Conventional Filtered Back-projection Method Research Articles

A novel protocol for abdominal low-dose CT scans adapted with a model-based iterative reconstruction method.

This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images. Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared. The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol. With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis.

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy. To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model. A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments. This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( ), peak signal-to-noise ratio ( ), and Pearson correlation ( ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP. This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

Prior image-guided cone-beam computed tomography augmentation from under-sampled projections using a convolutional neural network.

Acquiring sparse-view cone-beam computed tomography (CBCT) is an effective way to reduce the imaging dose. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projection under-sampling. Existing deep learning models have demonstrated feasibilities in restoring volumetric structures from the highly under-sampled images. However, because of the inter-patient variabilities, they failed to restore the patient-specific details with the common restoring pattern learned from the group data. Although the patient-specific models have been developed by training models using the intra-patient data and have shown effectiveness in restoring the patient-specific details, the models have to be retrained to be exclusive for each patient. It is highly desirable to develop a generalized model that can utilize the patient-specific information for the under-sampled image augmentation. In this study, we proposed a merging-encoder convolutional neural network (MeCNN) to realize the prior image-guided under-sampled CBCT augmentation. Instead of learning the patient-specific structures, the proposed model learns a generalized pattern of utilizing the patient-specific information in the prior images to facilitate the under-sampled image enhancement. Specifically, the MeCNN consists of a merging-encoder and a decoder. The merging-encoder extracts image features from both the prior CT images and the under-sampled CBCT images, and merges the features at multi-scale levels via deep convolutions. The merged features are then connected to the decoders via shortcuts to yield high-quality CBCT images. The proposed model was tested on both the simulated CBCTs and the clinical CBCTs. The predicted CBCT images were evaluated qualitatively and quantitatively in terms of image quality and tumor localization accuracy. Mann-Whitney U test was conducted for the statistical analysis. P<0.05 was considered statistically significant. The proposed model yields CT-like high-quality CBCT images from only 36 half-fan projections. Compared to other methods, CBCT images augmented by the proposed model have significantly lower intensity errors, significantly higher peak signal-to-noise ratio, and significantly higher structural similarity with respect to the ground truth images. Besides, the proposed method significantly reduced the 3D distance of the CBCT-based tumor localization errors. In addition, the CBCT augmentation is nearly real-time. With the prior-image guidance, the proposed method is effective in reconstructing high-quality CBCT images from the highly under-sampled projections, considerably reducing the imaging dose and improving the clinical utility of the CBCT.

Task-Based Evaluation of Image Reconstruction Methods for Defect Detection and Radiation Dose Reduction in Myocardial Perfusion SPECT

We evaluated the ability of conventional and corrective image reconstruction methods to detect a myocardial perfusion defect in single-photon emission computed tomography (SPECT) images using the channelized hotelling observer (CHO). Using our previously developed 4-D extended cardiac-torso, we simulated realistic transmural and endocardial perfusion defects in various locations and sizes in the myocardium. Almost noise-free projection datasets were generated separately from the heart, blood pool, lungs, liver, kidneys, stomach, gall bladder, and the remaining body using Monte Carlo simulation techniques that included the effects of collimator detector response, photon attenuation, and scatter. The datasets were then scaled and combined to model 99mTc Sestamibi myocardial perfusion SPECT projection datasets from a typical patient with various reduced count levels, simulating reduced radiation doses. The final projection data were reconstructed using the 3-D filtered back-projection (FBP) without correction and a 3-D ordered-subset expectation-maximization (OS-EM) method with corrections of attenuation, collimator-detector response, and scatter (ADS), followed by smoothing filtering with several different cut-off frequencies. Task-based evaluations on the reconstructed images were performed by using the CHO followed by the receiver operating characteristics (ROC) methodology to evaluate the detectability of a myocardial perfusion defect by the two image reconstruction methods for various defect anatomies. Areas under the ROC curve (AUC) were computed to assess the changes in the detection of myocardial perfusion defects. The results showed that the 3-D OS-EM with ADS correction gave overall higher AUC values than FBP at several post smoothing levels, count levels, and myocardial perfusion defect sizes. The difference in AUC increased as image smoothness decreased, where the 3-D OS-EM with correction was able to provide similar AUC values with 20%–40% reduction in count levels compared to those of FBP. The AUC values for smaller myocardial perfusion defects were lower for both reconstruction methods with smaller differences. We concluded that the 3-D OS-EM with ADS correction provides higher performance in detecting myocardial perfusion defects. At the conventional count level, it allows for less counts or lower radiation doses without loss of defect detection in myocardial perfusion SPECT images compared to the conventional FBP method, especially regarding less smoothed images.

Improvement in automated quantitation of myocardial perfusion abnormality by using iterative reconstruction image in combination with resolution recovery, attenuation and scatter corrections for the detection of coronary artery disease

An iterative reconstruction method in combination with resolution recovery, attenuation and scatter corrections (IR-RASC) can improve image quality. It, however, is undetermined whether this technique can improve the detection of coronary artery disease (CAD) when automated quantitative analysis is used. This study evaluated diagnostic values of IR-RASC in combination with automated quantitative analysis in stress myocardial perfusion imaging (MPI) in the CAD detection. This study enrolled consecutive 64 patients (mean age 66.2±17.3years, 39 males) who had undergone both 99mTc-labeled tetrofosmin stress MPI and coronary angiography within 3months. Stress MPI abnormalities quantified as summed stress score (SSS), summed rest score (SRS) and summed difference score (SDS) by Heart Risk View-S (HRV-S) and Quantitative Perfusion SPECT (QPS) softwares using IR-RASC images were compared with those by using conventional filtered back-projection method (FBP) images and angiographic findings. Based on expert visual assessment, SSS and SRS by HRV-S/QPS softwares with IR-RASC were significantly lower than those by HRV-S/QPS softwares with FBP at mid- and basal left ventricular segments. Receiver-operating characteristics analysis showed that areas under the curve assessed by HRV-S (0.687) and QPS (0.678) with IR-RASC were nearly identical to those (0.717-0.724) by expert assessment with FBP, and were significantly (P<0.05) greater than those by HRV-S (0.505) and QPS (0.522) with FBP. When HRV-S was used, the specificity and diagnostic accuracy of IR-RASC in the CAD detection were significantly greater than those of FBP: 90.3 versus 51.6%, P<0.0001 and 79.7 versus 54.7%, P=0.0027, respectively. Likewise, when QPS was used, the specificity and diagnostic accuracy of IR-RASC in the CAD detection were significantly greater than those of FBP: 80.6 versus 41.9%, P<0.0001, and 78.1 versus 51.6%, P=0.0018, respectively. There, however, were no significant differences in sensitivity between IR-RASC and FBP images. IR-RASC can improve diagnostic accuracy of the CAD detection using an automated scoring system compared to FBP, by reducing false positivity due to artefactual appearance.

Unbiased Filtered Back-Projection in $4\pi$ Compton Imaging With 3D Position Sensitive Detectors

In Compton imaging, iterative methods provide good performance but are usually computationally intensive. Thus, direct inverse algorithms such as filtered back-projection (FBP) are preferable when computation time is limited. The conventional FBP method assumes that the point spread function of a back-projection image is isotropic and invariant to incident direction, as required by frequency spectrum analysis. However, most of the time this assumption is not true because the detector geometry is rarely uniform. Therefore the conventional FBP reconstructed image is biased. To solve the geometry non-uniformity problem, this paper proposes an unbiased FBP algorithm that groups Compton events having the same Compton rings using a system matrix decomposition strategy. The proposed method produces more isotropic resolution, and preserves the capability to use frequency spectrum analysis. The algorithm has been applied to data from a 3D position-sensitive detector array with 4 crystals and a digital readout system. The resulting images had more isotropic resolution than standard FBP.

Radiation dose optimization for the bolus tracking technique in abdominal computed tomography: usefulness of real-time iterative reconstruction for monitoring scan.

The purpose of this study was to optimize the monitoring dose, obtained using the conventional filtered back projection (FBP) method and iterative reconstruction algorithms, for the bolus tracking technique. A phantom study was performed to assess the effect of the scan start time in patients grouped according to different body weights. An oval torso phantom was used for simulating the time enhancement curve of the bolus tracking technique. To reproduce image noise levels in the two body weight groups, the phantom diameter was adjusted with a water-equivalent material. The tube currents were 10, 20, 30, and 50mA. The monitoring scan was performed with the conventional FBP method and real-time adaptive iterative dose reduction by three-dimensional processing (AIDR 3D). The results at different doses were compared with those at 50mA. The volume computed tomography dose index was 1.31, 2.65, 3.93, and 6.56mGy at tube currents of 10, 20, 30, and 50mA, respectively. The scan start time, reconstructed using FBP, was significantly faster at 10 and 20mA in group A (50-59kg) and at 20mA in group B (≥80kg). The CT values in the region of interest could not be measured at 10mA because of artifacts. With real-time AIDR 3D, both groups showed no significant differences between the measurements obtained at 30 or 20mA and those obtained at 50mA. Our study demonstrated that the real-time AIDR 3D algorithm improved the accuracy of the CT measurements with the bolus tracking technique.

Adaptation of filtered back-projection to compton imaging with non-uniform azimuthal geometry

For Compton image reconstruction, analytic reconstruction methods such as filtered backprojection have been used for real-time imaging. The conventional filtered back-projection method assumes a uniformly distributed azimuthal response in the detector system. In this study, we applied filtered back-projection to the experimental data from detector systems with limited azimuthal angle coverage ranges and estimated the limitations of the analytic reconstruction methods when applied to these systems. For the system with a uniform azimuthal response, the images reconstructed by using filtered back-projection showed better angular resolutions than the images obtained by using simple back-projection did. However, when filtered back-projection was applied to reconstruct Compton images based on measurements performed by using Compton cameras with limited response geometries, the reconstructed images exhibited artifacts caused by the geometrical limitations. Our proposed method employs the Compton camera’s rotation to overcome the angular response limitations; when the rotation method was applied in this study, the artifacts in the reconstructed images caused by angular response limitations were minimized. With this method, filtered back-projection can be applied to reconstruct real-time Compton images even when the radiation measurements are performed by using Compton cameras with non-uniform azimuthal response geometries.

An efficient reconstruction algorithm for differential phase-contrast tomographic images from a limited number of views

The main focus of this paper is reconstruction of tomographic phase-contrast image from a set of projections. We propose an efficient reconstruction algorithm for differential phase-contrast computed tomography that can considerably reduce the number of projections required for reconstruction. The key result underlying this research is a projection theorem that states that the second derivative of the projection set is linearly related to the Laplacian of the tomographic image. The proposed algorithm first reconstructs the Laplacian image of the phase-shift distribution from the second-derivative of the projections using total variation regularization. The second step is to obtain the phase-shift distribution by solving a Poisson equation whose source is the Laplacian image previously reconstructed under the Dirichlet condition. We demonstrate the efficacy of this algorithm using both synthetically generated simulation data and projection data acquired experimentally at a synchrotron. The experimental phase data were acquired from a human coronary artery specimen using dark-field-imaging optics pioneered by our group. Our results demonstrate that the proposed algorithm can reduce the number of projections to approximately 33% as compared with the conventional filtered backprojection method, without any detrimental effect on the image quality.

Image reconstruction for digital breast tomosynthesis (DBT) by using projection-angle-dependent filter functions

Digital breast tomosynthesis (DBT) is considered in clinics as a standard three-dimensional imaging modality, allowing the earlier detection of cancer. It typically acquires only 10–30 projections over a limited angle range of 15–60° with a stationary detector and typically uses a computationally-efficient filtered-backprojection (FBP) algorithm for image reconstruction. However, a common FBP algorithm yields poor image quality resulting from the loss of average image value and the presence of severe image artifacts due to the elimination of the dc component of the image by the ramp filter and to the incomplete data, respectively. As an alternative, iterative reconstruction methods are often used in DBT to overcome these difficulties, even though they are still computationally expensive. In this study, as a compromise, we considered a projection-angle-dependent filtering method in which one-dimensional geometry-adapted filter kernels are computed with the aid of a conjugate-gradient method and are incorporated into the standard FBP framework. We implemented the proposed algorithm and performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the proposed method is superior to a conventional FBP method for DBT imaging and has a comparable computational cost, while preserving good image homogeneity and edge sharpening with no serious image artifacts.

Statistical image reconstruction for low-dose CT using nonlocal means-based regularization

Low-dose computed tomography (CT) imaging without sacrifice of clinical tasks is desirable due to the growing concerns about excessive radiation exposure to the patients. One common strategy to achieve low-dose CT imaging is to lower the milliampere-second (mAs) setting in data scanning protocol. However, the reconstructed CT images by the conventional filtered back-projection (FBP) method from the low-mAs acquisitions may be severely degraded due to the excessive noise. Statistical image reconstruction (SIR) methods have shown potentials to significantly improve the reconstructed image quality from the low-mAs acquisitions, wherein the regularization plays a critical role and an established family of regularizations is based on the Markov random field (MRF) model. Inspired by the success of nonlocal means (NLM) in image processing applications, in this work, we propose to explore the NLM-based regularization for SIR to reconstruct low-dose CT images from low-mAs acquisitions. Experimental results with both digital and physical phantoms consistently demonstrated that SIR with the NLM-based regularization can achieve more gains than SIR with the well-known Gaussian MRF regularization or the generalized Gaussian MRF regularization and the conventional FBP method, in terms of image noise reduction and resolution preservation.

Deriving adaptive MRF coefficients from previous normal‐dose CT scan for low‐dose image reconstruction via penalized weighted least‐squares minimization

Repeated computed tomography (CT) scans are required for some clinical applications such as image-guided interventions. To optimize radiation dose utility, a normal-dose scan is often first performed to set up reference, followed by a series of low-dose scans for intervention. One common strategy to achieve the low-dose scan is to lower the x-ray tube current and exposure time (mAs) or tube voltage (kVp) setting in the scanning protocol, but the resulted image quality by the conventional filtered back-projection (FBP) method may be severely degraded due to the excessive noise. Penalized weighted least-squares (PWLS) image reconstruction has shown the potential to significantly improve the image quality from low-mAs acquisitions, where the penalty plays an important role. In this work, the authors' explore an adaptive Markov random field (MRF)-based penalty term by utilizing previous normal-dose scan to improve the subsequent low-dose scans image reconstruction. In this work, the authors employ the widely-used quadratic-form MRF as the penalty model and explore a novel idea of using the previous normal-dose scan to obtain the MRF coefficients for adaptive reconstruction of the low-dose images. In the coefficients determination, the authors further explore another novel idea of using the normal-dose scan to obtain a scale map, which describes an optimal neighborhood for the coefficients determination such that a local uniform region has a small spread of frequency spectrum and, therefore, a small MRF window, and vice versa. The proposed penalty term is incorporated into the PWLS image reconstruction framework, and the low-dose images are reconstructed via the PWLS minimization. The presented adaptive MRF based PWLS algorithm was validated by physical phantom and patient data. The experimental results demonstrated that the presented algorithm is superior to the PWLS reconstruction using the conventional Gaussian MRF penalty or the edge-preserving Huber penalty and the conventional FBP method, in terms of image noise reduction and edge/detail/contrast preservation. This study demonstrated the feasibility and efficacy of the proposed scheme in utilizing previous normal-dose CT scan to improve the subsequent low-dose scans.

Prior image constrained compressed sensing: Implementation and performance evaluation

Prior image constrained compressed sensing (PICCS) is an image reconstruction framework which incorporates an often available prior image into the compressed sensing objective function. The images are reconstructed using an optimization procedure. In this paper, several alternative unconstrained minimization methods are used to implement PICCS. The purpose is to study and compare the performance of each implementation, as well as to evaluate the performance of the PICCS objective function with respect to image quality. Six different minimization methods are investigated with respect to convergence speed and reconstruction accuracy. These minimization methods include the steepest descent (SD) method and the conjugate gradient (CG) method. These algorithms require a line search to be performed. Thus, for each minimization algorithm, two line searching algorithms are evaluated: a backtracking (BT) line search and a fast Newton-Raphson (NR) line search. The relative root mean square error is used to evaluate the reconstruction accuracy. The algorithm that offers the best convergence speed is used to study the performance of PICCS with respect to the prior image parameter α and the data consistency parameter λ. PICCS is studied in terms of reconstruction accuracy, low-contrast spatial resolution, and noise characteristics. A numerical phantom was simulated and an animal model was scanned using a multirow detector computed tomography (CT) scanner to yield the projection datasets used in this study. For λ within a broad range, the CG method with Fletcher-Reeves formula and NR line search offers the fastest convergence for an equal level of reconstruction accuracy. Using this minimization method, the reconstruction accuracy of PICCS was studied with respect to variations in α and λ. When the number of view angles is varied between 107, 80, 64, 40, 20, and 16, the relative root mean square error reaches a minimum value for α ≈ 0.5. For values of α near the optimal value, the spatial resolution of the reconstructed image remains relatively constant and the noise texture is very similar to that of the prior image, which was reconstructed using the filtered backprojection (FBP) algorithm. Regarding the performance of the minimization methods, the nonlinear CG method with NR line search yields the best convergence speed. Regarding the performance of the PICCS image reconstruction, three main conclusions can be reached. (1) The performance of PICCS is optimal when the weighting parameter of the prior image parameter is selected to be near α = 0.5. (2) The spatial resolution measured for static objects in images reconstructed using PICCS from undersampled datasets is not degraded with respect to the fully-sampled reconstruction for α near its optimal value. (3) The noise texture of PICCS reconstructions is similar to that of the prior image, which was reconstructed using the conventional FBP method.

Usefulness of the automatic quantitative estimation tool for cerebral blood flow: clinical assessment of the application software tool AQCEL

AQCEL enables automatic reconstruction of single-photon emission computed tomogram (SPECT) without image degradation and quantitative analysis of cerebral blood flow (CBF) after the input of simple parameters. We ascertained the usefulness and quality of images obtained by the application software AQCEL in clinical practice. Twelve patients underwent brain perfusion SPECT using technetium-99m ethyl cysteinate dimer at rest and after acetazolamide (ACZ) loading. Images reconstructed using AQCEL were compared with those reconstructed using conventional filtered back projection (FBP) method for qualitative estimation. Two experienced nuclear medicine physicians interpreted the image quality using the following visual scores: 0, same; 1, slightly superior; 2, superior. For quantitative estimation, the mean CBF values of the normal hemisphere of the 12 patients using ACZ calculated by the AQCEL method were compared with those calculated by the conventional method. The CBF values of the 24 regions of the 3-dimensional stereotaxic region of interest template (3DSRT) calculated by the AQCEL method at rest and after ACZ loading were compared to those calculated by the conventional method. No significant qualitative difference was observed between the AQCEL and conventional FBP methods in the rest study. The average score by the AQCEL method was 0.25 ± 0.45 and that by the conventional method was 0.17 ± 0.39 (P = 0.34). There was a significant qualitative difference between the AQCEL and conventional methods in the ACZ loading study. The average score for AQCEL was 0.83 ± 0.58 and that for the conventional method was 0.08 ± 0.29 (P = 0.003). During quantitative estimation using ACZ, the mean CBF values of 12 patients calculated by the AQCEL method were 3-8% higher than those calculated by the conventional method. The square of the correlation coefficient between these methods was 0.995. While comparing the 24 3DSRT regions of 12 patients, the squares of the correlation coefficient between AQCEL and conventional methods were 0.973 and 0.986 for the normal and affected sides at rest, respectively, and 0.977 and 0.984 for the normal and affected sides after ACZ loading, respectively. The quality of images reconstructed using the application software AQCEL were superior to that obtained using conventional method after ACZ loading, and high correlations were shown in quantity at rest and after ACZ loading. This software can be applied to clinical practice and is a useful tool for improvement of reproducibility and throughput.

Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing

Dual energy computed tomography (DECT) is currently a subject of extensive investigation. DECT is currently implemented using either a dual source scanner with high and low kVp data acquired from separate sources or a single source scanner with both high and low kVp data acquired in an alternating manner. Both methods require dedicated hardware to enable data acquisition and image reconstruction for DECT. In this paper, we present a method to enable DECT using a single x-ray source with a slow kVp switching data acquisition. The enabling reconstruction technique allowing for the reduction in slew rate is the prior image constrained compressed sensing (PICCS) algorithm. When a slow kVp switching data acquisition method is used, the projection data with high and low kVp values are undersampled and the conventional filtered backprojection (FBP) image reconstruction does not enable streaking artifact-free images for material decomposition in DECT. In this paper, all of the acquired high and low kVp projection data were used to generate a prior image using the conventional FBP method. The PICCS algorithm was then used to reconstruct both high and low kVp images to enable material decomposition in the image domain. Both numerical simulations and physical phantom experimental studies were conducted to validate the proposed DECT scheme. The results demonstrate that a slew rate corresponding to 123 views at high and low kVp (high and low kVp values used for dual energy decomposition) is sufficient for the PICCS-based DECT method. In contrast, the slew rate should be high enough to obtain over 500 projections at each kVp for artifact-free reconstruction using an FBP-based DECT method.

Cone-beam reconstruction using the backprojection of locally filtered projections

This paper describes a flexible new methodology for accurate cone beam reconstruction with source positions on a curve (or set of curves). The inversion formulas employed by this methodology are based on first backprojecting a simple derivative in the projection space and then applying a Hilbert transform inversion in the image space. The local nature of the projection space filtering distinguishes this approach from conventional filtered-backprojection methods. This characteristic together with a degree of flexibility in choosing the direction of the Hilbert transform used for inversion offers two important features for the design of data acquisition geometries and reconstruction algorithms. First, the size of the detector necessary to acquire sufficient data for accurate reconstruction of a given region is often smaller than that required by previously documented approaches. In other words, more data truncation is allowed. Second, redundant data can be incorporated for the purpose of noise reduction. The validity of the inversion formulas along with the application of these two properties are illustrated with reconstructions from computer simulated data. In particular, in the helical cone beam geometry, it is shown that 1) intermittent transaxial truncation has no effect on the reconstruction in a central region which means that wider patients can be accommodated on existing scanners, and more importantly that radiation exposure can be reduced for region of interest imaging and 2) at maximum pitch the data outside the Tam-Danielsson window can be used to reduce image noise and thereby improve dose utilization. Furthermore, the degree of axial truncation tolerated by our approach for saddle trajectories is shown to be larger than that of previous methods.

Clinical correlative evaluation of an iterative method for reconstruction of brain SPECT images

Background: Brain SPECT and PET investigations have showed discrepancies in Alzheimer’s disease (AD) when considering data deriving from deeply located structures, such as the mesial temporal lobe. These discrepancies could be due to a variety of factors, including substantial differences in gamma-cameras and underlying technology. Mesial temporal structures are deeply located within the brain and the commonly used Filtered Back-Projection (FBP) technique does not fully take into account either the physical parameters of gamma-cameras or geometry of collimators. In order to overcome these limitations, alternative reconstruction methods have been proposed, such as the iterative method of the Conjugate Gradients with modified matrix (CG). However, the clinical applications of these methods have so far been only anecdotal. The present study was planned to compare perfusional SPECT data as derived from the conventional FBP method and from the iterative CG method, which takes into account the geometrical and physical characteristics of the gamma-camera, by a correlative approach with neuropsychology. Methods: Correlations were compared between perfusion of the hippocampal region, as achieved by both the FBP and the CG reconstruction methods, and a short-memory test (Selective Reminding Test, SRT), specifically addressing one of its function. A brain-dedicated camera (CERASPECT) was used for SPECT studies with 99mTc-hexamethylpropylene-amine-oxime in 23 consecutive patients (mean age: 74.2 ± 6.5) with mild (Mini-Mental Status Examination score ≥15, mean 20.3 ± 3), probable AD. Counts from a hippocampal region in each hemisphere were referred to the average thalamic counts. Results: Hippocampal perfusion significantly correlated with the MMSE score with similar statistical significance (p < 0.01) between the two reconstruction methods. Correlation between hippocampal perfusion and the SRT score was better with the CG method (r = 0.50 for both hemispheres, p < 0.01) than with the FBP method (r = 0.37 and 0.43, respectively for the right and left hemisphere, p < 0.05 and p < 0.02). The bootstrap procedure showed that such correlation indexes were statistically different both in the right (p < 0.01) and in the left (p < 0.05) hemisphere. Conclusion: These results are interpreted as a better performance of the CG reconstruction method in correctly detecting counts from hippocampal ROI. By using the same gamma-camera or collimator, alternative methods for brain SPECT reconstruction may improve quality of data and then help SPECT diagnostic accuracy.

Algebraic 2D PET image reconstruction using depth-of-interaction information

Recently a high-performance PET scanner, which measures depth-of-interaction (DOI) information, is being developed for molecular imaging. DOI measurement of multi-layered thin crystals can improve spatial resolution and scanner sensitivity simultaneously. In this paper, we apply an algebraic image reconstruction method to 2-dimensional (2D) DOI-PET scanners using accurate system modeling, in order to evaluate the effects of using DOI information on PET image quality. Algebraic image reconstruction methods have been successfully used to improve PET image quality, compared with the conventional filtered backprojection method. The proposed method is applied to simulated data for a small 2D DOI-PET scanner. The results show that accurate system modeling improves spatial resolution without noise emphasis, and that DOI information improves uniformity of spatial resolution.

131 3次元密度分布計測へのGAの応用

Three-dimensional density distribution can be reconstructed using tomography technique from interferogram images. Conventional tomography techniques have low accuracy with a few interferogram images and truncated images. To improve the accuracy of the reconstruction, a new tomography technique is proposed. In the present method, density distribution is assumed to be mainly effected by diffusion processes, and to be expressed as the summation of elementary density distributions. The tomography is carried out as the optimization of the arrangement of elementary density distributions. As optimization technique, genetic algorithm (GA) is introduced.The GA usually needs lots of calculation time. In this study, the two-dimensional density distribution in cross sections are stacked, in order to construct the three-dimensional data. As an initial condition of one cross section, the converged data at previous cross section are used, resulting in the less calculation time.The three-dimensional density distribution is reconstructed using the present method (GA) and conventional Filtered Back Projection method (FBP). The target flow field is density stratified field, which is caused by the downward helium jet into air. Both the GA and FBP can reconstruct the three-dimensional density distributions. The GA shows high effectiveness with reconstructing the three-dimensional data with a few images and truncated images.

Backprojection with fourier series expansion and FFT

The filtered backprojection (FBP) method has recently been used for almost all the practical CT scanners because of its simple principle and high accuracy in reconstruction of images. However, it is difficult to use this method for some applications such as a real time display in which images are adjusted during displaying images, since a backprojection of the FBP method requires a very large amount of computation. This paper introduces a method of backprojection in which the projection data compensated by a filter are expanded using Fourier series, and the sum of the series is evaluated by a high-speed synthesis algorithm of complex exponential using Gaussian functions and the fast Fourier transform (FFT) method. The theory is explained based on a conventional FBP method; then the usefulness of the proposed method is demonstrated. The results show that the proposed method can realize interpolations with various frequency characteristics requiring about one-sixth the amount of computation compared with a conventional backprojection using a linear interpolation.