Accelerated median root prior reconstruction for pinhole single-photon emission tomography (SPET)

Pinhole collimation can be used to improve spatial resolution in SPET. However, the resolution improvement is achieved at the cost of reduced sensitivity, which leads to projection images with poor statistics. Images reconstructed from these projections using the maximum likelihood expectation maximization (ML-EM) algorithms, which have been used to reduce the artefacts generated by the filtered backprojection (FBP) based reconstruction, suffer from noise/bias trade-off: noise contaminates the images at high iteration numbers, whereas early abortion of the algorithm produces images that are excessively smooth and biased towards the initial estimate of the algorithm. To limit the noise accumulation we propose the use of the pinhole median root prior (PH-MRP) reconstruction algorithm. MRP is a Bayesian reconstruction method that has already been used in PET imaging and shown to possess good noise reduction and edge preservation properties. In this study the PH-MRP algorithm was accelerated with the ordered subsets (OS) procedure and compared to the FBP, OS-EM and conventional Bayesian reconstruction methods in terms of noise reduction, quantitative accuracy, edge preservation and visual quality. The results showed that the accelerated PH-MRP algorithm was very robust. It provided visually pleasing images with lower noise level than the FBP or OS-EM and with smaller bias and sharper edges than the conventional Bayesian methods.     

Prospects for quantitative computed tomography imaging in the presence of foreign metal bodies using statistical image reconstruction

X-ray computed tomography (CT) images of patients bearing metal intracavitary applicators or other metal foreign objects exhibit severe artifacts including streaks and aliasing. We have systematically evaluated via computer simulations the impact of scattered radiation, the polyenergetic spectrum, and measurement noise on the performance of three reconstruction algorithms: conventional filtered backprojection (FBP), deterministic iterative deblurring, and a new iterative algorithm, alternating minimization (AM), based on a CT detector model that includes noise, scatter, and polyenergetic spectra. Contrary to the dominant view of the literature, FBP streaking artifacts are due mostly to mismatches between FBP's simplified model of CT detector response and the physical process of signal acquisition. Artifacts on AM images are significantly mitigated as this algorithm substantially reduces detector-model mismatches. However, metal artifacts are reduced to acceptable levels only when prior knowledge of the metal object in the patient, including its pose, shape, and attenuation map, are used to constrain AM's iterations. AM image reconstruction, in combination with object-constrained CT to estimate the pose of metal objects in the patient, is a promising approach for effectively mitigating metal artifacts and making quantitative estimation of tissue attenuation coefficients a clinical possibility.     

Noise and resolution in images reconstructed with FBP and OSC algorithms for CT

This paper presents a comparison between an analytical and a statistical iterative reconstruction algorithm for computed transmission tomography concerning their noise and resolution performance. The reconstruction of two-dimensional images from simulated fan-beam transmission data is performed with a filtered back-projection (FBP) type reconstruction and an iterative ordered subsets convex (OSC) maximum-likelihood method. A special software phantom, which allows measuring the resolution and noise in a nonambiguous way, is used to simulate transmission tomography scans with different signal-to-noise ratios (SNR). The noise and modulation transfer function is calculated for FBP and OSC reconstruction at several positions, distributed over the field-of-view (FOV). The reconstruction with OSC using different numbers of subsets shows an inverse linear relation to the number of iterations that are necessary to reach a certain resolution and SNR, i.e., increasing the number of subsets by a factor x reduces the number of required iterations by the same factor. The OSC algorithm is able to achieve a nearly homogeneous high resolution over the whole FOV, which is not achieved with FBP. The OSC method achieves a lower level of noise compared with FBP at the same resolution. The reconstruction with OSC can save a factor of up to nine of x-ray dose compared with FBP in the investigated range of noise levels.     

Fan-beam filtered-backprojection reconstruction without backprojection weight

In this paper, we address the problem of two-dimensional image reconstruction from fan-beam data acquired along a full 2pi scan. Conventional approaches that follow the filtered-backprojection (FBP) structure require a weighted backprojection with the weight depending on the point to be reconstructed and also on the source position; this weight appears only in the case of divergent beam geometries. Compared to reconstruction from parallel-beam data, the backprojection weight implies an increase in computational effort and is also thought to have some negative impacts on noise properties of the reconstructed images. We demonstrate here that direct FBP reconstruction from full-scan fan-beam data is possible with no backprojection weight. Using computer-simulated, realistic fan-beam data, we compared our novel FBP formula with no backprojection weight to the use of an FBP formula based on equal weighting of all data. Comparisons in terms of signal-to-noise ratio, spatial resolution and computational efficiency are presented. These studies show that the formula we suggest yields images with a reduced noise level, at almost identical spatial resolution. This effect increases quickly with the distance from the center of the field of view, from 0% at the center to 20% less noise at 20 cm, and to 40% less noise at 25 cm. Furthermore, the suggested method is computationally less demanding and reduces computation time with a gain that was found to vary between 12% and 43% on the computers used for evaluation.     

A computer simulation platform for the optimization of a breast tomosynthesis system

In breast tomosynthesis there is a compromise between resolution, noise, and acquisition speed for a given glandular dose. The purpose of the present work is to develop a simulation platform to investigate the potential imaging performance for the many possible tomosynthesis system configurations. The simulation platform was used to investigate the dependence of image blur and signal difference to noise ratio (SDNR) for several different tomosynthesis acquisition configurations. Simulated projections of a slanted thin tungsten wire placed in different object planes were modified according to the detector's modulation transfer function (MTF), with or without pixel binning. In addition, the focal spot blur (FSB), which depends on the location of the wire, the system geometry, the source-detector movement speed, and the exposure time, was also incorporated into the projections. Both expectation maximization (EM) and filtered back projection (FBP) based algorithms were used for 3D image reconstruction. The in-plane MTF was calculated from the reconstructed image of the wire. To evaluate the noise performance, simulated noiseless projections of calcification and tumor in uniform breast tissue were modified with the noise power spectrum (NPS) calculated from a cascaded linear system model for the detector for a given x-ray dose. The SDNR of the reconstructed images was calculated with different tomosynthesis configurations, e.g., pixel binning, view number, and angular range. Our results showed that for a source-to-imager distance (SID) of 66 cm, pixel binning (2 x 2) caused more degradation to the in-plane MTF than the blur caused by the moving focal spot and reconstruction. The in-depth resolution can be improved by increasing the angular range.     

Survey of practitioners’ competency for diagnosis of acute diseases manifest on chest X-ray

Background

Model-based iterative reconstruction (MBIR) is a promising reconstruction method which could improve CT image quality with low radiation dose. The purpose of this study was to demonstrate the advantage of using MBIR for noise reduction and image quality improvement in low dose chest CT for children with necrotizing pneumonia, over the adaptive statistical iterative reconstruction (ASIR) and conventional filtered back-projection (FBP) technique.

Methods

Twenty-six children with necrotizing pneumonia (aged 2 months to 11 years) who underwent standard of care low dose CT scans were included. Thinner-slice (0.625 mm) images were retrospectively reconstructed using MBIR, ASIR and conventional FBP techniques. Image noise and signal-to-noise ratio (SNR) for these thin-slice images were measured and statistically analyzed using ANOVA. Two radiologists independently analyzed the image quality for detecting necrotic lesions, and results were compared using a Friedman’s test.

Results

Radiation dose for the overall patient population was 0.59 mSv. There was a significant improvement in the high-density and low-contrast resolution of the MBIR reconstruction resulting in more detection and better identification of necrotic lesions (38 lesions in 0.625 mm MBIR images vs. 29 lesions in 0.625 mm FBP images). The subjective display scores (mean ± standard deviation) for the detection of necrotic lesions were 5.0 ± 0.0, 2.8 ± 0.4 and 2.5 ± 0.5 with MBIR, ASIR and FBP reconstruction, respectively, and the respective objective image noise was 13.9 ± 4.0HU, 24.9 ± 6.6HU and 33.8 ± 8.7HU. The image noise decreased by 58.9 and 26.3% in MBIR images as compared to FBP and ASIR images. Additionally, the SNR of MBIR images was significantly higher than FBP images and ASIR images.

Conclusions

The quality of chest CT images obtained by MBIR in children with necrotizing pneumonia was significantly improved by the MBIR technique as compared to the ASIR and FBP reconstruction, to provide a more confident and accurate diagnosis for necrotizing pneumonia.

     

Dual-energy contrast-enhanced breast tomosynthesis: optimization of beam quality for dose and image quality

Dual-energy contrast-enhanced breast tomosynthesis is a promising technique to obtain three-dimensional functional information from the breast with high resolution and speed. To optimize this new method, this study searched for the beam quality that maximized image quality in terms of mass detection performance. A digital tomosynthesis system was modeled using a fast ray-tracing algorithm, which created simulated projection images by tracking photons through a voxelized anatomical breast phantom containing iodinated lesions. The single-energy images were combined into dual-energy images through a weighted log subtraction process. The weighting factor was optimized to minimize anatomical noise, while the dose distribution was chosen to minimize quantum noise. The dual-energy images were analyzed for the signal difference to noise ratio (SdNR) of iodinated masses. The fast ray-tracing explored 523?776 dual-energy combinations to identify which yields optimum mass SdNR. The ray-tracing results were verified using a Monte Carlo model for a breast tomosynthesis system with a selenium-based flat-panel detector. The projection images from our voxelized breast phantom were obtained at a constant total glandular dose. The projections were combined using weighted log subtraction and reconstructed using commercial reconstruction software. The lesion SdNR was measured in the central reconstructed slice. The SdNR performance varied markedly across the kVp and filtration space. Ray-tracing results indicated that the mass SdNR was maximized with a high-energy tungsten beam at 49 kVp with 92.5 μm of copper filtration and a low-energy tungsten beam at 49 kVp with 95 μm of tin filtration. This result was consistent with Monte Carlo findings. This mammographic technique led to a mass SdNR of 0.92 ± 0.03 in the projections and 3.68 ± 0.19 in the reconstructed slices. These values were markedly higher than those for non-optimized techniques. Our findings indicate that dual-energy breast tomosynthesis can be performed optimally at 49 kVp with alternative copper and tin filters, with reconstruction following weighted subtraction. The optimum technique provides best visibility of iodine against structured breast background in dual-energy contrast-enhanced breast tomosynthesis.     

Evaluation of Apparent Noise on CT Images Using Moving Average Filters

This study aims to devise a simple method for evaluating the magnitude of texture noise (apparent noise) observed on computed tomography (CT) images scanned at a low radiation dose and reconstructed using iterative reconstruction (IR) and deep learning reconstruction (DLR) algorithms, and to evaluate the apparent noise in CT images reconstructed using the filtered back projection (FBP), IR, and two types of DLR (AiCE Body and AiCE Body Sharp) algorithms. We set a square region of interest (ROI) on CT images of standard- and obese-sized low-contrast phantoms, slid different-sized moving average filters in the ROI vertically and horizontally in steps of 1 pixel, and calculated the standard deviation (SD) of the mean CT values for each filter size. The SD of the mean CT values was fitted with a curve inversely proportional to the filter size, and an apparent noise index was determined from the curve-fitting formula. The apparent noise index of AiCE Body Sharp images for a given mAs value was approximately 58, 23, and 18% lower than that of the FBP, AIDR 3D, and AiCE Body images, respectively. The apparent noise index was considered to reflect noise power spectrum values at lower spatial frequency. Moreover, the apparent noise index was inversely proportional to the square roots of the mAs values. Thus, the apparent noise index could be a useful indicator to quantify and compare texture noise on CT images obtained with different scan parameters and reconstruction algorithms.

     

Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization

The objective of this paper is to evaluate an iterative maximum likelihood (ML) cone-beam computed tomography (CBCT) reconstruction with total variation (TV) regularization with respect to the robustness of the algorithm due to data inconsistencies. Three different and (for clinical application) typical classes of errors are considered for simulated phantom and measured projection data: quantum noise, defect detector pixels and projection matrix errors. To quantify those errors we apply error measures like mean square error, signal-to-noise ratio, contrast-to-noise ratio and streak indicator. These measures are derived from linear signal theory and generalized and applied for nonlinear signal reconstruction. For quality check, we focus on resolution and CT-number linearity based on a Catphan phantom. All comparisons are made versus the clinical standard, the filtered backprojection algorithm (FBP). In our results, we confirm and substantially extend previous results on iterative reconstruction such as massive undersampling of the number of projections. Errors of projection matrix parameters of up to 1° projection angle deviations are still in the tolerance level. Single defect pixels exhibit ring artifacts for each method. However using defect pixel compensation, allows up to 40% of defect pixels for passing the standard clinical quality check. Further, the iterative algorithm is extraordinarily robust in the low photon regime (down to 0.05 mAs) when compared to FPB, allowing for extremely low-dose image acquisitions, a substantial issue when considering daily CBCT imaging for position correction in radiotherapy. We conclude that the ML method studied herein is robust under clinical quality assurance conditions. Consequently, low-dose regime imaging, especially for daily patient localization in radiation therapy is possible without change of the current hardware of the imaging system.     

A comparison of reconstruction algorithms for breast tomosynthesis

Three algorithms for breast tomosynthesis reconstruction were compared in this paper, including (1) a back-projection (BP) algorithm (equivalent to the shift-and-add algorithm), (2) a Feldkamp filtered back-projection (FBP) algorithm, and (3) an iterative Maximum Likelihood (ML) algorithm. Our breast tomosynthesis system acquires 11 low-dose projections over a 50 degree angular range using an a-Si (CsI:Tl) flat-panel detector. The detector was stationary during the acquisition. Quality metrics such as signal difference to noise ratio (SDNR) and artifact spread function (ASF) were used for quantitative evaluation of tomosynthesis reconstructions. The results of the quantitative evaluation were in good agreement with the results of the qualitative assessment. In patient imaging, the superimposed breast tissues observed in two-dimensional (2D) mammograms were separated in tomosynthesis reconstructions by all three algorithms. It was shown in both phantom imaging and patient imaging that the BP algorithm provided the best SDNR for low-contrast masses but the conspicuity of the feature details was limited by interplane artifacts; the FBP algorithm provided the highest edge sharpness for microcalcifications but the quality of masses was poor; the information of both the masses and the microcalcifications were well restored with balanced quality by the ML algorithm, superior to the results from the other two algorithms.     

Half-scan fan-beam computed tomography with improved noise and resolution properties

Half-scan strategy can be used for reducing scanning time and radiation dose delivered to the patient in fan-beam computed tomography (CT). In helical CT, the data weighting/interpolation functions are often devised based upon half-scan configurations. The half-scan fan-beam filtered backprojection (FFBP) algorithm is generally used for image reconstruction from half-scan data. It can, however, be susceptible to sample aliasing and data noise for configurations with short focal lengths and/or large fan-angles, leading to nonuniform resolution and noise properties in reconstructed images. Uniform resolution and noise properties are generally desired because they may lead to an increased utility of reconstructed images in estimation and/or detection/classification tasks. In this work, we propose an algorithm for reconstruction of images with uniform noise and resolution properties in half-scan CT. In an attempt to evaluate the image-noise properties, we derive analytic expressions for image variances obtained by use of the half-scan algorithms. We also perform numerical studies to assess quantitatively the resolution and noise properties of the algorithms. The results in these studies confirm that the proposed algorithm yields images with more uniform spatial resolution and with lower and more uniform noise levels than does the half-scan FFBP algorithm. Empirical results obtained in noise studies also verify the validity of the derived expressions for image variances. The proposed algorithm would be particularly useful for image reconstruction from data acquired by use of configurations with short focal lengths and large field of measurement, which may be encountered in compact micro-CT and radiation therapeutic CT applications. The analytic results of the image-noise properties can be used for image-quality assessment in detection/classification tasks by use of model-observers.     

Development and evaluation of an exact fan-beam reconstruction algorithm using an equal weighting scheme via locally compensated filtered backprojection (LCFBP)

A novel exact fan-beam image reconstruction formula is presented and validated using both phantom data and clinical data. This algorithm takes the form of the standard ramp filtered backprojection (FBP) algorithm plus local compensation terms. This algorithm will be referred to as a locally compensated filtered backprojection (LCFBP). An equal weighting scheme is utilized in this algorithm in order to properly account for redundantly measured projection data. The algorithm has the desirable property of maintaining a mathematically exact result for: the full scan mode (2pi), the short scan mode (pi+ full fan angle), and the supershort scan mode [less than (pi+ full fan angle)]. Another desirable feature of this algorithm is that it is derivative-free. This feature is beneficial in preserving the spatial resolution of the reconstructed images. The third feature is that an equal weighting scheme has been utilized in the algorithm, thus the new algorithm has better noise properties than the standard filtered backprojection image reconstruction with a smooth weighting function. Both phantom data and clinical data were utilized to validate the algorithm and demonstrate the superior noise properties of the new algorithm.     

An improved ordered subsets expectation maximization positron emission computerized tomography reconstruction

Ordered subsets expectation maximization (OS-EM) reconstruction method is usually used in positron emission tomography (PET). But it has some disadvantage such as long computation time and bad reconstruction quality. Filtered back projection (FBP), that has many advantages such as simple structure and short reconstruction time, is firstly introduced into the initialization stage of the OS-EM to fast the reconstruction process. Then, the smoothness method is applied after the OS-EM algorithm to improve the reconstruction speed and quality. The reconstructed images are compared for both the simulated phantom data and the brain magnetic resonance imaging data. The improved OS-EM is shown to be more feasible than the standard OS-EM within the same iteration steps and in higher signal noise ratio (SNR) condition.     

Iterative reconstruction of a region of interest for transmission tomography

It was shown that images reconstructed for transmission tomography with iterative maximum likelihood (ML) algorithms exhibit a higher signal-to-noise ratio than images reconstructed with filtered back-projection type algorithms. However, a drawback of ML reconstruction in particular and iterative reconstruction in general is the requirement that the reconstructed field of view (FOV) has to cover the whole volume that contributes to the absorption. In the case of a high resolution reconstruction, this demands a huge number of voxels. This article shows how an iterative ML reconstruction can be limited to a region of interest (ROI) without losing the advantages of a ML reconstruction. Compared with a full FOV ML reconstruction, the reconstruction speed is mainly increased by reducing the number of voxels which are necessary for a ROI reconstruction. In addition, the speed of convergence is increased.     

Gaussian prefiltering of 123I DAT SPECT images when using depth-independent resolution recovery

Previously we have investigated a depth-independent compensation for collimator detector response (CDR) included in the OSEM reconstruction, intended for SPECT images that have been corrected for scatter and septal penetration using convolution-based methods. In this work, the aim was to study how different filtering strategies affect contrast as a function of noise when using Gaussian smoothing filters in combination with the above-described CDR compensation. The evaluation was performed for (123)I dopamine transporter (DAT) SPECT images. Prefiltering with 2D Gaussian filter kernels, where the deterioration in resolution is included in the depth-independent CDR compensation, was compared to conventional postfiltering with 3D Gaussian filter kernels. Images reconstructed without filtering are also included in the comparison. It was found that there is little benefit in noise reduction when using CDR compensation. However, this variant of prefiltering gives consistently higher contrasts as a function of noise compared with the postfiltering alternative, and that could be of interest when using other types of filters with contrast improving properties.     

Megavoltage CT on a tomotherapy system.

A megavoltage computed tomography (MVCT) system was developed on the University of Wisconsin tomotherapy benchtop. This system can operate either axially or helically, and collect transmission data without any bounds on delivered dose. Scan times as low as 12 s per slice are possible, and scans were run with linac output rates of 100 MU min(-1), although the system can be tuned to deliver arbitrarily low dose rates. Images were reconstructed with clinically reasonable doses ranging from 8 to 12 cGy. These images delineate contrasts below 2% and resolutions of 3.0 mm. Thus, the MVCT image quality of this system should be sufficient for verifying the patient's position and anatomy prior to radiotherapy. Additionally, synthetic data were used to test the potential for improved MVCT contrast using maximum-likelihood (ML) reconstruction. Specifically, the maximum-likelihood expectation-maximization (ML-EM) algorithm and a transmission ML algorithm were compared with filtered backprojection (FBP). It was found that for expected clinical MVCT doses enough imaging photons are used such that little benefit is conferred by the improved noise model of ML algorithms. For significantly lower doses, some quantitative improvement is achieved through ML reconstruction. Nonetheless, the image quality at those lower doses is not satisfactory for radiotherapy verification.     

MicroPET reconstruction with random coincidence correction via a joint Poisson model

Positron emission tomography (PET) can provide in vivo, quantitative and functional information for diagnosis; however, PET image quality depends highly on a reconstruction algorithm. Iterative algorithms, such as the maximum likelihood expectation maximization (MLEM) algorithm, are rapidly becoming the standards for image reconstruction in emission-computed tomography. The conventional MLEM algorithm utilized the Poisson model in its system matrix, which is no longer valid for delay-subtraction of randomly corrected data. The aim of this study is to overcome this problem. The maximum likelihood estimation using the expectation maximum algorithm (MLE-EM) is adopted and modified to reconstruct microPET images using random correction from joint prompt and delay sinograms; this reconstruction method is called PDEM. The proposed joint Poisson model preserves Poisson properties without increasing the variance (noise) associated with random correction. The work here is an initial application/demonstration without applied normalization, scattering, attenuation, and arc correction. The coefficients of variation (CV) and full width at half-maximum (FWHM) values were utilized to compare the quality of reconstructed microPET images of physical phantoms acquired by filtered backprojection (FBP), ordered subsets-expected maximum (OSEM) and PDEM approaches. Experimental and simulated results demonstrate that the proposed PDEM produces better image quality than the FBP and OSEM approaches.     

Statistical reconstruction for x-ray computed tomography using energy-integrating detectors

Statistical image reconstruction (SR) algorithms have the potential to significantly reduce x-ray CT image artefacts because they use a more accurate model than conventional filtered backprojection and can incorporate effects such as noise, incomplete data and nonlinear detector response. Most SR algorithms assume that the CT detectors are photon-counting devices and generate Poisson-distributed signals. However, actual CT detectors integrate energy from the x-ray beam and exhibit compound Poisson-distributed signal statistics. This study presents the first assessment of the impact on image quality of the resultant mismatch between the detector and signal statistics models assumed by the sinogram data model and the reconstruction algorithm. A 2D CT projection simulator was created to generate synthetic polyenergetic transmission data assuming (i) photon-counting with simple Poisson-distributed signals and (ii) energy-weighted detection with compound Poisson-distributed signals. An alternating minimization (AM) algorithm was used to reconstruct images from the data models (i) and (ii) for a typical abdominal scan protocol with incident particle fluence levels ranging from 10(5) to 1.6 x 10(6) photons/detector. The images reconstructed from data models (i) and (ii) were compared by visual inspection and image-quality figures of merit. The reconstructed image quality degraded significantly when the means were mismatched from the assumed model. However, if the signal means are appropriately modified, images from data models (i) and (ii) did not differ significantly even when SNR is very low. While data-mean mismatches characteristic of the difference between particle-fluence and energy-fluence transmission can cause significant streaking and cupping artefacts, the mismatch between the actual and assumed CT detector signal statistics did not significantly degrade image quality once systematic data means mismatches were corrected.     

Fast and exact 2D image reconstruction by means of Chebyshev decomposition and backprojection

A new algorithm for the reconstruction of two-dimensional (2D) images from projections is described. The algorithm is based on the decomposition of the projections into Chebyshev polynomials of the second kind, which are the ideal basis functions for this application. The Chebyshev decomposition is done via the fast discrete sine transform. A discrete reconstruction filter is applied that corresponds to the ramp filter used in standard filtered backprojection (FBP) reconstruction. In contrast to FBP, the filter is applied to the Chebyshev coefficients and not to the Fourier coefficients of the projections. Then the reconstructed image is simply obtained by means of backprojection. Consequently, the method can be considered as a Chebyshev-domain filtered backprojection (CD-FBP). The total calculation time is dominated by the backprojection step only and is comparable to FBP. The merits of CD-FBP as compared with standard FBP are that: (a) The result is exact if the 2D function to be reconstructed can be decomposed into polynomials of finite degree, and if the sampling is adequate. Otherwise a polynomial approximation results. (b) The algorithm is inherently discrete. (c) It is particularly well suited for reconstructions from projections with non-equidistant samples that occur for instance in 2D PET (positron emission tomography) imaging and in a special form of fan beam scanning. Examples of applications comprise reconstructions of the Shepp and Logan head phantom in various sampling geometries, and a real PET test object. In the PET example an increased resolution is observed in comparison with standard FBP.     

A 3D model of non-uniform attenuation and detector response for efficient iterative reconstruction in SPECT

A 3D physical model for iterative reconstruction in SPECT has been developed and applied to experimental data. The model incorporates non-uniform attenuation using reconstructed transmission CT data and distance-dependent detector response based on response function measurements over a range of distances from the detector. The 3D model has been implemented in a computationally efficient manner with practical memory requirements. The features of the model that provide efficiency are described including a new region-dependent reconstruction (RDR) technique. With RDR, filtered backprojection is used to reconstruct areas of the image of minimal clinical importance, and the result is used to supplement the iterative reconstruction of the clinically important areas of the image. The 3D model was incorporated into the maximum likelihood-expectation maximization (ML-EM) reconstruction algorithm and tested in three phantom studies--a point source, a uniform cylinder, and an anthropomorphic thorax--and a patient 9Tc(m) sestamibi study. Reconstructed images with the 3D method exhibited excellent noise and resolution characteristics. With the sestamibi data, the RDR technique produced essentially the conventional ML-EM estimate in the cardiac region with substantial time savings.