the effects of random amplification and secondary quantum blur, integration over pixel area, and electronic noise. The observer figures of merit that have been previously shown to bracket human performance directly specify the usefulness of the images for the stated diagnostic task. In addition, the observer figures of merit give a task-dependent measure of imaging system efficiency in terms of the ratio of an output SNR2 to an input SNR2. Thus, the concept of "detective quantum efficiency" reappears in a natural way but based in the spatial domain and not dependent on shift invariance and stationarity assumptions. With respect to the optimum amount of system blur, our simulations indicate that under certain task-dependent conditions, large signals are fairly insensitive to blur in the x-ray transducer, while an optimum blur is found for small signals.     

X-ray imaging optimization using virtual phantoms and computerized observer modelling

This study develops and demonstrates a realistic x-ray imaging simulator with computerized observers to maximize lesion detectability and minimize patient exposure. A software package, ViPRIS, incorporating two computational patient phantoms, has been developed for simulating x-ray radiographic images. A tomographic phantom, VIP-Man, constructed from Visible Human anatomical colour images is used to simulate the scattered portion using the ESGnrc Monte Carlo code. The primary portion of an x-ray image is simulated using the projection ray-tracing method through the Visible Human CT data set. To produce a realistic image, the software simulates quantum noise, blurring effects, lesions, detector absorption efficiency and other imaging artefacts. The primary and scattered portions of an x-ray chest image are combined to form a final image for computerized observer studies and image quality analysis. Absorbed doses in organs and tissues of the segmented VIP-Man phantom were also obtained from the Monte Carlo simulations. Approximately 25,000 simulated images and 2,500,000 data files were analysed using computerized observers. Hotelling and Laguerre-Gauss Hotelling observers are used to perform various lesion detection tasks. Several model observer tasks were used including SKE/BKE, MAFC and SKEV. The energy levels and fluence at the minimum dose required to detect a small lesion were determined with respect to lesion size, location and system parameters.     

Image covariance and lesion detectability in direct fan-beam x-ray computed tomography

We consider noise in computed tomography images that are reconstructed using the classical direct fan-beam filtered backprojection algorithm, from both full- and short-scan data. A new, accurate method for computing image covariance is presented. The utility of the new covariance method is demonstrated by its application to the implementation of a channelized Hotelling observer for a lesion detection task. Results from the new covariance method and its application to the channelized Hotelling observer are compared with results from Monte Carlo simulations. In addition, the impact of a bowtie filter and x-ray tube current modulation on reconstruction noise and lesion detectability are explored for full-scan reconstruction.     

Fast LROC analysis of Bayesian reconstructed emission tomographic images using model observers

Lesion detection and localization is an important task in emission computed tomography. Detection and localization performance with signal location uncertainty may be summarized by a scalar figure of merit, the area under the localization receiver operating characteristic (LROC) curve, A(LROC). We consider model observers to compute A(LROC) for two-dimensional maximum a posteriori (MAP) reconstructions. Model observers may be used to rapidly prototype studies that use human observers. We address the case background-known-exactly (BKE) and signal known except for location. Our A(LROC) calculation makes use of theoretical expressions for the mean and covariance of the reconstruction and, unlike conventional methods that also use model observers, does not require computation of a large number of sample reconstructions. We validate the results of the procedure by comparison to A(LROC) obtained using a gold-standard Monte Carlo method employing a large set of reconstructed noise samples. Under reasonable simulation conditions, our theoretical calculation is about one to two orders of magnitude faster than the conventional Monte Carlo method.     

Fully 4D motion-compensated reconstruction of cardiac SPECT images

In this paper, we investigate the benefits of a spatiotemporal approach for reconstruction of image sequences. In the proposed approach, we introduce a temporal prior in the form of motion compensation to account for the statistical correlations among the frames in a sequence, and reconstruct all the frames collectively as a single function of space and time. The reconstruction algorithm is derived based on the maximum a posteriori estimate, for which the one-step late expectation-maximization algorithm is used. We demonstrated the method in our experiments using simulated single photon emission computed tomography (SPECT) cardiac perfusion images. The four-dimensional (4D) gated mathematical cardiac-torso phantom was used for simulation of gated SPECT perfusion imaging with Tc-99m-sestamibi. In addition to bias-variance analysis and time activity curves, we also used a channelized Hotelling observer to evaluate the detectability of perfusion defects in the reconstructed images. Our experimental results demonstrated that the incorporation of temporal regularization into image reconstruction could significantly improve the accuracy of cardiac images without causing any significant cross-frame blurring that may arise from the cardiac motion. This could lead to not only improved detection of perfusion defects, but also improved reconstruction of the heart wall which is important for functional assessment of the myocardium.     

Dose efficiency and low-contrast detectability of an amorphous silicon x-ray detector for digital radiography

The effect of dose reduction on low-contrast detectability is investigated theoretically and experimentally for a production grade amorphous silicon (a-Si) x-ray detector and compared with a standard thoracic screen-film combination. A non-prewhitening matched filter observer model modified to include a spatial response function and internal noise for the human visual system (HVS) is used to calculate a signal-to-noise ratio (SNR) related to object detectability. Other inputs to the SNR calculation are the detective quantum efficiency (DQE) and the modulation transfer function (MTF) of the imaging system. Besides threshold detectability, the model predicts the equivalent perception dose ratio (EPDR), which is the fraction of the screen film exposure for which the digital detector provides equal detectability. Images of a contrast-detail phantom are obtained with the digital detector at dose levels corresponding to 27%, 41%, 63% and 100% of the dose used for screen-film. The images are used in a four-alternative forced choice (4-AFC) observer perception study in order to measure threshold detectability. A statistically significant improvement in contrast detectability is measured with the digital detector at 100% and 63% of the screen-film dose. There is no statistical difference between screen-film and digital at 41% of the dose. On average, the experimental EPDR is 44%, which agrees well with the model prediction of 40%.     

Amorphous selenium flat panel detectors for digital mammography: validation of a NPWE model observer with CDMAM observer performance experiments

Model observers have been developed which incorporate a specific imaging task, system performance, and human observer characteristics and can potentially overcome some of the limitations in using detective quantum efficiency for optimization and comparison of detectors. In this paper, a modified nonprewhitening matched filter (NPWE) model observer was developed and validated to predict object detectability for an amorphous selenium (a-Se) direct flat-panel imager (FPI) where aliasing is severe. A preclinical a-Se digital mammography FPI with 85 microm pixel size was used in this investigation. Its physical imaging properties including modulation transfer function (MTF), noise power spectrum, and DQE were fully characterized. An observer performance study was conducted by imaging the CDMAM 3.4 contrast-detail phantom designed specifically for digital mammography and presenting these images to a panel of seven observers. X-ray attenuation and scatter due to the phantom were determined experimentally for use in development of the model observer. The observer study results were analyzed via threshold averaging and signal detection theory (SDT) based techniques to produce contrast-detail curves where threshold contrast is plotted as a function of disk diameter. Validity of the model was established using SDT analysis of the experimental data. The effect of aliasing on the detectability of small diameter disks was determined using the NPWE model observer. The signal spectrum was calculated using the presampling MTF of the detector with and without including the aliased terms. Our results indicate that the NPWE model based on Fourier domain parameters provides reasonable prediction of object detectability for the signal-known-exactly task in uniform image noise for a-Se direct FPI.     

Toward objective and quantitative evaluation of imaging systems using images of phantoms

The use of imaging phantoms is a common method of evaluating image quality in the clinical setting. These evaluations rely on a subjective decision by a human observer with respect to the faintest detectable signal(s) in the image. Because of the variable and subjective nature of the human-observer scores, the evaluations manifest a lack of precision and a potential for bias. The advent of digital imaging systems with their inherent digital data provides the opportunity to use techniques that do not rely on human-observer decisions and thresholds. Using the digital data, signal-detection theory (SDT) provides the basis for more objective and quantitative evaluations which are independent of a human-observer decision threshold. In a SDT framework, the evaluation of imaging phantoms represents a "signal-known-exactly/background-known-exactly" ("SKE/ BKE") detection task. In this study, we compute the performance of prewhitening and nonprewhitening model observers in terms of the observer signal-to-noise ratio (SNR) for these "SK E/BKE" tasks. We apply the evaluation methods to a number of imaging systems. For example, we use data from a laboratory implementation of digital radiography and from a full-field digital mammography system in a clinical setting. In addition, we make a comparison of our methods to human-observer scoring of a set of digital images of the CDMAM phantom available from the internet (EUREF-European Reference Organization). In the latter case, we show a significant increase in the precision of the quantitative methods versus the variability in the scores from human observers on the same set of images. As regards bias, the performance of a model observer estimated from a finite data set is known to be biased. In this study, we minimize the bias and estimate the variance of the observer SNR using statistical resampling techniques, namely, "bootstrapping" and "shuffling" of the data sets. Our methods provide objective and quantitative evaluation of imaging systems with increased precision and reduced bias.     

Evaluation of internal noise methods for Hotelling observer models

The inclusion of internal noise in model observers is a common method to allow for quantitative comparisons between human and model observer performance in visual detection tasks. In this article, we studied two different strategies for inserting internal noise into Hotelling model observers. In the first strategy, internal noise was added to the output of individual channels: (a) Independent nonuniform channel noise, (b) independent uniform channel noise. In the second strategy, internal noise was added to the decision variable arising from the combination of channel responses. The standard deviation of the zero mean internal noise was either constant or proportional to: (a) the decision variable's standard deviation due to the external noise, (b) the decision variable's variance caused by the external noise, (c) the decision variable magnitude on a trial to trial basis. We tested three model observers: square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO) using a four alternative forced choice (4AFC) signal known exactly but variable task with a simulated signal embedded in real x-ray coronary angiogram backgrounds. The results showed that the internal noise method that led to the best prediction of human performance differed across the studied model observers. The CHO model best predicted human observer performance with the channel internal noise. The HO and LGHO best predicted human observer performance with the decision variable internal noise. The present results might guide researchers with the choice of methods to include internal noise into Hotelling model observers when evaluating and optimizing medical image quality.     

Validation of a digital mammographic unit model for an objective and highly automated clinical image quality assessment

In mammography, image quality assessment has to be directly related to breast cancer indicator (e.g. microcalcifications) detectability. Recently, we proposed an X-ray source/digital detector (XRS/DD) model leading to such an assessment. This model simulates very realistic contrast-detail phantom (CDMAM) images leading to gold disc (representing microcalcifications) detectability thresholds that are very close to those of real images taken under the simulated acquisition conditions. The detection step was performed with a mathematical observer. The aim of this contribution is to include human observers into the disc detection process in real and virtual images to validate the simulation framework based on the XRS/DD model. Mathematical criteria (contrast-detail curves, image quality factor, etc.) are used to assess and to compare, from the statistical point of view, the cancer indicator detectability in real and virtual images. The quantitative results given in this paper show that the images simulated by the XRS/DD model are useful for image quality assessment in the case of all studied exposure conditions using either human or automated scoring. Also, this paper confirms that with the XRS/DD model the image quality assessment can be automated and the whole time of the procedure can be drastically reduced. Compared to standard quality assessment methods, the number of images to be acquired is divided by a factor of eight.     

CHO数字观察器在PET图像滤波方法评估中的应用

在临床应用中需要限制扫描时间和药物剂量,这往往会使正电子发射断层扫描(PET)的图像的分辨率变低,噪声变多。为提供可供临床诊断的图像,去噪是一个必须的手段,而在重建后增加一个滤波器是目前最常用的去噪方法。因此对不同滤波器滤波效果的比较是PET图像重建中的重要环节,其中最关键的是滤波参数的选取。目前采用的信噪比(SNR)以及恢复系数(RC)等评估方法可以用来非定量地选取参数,研究者们只能凭经验选取最优参数。而通道化霍特林观察器(CHO)作为一个比较通用的数字观察器,已被用于与PET图像质量相关的各种参数的选择,如重建算法参数、系统设计参数、临床协议参数等,然而其在评估不同滤波方法对图像重建质量的影响中的应用研究还比较少。通过比较CHO计算得到的ROC(receiver operating characteristic)曲线下面积(area under the ROC curve,AUC),选择两种常用的滤波器(即高斯滤波器和非局部均值(Non-Local Mean, NLM)滤波器)的最优参数,并评估它们在PET中的滤波效果。结果表明,对于13 mm球体,σ为1.1~1.4的高斯滤波器和f为0.5~0.9的NLM滤波器可以达到最大的检测能力值,而对于10 mm球体,σ为1.4~2.0的高斯滤波器和f为0.5~0.9的NLM滤波器可以达到最大的检测能力值。虽然两个滤波器所对应的AUC值都能高达0.9,但是NLM滤波器的AUC值高于高斯滤波器。通过IEC图像和病人图像也能发现,NLM滤波后的PET图像中的亮点比高斯滤波的更加清晰,噪声更少。该结论和传统滤波器评估方法得到的结论一致,这说明在PET的病灶检测任务中,CHO能够准确地比较这两种滤波器的性能。     

Experimental results from a prototype slit-slat collimator with mixed multiplexed and non-multiplexed data

We have previously shown with simulations that a gain in signal-to-noise ratio (SNR) can be obtained by using mixed multiplexed (MX) and non-MX data in a slit-slat SPECT system as compared to using non-MX data only. We have now developed a prototype slit-slat collimator for a conventional gamma camera in order to validate these simulation results. The prototype collimator consists of seven slits and multiple parallel slats. Image reconstruction is performed using a modified OSEM algorithm, which takes into account geometric sensitivity variations and attenuation, but not scatter or resolution effects. Here, we first describe the calibration of the system and then we present the experimental validation with phantom experiments. SPECT acquisitions using different geometric and anthropomorphic phantoms were performed with and without multiplexing. The results show that reconstruction of the MX projections with the non-MX-projections eliminates artefacts caused by multiplexing. SNR gains obtained using the mixed MX and non-MX configurations were in the range of 26% to 51% for different phantoms. The results were in agreement with our previously published simulation work, proving that combining MX and non-MX data can result in artefact-free reconstructed images with improved SNR.     

Comparison of region-of-interest analysis and human observers in the diagnosis of Parkinson's disease using [99mTc]TRODAT-1 and SPECT

This study determined the relative accuracy of diagnosis of Parkinson's disease (PD) using SPECT imaging data, comparing a semi-quantitative region-of-interest (ROI) approach and human observers. A set of patients with PD and normal healthy control subjects were studied using the dopamine transporter tracer [(99m)Tc]TRODAT-1 and SPECT. The sample comprised 81 patients (mean age +/- SD, 63.4 +/- 10.4 years; age range, 39.0-84.2 years) and 94 healthy controls (mean age +/- SD, 61.8 +/- 11.0 years; age range, 40.9-83.3 years). A standardized template containing six ROIs was transposed onto subregions of the brain, and the ratio of striatal to background ROI values was used as a semi-quantitative outcome measure. All images were used in a human observer study, with four experienced investigators. The data from the observer and ROI studies were analysed using a receiver operating characteristic (ROC) analysis, where the area under the ROC curve (AUC) indicated the diagnostic accuracy. ROI analysis and human observers gave similar diagnostic performance (mean observer AUC = 0.89, best ROI AUC = 0.90). This suggested that the human observers are visually acquiring similar information from the images that are contained in the semi-quantitative striatal uptake.     

A channelized Hotelling observer study of lesion detection in SPECT MAP reconstruction using anatomical priors

In emission tomography, anatomical side information, in the form of organ and lesion boundaries, derived from intra-patient coregistered CT or MR scans can be incorporated into the reconstruction. Our interest is in exploring the efficacy of such side information for lesion detectability. To assess detectability we used the SNR of a channelized Hotelling observer and a signal-known exactly/background-known exactly detection task. In simulation studies, we incorporated anatomical side information into a SPECT MAP (maximum a posteriori) reconstruction by smoothing within but not across organ or lesion boundaries. A non-anatomical prior was applied by uniform smoothing across the entire image. We investigated whether the use of anatomical priors with organ boundaries alone or with perfect lesion boundaries alone would change lesion detectability relative to the case of a prior with no anatomical information. Furthermore, we investigated whether any such detectability changes for the organ-boundary case would be a function of the distance of the lesion to the organ boundary. We also investigated whether any detectability changes for the lesion-boundary case would be a function of the degree of proximity, i.e. a difference in the radius of the true functional lesion and the radius of the anatomical lesion boundary. Our results showed almost no detectability difference with versus without organ boundaries at any lesion-to-organ boundary distance. Our results also showed no difference in lesion detectability with and without lesion boundaries, and no variation of lesion detectability with degree of proximity.     

Investigation of basic imaging properties in digital radiography. 13. Effect of simple structured noise on the detectability of simulated stenotic lesions

We investigated the effects of structured background noise on the detectability of stenotic lesions. Digital subtraction angiographic (DSA) images of stenotic blood vessels were simulated and superimposed onto uniform noise samples. Eighteen-alternative forced choice (18-AFC) experiments were employed to determine the detectability of the stenotic lesion in the structured-noise background of a blood vessel. In this study, the dependence of detectability on lesion size, vessel size, and incident x-ray exposure was examined. Our results indicate that the presence of structured noise in an image will reduce the detectability of a lesion. However, the relative performance of an observer when the lesion size and incident exposure were varied was the same with and without the presence of the structured background. Thus, conclusions obtained previously with regard to changes in the detectability of a lesion in the presence of uniform background noise can be applied directly to conditions in which simple structured anatomic background is present.     

La théorie statistique de la décision: application à l''étude du rapport signal sur bruit en tomographie d''émission monophotoniqueStatistical decision theory: application to the study of signal to noise ratio in single photon emission tomography

The statistical decision theory (SDT) is applied in all direct imaging modalities. The detection stage can be quantified exactly from the ideal observer concept. The ideal observer depend on the noise power spectrum (NPS). An expression of NPS in single photon emission tomography (SPECT) of the uncertainty over the number of counts is derived in order to estimate the noise and the signal to noise ratio (SNR). The reconstruction algorithm is the method of back-projection of filtered projection. It is shown that derived expressions of noise and SNR compare well with expressions derived from classical approaches. So, we can affirm that the SDT can be considered in tomography (indirect imaging).     

Penalized maximum-likelihood image reconstruction for lesion detection

Detecting cancerous lesions is one major application in emission tomography. In this paper, we study penalized maximum-likelihood image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modelling the photon detection process and measurement noise in imaging systems. To explore the full potential of penalized maximum-likelihood image reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters to improve lesion detectability. We conducted computer-based Monte Carlo simulations to compare the proposed penalty function, conventional penalty function, and a penalty function for isotropic point spread function. The lesion detectability is measured by a channelized Hotelling observer. The results show that the proposed penalty function outperforms the other penalty functions for lesion detection. The relative improvement is dependent on the size of the lesion. However, we found that the penalty function optimized for a 5 mm lesion still outperforms the other two penalty functions for detecting a 14 mm lesion. Therefore, it is feasible to use the penalty function designed for small lesions in image reconstruction, because detection of large lesions is relatively easy.     

Fast Monte Carlo based joint iterative reconstruction for simultaneous 99mTc/ 123I SPECT imaging

Simultaneous 99mTC/ 123I SPECT allows the assessment of two physiological functions under identical conditions. The separation of these radionuclides is difficult, however, because their energies are close. Most energy-window-based scatter correction methods do not fully model either physical factors or patient-specific activity and attenuation distributions. We have developed a fast Monte Carlo (MC) simulation-based multiple-radionuclide and multiple-energy joint ordered-subset expectation-maximization (JOSEM) iterative reconstruction algorithm, MC-JOSEM. MC-JOSEM simultaneously corrects for scatter and cross talk as well as detector response within the reconstruction algorithm. We evaluated MC-JOSEM for simultaneous brain profusion (99mTc-HMPAO) and neurotransmission (123I-altropane) SPECT. MC simulations of 99mTc and 123I studies were generated separately and then combined to mimic simultaneous 99mTc/ 123I SPECT. All the details of photon transport through the brain, the collimator, and detector, including Compton and coherent scatter, septal penetration, and backscatter from components behind the crystal, were modeled. We reconstructed images from simultaneous dual-radionuclide projections in three ways. First, we reconstructed the photopeak-energy-window projections (with an asymmetric energy window for 1231) using the standard ordered-subsets expectation-maximization algorithm (NSC-OSEM). Second, we used standard OSEM to reconstruct 99mTc photopeak-energy-window projections, while including an estimate of scatter from a Compton-scatter energy window (SC-OSEM). Third, we jointly reconstructed both 99mTc and 123I images using projection data associated with two photo-peak energy windows and an intermediate-energy window using MC-JOSEM. For 15 iterations of reconstruction, the bias and standard deviation of 99mTc activity estimates in several brain structures were calculated for NSC-OSEM, SC-OSEM, and MC-JOSEM, using images reconstructed from primary (unscattered) photons as a reference. Similar calculations were performed for 123I images for NSC-OSEM and MC-JOSEM. For 123I images, dopamine binding potential (BP) at equilibrium and its signal-to-noise ratio (SNR) were also calculated. Our results demonstrate that MC-JOSEM performs better than NSC- and SC-OSEM for quantitation tasks. After 15 iterations of reconstruction, the relative bias of 99mTc activity estimates in the thalamus, striata, white matter, and gray matter volumes from MC-JOSEM ranged from -2.4% to 1.2%, while the same estimates for NSC-OSEM (SC-OSEM) ranged from 20.8% to 103.6% (7.2% to 41.9%). Similarly, the relative bias of 123I activity estimates from 15 iterations of MC-JOSEM in the striata and background ranged from -1.4% to 2.9%, while the same estimates for NSC-OSEM ranged from 1.6% to 10.0%. The relative standard deviation of 99mTc activity estimates from MC-JOSEM ranged from 1.1% to 4.8% versus 1.2% to 6.7% (1.2% to 5.9%) for NSC-OSEM (SC-OSEM). The relative standard deviation of 123I activity estimates using MC-JOSEM ranged from 1.1% to 1.9% versus 1.5% to 2.7% for NSC-OSEM. Using the 123I dopamine BP obtained from the reconstruction produced by primary photons as a reference, the result for MC-JOSEM was 50.5% closer to the reference than that of NSC-OSEM after 15 iterations. The SNR for dopamine BP was 23.6 for MC-JOSEM as compared to 18.3 for NSC-OSEM.