Search

Objective. Single-photon emission computed tomography (SPECT) with pinhole collimators can provide high-resolution imaging, but is often limited by low sensitivity. Acquiring projections simultaneously through multiple pinholes affords both high resolution and high sensitivity. However, the overlap of projections from different pinholes on detectors, known as multiplexing, has been shown to cause artefacts which degrade reconstructed images. Approach. Multiplexed projection sets were considered here using an analytic simulation model of AdaptiSPECT-C-a brain-dedicated multi-pinhole SPECT system. AdaptiSPECT-C has fully adaptable aperture shutters, so can acquire projections with a combination of multiplexed and non-multiplexed frames using temporal shuttering. Two strategies for reducing multiplex artefacts were considered: an algorithm to de-multiplex projections, and an alternating reconstruction strategy for projections acquired with a combination of multiplexed and non-multiplexed frames. Geometric and anthropomorphic digital phantoms were used to assess a number of metrics. Main results. Both de-multiplexing strategies showed a significant reduction in image artefacts and improved fidelity, image uniformity, contrast recovery and activity recovery (AR). In all cases, the two de-multiplexing strategies resulted in superior metrics to those from images acquired with only mux-free frames. The de-multiplexing algorithm provided reduced image noise and superior uniformity, whereas the alternating strategy improved contrast and AR. Significance. The use of these de-multiplexing algorithms means that multi-pinhole SPECT systems can acquire projections with more multiplexing without degradation of images., (© 2024 Institute of Physics and Engineering in Medicine.)

Purpose: Dual respiratory-cardiac gating reduces respiratory and cardiac motion blur in myocardial perfusion single-photon emission computed tomography (MP-SPECT). However, image noise is increased as detected counts are reduced in each dual gate (DG). We aim to develop a denoising method for dual gating MP-SPECT images using a 3D conditional generative adversarial network (cGAN)., Methods: Twenty extended cardiac-torso phantoms with various 99m Tc-sestamibi distributions, defect characteristics, and body and organ sizes were used in the simulation, modeling six respiratory and eight cardiac gates (CGs), that is, 48 DGs for ordered subset expectation maximization reconstruction. Twenty clinical 99m Tc-sestamibi SPECT/CT datasets were re-binned into 7 respiratory gates and 8 CGs, that is, 56 DGs for maximum likelihood expectation maximization reconstruction. We evaluated the use of (i) phantoms' own datasets (patient-specific denoising [PD]) or other phantoms' datasets (cross-patient denoising) for training; (ii) the CG or the static (non-gated [NG]) data as the training references for cGAN; and (iii) cGAN as compared to conventional 3D post-reconstruction filtering, cardiac gating methods, and convolutional neural network. Normalized mean squared error, noise as assessed by normalized standard deviation, spatial blurring measured as the full-width-at-half-maximum of left ventricular wall, ejection fraction, joint correlation histogram, and defect size were analyzed as metrics of image quality., Results: Training using patients' own dataset is superior to conventional training based on other patients' data. Using CG image as training reference provides a better trade-off in terms of noise and image blur as compared to the use of NG. cGAN-CG-PD provides superior performance as compared to other denoising methods for all physical and diagnostic indices evaluated in both simulation and clinical studies., Conclusions: cGAN denoising is promising for dual gating MP-SPECT based on the metrics mentioned earlier., (© 2022 American Association of Physicists in Medicine.)

Purpose: The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100 ms) single photon emission computed tomography (SPECT) projection data derived from list-mode acquisitions., Methods: For our initial evaluation, we acquired list-mode projection data from an anthropomorphic cardiac phantom mounted on a Quasar respiratory motion platform simulating 15 mm amplitude respiratory motion. We also selected 302 consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson's correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements were calculated for individual projection data sets and for the patient group as a whole. Both the VTS- and COM-derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom rigid-body motion estimation was done in two ways: (1) using three iterations of ordered-subsets expectation-maximization (OSEM) with four subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (four projection angles per subset) and five iterations or MLEM and 80 iterations, using the two respiratory estimates, respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff was calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values)., Results: The r-value calculated when comparing the VTS and COM respiratory signals varied widely between -0.01 and 0.96 with an average of 0.70, while the SEE varied between 0.80 and 6.48 mm with an average of 2.05 mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11 mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r = 0.90 (0.94), and an SEE of 1.56 mm (1.20 mm) for OSEM (MLEM), respectively. Bland-Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 (9.2 mm) and 9.01 mm (9.14 mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 (14.10 mm) and 12.55 mm (14.29 mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS-derived respiratory estimates to correct for respiration compared to the COM-derived estimates., Conclusions: The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the drawbacks of our VTS methodology. However, it is not generally equivalent to the VTS method in extent of correction., (© 2021 American Association of Physicists in Medicine.)

We are developing a multi-detector pinhole-based stationary brain-dedicated SPECT system: AdaptiSPECT-C. In this work, we introduced a new design prototype with multiple adaptable pinhole apertures for each detector to modulate the multiplexing by employing temporal shuttering of apertures. Temporal shuttering of apertures over the scan time provides the AdaptiSPECT-C with the capability of multiple-frame acquisition. We investigated, through analytic simulation, the impact of projection multiplexing on image quality using several digital phantoms and a customized anthropomorphic phantom emulating brain perfusion clinical distribution. The 105 pinholes in the collimator of the system were categorized into central, axial, and lateral apertures. We generated, through simulation, collimators of different multiplexing levels. Several data acquisition schemes were also created by changing the imaging time share of the acquisition frames. Sensitivity increased by 35% compared to the single-pinhole-per-detector base configuration of the AdaptiSPECT-C when using the central, axial, and lateral apertures with equal acquisition time shares within a triple-frame scheme with a high multiplexing scenario. Axial and angular sampling of the base configuration was enhanced by adding the axial and lateral apertures. We showed that the temporal shuttering of apertures can be exploited, trading the sensitivity, to modulate the multiplexing and to acquire a set of non-multiplexed non-truncated projections. Our results suggested that reconstruction benefited from utilizing both non-multiplexed projections and projections with modulated multiplexing resulting in a noticeably reduction in the multiplexing-induced image artefacts. Contrast recovery factor improved by 20% (9%) compared to the base configuration for a Defrise (hot-rod) phantom study when the central and axial (lateral) apertures with equal time shares were combined. The results revealed that, as an overall trend at each simulated multiplexing level, lowest normalized root-mean-square errors for the brain gray-matter regions were achieved with the combined usage of the central apertures and axial/lateral apertures.

Purpose: Post-reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoising in conventional SPECT-MPI acquisitions, and investigate whether it can be more effective for improving the detectability of perfusion defects compared to traditional postfiltering., Methods: Owing to the lack of ground truth in clinical studies, we adopt a noise-to-noise (N2N) training approach for denoising in SPECT-MPI images. We consider a coupled U-Net (CU-Net) structure which is designed to improve learning efficiency through feature map reuse. For network training we employ a bootstrap procedure to generate multiple noise realizations from list-mode clinical acquisitions. In the experiments we demonstrated the proposed approach on a set of 895 clinical studies, where the iterative OSEM algorithm with three-dimensional (3D) Gaussian postfiltering was used to reconstruct the images. We investigated the detection performance of perfusion defects in the reconstructed images using the non-prewhitening matched filter (NPWMF), evaluated the uniformity of left ventricular (LV) wall in terms of image intensity, and quantified the effect of smoothing on the spatial resolution of the reconstructed LV wall by using its full-width at half-maximum (FWHM)., Results: Compared to OSEM with Gaussian postfiltering, the DL denoised images with CU-Net significantly improved the detection performance of perfusion defects at all contrast levels (65%, 50%, 35%, and 20%). The signal-to-noise ratio (SNR D ) in the NPWMF output was increased on average by 8% over optimal Gaussian smoothing (P < 10 -4 , paired t-test), while the inter-subject variability was greatly reduced. The CU-Net also outperformed a 3D nonlocal means (NLM) filter and a convolutional autoencoder (CAE) denoising network in terms of SNR D . In addition, the FWHM of the LV wall in the reconstructed images was varied by less than 1%. Furthermore, CU-Net also improved the detection performance when the images were processed with less post-reconstruction smoothing (a trade-off of increased noise for better LV resolution), with SNR D improved on average by 23%., Conclusions: The proposed DL with N2N training approach can yield additional noise suppression in SPECT-MPI images over conventional postfiltering. For perfusion defect detection, DL with CU-Net could outperform conventional 3D Gaussian filtering with optimal setting as well as NLM and CAE., (© 2020 American Association of Physicists in Medicine.)

In cardiac SPECT perfusion imaging, cardiac motion can lead to motion blurring of anatomical detail and perfusion defects in the reconstructed myocardium. In this study, we investigated the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. We considered a post-reconstruction motion correction (PMC) approach in which the image motion between two cardiac gates is obtained with optical flow estimation. In the experiments, we demonstrated the proposed post-reconstruction motion correction with optical flow estimation (PMC-OFE) approach on a set of clinical acquisitions from 194 subjects. We quantified the detectability of perfusion defects in the reconstructed images by using the total perfusion deficit scores, calculated by the clinical software tool QPS, and conducted a receiver-operating-characteristic (ROC) study to obtain the detection performance. Besides imaging with conventional standard dose, we also evaluated the approach for reduced dose SPECT imaging where the imaging dose was retrospectively reduced to 50%, 25%, and 12.5% of the standard dose. The proposed PMC-OFE approach achieved at each dose level higher area-under-the-ROC-curve (AUC) for perfusion defect detection than the traditional approach of using ungated data (Non-MC) (p -value  <  0.05); in particular, with half dose, PMC-OFE achieved AUC  =  0.813, which is comparable to Non-MC with standard dose (AUC  =  0.795). Moreover, the proposed PMC-OFE approach also outperformed the 'Motion Frozen' (MF) method implemented in the clinical quantitative gated SPECT (QGS) software. In particular, at 25% and 12.5% of standard dose, the AUC values obtained by PMC-OFE are 0.788 and 0.779, respectively, compared to 0.758 and 0.731 for MF (p -value  <  0.05).

Background: We investigated the extent to which the administered dose (activity) level can be reduced without sacrificing diagnostic accuracy for three reconstruction strategies for SPECT-myocardial perfusion imaging (MPI)., Methods: We optimized the parameters of the three reconstruction strategies for perfusion-defect detection over a range of simulated administered dose levels using a set of hybrid studies (derived from 190 subjects) consisting of clinical SPECT-MPI data modified to contain realistic simulated lesions. The optimized strategies we considered are filtered backprojection (FBP) with no correction for degradations, ordered-subsets expectation-maximization (OS-EM) with attenuation correction (AC), scatter correction (SC), and resolution correction (RC), and OS-EM with scatter and resolution correction only. Each study was evaluated using a total perfusion deficit (TPD) score computed by the Quantitative Perfusion SPECT (QPS) software package. We conducted a receiver operating characteristics (ROC) study based on the TPD scores for each dose level and reconstruction strategy., Results: For FBP, the achieved optimum values of the area under the ROC curve (AUC) at 100%, 50%, 25%, and 12.5% of standard dose were 0.75, 0.74, 0.72, and 0.70, respectively, compared to 0.81, 0.79, 0.76, and 0.74 for OS-EM with AC-SC-RC and 0.78, 0.77, 0.74, 0.72 for OS-EM with SC-RC., Conclusions: Our results suggest that studies reconstructed by OS-EM with AC-SC-RC could possibly be reduced, on average, to 25% of the originally administered dose without causing diagnostic accuracy (AUC) to decrease below that of FBP.

Purpose: Cardiac perfusion images in single-photon emission computed tomography (SPECT) can suffer from respiratory motion blur. We investigated a reconstruction approach for correcting respiratory motion in respiratory-binned acquisitions and assessed the benefit of this approach in both standard dose and reduced dose., Methods: We modeled the acquired data from different respiratory bins by a joint probability distribution which was parameterized with respect to a common reference bin. The acquired data from all the respiratory bins were then utilized simultaneously for determining the source distribution in the reference bin using maximum a posteriori (MAP) estimation. We evaluated this approach with simulated imaging data and ten sets of clinical acquisitions, and compared it with a postreconstruction motion correction approach developed previously. We quantified the accuracy of the reconstruction results both at standard dose and with imaging dose reduced by 50% and 75%, respectively., Results: The proposed motion-compensated reconstruction (MCR) approach led to improved reconstruction of the myocardium in terms of both noise level and LV wall resolution. Compared to traditional acquisition (without motion correction), the proposed approach reduced the mean squared error of the image intensity in the myocardium by 27.59%, 20.59%, and 12.05% at full, half-, and quarter dose, respectively; the LV resolution, quantified by the full width at half-maximum (FWHM), was improved by 17.34%, 14.35%, and 12.95% at full, half-, and quarter dose, respectively; in addition, the proposed approach also improved the perfusion defect detectability at both full dose and reduced dose. Furthermore, with motion correction, the reconstruction results obtained at half-dose were comparable to that obtained at full dose without correction. Similar improvements were also demonstrated in the clinical acquisitions at different dose levels., Conclusions: Respiratory motion correction in perfusion SPECT can improve the reconstruction of the myocardium at both standard and reduced dose. At half-dose, the results obtained with motion correction are comparable to that of traditional reconstruction obtained at full dose. MCR can be more accurate than postreconstruction correction., (© 2018 American Association of Physicists in Medicine.)

Cardiac gated images often suffer from increased noise in single photon emission computed tomography (SPECT) due to reduced data counts compared to non-gated studies. We investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in gated SPECT images. In this filter, the output at a voxel location is computed from a weighted average of voxels in its four-dimensional (4D) neighborhood, wherein the filter coefficients are adjusted according to the similarity level in the local image pattern of individual voxels with respect to the output voxel. This adaptive property allows the filter to achieve noise reduction while avoiding excessive blur of the heart wall. In the experiments, we first evaluated the accuracy of the proposed NLM filtering approach using simulated SPECT imaging data. We then demonstrated the approach on eight sets of clinical acquisitions. In addition, we also explored the robustness of the NLM filter with imaging dose reduced by 50% in these clinical acquisitions. The quantitative results show that 4D NLM filtering could effectively reduce the noise level in gated images, leading to more accurate reconstruction of the myocardium. Compared to spatial filtering alone, using temporal filtering in NLM could reduce the mean-squared-error of the myocardium by 55.63% and improve the left ventricle resolution by 19.92%. It could also improve the visibility of perfusion defects in gated images. Similar results are also observed on the clinical acquisitions both at standard dose and at 50% reduced dose. The 4D NLM results are also found to be comparable to that of a motion-compensated 4D reconstruction approach which is computationally more demanding.

Purpose: Head motion during PET brain imaging can cause significant degradation of image quality. Several authors have proposed ways to compensate for PET brain motion to restore image quality and improve quantitation. Head restraints can reduce movement but are unreliable; thus the need for alternative strategies such as data-driven motion estimation or external motion tracking. Herein, the authors present a data-driven motion estimation method using a preprocessing technique that allows the usage of very short duration frames, thus reducing the intraframe motion problem commonly observed in the multiple frame acquisition method., Methods: The list mode data for PET acquisition is uniformly divided into 5-s frames and images are reconstructed without attenuation correction. Interframe motion is estimated using a 3D multiresolution registration algorithm and subsequently compensated for. For this study, the authors used 8 PET brain studies that used F-18 FDG as the tracer and contained minor or no initial motion. After reconstruction and prior to motion estimation, known motion was introduced to each frame to simulate head motion during a PET acquisition. To investigate the trade-off in motion estimation and compensation with respect to frames of different length, the authors summed 5-s frames accordingly to produce 10 and 60 s frames. Summed images generated from the motion-compensated reconstructed frames were then compared to the original PET image reconstruction without motion compensation., Results: The authors found that our method is able to compensate for both gradual and step-like motions using frame times as short as 5 s with a spatial accuracy of 0.2 mm on average. Complex volunteer motion involving all six degrees of freedom was estimated with lower accuracy (0.3 mm on average) than the other types investigated. Preprocessing of 5-s images was necessary for successful image registration. Since their method utilizes nonattenuation corrected frames, it is not susceptible to motion introduced between CT and PET acquisitions., Conclusions: The authors have shown that they can estimate motion for frames with time intervals as short as 5 s using nonattenuation corrected reconstructed FDG PET brain images. Intraframe motion in 60-s frames causes degradation of accuracy to about 2 mm based on the motion type.

Purpose: In cardiac single photon emission computed tomography (SPECT), respiratory-binned study is used to combat the motion blur associated with respiratory motion. However, owing to the variability in respiratory patterns during data acquisition, the acquired data counts can vary significantly both among respiratory bins and among projection angles within individual bins. If not properly accounted for, such variation could lead to artifacts similar to limited-angle effect in image reconstruction. In this work, the authors aim to investigate several reconstruction strategies for compensating the limited-angle effect in respiratory binned data for the purpose of reducing the image artifacts., Methods: The authors first consider a model based correction approach, in which the variation in acquisition time is directly incorporated into the imaging model, such that the data statistics are accurately described among both the projection angles and respiratory bins. Afterward, the authors consider an approximation approach, in which the acquired data are rescaled to accommodate the variation in acquisition time among different projection angles while the imaging model is kept unchanged. In addition, the authors also consider the use of a smoothing prior in reconstruction for suppressing the artifacts associated with limited-angle effect. In our evaluation study, the authors first used Monte Carlo simulated imaging with 4D NCAT phantom wherein the ground truth is known for quantitative comparison. The authors evaluated the accuracy of the reconstructed myocardium using a number of metrics, including regional and overall accuracy of the myocardium, uniformity and spatial resolution of the left ventricle (LV) wall, and detectability of perfusion defect using a channelized Hotelling observer. As a preliminary demonstration, the authors also tested the different approaches on five sets of clinical acquisitions., Results: The quantitative evaluation results show that the three compensation methods could all, but to different extents, reduce the reconstruction artifacts over no compensation. In particular, the model based approach reduced the mean-squared-error of the reconstructed myocardium by as much as 40%. Compared to the approach of data rescaling, the model based approach further improved both the overall and regional accuracy of the myocardium; it also further improved the lesion detectability and the uniformity of the LV wall. When ML reconstruction was used, the model based approach was notably more effective for improving the LV wall; when MAP reconstruction was used, the smoothing prior could reduce the noise level and artifacts with little or no increase in bias, but at the cost of a slight resolution loss of the LV wall. The improvements in image quality by the different compensation methods were also observed in the clinical acquisitions., Conclusions: Compensating for the uneven distribution of acquisition time among both projection angles and respiratory bins can effectively reduce the limited-angle artifacts in respiratory-binned cardiac SPECT reconstruction. Direct incorporation of the time variation into the imaging model together with a smoothing prior in reconstruction can lead to the most improvement in the accuracy of the reconstructed myocardium.

Purpose: Binning list-mode acquisitions as a function of a surrogate signal related to respiration has been employed to reduce the impact of respiratory motion on image quality in cardiac emission tomography (SPECT and PET). Inherent in amplitude binning is the assumption that there is a monotonic relationship between the amplitude of the surrogate signal and respiratory motion of the heart. This assumption is not valid in the presence of hysteresis when heart motion exhibits a different relationship with the surrogate during inspiration and expiration. The purpose of this study was to investigate the novel approach of using the Bouc-Wen (BW) model to provide a signal accounting for hysteresis when binning list-mode data with the goal of thereby improving motion correction. The study is based on the authors' previous observations that hysteresis between chest and abdomen markers was indicative of hysteresis between abdomen markers and the internal motion of the heart., Methods: In 19 healthy volunteers, they determined the internal motion of the heart and diaphragm in the superior-inferior direction during free breathing using MRI navigators. A visual tracking system (vts) synchronized with MRI acquisition tracked the anterior-posterior motions of external markers placed on the chest and abdomen. These data were employed to develop and test the Bouc-Wen model by inputting the vts derived chest and abdomen motions into it and using the resulting output signals as surrogates for cardiac motion. The data of the volunteers were divided into training and testing sets. The training set was used to obtain initial values for the model parameters for all of the volunteers in the set, and for set members based on whether they were or were not classified as exhibiting hysteresis using a metric derived from the markers. These initial parameters were then employed with the testing set to estimate output signals. Pearson's linear correlation coefficient between the abdomen, chest, average of chest and abdomen markers, and Bouc-Wen derived signals versus the true internal motion of the heart from MRI was used to judge the signals match to the heart motion., Results: The results show that the Bouc-Wen model generated signals demonstrated strong correlation with the heart motion. This correlation was slightly larger on average than that of the external surrogate signals derived from the abdomen marker, and average of the abdomen and chest markers, but was not statistically significantly different from them., Conclusions: The results suggest that the proposed model has the potential to be a unified framework for modeling hysteresis in respiratory motion in cardiac perfusion studies and beyond.

The development of methods for correcting patient motion in emission tomography has been receiving increased attention. Often the performance of these methods is evaluated through simulations using digital anthropomorphic phantoms, such as the commonly used extended cardiac torso (XCAT) phantom, which models both respiratory and cardiac motion based on human studies. However, non-rigid body motion, which is frequently seen in clinical studies, is not present in the standard XCAT phantom. In addition, respiratory motion in the standard phantom is limited to a single generic trend. In this work, to obtain a more realistic representation of motion, we developed a series of individual-specific XCAT phantoms, modeling non-rigid respiratory and non-rigid body motions derived from the magnetic resonance imaging (MRI) acquisitions of volunteers. Acquisitions were performed in the sagittal orientation using the Navigator methodology. Baseline (no motion) acquisitions at end-expiration were obtained at the beginning of each imaging session for each volunteer. For the body motion studies, MRI was again acquired only at end-expiration for five body motion poses (shoulder stretch, shoulder twist, lateral bend, side roll, and axial slide). For the respiratory motion studies, an MRI was acquired during free/regular breathing. The magnetic resonance slices were then retrospectively sorted into 14 amplitude-binned respiratory states, end-expiration, end-inspiration, six intermediary states during inspiration, and six during expiration using the recorded Navigator signal. XCAT phantoms were then generated based on these MRI data by interactive alignment of the organ contours of the XCAT with the MRI slices using a graphical user interface. Thus far we have created five body motion and five respiratory motion XCAT phantoms from the MRI acquisitions of six healthy volunteers (three males and three females). Non-rigid motion exhibited by the volunteers was reflected in both respiratory and body motion phantoms with a varying extent and character for each individual. In addition to these phantoms, we recorded the position of markers placed on the chest of the volunteers for the body motion studies, which could be used as external motion measurement. Using these phantoms and external motion data, investigators will be able to test their motion correction approaches for realistic motion obtained from different individuals. The non-uniform rational B-spline data and the parameter files for these phantoms are freely available for downloading and can be used with the XCAT license.

Motion estimation methods in single photon emission computed tomography (SPECT) can be classified into methods which depend on just the emission data (data-driven), or those that use some other source of information such as an external surrogate. The surrogate-based methods estimate the motion exhibited externally which may not correlate exactly with the movement of organs inside the body. The accuracy of data-driven strategies on the other hand is affected by the type and timing of motion occurrence during acquisition, the source distribution, and various degrading factors such as attenuation, scatter, and system spatial resolution. The goal of this paper is to investigate the performance of two data-driven motion estimation schemes based on the rigid-body registration of projections of motion-transformed source distributions to the acquired projection data for cardiac SPECT studies. Comparison is also made of six intensity based registration metrics to an external surrogate-based method. In the data-driven schemes, a partially reconstructed heart is used as the initial source distribution. The partially-reconstructed heart has inaccuracies due to limited angle artifacts resulting from using only a part of the SPECT projections acquired while the patient maintained the same pose. The performance of different cost functions in quantifying consistency with the SPECT projection data in the data-driven schemes was compared for clinically realistic patient motion occurring as discrete pose changes, one or two times during acquisition. The six intensity-based metrics studied were mean-squared difference, mutual information, normalized mutual information (NMI), pattern intensity (PI), normalized cross-correlation and entropy of the difference. Quantitative and qualitative analysis of the performance is reported using Monte-Carlo simulations of a realistic heart phantom including degradation factors such as attenuation, scatter and system spatial resolution. Further the visual appearance of motion-corrected images using data-driven motion estimates was compared to that obtained using the external motion-tracking system in patient studies. Pattern intensity and normalized mutual information cost functions were observed to have the best performance in terms of lowest average position error and stability with degradation of image quality of the partial reconstruction in simulations. In all patients, the visual quality of PI-based estimation was either significantly better or comparable to NMI-based estimation. Best visual quality was obtained with PI-based estimation in one of the five patient studies, and with external-surrogate based correction in three out of five patients. In the remaining patient study there was little motion and all methods yielded similar visual image quality.

Background: Chest CT scans are commonly used to clinically assess disease severity in patients presenting with pulmonary sarcoidosis. Despite their ability to reliably detect subtle changes in lung disease, the utility of chest CT scans for guiding therapy is limited by the fact that image interpretation by radiologists is qualitative and highly variable. We sought to create a computerized CT image analysis tool that would provide quantitative and clinically relevant information., Methods: We established that a two-point correlation analysis approach reduced the background signal attendant to normal lung structures, such as blood vessels, airways, and lymphatics while highlighting diseased tissue. This approach was applied to multiple lung fields to generate an overall lung texture score (LTS) representing the quantity of diseased lung parenchyma. Using deidentified lung CT scan and pulmonary function test (PFT) data from The Ohio State University Medical Center's Information Warehouse, we analyzed 71 consecutive CT scans from patients with sarcoidosis for whom simultaneous matching PFTs were available to determine whether the LTS correlated with standard PFT results., Results: We found a high correlation between LTS and FVC, total lung capacity, and diffusing capacity of the lung for carbon monoxide (P &lt; .0001 for all comparisons). Moreover, LTS was equivalent to PFTs for the detection of active lung disease. The image analysis protocol was conducted quickly (&lt; 1 min per study) on a standard laptop computer connected to a publicly available National Institutes of Health ImageJ toolkit., Conclusions: The two-point image analysis tool is highly practical and appears to reliably assess lung disease severity. We predict that this tool will be useful for clinical and research applications.

Purpose: Motion-compensated temporal processing can have a major impact on improving the image quality in gated cardiac single photon emission computed tomography (SPECT). In this work, we investigate the effect of different optical flow estimation methods for motion-compensated temporal processing in gated SPECT. In particular, we explore whether better motion estimation can substantially improve reconstructed image quality, and how the estimated motion would compare to the ideal case of known motion in terms of reconstruction., Methods: We consider the following three methods for obtaining the image motion in 4D reconstruction: (1) the Horn-Schunck optical flow equation (OFE) method, (2) a recently developed periodic OFE method, and (3) known cardiac motion derived from the NURBS-based cardiac-torso (NCAT) phantom. The periodic OFE method is used to exploit the inherent periodic nature in cardiac gated images. In this method, the optical flow in a sequence is modeled by a Fourier harmonic representation, which is then estimated from the image data. We study the impact of temporal processing on 4D reconstructions when the image motion is obtained with the different methods above. For quantitative evaluation, we use simulated imaging with multiple noise realizations from the NCAT phantom, where different patient geometry and lesion sizes are also considered. To quantify the reconstruction results, we use the following measures of reconstruction accuracy and defect detection in the myocardium: (1) overall error level in the myocardium, (2) regional accuracy of the left ventricle (LV) wall, (3) accuracy of regional time activity curves of the LV, and (4) perfusion defect detectability with a channelized Hotelling observer (CHO). In addition, we also examine the effect of noise on the distortion in the reconstructed LV wall shape by detecting its contours. As a preliminary demonstration, these methods are also tested on two sets of clinical acquisitions., Results: For the different quantitative measures considered, the periodic OFE further improved the reconstruction accuracy of the myocardium compared to OFE in 4D reconstruction; its improvement in reconstruction almost matched that of the known motion. Specifically, the overall mean-squared error in the myocardium was reduced by over 20% with periodic OFE; with noise level fixed at 10%, the regional bias on the LV was reduced from 20% (OFE) to 14% (periodic OFE), compared to 11% by the known motion. In addition, the CHO results show that there was also improvement in lesion detectability with the periodic OFE. The regional time activity curves obtained with the periodic OFE were also observed to be more consistent with the reference; in addition, the contours of the reconstructed LV wall with the periodic OFE were demonstrated to show less degree of variations among different noise realizations. Such improvements were also consistent with the results obtained from the clinical acquisitions., Conclusions: Use of improved optical flow estimation can further improve the accuracy of reconstructed images in 4D. The periodic OFE method not only can achieve improvements over the traditional OFE, but also can almost match that of the known motion in terms of the several quality measures considered.

Purpose: One issue with amplitude binning list-mode studies in SPECT for respiratory motion correction is that variation in the patient's respiratory pattern will result in binned motion states with little or no counts at various projection angles. The reduced counts result in limited-angle reconstruction artifacts which can impact the accuracy of the necessary motion estimation needed to correct the images. In this work, the authors investigate a method to overcome the effect of limited-angle reconstruction artifacts in SPECT when estimating respiratory motion., Methods: In the first pass of the reconstruction method, only the projection angles with significant counts in common between the binned respiratory states are used in order to better estimate the motion between them. After motion estimation, the estimates are used to correct for motion within iterative reconstruction using all of the acquired projection data., Results: Using simulated SPECT studies based on the NCAT phantom, the authors demonstrate the problem caused by having data available for only a limited number of angles when estimating motion and the utility of the proposed method in diminishing this error. For NCAT data sets with a clinically appropriate level of Poisson noise, the average registration error for motion with the proposed method was always less with the use of their algorithm, the reduction being statistically significant (p<0.05) in the majority of cases. The authors illustrate the ability of their method to correct the degradations caused by respiratory motion in short-axis slices and polar maps of the NCAT phantom for cases with 1 and 2 cm amplitudes of respiratory motion. In four cardiac-perfusion patients acquired on the same day, the authors demonstrate the large variability of the number of counts in the amplitude-binned projections. Finally, the authors demonstrate a visual improvement in the slices and polar maps of patient studies with the algorithm for respiratory motion correction., Conclusions: The authors' method shows promise in reducing errors in respiratory motion estimation despite the presence of limited-angle reconstruction effects due to irregularity in respiration. Improvements in image quality were observed in both simulated and clinical studies.

With increasing availability of multimodality imaging systems, high-resolution anatomical images can be used to guide the reconstruction of emission tomography studies. By measuring reader performance on a lesion detection task, this study investigates the improvement in image-quality due to use of prior anatomical knowledge, for example organ or lesion boundaries, during SPECT reconstruction. Simulated (67)Ga -citrate source and attenuation distributions were created from the mathematical cardiac-torso (MCAT) anthropomorphic digital phantom. The SIMIND Monte Carlo software was then used to generate SPECT projection data. The data were reconstructed using the De Pierro maximum a posteriori (MAP) algorithm and the rescaled-block-iterative (RBI) algorithm for comparison. We compared several degrees of prior knowledge about the anatomy: no knowledge about the anatomy; knowledge of organ boundaries; knowledge of organ and lesion boundaries; and knowledge of organ, lesion, and pseudo-lesion (non-emission uptake altering) boundaries. The MAP reconstructions used quadratic smoothing within anatomical regions, but not across any provided region boundaries. The reconstructed images were read by human observers searching for lesions in a localization receiver operating characteristic (LROC) study of the relative detection/localization accuracies of the reconstruction algorithms. Area under the LROC curve was computed for each algorithm as the comparison metric. We also had humans read images reconstructed using different prior strengths to determine the optimal trade-off between data consistency and the anatomical prior. Finally by mixing together images reconstructed with and without the prior, we tested to see if having an anatomical prior only some of the time changes the observer's detection/localization accuracy on lesions where no boundary prior is available. We found that anatomical priors including organ and lesion boundaries improve observer performance on the lesion detection/localization task. Use of just organ boundaries did not provide a statistically significant improvement in performance however. We also found that optimal prior strength depends on the level of anatomical knowledge, with a broad plateau in which observer performance is near optimal. We found no evidence that having anatomical priors use lesion boundaries only when available changes the observer's performance when they are not available. We conclude that use of anatomical priors with organ and lesion boundaries improves reader performance on a lesion-detection/localization task, and that pseudo-lesion boundaries do not hurt reader performance. However, we did not find evidence that a prior using only organ boundaries helps observer performance. Therefore we suggest prior strength should be tuned to the organ-only case, since a prior will likely not be available for all lesions.

Using psychophysical studies, the authors have evaluated the effectiveness of various strategies for compensating for physical degradations in SPECT imaging. The particular application was Ga-67-citrate imaging of mediastinal tumors, which was chosen because Ga-67 is a particularly challenging radionuclide for imaging. The test strategies included compensations for nonuniform attenuation, distance-dependent spatial resolution, and scatter applied in various combinations as part of iterative reconstructions with the rescaled block iterative-expectation maximization (RBI-EM) algorithm. The authors also evaluated filtered backprojection reconstructions. Strategies were compared on the basis of human-observer studies of lesion localization and detection accuracy using the localization receiver operating characteristics (LROC) paradigm. These studies involved hybrid images which were obtained by adding the projections of Monte Carlo-simulated lesions to disease-free clinical projection data. The background variability in these images can provide a more realistic assessment of the relative utility of reconstruction strategies than images from anthropomorphic digital phantoms. The clinical datasets were obtained using a GE-VG dual-detector SPECT system with CT-estimated attenuation maps. After determining a target lesion contrast, they conducted pilot LROC studies to obtain a near-optimal set of reconstruction parameters for each strategy, and then conducted the strategy comparison study. The results indicate improved detection accuracy with RBI-EM as more compensations are applied within the reconstruction. The relative rankings of the test strategies agreed in most cases with those of previous studies that employed simulated projections of digital anthropomorphic phantoms, thus confirming the findings of those studies.

The purpose of the study was to evaluate the resolution recovery in the list-mode iterative reconstruction algorithm (LMIRA) for SPECT. In this study we compare the performance of the proposed method with other iterative resolution recovery methods for different noise levels. We developed an iterative reconstruction method which uses list-mode data instead of binned data. The new algorithm makes use of a more accurate model of the collimator structure. We compared the SPECT list-mode reconstruction with MLEM, OSEM and RBI, all including resolution recovery. For the evaluation we used Gaussian shaped sources with different FWHM at three different locations and three noise levels. For these distributions we calculated the reconstructed images for a different number of iterations. The absolute error for the reconstructed images was used to evaluate the performance. The performance of all four methods is comparable for the sources located in the centre of the field of view. For the sources located out of the centre, the error of the list-mode method is significantly lower than that of the other methods. Splitting the system model into a separate object-dependent and detector-dependent module gives us a flexible reconstruction method. With this we can very easily adapt the resolution recovery to different collimator types.

The algorithm we consider here is a block-iterative (or ordered subset) version of the interior point algorithm for transmission reconstruction. Our algorithm is an interior point method because each vector of the iterative sequence [x(k)], k = 0, 1, 2, ... satisfies the constraints a(j) < x(j)k < b(j), j = 1, ..., J. Because it is a block-iterative algorithm that reconstructs the transmission attenuation map and places constraints above and below the pixel values of the reconstructed image, we call it the BITAB method. Computer simulations using the three-dimensional mathematical cardiac and torso phantom, reveal that the BITAB algorithm in conjunction with reasonably selected prior upper and lower bounds has the potential to improve the accuracy of the reconstructed attenuation coefficients from truncated fan beam transmission projections. By suitably selecting the bounds, it is possible to restrict the over estimation of coefficients outside the fully sampled region, that results from reconstructing truncated fan beam projections with iterative transmission algorithms such as the maximum-likelihood gradient type algorithm.

Spatiotemporal reconstruction of cardiac-gated SPECT images permits us to obtain valuable information related to cardiac function. However, the task of reconstructing this four-dimensional (4-D) data set is computation intensive. Typically, these studies are reconstructed frame-by-frame: a nonoptimal approach because temporal correlations in the signal are not accounted for. In this work, we show that the compression and signal decorrelation properties of the Karhunen-Loève (KL) transform may be used to greatly simplify the spatiotemporal reconstruction problem. The gated projections are first KL transformed in the temporal direction. This results in a sequence of KL-transformed projection images for which the signal components are uncorrelated along the time axis. As a result, the 4-D reconstruction task is simplified to a series of three-dimensional (3-D) reconstructions in the KL domain. The reconstructed KL components are subsequently inverse KL transformed to obtain the entire spatiotemporal reconstruction set. Our simulation and clinical results indicate that KL processing provides image sequences that are less noisy than are conventional frame-by-frame reconstructions. Additionally, by discarding high-order KL components that are dominated by noise, we can achieve savings in computation time because fewer reconstructions are needed in comparison to conventional frame-by-frame reconstructions.

This paper presents a general method for synchronising digitised video data using a mathematical approach based upon the direct linear transformation reconstruction technique. The method was tested using digitised data from genlocked video recordings of gymnastic vaulting, tumbling, high bar and rings. The mean synchronisation error was less than 0.002 s for vaulting and less than 0.001 s for the other activities.

Unlabelled: Image artifacts from variable self-attenuation are recognized as major sources of diagnostic uncertainty in SPECT. For myocardial perfusion studies, an attenuation map is often obtained from a separate transmission study. However, for many applications such as bone SPECT, it has been believed to be unnecessary to obtain a transmission study to correct for the effects of attenuation. We have had significant success in clinical management of lower spine pain using bone SPECT. This success has led us to consider SPECT for the management of cervical spine pain. Cervical spine reconstructions without attenuation correction are difficult to interpret, because the high attenuation in the mandible and skull tends to decrease estimates of activity of the upper cervical spine, and the lower cervical/upper thoracic vertebrae are obscured by the shoulders. We present a technique that uses downscatter to provide attenuation correction for these acquisitions and compare it with other recognized attenuation correction techniques., Methods: An emission study is acquired using two windows: one for obtaining the photopeak data and another for obtaining the downscattered photons. A body outline is estimated from these datasets using a projection data thresholding method. From this outline, a uniform attenuation map is created using attenuation coefficients appropriate for 99mTc in water (0.154 cm(-1)). These maps are used in SPECT reconstruction using ordered-subset expectation maximization (OSEM). This method is compared with (a) no attenuation correction (NC), (b) conventional Chang attenuation correction based on the interactive determination of the body outline from the 99mTc emission photopeak data (ChangAC) and (c) OSEM correction using attenuation maps estimated with a line source and fanbeam collimators (transAC)., Results: Patient studies using scatterAC demonstrated a significant improvement in the uniformity of estimated cervical spine uptake in normal patients, compared with either NC or ChangAC. Results using scatterAC were similar to those of transAC. We also observed significant improvement in uniformity using scatterAC in SPECT of the lower back in obese patients, as well as the relative limitations of scatterAC versus nonuniform, transmission-based attenuation correction., Conclusion: Comparisons with reconstructions using transmission data for estimating attenuation demonstrate that reasonable quantitative accuracy can be obtained in SPECT of the cervical spine using this simple attenuation estimate. Both scatterAC and transAC appear to provide consistent and expected uniform spine uptake in the cervical spines of normal patients.

Unlabelled: Brain SPECT imaging using 99mTc lipophilic tracers such as hexamethyl propyleneamine oxime (HMPAO) attempts to estimate cerebral, cerebellar and subcortical perfusion by assessing the relative amount of tracer uptake among these regions. Most commonly, comparison is made with cerebellar activity. Because the assessment of relative tracer uptake may be rendered inaccurate by photon attenuation by the nonuniform attenuation properties of the head, brain SPECT reconstructions have been compared using attenuation correction (AC) with various methods for estimating the attenuation map., Methods: Patients underwent 99mTc-HMPAO brain SPECT with transmission line source AC hardware. In addition to the emission dataset, emission downscatter and transmission datasets were acquired. Iterative reconstructions using three different attenuation maps were investigated. These included: (a) that obtained from transmission imaging, (b) that obtained from segmentation of a reconstruction from a lower energy Compton scatter window and (c) a slice-independent, uniform, elliptical attenuation map. No AC was also compared., Results: Count profiles in patients having brain perfusion SPECT scans showed a significant difference in region count estimates in the brain depending on whether AC is used as well as on the attenuation map used. Scatter-based AC is able to provide external contour detection and attenuation compensation based on that contour, whereas transmission-based AC provides external contour detection as well as internal, nonuniform attenuation estimation and AC. If one considers transmission AC to be the clinical "gold standard," non-attenuation-corrected as well as fixed-ellipsoid, uniform attenuation-corrected studies provided unreliable regional estimates of tracer activity., Conclusion: This study shows the significant difference in clinical brain SPECT count profiles depending on how and whether there is compensation for attenuation. Based on prior studies validating the improved quantitative accuracy of SPECT using transmission-based AC, this study suggests that clinical 99mTc brain perfusion SPECT would benefit from and, in situations demanding rigorous quantitative assessment, requires transmission-based AC. Estimating attenuation maps with scatter-based methods was the next most accurate (clinical) method tested and can be used if and when transmission imaging cannot be used.

The goal of this investigation was to compare resolution recovery versus noise level of two methods for compensation of distance-dependent resolution (DDR) in SPECT imaging. The two methods of compensation were restoration filtering based on the frequency-distance relationship (FDR) prior to iterative reconstruction, and modelling DDR in the projector/backprojector pair employed in iterative reconstruction. FDR restoration filtering was computationally faster than modelling the detector response in iterative reconstruction. Using Gaussian diffusion to model the detector response in iterative reconstruction sped up the process by a factor of 2.5 over frequency domain filtering in the projector/backprojector pair. Gaussian diffusion modelling resulted in a better resolution versus noise tradeoff than either FDR restoration filtering or solely modelling attenuation in the projector/backprojector pair of iterative reconstruction. For the pixel size investigated herein (0.317 cm), accounting for DDR in the projector/backprojector pair by Gaussian diffusion, or by applying a blurring function based on the distance from the face of the collimator at each distance, resulted in very similar resolution recovery and slice noise level.

Quantitative parameters such as the maximum and total counts in a volume are influenced by the partial volume effect. The magnitude of this effect varies with the non-stationary and anisotropic spatial resolution in SPECT slices. The objective of this investigation was to determine whether iterative reconstruction which includes modelling of the three-dimensional (3D) spatial resolution of SPECT imaging can reduce the impact of the partial volume effect on the quantitation of activity compared with filtered backprojection (FBP) techniques which include low-pass, and linear restoration filtering using the frequency distance relationship (FDR). The iterative reconstruction algorithms investigated were maximum-likelihood expectation-maximization (MLEM), MLEM with ordered subset acceleration (ML-OS), and MLEM with acceleration by the rescaled-block-iterative technique (ML-RBI). The SIMIND Monte Carlo code was used to simulate small hot spherical objects in an elliptical cylinder with and without uniform background activity as imaged by a low-energy ultra-high-resolution (LEUHR) collimator. Centre count ratios (CCRs) and total count ratios (TCRs) were determined as the observed counts over true counts. CCRs were unstable while TCRs had a bias of approximately 10% for all iterative techniques. The variance in the TCRs for ML-OS and ML-RBI was clearly elevated over that of MLEM, with ML-RBI having the smaller elevation. TCRs obtained with FDR-Wiener filtering had a larger bias (approximately 30%) than any of the iterative reconstruction methods but near stationarity is also reached. Butterworth filtered results varied by 9.7% from the centre to the edge. The addition of background has an influence on the convergence rate and noise properties of iterative techniques.

Background: In single photon-emission computed tomographic imaging of the chest, nonuniform attenuation correction requires use of a patient-specific attenuation map. The aim of this study was to determine whether an estimate of the regions of the lungs and nonpulmonary tissues of the chest could be obtained by segmenting the photopeak and Compton scatter window images in a phantom and in patients to estimate patient-specific attenuation maps., Methods and Results: The photopeak and scatter window slices from 16 consecutive 99mTc-labeled sestamibi perfusion studies were segmented interactively. In these studies, visually reasonable regions could be obtained by estimating a "cold" lung region from scatter window data with additional anatomic information of the myocardium region, the backbone and sternum locations, the liver, and the rib cage from the photopeak window data. In an anthropomorphic torso phantom study and a patient study, comparison was made between the attenuation maps based on segmentation of the emission images and transmission imaging with a slant-hole collimator. It was determined that good agreement in the estimation of the body regions can be achieved with segmentation of the emission images in both the phantom and patient data. Attenuation correction using the maximum-likelihood expectation maximization method was performed on the phantom and the patient data. In both studies, attenuation correction with the segmented attenuation map improved uniformity of the inferior wall region in comparison with the other walls., Conclusions: The estimation of patient-specific attenuation maps by segmenting the scatter and photopeak window slices of 99mTc-labeled sestamibi studies may be a way of reducing the loss of specificity due to attenuation artifacts. The potential limitations on the accuracy of correction inherent in the method due to the estimation of the regions and assignment of the attenuation coefficients need to be determined further, and the method needs to be further automated before it can be considered for routine clinical use.

The application of stationary restoration techniques to SPECT images assumes that the modulation transfer function (MTF) of the imaging system is shift invariant. It was hypothesized that using intrinsic attenuation correction (i.e., methods which explicitly invert the exponential radon transform) would yield a three-dimensional (3-D) MTF which varies less with position within the transverse slices than the combined conjugate view two-dimensional (2-D) MTF varies with depth. Thus the assumption of shift invariance would become less of an approximation for 3-D post- than for 2-D pre-reconstruction restoration filtering. SPECT acquisitions were obtained from point sources located at various positions in three differently shaped, water-filled phantoms. The data were reconstructed with intrinsic attenuation correction, and 3-D MTFs were calculated. Four different intrinsic attenuation correction methods were compared: (1) exponentially weighted backprojection, (2) a modified exponentially weighted backprojection as described by Tanaka et al. [Phys. Med. Biol. 29, 1489-1500 (1984)], (3) a Fourier domain technique as described by Bellini et al. [IEEE Trans. ASSP 27, 213-218 (1979)], and (4) the circular harmonic transform (CHT) method as described by Hawkins et al. [IEEE Trans. Med. Imag. 7, 135-148 (1988)]. The dependence of the 3-D MTF obtained with these methods, on point source location within an attenuator, and on shape of the attenuator, was studied. These 3-D MTFs were compared to: (1) those MTFs obtained with no attenuation correction, and (2) the depth dependence of the arithmetic mean combined conjugate view 2-D MTFs.(ABSTRACT TRUNCATED AT 250 WORDS)

A wide variety of image segmentation techniques have been proposed for the measurement of organ or lesion volumes in SPECT images. Evaluation of the relative performance of the various methods is difficult due to wide variations in system response characteristics, size, shape, and contrast of the imaged objects, and image acquisition and processing techniques. Selected image segmentation methods for volume quantitation in SPECT were applied to a set of simulated SPECT images containing objects ranging in volume from 1.8 to 113.1 cc. The specific segmentation methods included: (1) operator drawn regions of interest, (2) count-based methods, (3) three levels of fixed thresholds, (4) an adaptive threshold (GLH method), (5) a two-dimensional (2-D) edge detection method, and (6) a three-dimensional (3-D) edge detection method. In general, the 3-D edge detection method required minimal operator intervention while providing the most accurate and consistent estimates of object volume across changes in object contrast and size.

A study of activity quantitation with prereconstruction Metz filtering and use of the scatter degradation factor (SDF) to numerically correct for scatter was conducted. The ratio of the count rate per unit activity for source locations within a 30 x 23-cm water-filled tub phantom to the count rate per unit activity for Tc-99m point sources of known activity imaged in air was used to judge the accuracy of activity determination. The investigation was conducted for certain locations within the tub when it was uniformly filled with Tc-99m activity, and for the same locations at the center of 5, 4, 3, and 2-cm diam, hot spheres imaged in a cold background. The source locations were the center, and one-fourth, one-half, and three-fourths the major axis. Various methods of combining the conjugate views for use with prereconstruction attenuation correction (arithmetic and geometric mean), and extent to which the Metz filter followed the inverse filter before rolling off to suppress noise were investigated. Without Metz filtering, attenuation correction was performed using a transmission curve that included buildup. With Metz filtering, the good-geometry attenuation coefficient was used and the combined views were scaled by the SDF calculated for the average body thickness. Depending on the size of the sphere and the extent to which the inverse filter was followed, Metz filtering combined with use of the SDF improved the accuracy of activity quantitation.

Stationary and nonstationary finite-impulse-response (FIR) implementations of the count-dependent Metz filter were investigated in this study. Filter size was observed to be an important variable controlling image quality. For Metz filtering of 128 X 128 pixel images at least a 15 X 15 term FIR filter was deemed necessary. By using an algorithm which selected between a set of preformed FIR filters based on pixel count, a nonstationary FIR implementation of the Metz filter was developed which required very little increase in execution time to stationary filtering. In a limited comparison of 'tumor' detection with stationary and nonstationary FIR filtering all of the Metz filtering techniques showed a significant improvement in detection when compared to the unprocessed images. However, no significant difference was observed between the stationary and nonstationary Metz filtering techniques. Thus, for Metz filters optimized solely on the basis of count, nonstationary FIR filtering does not seem to offer an advantage when compared to stationary filtering.

Image restoration using the constrained least-squares (CLS) method theoretically adapts to the image being processed. In addition, it only requires knowing the modulation transfer function of the imaging system when applied to nuclear medicine images. Prompted by these observations, a systematic evaluation of the effects of the form of the "coarseness function" [C(f)] used by the CLS method has been conducted. Nine C(f)'s are evaluated using an observer preference and a normalized mean-squared error (NMSE) criterion. This evaluation is conducted for three modulation transfer functions and a wide range of count levels. The results of the subjective studies support using the form of C(f) which has been most widely employed in previous studies, i.e., the form designed to minimize the energy in the second derivative of the restored image. A different form of C(f) is generally found to be optimal by the mean-squared error criterion. The CLS method is then compared to: (1) no processing, (2) count-dependent smoothing, and (3) count-dependent Metz restoration. When evaluated using objective measurements of error and contrast, the CLS method is found to be slightly inferior to the best method, Metz restoration. However, CLS restoration is found to be equal to or better than the other methods when judged by the results of observer preference studies.

Three-dimensional patterns of variation in the intensity of acetylcholinesterase histochemical staining and the width of stain-defined subregions were quantified for the dentate gyrus of the adult male Long-Evans rat. Matched tissue sections sampled through the central hippocampal formation of five rats were measured with a digital image analysis computer system. The width and stain intensity were determined for defined portions of the dentate gyrus related to gross acetylcholinesterase staining patterns and the known distribution of dentate afferents. Normalized values reflecting stain intensity at defined positions within this standardized sampling array were examined to investigate regional differences in acetylcholinesterase distribution along the primary dendritic axis of dentate granule neurons. The data illustrate quantitative differences in the partitioning of acetylcholinesterase as a function of intrahippocampal position. The variation is more pronounced in the septal-temporal axis than the granule cell layer crest-tip axis. Furthermore, the septal-temporal variations in acetylcholinesterase intensity demonstrate some independence according to proximal-distal location within the molecular layer. The results suggest that acetylcholinesterase distribution within the dentate gyrus may reflect local physiological characteristics of those afferent systems related to this enzyme, including but not necessarily limited to those that are specifically cholinergic.