Single Photon Emission Computed Tomography Reconstruction Research Articles

High quality low-dose SPECT reconstruction using CGAN-based transformer network with geometric tight framelet

Single-photon emission computed tomography (SPECT) is a widely used diagnostic tool, but radioactive radiation during imaging poses potential health risks to subjects. Accurate low-dose single-photon emission computed tomography reconstruction is crucial in clinical applications of single-photon emission computed tomography. However, it remains a challenging problem due to the high noise and low spatial resolution of the low-dose reconstructed single-photon emission computed tomography images. The aim of the study is to develop a deep learning based framework for high quality low-dose single-photon emission computed tomography reconstruction. In the proposed framework, the conditional generative adversarial network (CGAN) was used as backbone structure and a Residual Attention CSwin Transformer (RACT) block was introduced as the basic building block for the generator of conditional generative adversarial network. The proposed residual attention CSwin transformer block has a dual-branch structure, which integrates the local modeling capability of CNN and the global dependency modeling capability of Transformer to improve the quality of single-photon emission computed tomography reconstructed images. More importantly, a novel loss term based on the geometric tight framelet (GTF) was designed to better suppress noise for the single-photon emission computed tomography reconstructed image while preserving the details of image to the greatest extent. Monte Carlo simulation software SIMIND was used to produce low-dose single-photon emission computed tomography images dataset to evaluate the performance of the proposed method. The simulation results showed that the proposed method can reduce more noise and preserve more details of various situations compared to several recent methods. To further validate the performance of the proposed method, we also verified the generalization ability of the proposed method, which is more adaptable to different noise level scenarios than other methods. Our results indicated that the proposed framework has the potential to reduce the tracer dose required for single-photon emission computed tomography imaging without compromising the quality of the reconstructed images.

Influence on voxel-based dosimetry: noise effect on absorbed dose dosimetry at single time-point versus sequential single-photon emission computed tomography.

The purpose of this study was to evaluate how statistical fluctuation in single-photon emission computed tomography (SPECT) images propagate to absorbed dose maps. SPECT/computed tomography (CT) images of iodine-131 filled phantoms, using different acquisition and processing protocols, were evaluated using STRATOS software to assess the absorbed dose distribution at the voxel level. Absorbed dose values and coefficient of variation (COV) were analyzed for dosimetry based on single time-point SPECT images and time-integrated activities of SPECT sequences with low and high counts. Considering dosimetry based on a single time-point, the mean absorbed dose was not significantly affected by total counts or reconstruction parameters, but the uniformity of the absorbed dose maps had an almost linear correlation with SPECT noise. When high- and low-count SPECT sequences were used to generate an absorbed dose map, the absorbed dose COV for each of the temporal sequences was slightly lower than the absorbed dose COV based on the single SPECT image with the highest count included in the sequence. The impact of changes in SPECT counts and reconstruction parameters is almost linear when dosimetry is based on isolated SPECT images, but less pronounced when dosimetry is based on sequential SPECTs.

Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction

Objective. Monte-Carlo simulation studies have been essential for advancing various developments in single photon emission computed tomography (SPECT) imaging, such as system design and accurate image reconstruction. Among the simulation software available, Geant4 application for tomographic emission (GATE) is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes. Approach. In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system. Main results. We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of 123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like 123I-IMP brain perfusion source distributions. Significance. This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.

Anatomically guided reconstruction improves lesion quantitation and detectability in bone SPECT/CT.

Bone single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging suffers from poor spatial resolution, but the image quality can be improved during SPECT reconstruction by using anatomical information derived from CT imaging. The purpose of this work was to compare two different anatomically guided SPECT reconstruction methods to ordered subsets expectation maximization (OSEM) which is the most commonly used reconstruction method in nuclear medicine. The comparison was done in terms of lesion quantitation and lesion detectability. Anatomically guided Bayesian reconstruction (AMAP) and kernelized ordered subset expectation maximization (KEM) algorithms were implemented and compared against OSEM. Artificial lesions with a wide range of lesion-to-background contrasts were added to normal bone SPECT/CT studies. The quantitative accuracy was assessed by the error in lesion standardized uptake values and lesion detectability by the area under the receiver operating characteristic curve generated by a non-prewhitening matched filter. AMAP and KEM provided significantly better quantitative accuracy than OSEM at all contrast levels. Accuracy was the highest when SPECT lesions were matched to a lesion on CT. Correspondingly, AMAP and KEM also had significantly better lesion detectability than OSEM at all contrast levels and reconstructions with matching CT lesions performed the best. Quantitative differences between AMAP and KEM algorithms were minor. Visually AMAP and KEM images looked similar. Anatomically guided reconstruction improves lesion quantitation and detectability markedly compared to OSEM. Differences between AMAP and KEM algorithms were small and thus probably clinically insignificant.

Data-Driven Respiration-Gated SPECT for Liver Radioembolization

Respiratory motion leads to blur and artifacts in single-photon emission computed tomography (SPECT) images. This may be an issue in some treatments such as liver radioembolization. The purpose of this work was to reconstruct 4-D respiration-gated SPECT images using a data-driven approach. A respiratory signal was extracted from SPECT list-mode data using Laplacian eigenmaps and used for retrospective phase gating. The method was evaluated on various SPECT datasets: Monte Carlo simulation of a patient acquisition, several acquisitions of a dynamic phantom, and 20 acquisitions of patients treated with liver radioembolization. We observed an increase of the mean activity inside the tumor (A) and of the tumor-to-normal liver ratio (T/N) in the respiration-gated SPECT image compared to the 3-D image without motion correction. For the patient acquisitions, an average 3.3% (up to 11.9%) and 3.2% (up to 9.7%) increase was observed for A and T/N, respectively. For the simulation, the activity was within 2.6% of the reference without movement and around 3% for the three patterns of tumor motion simulated with the mechanical phantom. Overall, the proposed respiration-gated SPECT reconstruction improves the estimation of the activity in the tumor without the need for extra hardware.

Influence of brain atrophy using semiquantitative analysis in [123I]FP-CIT single-photon emission computed tomography by a Monte Carlo simulation study

The specific binding ratio (SBR) is an objective indicator of N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-[123I] iodophenyl) nortropane ([123I]FP-CIT) single-photon emission computed tomography (SPECT) that could be used for the diagnosis of Parkinson’s disease and Lewy body dementia. One of the issues of the SBR analysis is that the setting position of the volume of interest (VOI) may contain cerebral ventricles and cerebral grooves. These areas may become prominent during the brain atrophy analysis; however, this phenomenon has not been evaluated enough. This study thus used Monte Carlo simulations to examine the effect of brain atrophy on the SBR analysis. The brain atrophy model (BAM) used to simulate the three stages of brain atrophy was made using a morphological operation. Brain atrophy levels were defined in the descending order from 1 to 3, with Level 3 indicating to the most severe damage. Projection data were created based on BAM, and the SPECT reconstruction was performed. The ratio of the striatal to background region accumulation was set to a rate of 8:1, 6:1, and 4:1. The striatal and the reference VOI mean value were decreased as brain atrophy progressed. Additionally, the Bolt’s analysis methods revealed that the reference VOI value was more affected by brain atrophy than the striatal VOI value. Finally, the calculated SBR value was overestimated as brain atrophy progressed, and a similar trend was observed when the ratios of the striatal to background region accumulation were changed. This study thus suggests that the SBR can be overestimated in cases of advanced brain atrophy.

Combination of compressed sensing-based iterative reconstruction and offset acquisition for I-123 FP-CIT SPECT: a simulation study

Objective(s):The purpose of this study was to validate undersampled single-photon emission computed tomography (SPECT) imaging using a combination of compressed sensing (CS) iterative reconstruction (CS-IR) and offset acquisition.Methods:Three types of numerical phantoms were used to evaluate image quality and quantification derived from CS with offset acquisition. SPECT images were reconstructed using filtered back-projection (FBP), maximum likelihood-expectation maximization (ML-EM), CS-IR, and CS-IR with offset acquisition. The efficacy of CS-IR with offset acquisition was examined in terms of spatial resolution, aspect ratio (ASR), activity concentration linearity, contrast, percent coefficient of variation (%CV), and specific binding ratio (SBR).Results:The full widths at half maximum remained unchanged as the number of projections decreased in CS-IR with offset acquisition. Changes in ASRs and linearities of count density were observed for ML-EM and CS-IR from undersampled projections. The %CV obtained by CS-IR with offset acquisition was substantially lower than that obtained by ML-EM and CS-IR. There were no significant differences between the %CVs obtained from 60 projections by CS-IR with offset acquisition and from 120 projections by FBP. Although the SBRs for CS-IR with offset acquisition tended to be slightly lower than for FBP, the SBRs for CS-IR with offset acquisition did not change with the number of projections.Conclusions:CS-IR with offset acquisition can provide good image quality and quantification compared with a commonly used SPECT reconstruction method, especially from undersampled projection data. Our proposed method could shorten overall SPECT acquisition times, which would benefit patients and enable quantification with dynamic SPECT acquisitions.

LU-Net: combining LSTM and U-Net for sinogram synthesis in sparse-view SPECT reconstruction.

Lowering the dose in single-photon emission computed tomography (SPECT) imaging to reduce the radiation damage to patients has become very significant. In SPECT imaging, lower radiation dose can be achieved by reducing the activity of administered radiotracer, which will lead to projection data with either sparse projection views or reduced photon counts per view. Direct reconstruction of sparse-view projection data may lead to severe ray artifacts in the reconstructed image. Many existing works use neural networks to synthesize the projection data of sparse-view to address the issue of ray artifacts. However, these methods rarely consider the sequence feature of projection data along projection view. This work is dedicated to developing a neural network architecture that accounts for the sequence feature of projection data at adjacent view angles. In this study, we propose a network architecture combining Long Short-Term Memory network (LSTM) and U-Net, dubbed LU-Net, to learn the mapping from sparse-view projection data to full-view data. In particular, the LSTM module in the proposed network architecture can learn the sequence feature of projection data at adjacent angles to synthesize the missing views in the sinogram. All projection data used in the numerical experiment are generated by the Monte Carlo simulation software SIMIND. We evenly sample the full-view sinogram and obtain the 1/2-, 1/3- and 1/4-view projection data, respectively, representing three different levels of view sparsity. We explore the performance of the proposed network architecture at the three simulated view levels. Finally, we employ the preconditioned alternating projection algorithm (PAPA) to reconstruct the synthesized projection data. Compared with U-Net and traditional iterative reconstruction method with total variation regularization as well as PAPA solver (TV-PAPA), the proposed network achieves significant improvement in both global and local quality metrics.

IMPROVEMENTS OF 111IN SPECT IMAGES RECONSTRUCTED WITH SPARSELY ACQUIRED PROJECTIONS BY DEEP LEARNING GENERATED SYNTHETIC PROJECTIONS.

The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30–120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30–120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.

Technical Note: Nuclear imaging with an x-ray flat panel detector: A proof-of-concept study.

PurposeInterventional procedures involving radionuclides (e.g., radioembolization) would benefit from single‐photon emission computed tomography (SPECT) performed in the intervention room because the activity distribution could be immediately visualized. We believe it might be possible to perform SPECT with the C‐arm cone beam computed tomography (CBCT) scanner present in the intervention room by equipping the x‐ray flat panel detector with a collimator. The purpose of this study is to demonstrate the approach and to investigate the achievable SPECT reconstruction quality.MethodsA proof‐of‐concept experiment was performed to evaluate the possibility of nuclear imaging with an x‐ray flat panel detector. The experiment was digitally replicated to study the accuracy of the simulations. Three flat panel configurations (with standard hardware and reconstruction methodology, with sophisticated reconstruction methodology, and with expected future hardware) and a conventional gamma camera were evaluated. The Jaszczak and the NEMA IQ phantom (filled with 99mTc) were simulated and assessed on resolution and contrast‐to‐noise ratio (CNR).ResultsThe proof‐of‐concept experiment demonstrated that nuclear images could be obtained from the flat panel detector. The simulation of the same configuration demonstrated that simulations could accurately predict the flat panel detector response. The CNR of the 37 mm sphere in the NEMA IQ phantom was 22.8 ± 1.2 for the gamma camera reconstructions, while it was 11.3 ± 0.7 for the standard flat panel detector. With sophisticated reconstruction methodology, the CNR improved to 13.5 ± 1.4. The CNR can be expected to advance to 18.1 ± 1.3 for future flat panel detectors.ConclusionsThe x‐ray flat panel detector of a CBCT scanner might be used to perform nuclear imaging. The SPECT reconstruction quality will be lower than that achieved by a conventional gamma camera. The flat panel detector approach could, however, be useful in providing a cost‐effective alternative to the purchase of a mobile SPECT scanner for enabling interventional scanning.

A content-adaptive unstructured grid based integral equation method with the TV regularization for SPECT reconstruction

Existing reconstruction methods for single photon emission computed tomography (SPECT) are most based on discrete models, leading to low accuracy in reconstruction. Reconstruction methods based on integral equation models (IEMs) with a higher order piecewise polynomial discretization on the pixel grid for SEPCT imaging were recently proposed to overcome the accuracy deficiency of the discrete models. Discretization of IEMs based on the pixel grid leads to a system of a large dimension, which may require higher computational costs to solve. We develop a SPECT reconstruction method which employs an IEM of the SPECT data acquisition process and discretizes it on a content-adaptive unstructured grid (CAUG) with the total variation (TV) regularization aiming at reducing computational costs of the integral equation method. Specifically, we design a CAUG of the image domain for the discretization of the IEM, and propose a TV regularization defined on the CAUG for the resulting ill-posed problem. We then apply a preconditioned fixed-point proximity algorithm to solve the resulting non-smooth optimization problem, and provide convergence analysis of the algorithm. Numerical experiments are presented to demonstrate the superiority of the proposed method over the competing methods in terms of suppressing noise, preserving edges and reducing computational costs.

Simultaneous respiratory motion correction and image reconstruction in 4D-multi pinhole small animal SPECT.

Respiratory motion in the chest region during single photon emission computed tomography (SPECT) is a major degrading factor that reduces the accuracy of image quantification. This effect is more notable when the tumor is very small, or the spatial resolution of the imaging system is less than the respiratory motion amplitude. Small animals imaging systems with sub-millimeter spatial resolution need more attention to the respiratory motion for quantitative studies. We developed a motion-embedded four-dimensional (4D)-multi pinhole SPECT (MPS) reconstruction algorithm for respiratory motion correction. This algorithm makes full use of projection statistics for reconstruction of every individual frame. The ROBY phantom with small tumors in liver was generated in eight different phases during one respiratory cycle. The MPS projections were modeled using a fast ray tracing method simulating an MPS acquisition. Individual frames were reconstructed and used for motion estimation. The Demons non-rigid registration algorithm was used to calculate deformation vector fields (DVFs) for simultaneous motion correction and image reconstruction. A motion-embedded 4D-MPS method was used to reconstruct images using all the projections and corresponding DVFs, simultaneously. The 4D-MPS reconstructed images were compared to the low-count single frame (LCSF) reconstructed image, the three-dimensional (3D)-MPS images reconstructed using individual frames, and post reconstruction registration (PRR) that aligns all individual phases to a reference frame using Demons-derived DVFs. The tumor volume relative error (TVE), tumor contrast relative error (TCE), and dice index (DI) for 2, 3, and 4mm liver were calculated and compared for different reconstruction methods. For the 4D-MPS reconstruction method, TVE was reduced and DI was higher compared to PRR, 3D-MPS, and LCSF. The extent of the improvement was higher for the small tumor size (i.e. 2mm). For the biggest tumor in contrast 3 (i.e. 4mm) TVE for 4D-MPS, PRR, 3D-MPS and, LCSF were 1.33%, 8%, 8%, and 14.67%, respectively. The results suggest that motion-embedded 4D-MPS method is an effective and practical way for respiratory motion correction. It reconstructs high quality gated frames while using all projection data to reconstruct each frame.

Respiratory motion compensation in interventional liver SPECT using simultaneous fluoroscopic and nuclear imaging.

PurposeQuantitative accuracy of the single photon emission computed tomography (SPECT) reconstruction of the pretreatment procedure of liver radioembolization is crucial for dosimetry; visual quality is important for detecting doses deposited outside the planned treatment volume. Quantitative accuracy is limited by respiratory motion. Conventional gating eliminates motion by count rejection but increases noise, which degrades the visual reconstruction quality. Motion compensation using all counts can be performed if the motion signal and motion vector field over time are known. The measurement of the motion signal of a patient currently requires a device (such as a respiratory belt) attached to the patient, which complicates the acquisition. The motion vector field is generally extracted from a previously acquired four‐dimensional scan and can differ from the motion in the scan performed during the intervention. The simultaneous acquisition of fluoroscopic and nuclear projections can be used to obtain both the motion vector field and the projections of the corresponding (moving) activity distribution. This eliminates the need for devices attached to the patient and provides an accurate motion vector field for SPECT reconstruction. Our approach to motion compensation would primarily be beneficial for interventional SPECT because the time‐critical setting requires fast scans and no inconvenience of an external apparatus. The purpose of this work is to evaluate the performance of the motion compensation approach for interventional liver SPECT by means of simulations.MethodsNuclear and fluoroscopic projections of a realistic digital human phantom with respiratory motion were generated using fast Monte Carlo simulators. Fluoroscopic projections were sampled at 1–5 Hz. Nuclear data were acquired continuously in list mode. The motion signal was extracted from the fluoroscopic projections by calculating the center‐of‐mass, which was then used to assign each photon to a corresponding motion bin. The fluoroscopic projections were reconstructed per bin and coregistered, resulting in a motion vector field that was used in the SPECT reconstruction. The influence of breathing patterns, fluoroscopic imaging dose, sampling rate, number of bins, and scanning time was studied. In addition, the motion compensation method was compared with conventional gating to evaluate the detectability of spheres with varying uptake ratios.ResultsThe liver motion signal was accurately extracted from the fluoroscopic projections, provided the motion was stable in amplitude and the sampling rate was greater than 2 Hz. The minimum total fluoroscopic dose for the proposed method to function in a 5‐min scan was 10 µGy. Although conventional gating improved the quantitative reconstruction accuracy, substantial background noise was observed in the short scans because of the limited counts available. The proposed method similarly improved the quantitative accuracy, but generated reconstructions with higher visual quality. The proposed method provided better visualization of low‐contrast features than when using gating.ConclusionThe proposed motion compensation method has the potential to improve SPECT reconstruction quality. The method eliminates the need for external devices to measure the motion signal and generates an accurate motion vector field for reconstruction. A minimal increase in the fluoroscopic dose is required to substantially improve the results, paving the way for clinical use.

Performance of compressed sensing-based iterative reconstruction for single-photon emission computed tomography from undersampled projection data: a simulation study in 123I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane imaging.

The aim of this study was to investigate the efficacy of compressed sensing (CS)-based iterative reconstruction (CS-IR) from undersampled projection data in I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography (SPECT). We used the cylinder/sphere and the striatal digital phantom models. The number of projections was set at 120, 90, 60, 40, and 30 projections. SPECT images were reconstructed using filtered back-projection (FBP), maximum likelihood-expectation maximization (ML-EM), and CS-IR. The total-variation transform with local image gradient in L1-norm was adopted in our CS algorithm. The efficacy of CS-IR was examined in terms of the spatial resolution, recovery coefficient, aspect ratio (ASR), activity concentration linearity, percent coefficient of variation (%CV), and specific binding ratio. As the number of projections decreased, the following results were observed. No differences of the spatial resolution and activity concentration linearity were observed between reconstruction methods. However, ASR for FBP slightly increased in contrast to ML-EM and CS-IR for which ASR remained constant. There were not any clear differences between recovery coefficients obtained from each reconstruction. The %CV obtained by CS-IR was significantly superior to that obtained by other reconstructions at all number of projections: for example, the %CV obtained by 60 projection CS-IR was equivalent to that obtained by 120 projection FBP and ML-EM. The specific binding ratio did not change with the number of projections, and there were no significant differences between FBP, ML-EM, and CS-IR. We have demonstrated that CS-IR with decreased number of projections can provide a good image quality compared with commonly used SPECT reconstruction methods. This CS could help to reduce overall acquisition time in I-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane SPECT, particularly.

Impact of Novel Incorporation of CT-based Segment Mapping into a Conjugated Gradient Algorithm on Bone SPECT Imaging: Fundamental Characteristics of a Context-specific Reconstruction Method.

Objective(s):The latest single-photon emission computed tomography (SPECT)/computed tomography (CT) reconstruction system, referred to as xSPECT Bone™, is a context-specific reconstruction system utilizing tissue segmentation information from CT data, which is called a zone map. The aim of this study was to evaluate the effects of zone-map enhancement incorporated into the ordered-subset conjugated gradient minimization (OSCGM) reconstruction method on SPECT images.Methods: Image quality with zone-map enhanced OSCGM (OSCGMz) and non-enhanced OSCGM methods was compared using various reconstruction parameters. The compartment phantom had 3 radioactive sections with CT values of about 1000, 250, and 0 HU. SPECT data were acquired using a low-energy high-resolution (LEHR) collimator, with a 256×256 matrix and 2.4-mm pixel size. The performances of the 2 reconstruction methods (OSCGM vs. OSCGMz) were evaluated on the basis of %error, coefficient of variation (%CV), and normalized mean squared error (NMSE), and adequate iterative update numbers were determined. The relative CV representing the ratio of smoothed images to non-smoothed images was calculated to evaluate the effects of the Gaussian filter on each section set with different CT values.Results:On comparing the OSCGM and OSCGMz methods, it was found that the %error of the OSCGMz method tended to show convergence with fewer updates, especially in the high CT value section mimicking bone absorption. In the water section, the %CV of the OSCGMz method was lower than that of the OSCGM method. The NMSE minimum values for the OSCGM and OSCGMz methods were obtained at 30 and 20 updates, respectively. The relative CV for the OSCGMz method in the water section decreased remarkably according to the size of the full width at half maximum (FWHM) of the Gaussian filter.Conclusion:The zone-map enhancement contributed to SPECT reconstruction for the reproduction of radioactive concentrations in bone tissues, using a low number of OSCGM updates. Our findings indicated that the incorporation of zone maps into SPECT reconstruction might improve image quality.

A Higher-Order Polynomial Method for SPECT Reconstruction.

Existing single-photon emission computed tomography (SPECT) reconstruction methods are mostly based on discrete models that may be viewed as piecewise constant approximations of a continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, a traditional discrete model introduces irreducible model errors which are a bottleneck of the quality improvement of reconstructed images in clinical applications. To overcome this drawback, we develop a higher-order polynomial method for SPECT reconstruction. Specifically, we represent the data acquisition of SPECT imaging by using an integral equation model, approximate the solution of the underlying integral equation by higher-order piecewise polynomials leading to a new discrete system and introduce two novel regularizers for the system, by exploring the a priori knowledge of the radiotracer distribution, suitable for the approximation. The proposed higher-order polynomial method outperforms significantly the cutting edge reconstruction method based on a traditional discrete model in terms of model error reduction, noise suppression, and artifact reduction. In particular, the coefficient of variation of images reconstructed by the piecewise linear polynomial method is reduced by a factor of 10 in comparison to that of a traditional discrete model-based method.

Initial value selection of the model parameters in the curve fitting phase of the dynamic SPECT imaging

Abstract The dynamic SPECT (Single Photon Emission Computed Tomography) reconstruction algorithm developed in our prior work reconstructs the parameters of the time activity curve for each image voxel directly from the projection images. In each iterations of the SPECT reconstruction beyond the static 3D MLEM (Maximum Likelihood Expectation Maximization) step, the algorithm performs a fitting process for each voxel in order to estimate the parameters describing the function of the examined organ considering that the time frames are not independent from each other. In real cases the fitted curve is nonlinear function of these parameters, it is usually described as the sum of exponential functions. In order to estimate the parameters properly, an iterative root-finding method is applied. In the current study the Newton-Raphson method is used. The selection of a proper initial value for the root-finding method is critical in order to achieve convergence of the fitting process. If the initial guess is not appropriate, the root-finding algorithm can diverge or converge to an inappropriate parameter set that can result in unacceptable reconstructed parameters. This affects then the subsequent MLEM iterations, also neighboring voxels and breaks the reconstruction. In this work we investigated different methods to calculate the initial values of the fitting process and evaluated the reconstructed parameter set of the dynamic SPECT reconstruction algorithm. Three different methods are investigated, one that uses the fitted parameters of the previous MLEM iteration, one that is based on the sum of the geometrical series of the exponentials and one that calculates the best guess using both methods. The three methods were compared by benchmark reconstruction cases using a mathematical phantom. In each reconstruction different initial value selection method was applied then the time activity curves of the voxels belonging to the same tissue were statistically evaluated using the reconstructed parameters. In the study no significant differences were found in the mean value of the reconstructed parameters. The standard deviation of the parameters was similar between the two simple approaches, however, the combination of the methods resulted in better statistical performance.

Advances in SPECT Methodology.

The emerging role of molecular imaging has made possible to evaluate and quantify biochemical changes of molecular targets in specific neurochemical systems involved in movement disorders, providing neurochemical information of clinical changes before the pathological features occurred. In detail, radionuclides imaging techniques are frequently used for the in vivo study of neurotransmitter and receptor function, alterations in cerebral blood flow and metabolic activity, abnormal protein deposition, and inflammation, with a central role in molecular imaging for preclinical and clinical studies. The present chapter represents an overview of main use of single-photon emission computed tomography (SPECT) in movement disorders, with a focal attention on specific radiotracers used, recent advances in SPECT technology and reconstruction algorithm and added specific value of semiquantitative methods for images analysis. Finally, a brief paragraph is dedicated to description of SPECT/CT devices and advantages of using hybrid technology.

Monte Carlo-based quantitative pinhole SPECT reconstruction using a ray-tracing back-projector

BackgroundMonte Carlo simulations provide accurate models of nuclear medicine imaging systems as they can properly account for the full physics of photon transport. The accuracy of the model included in the maximum-likelihood–expectation-maximization (ML-EM) reconstruction limits the overall accuracy of the reconstruction results. In this paper, we present a Monte Carlo-based ML-EM reconstruction method for pinhole single-photon emission computed tomography (SPECT) that has been incorporated into the SIMIND Monte Carlo program. The Monte Carlo-based model, which accounts for all of the physical and geometrical characteristics of the camera system, is used in the forward-projection step of the reconstruction, while a simpler model based on ray-tracing is used for back-projection. The aim of this work was to investigate the quantitative accuracy of this combination of forward- and back-projectors in the clinical pinhole camera GE Discovery NM 530c.ResultsThe total activity was estimated in 99mTc-filled spheres with volumes between 0.5 and 16 mL. The total sphere activity was generally overestimated but remained within 10% of the reference activity defined by the phantom preparation. The recovered activity converged towards the reference activity as the number of iterations increased. Furthermore, the recovery of the activity concentrations within the physical boundaries of the spheres increased with increasing sphere volume. Additionally, the Monte Carlo-based reconstruction enabled recovery of the true activity concentration in the myocardium of a cardiac phantom mounted in a torso phantom regardless of whether the torso was empty or water-filled. A qualitative comparison to data reconstructed using the clinical reconstruction algorithm showed that the two methods performed similarly, although the images reconstructed using the clinical software were more uniform due to the incorporation of noise regularization and post-filtration in that reconstruction technique.ConclusionsWe developed a Monte Carlo-based reconstruction method for pinhole SPECT and evaluated it using phantom measurements. The combination of a Monte Carlo-based forward-projector and a simplified analytical ray-tracing back-projector produced quantitative images of acceptable image quality. No explicit calibration is necessary in this method since the forward-projector model maintains a relationship between the number of counts and activity.

Simplifying volumes-of-interest (VOIs) definition in quantitative SPECT: Beyond manual definition of 3D whole-organ VOIs.

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