3D RBI-EM reconstruction with spherically-symmetric basis function for SPECT rotating slat collimator

A single photon emission computed tomography (SPECT) rotating slat collimator with strip detector acquires distance-weighted plane integral data, along with the attenuation factor and distance-dependent detector response. In order to image a 3D object, the slat collimator device has first to spin around its axis and then rotate around the object to produce 3D projection measurements. Compared to the slice-by-slice 2D reconstruction for the parallel-hole collimator and line integral data, a more complex 3D reconstruction is needed for the slat collimator and plane integral data. In this paper, we propose a 3D RBI-EM reconstruction algorithm with spherically-symmetric basis function, also called 'blobs', for the slat collimator. It has a closed and spherically symmetric analytical expression for the 3D Radon transform, which makes it easier to compute the plane integral than the voxel. It is completely localized in the spatial domain and nearly band-limited in the frequency domain. Its size and shape can be controlled by several parameters to have desired reconstructed image quality. A mathematical lesion phantom study has demonstrated that the blob reconstruction can achieve better contrast-noise trade-offs than the voxel reconstruction without greatly degrading the image resolution. A real lesion phantom study further confirmed this and showed that a slat collimator with CZT detector has better image quality than the conventional parallel-hole collimator with NaI detector. The improvement might be due to both the slat collimation and the better energy resolution of the CZT detector.     

Image reconstruction algorithm for a rotating slat collimator

A slat collimator in single photon emission computed tomography consists of a set of parallel slats. As the collimator spins, the detector measures a one-dimensional projection data set. A complete data set can be obtained by rotating the detector/collimator assembly around the object (patient) while the collimator spins continuously. The measured projection data are assumed to be weighted planar integrals of the object. This paper describes the development of an approximate three-dimensional image reconstruction algorithm for a rotating/spinning slat collimator. This algorithm is in filtered backprojection form. Computer simulations were performed to verify the effectiveness of the algorithm.     

Comparing planar image quality of rotating slat and parallel hole collimation: influence of system modeling

The main remaining challenge for a gamma camera is to overcome the existing trade-off between collimator spatial resolution and system sensitivity. This problem, strongly limiting the performance of parallel hole collimated gamma cameras, can be overcome by applying new collimator designs such as rotating slat (RS) collimators which have a much higher photon collection efficiency. The drawback of a RS collimated gamma camera is that, even for obtaining planar images, image reconstruction is needed, resulting in noise accumulation. However, nowadays iterative reconstruction techniques with accurate system modeling can provide better image quality. Because the impact of this modeling on image quality differs from one system to another, an objective assessment of the image quality obtained with a RS collimator is needed in comparison to classical projection images obtained using a parallel hole (PH) collimator. In this paper, a comparative study of image quality, achieved with system modeling, is presented. RS data are reconstructed to planar images using maximum likelihood expectation maximization (MLEM) with an accurate Monte Carlo derived system matrix while PH projections are deconvolved using a Monte Carlo derived point-spread function. Contrast-to-noise characteristics are used to show image quality for cold and hot spots of varying size. Influence of the object size and contrast is investigated using the optimal contrast-to-noise ratio (CNR(o)). For a typical phantom setup, results show that cold spot imaging is slightly better for a PH collimator. For hot spot imaging, the CNR(o) of the RS images is found to increase with increasing lesion diameter and lesion contrast while it decreases when background dimensions become larger. Only for very large background dimensions in combination with low contrast lesions, the use of a PH collimator could be beneficial for hot spot imaging. In all other cases, the RS collimator scores better. Finally, the simulation of a planar bone scan on a RS collimator revealed a hot spot contrast improvement up to 54% compared to a classical PH bone scan.     

Monte Carlo modelling of the performance of a rotating slit-collimator for improved planar gamma-camera imaging.

Planar imaging with a gamma camera is currently limited by the performance of the collimator. Spatial resolution and sensitivity trade off against each other; it is not possible with conventional parallel-hole collimation to have high geometric sensitivity and at the same time excellent spatial resolution unless field-of-view is sacrificed by using fan- or cone-beam collimators. We propose a rotating slit-collimator which collects one-dimensional projections from which the planar image may be reconstructed by the theory of computed tomography. The performance of such a collimator is modelled by Monte Carlo methods and images are reconstructed by a convolution and backprojection technique. The performance is compared with that of a conventional parallel-hole collimator and it is shown that higher spatial resolution with increased sensitivity is possible with the slit-collimator. For a point source a spatial resolution of some 6 mm at a distance of 100 mm from the collimator with a x7 sensitivity compared with a parallel-hole collimator was achieved. Applications to bone scintigraphy are modelled and an improved performance in hot-spot imaging is demonstrated. The expected performance in cold-spot imaging is analytically investigated. The slit-collimator is not expected to improve cold-spot imaging. Practical design considerations are discussed.     

Noise and signal decoupling in maximum-likelihood reconstructions and Metz filters for PET brain images

Images reconstructed with the maximum-likelihood-by-expectation-maximization (ML) algorithm have lower noise in some regions, particularly low count areas, compared with images reconstructed with filtered backprojection (FBP). The use of statistically correct noise model coupled with the positivity constraint in the ML algorithm provides this noise improvement, but whether this model confers a general advantage for ML over FBP with no noise model and any reconstruction filter, is unclear. We have studied the quantitative impact of the correct noise model in the ML algorithm applied to simulated and real PET fluoro-deoxyglucose (FDG) brain images, given a simplified but accurate reconstruction model with spatially invariant resolution. For FBP reconstruction, several Metz filters were chosen and images with different resolution were obtained depending on the order (1-400) of the Metz filters. Comparisons were made based on the mean Fourier spectra of the projection amplitudes, the noise-power spectra, and the mean region-of-interest signal and noise behaviour in the images. For images with resolution recovery beyond the intrinsic detector resolution, the noise increased significantly for FBP compared with ML. This indicates that in the process of signal recovery using ML, the noise is decoupled from the signal. Such noise decoupling is not possible for FBP. However, for image resolution equivalent to or less than the intrinsic detector resolution, FBP with Metz filters of various orders can achieve a performance similar to ML. The significance of the noise decoupling advantage in ML is dependent on the reconstructed image resolution required for specific imaging tasks.     

System characteristics of SPECT with a slat collimated strip detector

In classical SPECT with parallel hole collimation, the sensitivity is constant over the field of view (FOV). This is no longer the case if a rotating slat collimator with planar photon collection is used: there will be a significant variation of the sensitivity within the FOV. Since not compensating for this inhomogeneous sensitivity distribution would result in non-quantitative images, an accurate knowledge of the sensitivity is mandatory to account for it during reconstruction. On the other hand, the spatial resolution versus distance dependency remains unaltered compared to parallel hole collimation. For deriving the sensitivity, different factors have to be taken into account: a first factor concerns the intrinsic detector properties and will be incorporated into the calculations as a detection efficiency term depending on the incident angle. The calculations are based on a second and more pronounced factor: the collimator and detector geometry. Several assumptions will be made for the calculation of the sensitivity formulae and it will be proven that these calculations deliver a valid prediction of the sensitivity at points far enough from the collimator. To derive a close field model which also accounts for points close to the collimator surface, a modified calculation method is used. After calculating the sensitivity in one plane it is easy to obtain the tomographic sensitivity. This is done by rotating the sensitivity maps for spin and camera rotation. The results derived from the calculations are then compared to simulation results and both show good agreement after including the aforementioned detection efficiency term. The validity of the calculations is also proven by measuring the sensitivity in the FOV of a prototype rotating slat gamma camera. An expression for the resolution of these planar collimation systems is obtained. It is shown that for equal collimator dimensions the same resolution-distance relationship is obtained as for parallel hole collimators. Although, a better spatial resolution can be obtained with our prototype camera due to the smaller pitch of the slats. This can be achieved without a major drop in system sensitivity due to the fact that the slats consist of less collimator material compared to a parallel hole collimator. The accuracy of the calculated resolution is proven by comparison with Monte Carlo simulation and measurement resolution values.     

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.     

Resolution recovery for list-mode reconstruction in SPECT

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 cardiofocal collimator: a variable-focus collimator for cardiac SPECT

Investigators in nuclear medicine have long been in search of a practical method to increase the number of detected events in cardiac SPECT. A clinically practical method requires a simple data acquisition protocol, clinically acceptable reconstruction times, artifact levels near or below visual threshold, and the use of currently available cameras and computers. Towards this end, we have developed the Cardiofocal collimator, a variable-focus collimator for cardiac SPECT that increases the number of detected events from the heart by more than a factor of two compared to that of a parallel-hole collimator with equivalent resolution. In both the transverse and axial dimensions, the focusing is strongest at the centre of the collimator, and gradually relaxes to nearly parallel-hole collimation at the edge of the collimator. The variable-focus concept provides an increase in the number of counts from organs imaged near the centre of the collimator, where the heart will spend most of the time during a cardiac SPECT study, while adequately sampling enough of the background activity distribution to prevent truncation artifacts in the reconstructed images. Images are reconstructed in clinically acceptable times using a filtered backprojection reconstruction algorithm. The algorithm supports both full-scan (360 degrees) and short-scan (180 degrees plus the fan angle) acquisitions. The results of simulations and phantom studies are included to demonstrate the performance of the Cardiofocal collimator.     

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.     

Compensation of spatial system response in SPECT with conjugate gradient reconstruction technique

A procedure for the determination of the system matrix in single photon emission tomography (SPECT) is described which uses the conjugate gradient reconstruction technique in order to take into account the variable system resolution of a camera equipped with parallel-hole collimators. The procedure involves the acquisition of the system line spread functions (LSF) in the region occupied by the object to be studied. Those data are used to generate a set of weighting factors based on the assumption that the LSFs of the collimated camera are of Gaussian shape with the full width at half maximum (FWHM) linearly dependent on the source depth in the span of image space. The factors are stored on a disc file for subsequent use in the reconstruction process. Afterwards the reconstruction is performed using the conjugate gradient method with the system matrix modified by the incorporation of these precalculated factors in order to take into account the variable geometrical system response. The set of weighting factors is regenerated whenever the acquisition conditions are changed (collimator, radius of rotation). In the case of an ultra high resolution (UHR) collimator 2000 weighting factors need to be calculated. The modification of the system matrix for the geometrical response allows the number of iterations to increase, considerably improving image definition without the appearance of noise artifacts. Moreover, phantom studies show that the number of iterations is less critical because of improved stability in the convergence to the solution. For brain studies of patients 10-15 iterations are usually performed. Studies with a single line source give a value between 7 and 8 mm for the FWHM of the point spread function (PSF) when the conjugate gradient method with modified system matrix is used on data acquired with a UHR collimator, whereas without the modification of the system matrix the result is 9 mm FWHM, if filtered backprojection (FBP) is used with the same filter as in the clinical studies the result is 15 mm FWHM. The results of this work show that proper definition of the system matrix using conjugate gradients influences the quality of the reconstruction remarkably. Nevertheless, further work has to be done in order to assess to what extent the system matrix is ill-conditioned and, eventually, to define a suitable regularization technique.     

Noise, resolution, and sensitivity considerations in the design of a single-slice emission-transmission computed tomographic system

A prototype Emission-Transmission Computed Tomography (ETCT) system is being developed that will acquire single-slice x-ray transmission CT images simultaneously with single photon emission computed tomography (SPECT) images. This system will permit the correlation of anatomical information from x-ray CT with functional information from SPECT images. The patient-specific attenuation map derived from the x-ray CT images can be used to perform attenuation correction of the SPECT images, so that accurate quantitative information can be obtained. The fan-beam scanning geometry and the use of a segmented HPGe detector array impose special constraints on the design of the collimator for the system. Based on a signal detection model, an efficiency-resolution figure of merit (ERFM) as a function of the collimator geometric efficiency, system resolution width, and object diameter is defined. The ERFM is proportional to the square of the detection signal-to-noise ratio. The collimator design parameters can then be optimized by optimizing the ERFM for an anticipated object diameter. The collimator point-spread function, geometric efficiency, and resolution are calculated. The collimator optimized for the detection of a 1-cm object will have a single-slice point source efficiency of 1.2 X 10(-4), and a FWHM of 6.5 mm at the center of the reconstruction circle, at 12 cm from the collimator face. The minimum object contrast which will give a detection SNR of 5 is 74%, for a total accumulated count per slice of 2 X 10(6).     

Tomographic image quality of rotating slat versus parallel hole-collimated SPECT

Parallel and converging hole collimators are most frequently used in nuclear medicine. Less common is the use of rotating slat collimators for single photon emission computed tomography (SPECT). The higher photon collection efficiency, inherent to the geometry of rotating slat collimators, results in much lower noise in the data. However, plane integrals contain spatial information in only one direction, whereas line integrals provide two-dimensional information. It is not a trivial question whether the initial gain in efficiency will compensate for the lower information content in the plane integrals. Therefore, a comparison of the performance of parallel hole and rotating slat collimation is needed. This study compares SPECT with rotating slat and parallel hole collimation in combination with MLEM reconstruction with accurate system modeling and correction for scatter and attenuation. A contrast-to-noise study revealed an improvement of a factor 2-3 for hot lesions and more than a factor of 4 for cold lesion. Furthermore, a clinically relevant case of heart lesion detection is simulated for rotating slat and parallel hole collimators. In this case, rotating slat collimators outperform the traditional parallel hole collimators. We conclude that rotating slat collimators are a valuable alternative for parallel hole collimators.     

Joint optimization of collimator and reconstruction parameters in SPECT imaging for lesion quantification

Obtaining the best possible task performance using reconstructed SPECT images requires optimization of both the collimator and reconstruction parameters. The goal of this study is to determine how to perform this optimization, namely whether the collimator parameters can be optimized solely from projection data, or whether reconstruction parameters should also be considered. In order to answer this question, and to determine the optimal collimation, a digital phantom representing a human torso with 16 mm diameter hot lesions (activity ratio 8:1) was generated and used to simulate clinical SPECT studies with parallel-hole collimation. Two approaches to optimizing the SPECT system were then compared in a lesion quantification task: sequential optimization, where collimation was optimized on projection data using the Cramer–Rao bound, and joint optimization, which simultaneously optimized collimator and reconstruction parameters. For every condition, quantification performance in reconstructed images was evaluated using the root-mean-squared-error of 400 estimates of lesion activity. Compared to the joint-optimization approach, the sequential-optimization approach favoured a poorer resolution collimator, which, under some conditions, resulted in sub-optimal estimation performance. This implies that inclusion of the reconstruction parameters in the optimization procedure is important in obtaining the best possible task performance; in this study, this was achieved with a collimator resolution similar to that of a general-purpose (LEGP) collimator. This collimator was found to outperform the more commonly used high-resolution (LEHR) collimator, in agreement with other task-based studies, using both quantification and detection tasks.     

A neural network approach for image reconstruction in electron magnetic resonance tomography

An object-oriented, artificial neural network (ANN) based, application system for reconstruction of two-dimensional spatial images in electron magnetic resonance (EMR) tomography is presented. The standard back propagation algorithm is utilized to train a three-layer sigmoidal feed-forward, supervised, ANN to perform the image reconstruction. The network learns the relationship between the 'ideal' images that are reconstructed using filtered back projection (FBP) technique and the corresponding projection data (sinograms). The input layer of the network is provided with a training set that contains projection data from various phantoms as well as in vivo objects, acquired from an EMR imager. Twenty five different network configurations are investigated to test the ability of the generalization of the network. The trained ANN then reconstructs two-dimensional temporal spatial images that present the distribution of free radicals in biological systems. Image reconstruction by the trained neural network shows better time complexity than the conventional iterative reconstruction algorithms such as multiplicative algebraic reconstruction technique (MART). The network is further explored for image reconstruction from 'noisy' EMR data and the results show better performance than the FBP method. The network is also tested for its ability to reconstruct from limited-angle EMR data set.     

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.

     

Pinhole collimation for ultra-high-resolution, small-field-of-view SPECT

The objective of this investigation was to evaluate small-field-of-view, ultra-high-resolution pinhole collimation for a rotating-camera SPECT system that could be used to image small laboratory animals. Pinhole collimation offers distinct advantages over conventional parallel-hole collimation when used to image small objects. Since geometric sensitivity increases markedly for points close to the pinhole, small-diameter and high-magnification pinhole geometries may be useful for selected imaging tasks when used with large-field-of-view scintillation cameras. The use of large magnifications can minimize the loss of system resolution caused by the intrinsic resolution of the scintillation camera. A pinhole collimator has been designed and built that can be mounted on one of the scintillation cameras of a triple-head SPECT system. Three pinhole inserts with approximate aperture diameters of 0.6, 1.2 and 2.0 mm have been built and can be mounted individually on the collimator housing. When a ramp filter is used with a three-dimensional (3D) filtered backprojection (FBP) algorithm, the three apertures have in-plane SPECT spatial resolutions (FWHM) at 4 cm of 1.5, 1.9 and 2.8 mm, respectively. In-air point source sensitivities at 4 cm from the apertures are 0.9, 2.6 and 5.7 counts s(-1) microCi(-1) (24, 70 and 154 counts s(-1) MBq(-1)) for the 0.6, 1.2 and 2.0 mm apertures, respectively. In vitro image quality was evaluated with a micro-cold-rod phantom and a micro-Defrise phantom using both the 3D FBP algorithm and a 3D maximum likelihood-expectation maximization (ML-EM) algorithm. In vivo image quality was evaluated using two (315 and 325 g) rats. Ultra-high-resolution pinhole SPECT is an inexpensive and simple approach for imaging small animals that can be used with existing rotating-camera SPECT system.     

Automatic estimation of detector radial position for contoured SPECT acquisition using CT images on a SPECT/CT system

An algorithm was developed to estimate noncircular orbit (NCO) single-photon emission computed tomography (SPECT) detector radius on a SPECT/CT imaging system using the CT images, for incorporation into collimator resolution modeling for iterative SPECT reconstruction. Simulated male abdominal (arms up), male head and neck (arms down) and female chest (arms down) anthropomorphic phantom, and ten patient, medium-energy SPECT/CT scans were acquired on a hybrid imaging system. The algorithm simulated inward SPECT detector radial motion and object contour detection at each projection angle, employing the calculated average CT image and a fixed Hounsfield unit (HU) threshold. Calculated radii were compared to the observed true radii, and optimal CT threshold values, corresponding to patient bed and clothing surfaces, were found to be between -970 and -950 HU. The algorithm was constrained by the 45 cm CT field-of-view (FOV), which limited the detected radii to < or = 22.5 cm and led to occasional radius underestimation in the case of object truncation by CT. Two methods incorporating the algorithm were implemented: physical model (PM) and best fit (BF). The PM method computed an offset that produced maximum overlap of calculated and true radii for the phantom scans, and applied that offset as a calculated-to-true radius transformation. For the BF method, the calculated-to-true radius transformation was based upon a linear regression between calculated and true radii. For the PM method, a fixed offset of +2.75 cm provided maximum calculated-to-true radius overlap for the phantom study, which accounted for the camera system's object contour detect sensor surface-to-detector face distance. For the BF method, a linear regression of true versus calculated radius from a reference patient scan was used as a calculated-to-true radius transform. Both methods were applied to ten patient scans. For -970 and -950 HU thresholds, the combined overall average root-mean-square (rms) error in radial position for eight patient scans without truncation were 3.37 cm (12.9%) for PM and 1.99 cm (8.6%) for BF, indicating BF is superior to PM in the absence of truncation. For two patient scans with truncation, the rms error was 3.24 cm (12.2%) for PM and 4.10 cm (18.2%) for BF. The slightly better performance of PM in the case of truncation is anomalous, due to FOV edge truncation artifacts in the CT reconstruction, and thus is suspect. The calculated NCO contour for a patient SPECT/CT scan was used with an iterative reconstruction algorithm that incorporated compensation for system resolution. The resulting image was qualitatively superior to the image obtained by reconstructing the data using the fixed radius stored by the scanner. The result was also superior to the image reconstructed using the iterative algorithm provided with the system, which does not incorporate resolution modeling. These results suggest that, under conditions of no or only mild lateral truncation of the CT scan, the algorithm is capable of providing radius estimates suitable for iterative SPECT reconstruction collimator geometric resolution modeling.     

Quantitative reconstruction for myocardial perfusion SPECT: an efficient approach by depth-dependent deconvolution and matrix rotation

An efficient reconstruction method for myocardial perfusion single-photon emission computed tomography (SPECT) has been developed which compensates simultaneously for attenuation, scatter, and resolution variation. The scattered photons in the primary-energy-window measurements are approximately removed by subtracting the weighted scatter-energy-window samples. The resolution variation is corrected by deconvolving the subtracted data with the detector-response kernel in frequency space using the depth-dependent frequency relation. The attenuated photons are compensated by recursively tracing the attenuation factors through the object-specific attenuation map. An experimental chest phantom with defects inside myocardium was used to test the method. The attenuation map of the phantom was reconstructed from transmission scans using a flat external source and a high-resolution parallel-hole collimator of a single-detector system. The detector-response kernel was approximated from measurements of a point source in air at several depths from the collimator surface. The emission data were acquired by the same detector setting. A computer simulation using similar protocols as in the experiment was performed. Both the simulation and experiment showed significant improvement in quantification with the proposed method, as compared to the conventional filtered-backprojection technique. The quantitative gain by the additional deconvolution was demonstrated. The computation time was less than 20 min on a HP/730 desktop computer for reconstruction of a 1282 x 64 array from 128 projections of 128 x 64 samples.