Single Photon Emission Computed Tomography Projection Research Articles

Sinogram-characteristic-informed network for efficient restoration of low-dose SPECT projection data.

Single Photon Emission Computed Tomography (SPECT) sinogram restoration for low-dose imaging is a critical challenge in medical imaging. Existing methods often overlook the characteristics of the sinograms, necessitating innovativeapproaches. In this study, we introduce the Sinogram-characteristic-informed network (SCI-Net) to address the restoration of low-dose SPECT sinograms. Our aim is to build and train a model based on the characteristics of sinograms, including continuity, periodicity, multi-scale properties of lines in sinograms, and others, to enhance the model's understanding of the restorationprocess. SCI-Net incorporates several novel mechanisms tailored to exploit the inherent characteristics of sinograms. We implement a channel attention module with a decay mechanism to leverage continuity across adjacent sinograms, while a position attention module captures global correlations within individual sinograms. Additionally, we propose a multi-stage progressive integration mechanism to balance local detail and overall structure. Multiple regularization terms, customized to sinogram image characteristics, are embedded into the loss function for modeltraining. The experimental evaluations are divided into two parts: simulation data evaluation and clinical evaluation. The simulation data evaluation is conducted on a dataset comprising ten organ types, generated by the SIMIND Monte Carlo program from extended cardiac-torso (XCAT) anatomical phantoms. The dataset includes a total of SPECT sinograms with low-dose as input data and normal-dose as ground truth, consisting of 3881 sinograms in the training dataset and 849 sinograms in the testing set. When comparing the restoration of low-dose sinograms to normal-dose references, SCI-Net effectively improves performance. Specifically, the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) on sinograms increase from 15.72 to 34.66 ( 0.001) and 0.6297 to 0.9834 ( 0.001), respectively, and on reconstructed images, reconstructed by maximum likelihood-expectation maximization (ML-EM), the PSNR and the SSIM improve from 21.95 to 33.14 ( 0.001) and 0.9084 to 0.9866 ( 0.001), respectively. We compared SCI-Net with existing methods, including baseline models, traditional reconstruction algorithms, end-to-end methods, sinogram restoration methods, and image post-processing methods. The experimental results and visual examples demonstrate that SCI-Net surpasses these existing methods in SPECT sinogram restoration. The clinical evaluation is conducted on clinical data of low-dose SPECT sinograms for spleen, thyroid, skull, and bone. These SPECT projection data are obtained from Discovery NM/CT670 scans. We compare the reconstructed images from the SCI-Net restored sinograms, the reconstructed images from the original low-dose sinograms, and the reconstructed images using the built-in algorithm of the Discovery NM/CT670. The results show that our method effectively reduces the coefficient of variation (COV) in the regions of interest (ROI) of the reconstructed images, thereby enhancing the quality of the reconstructed images through SPECT sinogramrestoration. Our proposed SCI-Net exhibits promising performance in the restoration of low-dose SPECT projection data. In the SCI-Net, we have implemented three mechanisms based on distinct forms, which are advantageous for the model to more effectively leverage the characteristics of sinograms and achieve commendable restorationoutcomes.

DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.

Single-Photon Emission Computed Tomography (SPECT) is widely applied for the diagnosis of coronary artery diseases. Low-dose (LD) SPECT aims to minimize radiation exposure but leads to increased image noise. Limited-view (LV) SPECT, such as the latest GE MyoSPECT ES system, enables accelerated scanning and reduces hardware expenses but degrades reconstruction accuracy. Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ( μ -maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments. Although various methods have been developed to solely focus on LD denoising, LV reconstruction, or CT-free AC in SPECT, the solution for simultaneously addressing these tasks remains challenging and under-explored. Furthermore, it is essential to explore the potential of fusing cross-domain and cross-modality information across these interrelated tasks to further enhance the accuracy of each task. Thus, we propose a Dual-Domain Coarse-to-Fine Progressive Network (DuDoCFNet), a multi-task learning method for simultaneous LD denoising, LV reconstruction, and CT-free μ -map generation of cardiac SPECT. Paired dual-domain networks in DuDoCFNet are cascaded using a multi-layer fusion mechanism for cross-domain and cross-modality feature fusion. Two-stage progressive learning strategies are applied in both projection and image domains to achieve coarse-to-fine estimations of SPECT projections and CT-derived μ -maps. Our experiments demonstrate DuDoCFNet's superior accuracy in estimating projections, generating μ -maps, and AC reconstructions compared to existing single- or multi-task learning methods, under various iterations and LD levels. The source code of this work is available at https://github.com/XiongchaoChen/DuDoCFNet-MultiTask.

A Learned Reconstruction Network for SPECT Imaging.

A neural network designed specifically for SPECT image reconstruction was developed. The network reconstructed activity images from SPECT projection data directly. Training was performed through a corpus of training data including that derived from digital phantoms generated from custom software and the corresponding projection data obtained from simulation. When using the network to reconstruct images, input projection data were initially fed to two fully connected (FC) layers to perform a basic reconstruction. Then the output of the FC layers and an attenuation map were delivered to five convolutional layers for signal-decay compensation and image optimization. To validate the system, data not used in training, simulated data from the Zubal human brain phantom, and clinical patient data were used to test reconstruction performance. Reconstructed images from the developed network proved closer to the truth with higher resolution and quantitative accuracy than those from conventional OS-EM reconstruction. To understand better the operation of the network for reconstruction, intermediate results from hidden layers were investigated for each step of the processing. The network system was also retrained with noisy projection data and compared with that developed with noise-free data. The retrained network proved even more robust after having learned to filter noise. Finally, we showed that the network still provided sharp images when using reduced view projection data (retrained with reduced view data).

Contrast optimization using triple energy window scatter correction method for In-111 SPECT imaging: A SIMIND Monte Carlo study

Introduction: Detection of scattered photons in photo-peak window degrades image contrast and quantitative accuracy of single-photon emission computed tomography (SPECT) imaging. Increases image contrast lead to significant improvement of image quality. The triple energy window (TEW) method, which has developed to eliminate the counts of scatter photons in measured counts, was applied to I-111 SPECT study and its effect was examined in a simulation study. Materials and method: The Siemens SYMBIA gamma camera equipped with a medium energy (ME) collimator was simulated by the Simulating Medical Imaging Nuclear Detectors (SIMIND) program. We used the SIMIND Monte Carlo program to generate the I-111 SPECT projection data of the Jaszczak phantom. The phantom consists of six spheres with different diameters (9.5, 12.7, 19.1, 15.9, 25.4, and 31.8 mm) which are used to evaluate the image contrast. Geometric, scatter and penetration fraction and also, point-spread functions (PSFs) and contrast curves were drawn and compared. Results: Results showed that 171keV photo-peak compared to 245keV gave the best results with a ME collimator when the TEW scatter correction method was applied. This can be explained by the large amount of collimator scatter and penetration from the photo-peak and by the collimator for 245 photo-peak window. Conclusion: With TEW scatter correction method, it is better to use a 171keV photo-peak window because of the better spatial resolution and image contrast.

Interpolated CT for attenuation correction on respiratory gating cardiac SPECT/CT - A simulation study.

Respiratory gated four-dimensional (4D) single photon emission computed tomography (SPECT) with phase-matched CT reduces respiratory blurring and attenuation correction (AC) artifacts in cardiac SPECT. This study aims to develop and investigate the effectiveness of an interpolated CT (ICT) method for improved cardiac SPECT AC using simulations. We used the 4D XCAT phantom to simulate a population of ten patients varied in gender, anatomy, 99m Tc-sestamibi distribution, respiratory patterns, and disease states. Simulated 120 SPECT projection data were rebinned into six equal count gates. Activity and attenuation maps in each gate were averaged as gated SPECT and CT (GCT). Three helical CTs were simulated at end-inspiration (HCT-IN), end-expiration (HCT-EX), and mid-respiration (HCT-MID). The ICTs were obtained from HCT-EX and HCT-IN using the motion vector field generated between them from affine plus b-spline registration. Projections were reconstructed by OS-EM method, using GCT, ICT, and three HCTs for AC. Reconstructed images of each gate were registered to end-expiration and averaged to generate the polar plots. Relative difference for each segment and relative defect size were computed using images of GCT AC as reference. The average of maximum relative difference through ten phantoms was 7.93±4.71%, 2.50±0.98%, 3.58±0.74%, and 2.14±0.56% for noisy HCT-IN, HCT-MID, HCT-EX, and ICT AC data, respectively. The ICT showed closest defect size to GCT while the differences from HCTs can be over 40%. We conclude that the performance of ICT is similar to GCT. It improves the image quality and quantitative accuracy for respiratory-gated cardiac SPECT as compared to conventional HCT, while it can potentially further reduce the radiation dose of GCT.

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.

Collimator and energy window optimization for 90Y bremsstrahlung SPECT imaging: A SIMIND Monte Carlo study

Treatment efficacy of radioembolization using Yttrium-90 (90Y) microspheres is assessed by the 90Y bremsstrahlung single photon emission computed tomography (SPECT) imaging following radioembolization. The radioisotopic image has the potential of providing reliable activity map of 90Y microspheres distribution. One of the main reasons of the poor image quality in 90Y bremsstrahlung SPECT imaging is the continuous and broad energy spectrum of the related bremsstrahlung photons. Furthermore, collimator geometry plays an impressive role in the spatial resolution, sensitivity and image contrast. Due to the relatively poor quality of the 90Y bremsstrahlung SPECT images, we intend to optimize the medium-energy (ME) parallel-hole collimator and energy window. The Siemens e.cam gamma camera equipped with a ME collimator and a voxelized phantom was simulated by the SImulating Medical Imaging Nuclear Detectors (SIMIND) program. We used the SIMIND Monte Carlo program to generate the 90Y bremsstrahlung SPECT projection of the digital Jaszczak phantom. The phantom consist of the six hot spheres ranging from 9.5 to 31.8mm in diameter, which are used to evaluate the image contrast. In order to assess the effect of the energy window on the image contrast, three energy windows ranging from 60 to 160KeV, 160 to 400KeV, and 60 to 400KeV were set on a 90Y bremsstrahlung spectrum. As well, the effect of the hole diameter of a ME collimator on the image contrast and bremsstrahlung spectrum were investigated. For the fixed collimator and septa thickness values (3.28cm and 1.14mm, respectively), a hole diameter range (2.35–3.3mm) was chosen based on the appropriate balance between the spatial resolution and sensitivity. The optimal energy window for 90Y bremsstrahlung SPECT imaging was extended energy window from 60 to 400KeV. Besides, The optimal value of the hole diameter of ME collimator was obtained 3.3mm. Geometry of the ME parallel-hole collimator and energy window are indeed important indicators of the image quality in 90Y bremsstrahlung imaging. The obtained optimal ME collimator and optimal energy window have the potential to improve the image contrast of 90Y bremsstrahlung images. Subsequently, high quality 90Y bremsstrahlung images can provide reliable estimate of the 90Y microsphere activity distribution after radioembolization.

Nonlinear dual reconstruction of SPECT activity and attenuation images.

In single photon emission computed tomography (SPECT), accurate attenuation maps are needed to perform essential attenuation compensation for high quality radioactivity estimation. Formulating the SPECT activity and attenuation reconstruction tasks as coupled signal estimation and system parameter identification problems, where the activity distribution and the attenuation parameter are treated as random variables with known prior statistics, we present a nonlinear dual reconstruction scheme based on the unscented Kalman filtering (UKF) principles. In this effort, the dynamic changes of the organ radioactivity distribution are described through state space evolution equations, while the photon-counting SPECT projection data are measured through the observation equations. Activity distribution is then estimated with sub-optimal fixed attenuation parameters, followed by attenuation map reconstruction given these activity estimates. Such coupled estimation processes are iteratively repeated as necessary until convergence. The results obtained from Monte Carlo simulated data, physical phantom, and real SPECT scans demonstrate the improved performance of the proposed method both from visual inspection of the images and a quantitative evaluation, compared to the widely used EM-ML algorithms. The dual estimation framework has the potential to be useful for estimating the attenuation map from emission data only and thus benefit the radioactivity reconstruction.

Evaluation of dead-time corrections for post-radionuclide-therapy 177Lu quantitative imaging with low-energy high-resolution collimators

Dead-time (DT) effects rarely cause problems in diagnostic single-photon emission computed tomography (SPECT) studies; however, in post-radionuclide-therapy imaging, DT can be substantial. Therefore, corrections may be necessary if quantitative images are used in image-based dosimetry or for evaluation of therapy outcomes. This task is particularly challenging if low-energy collimators are used. Our goal was to design a simple method to determine the dead-time correction factor (DTCF) without the need for phantom experiments and complex calculations. Planar and SPECT/CT scans of a water phantom containing a 70 ml bottle filled with lutetium-177 (Lu) were acquired over 60 days. Two small Lu markers were used in all scans. The DTCF based on the ratio of observed to true count rates measured over the entire spectrum and using photopeak primary photons only was estimated for phantom (DT present) and marker (no DT) scans. In addition, variations in counts in SPECT projections (potentially caused by varying bremsstrahlung and scatter) were investigated. For count rates that were about two-fold higher than typically seen in post-therapy Lu scans, the maximum DTCF reached a level of about 17%. The DTCF values determined directly from the phantom experiments using the total energy spectrum and photopeak counts only were equal to 13 and 16%, respectively. They were closely matched by those from the proposed marker-based method, which uses only two energy windows and measures photopeak primary photons (15-17%). A simple, marker-based method allowing for determination of the DTCF in high-activity Lu imaging studies has been proposed and validated using phantom experiments.

A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list-mode acquisitions.

This technical note documents a method that the authors developed for combining a signal to synchronize a patient-monitoring device with a second physiological signal for inclusion into list-mode acquisition. Our specific application requires synchronizing an external patient motion-tracking system with a medical imaging system by multiplexing the tracking input with the ECG input. The authors believe that their methodology can be adapted for use in a variety of medical imaging modalities including single photon emission computed tomography (SPECT) and positron emission tomography (PET). The authors insert a unique pulse sequence into a single physiological input channel. This sequence is then recorded in the list-mode acquisition along with the R-wave pulse used for ECG gating. The specific form of our pulse sequence allows for recognition of the time point being synchronized even when portions of the pulse sequence are lost due to collisions with R-wave pulses. This was achieved by altering our software used in binning the list-mode data to recognize even a portion of our pulse sequence. Limitations on heart rates at which our pulse sequence could be reliably detected were investigated by simulating the mixing of the two signals as a function of heart rate and time point during the cardiac cycle at which our pulse sequence is mixed with the cardiac signal. The authors have successfully achieved accurate temporal synchronization of our motion-tracking system with acquisition of SPECT projections used in 17 recent clinical research cases. In our simulation analysis the authors determined that synchronization to enable compensation for body and respiratory motion could be achieved for heart rates up to 125 beats-per-minute (bpm). Synchronization of list-mode acquisition with external patient monitoring devices such as those employed in motion-tracking can reliably be achieved using a simple method that can be implemented using minimal external hardware and software modification through a single input channel, while still recording cardiac gating signals.

An evaluation of data-driven motion estimation in comparison to the usage of external-surrogates in cardiac SPECT imaging

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

Fast direct estimation of the blood input function and myocardial time activity curve from dynamic SPECT projections via reduction in spatial and temporal dimensions

Reconstruction of parametric images from dynamic single photon emission computed tomography (SPECT) data acquired with slow rotating cameras is a challenge because the estimation of the time-activity curves (TACs) may involve fitting data to an inconsistent underdetermined system of equations. This work presents a novel algorithm for the estimation of the blood input function and myocardial TAC with high accuracy and high efficiency directly from these projections. In the proposed dynamic reconstruction method, the information from the segmentation of functional regions from the static reconstructed image was used as a prior to construct a sparse matrix, through which the spatial distribution of the radioactive tracer was represented. Then the temporal distribution of the radioactive tracer was modeled by nonuniform B-spline basis functions which were determined according to a new selection rule. With reduction in both the spatial and temporal dimensions of the reconstructed image, the blood input function and myocardial TAC were estimated using the 4D maximum likelihood expectation maximization algorithm. The method was validated using data from both digital phantom simulations and an experimental rat study. Compared with the conventional dynamic SPECT reconstruction method without the reduction in spatial dimensions, the proposed method provides more accurate TACs with less computation time in both phantom simulation studies and a rat experimental study. The proposed method is promising in both providing more accurate time-activity curves and reducing the computation time, which makes it practical for small animal studies using clinical systems with slow rotating cameras.

Position mapping and a uniformity correction method for small-animal SPECT based on connected regional recognition

We describe a novel position mapping and the uniformity correction method to improve the performance of small animal single-photon emission computed tomography (SPECT) imaging. The SPECT system consists of a cerium doped lutetium−yttrium oxyorthosilicate (LYSO) scintillation crystal (22×22 pixel array, 2mm×2mm×3mm pixel size), a position sensitive photomultiplier tube (H8500c, Hamamatsu Photonics Co., Ltd., Shizuoka Prefecture, Japan), and a parallel-hole collimator (Nuclear Fields Pty. Ltd., St. Marys, Australia). The position mapping method was based on a connected regional recognition algorithm. We present the algorithm and step-by-step details of image boundary detection, dynamic binarization, connected regional recognition, center-of-gravity computing, and look-up table establishment. The position mapping and uniformity correction tables were generated and applied to the SPECT projection data. The corrected projection images demonstrated that this correction method improved the uniformity of the raw projection image by ∼16%. The preliminary SPECT reconstruction results (using algebraic reconstruction technology) are also presented. A comparison between the reconstructed images before and after correction further confirms the performance of this correction method.

Quantification and reduction of the collimator-detector response effect in SPECT by applying a system model during iterative image reconstruction

Detector blurring and non-ideal collimation decrease the spatial resolution of the single-photon emission computed tomography (SPECT) images. Iterative reconstruction algorithms such as ordered subsets expectation maximization (OSEM) can incorporate degrading factors during reconstruction. We investigated the quantitative errors associated with poor SPECT resolution and evaluated the importance of two-dimensional (2D) and three-dimensional (3D) resolution recovery by modelling system response during iterative image reconstruction. Different phantoms consisted of the NURBS-based cardiac-torso (NCAT) liver phantom with small tumors, the Zubal brain phantom and the NCAT heart phantom were used in this study. Monte Carlo simulation was used to create SPECT projections. Gaussian functions were used to model collimator detector response (CDR). Modeled CDRs were applied during OSEM. Both noise-free and noisy projections were created. Even with noise-free projections, conventional OSEM algorithm provided limited quantitative accuracy compared to both 2D and 3D resolution recovery. The 3D implementation of resolution recovery, however, yielded superior results compared to its 2D implementation. For the liver phantom, the ability to distinguish small tumors in both transverse and axial planes was improved. For the brain phantom, gray to white matter activity ratio was increased from 3.14 ± 0.04 in simple OSEM to 3.84 ± 0.06 in 3D OSEM. For the NCAT heart phantom, 3D resolution recovery, results in images with thinner wall and higher contrast for different noise levels. There are considerable quantitative errors associated with CDR, especially when the size of the target is comparable with the spatial resolution of the system. Between different reconstruction algorithms, 3D OSEM that consider the 3D nature of CDR, improve both the visual quality and the quantitative accuracy of any SPECT studies.

High-order total variation minimization for interior SPECT

Recently, we developed an approach for solving the computed tomography (CT) interior problem based on the high-order TV (HOT) minimization, assuming that a region-of-interest (ROI) is piecewise polynomial. In this paper, we generalize this finding from the CT field to the single-photon emission computed tomography (SPECT) field, and prove that if an ROI is piecewise polynomial, then the ROI can be uniquely reconstructed from the SPECT projection data associated with the ROI through the HOT minimization. Also, we propose a new formulation of HOT, which has an explicit formula for any n-order piecewise polynomial function, while the original formulation has no explicit formula for n ⩾ 2. Finally, we verify our theoretical results in numerical simulation, and discuss relevant issues.

Half-time myocardial perfusion SPECT imaging with attenuation and Monte Carlo-based scatter correction

To test the potential of a new reconstruction algorithm with Monte Carlo-based scatter correction in half-time myocardial perfusion single-photon emission computed tomography (SPECT). The mathematical four-dimensional NURBS-based Cardiac-Torso phantom and the SIMIND Monte Carlo simulation package were used to simulate full-time and half-time SPECT projection data. The data were reconstructed using the standard ordered subset expectation maximization-based algorithm and the new Monte Carlo-based algorithm. Defect contrast, myocardium versus ventricle contrast and resolution were calculated. In addition to the simulation studies, full-time and half-time SPECT projection data of 30 patients were reconstructed with the standard and the new method. The patient data were qualitatively evaluated by four nuclear medicine experts on a scale from 1 (poor quality) to 5 (high quality). The new reconstruction method with half-time data produced higher contrast and better resolution in the simulations and also achieved higher qualitative scores in the patient study than the standard reconstruction with full-time data. Half-time myocardial perfusion imaging using the new reconstruction algorithm with Monte Carlo-based scatter correction produced images with superior quality when compared with full-time imaging with standard reconstruction.

Reconstruction From Uniformly Attenuated SPECT Projection Data Using the DBH Method

An algorithm was developed for the 2-D reconstruction of truncated and nontruncated uniformly attenuated data acquired from single photon emission computed tomography (SPECT). The algorithm is able to reconstruct data from half-scan (180 ( degrees ) ) and short-scan (180 ( degrees ) +fan angle) acquisitions for parallel- and fan-beam geometries, respectively, as well as data from full-scan (360 ( degrees )) acquisitions. The algorithm is a derivative, backprojection, and Hilbert transform (DBH) method, which involves the backprojection of differentiated projection data followed by an inversion of the finite weighted Hilbert transform. The kernel of the inverse weighted Hilbert transform is solved numerically using matrix inversion. Numerical simulations confirm that the DBH method provides accurate reconstructions from half-scan and short-scan data, even when there is truncation. However, as the attenuation increases, finer data sampling is required.

Subendocardial versus transmural ischaemia in myocardial perfusion SPECT – a Monte Carlo study

Myocardial perfusion imaging with single-photon emission computed tomography (SPECT) is useful for the evaluation of patients with known or suspected coronary artery disease. Parameters of interest are the reduction in the blood perfusion (severity) and the lesion volume (extent). The aim of this study was to evaluate these parameters, as calculated by automatic quantification software, for different cases of subendocardial and transmural myocardial lesions. A computer phantom was used to simulate 32 male patients with different defect locations and activity uptakes, which were based on clinical patient studies. The Monte Carlo program SIMIND was used to simulate realistic SPECT projections which were reconstructed to give short-axis images, analysed by the AutoQUANT program using the same procedure as for a real patient. The results showed a disparity between the quantification of transmural and subendocardial lesions with the same lesion activity uptake reduction and this could be confirmed by visual interpretation. Neither the parameters given by the quantification program nor visual interpretation could distinguish between the transmural lesions and the subendocardial lesions with activity uptake reduction twice as high as in the corresponding transmural lesions. Transmural lesions and the corresponding subendocardial lesions with the same activity uptake reduction could be separated by the quantification software for SPECT imaging and visual analysis. The subendocardial lesions with activity uptake reduction twice as high as in the corresponding transmural lesions could not be differentiated neither by the quantification software nor by visual interpretation. Thus these lesions will get the same scoring when analysed by the AutoQUANT program.

Fusion of the MR image to SPECT with possible correction for partial volume effects

Low spatial resolution and the related partial volume effects limit the diagnostic potential of brain single photon emission computed tomography (SPECT) imaging. As a possible remedy for this problem we propose a technique for the fusion of SPECT and MR images, which requires for a given patient the SPECT data and the T1-weighted MR image. Basically, after the reconstruction and coregistration steps, the high-frequency part of the MR, which would be unrecoverable by the set SPECT acquisition system + reconstruction algorithm, is extracted and added to the SPECT image. The tuning of the weight of the MR on the resulting fused image can be performed very quickly, any iterative reconstruction algorithm can be used and, in the case that the SPECT projections are not available, the proposed technique can also be applied directly to the SPECT image, provided that the performance of the scanner is known. The procedure has the potential of increasing the diagnostic value of a SPECT image. Even in the locations of SPECT-MR mismatch it does not significantly affect quantitation over regions of interest (ROIs) whose dimensions are decidedly larger than the SPECT resolution distance. On the other hand, appreciable corrections for partial volume effects are expected in the locations where the contrast in the structural MR matches the corresponding contrast in functional activity.

Do short-time SPECT images of bone scintigraphy improve the diagnostic value in the evaluation of solitary lesions in the thoracic spine in patients with extraskeletal malignancies?

Single photon emission computed tomography (SPECT) images provide many details of the anatomical structure. Also about bone scintigraphy, there are many reports of the improvement of diagnosis by SPECT images. Although SPECT is useful, it requires much time. So to perform SPECT for all cases is difficult in the clinical situation. Recently, due to technical improvements in gamma cameras, we can get SPECT images in a short time. We examined diagnosis of solitary hot spots of thoracic spine in cancer patients using short-time SPECT. And we considered whether short-time SPECT contributes to the precise diagnosis of the lesion. We performed bone scintigraphy image acquisition and both planar and short-time SPECT of the chest. Short-time SPECT was acquired in 6 minutes. We selected 36 cases with malignancy, whose bone scintigraphy demonstrated a solitary accumulation hot spot in the thoracic spine. Three experienced radiologists in nuclear medicine and 4 beginners diagnosed the images. They interpreted planar, short-time SPECT and maximum intensity projection (MIP) view of the chest of each case. The observers' response data were analyzed with receiver operating characteristic (ROC) curve analysis. Of the three types of images, the Az (the area under ROC curve) values of short-time SPECT were the highest in all the observers except for only one beginner. Compared with experienced observers, beginners scored lower Az values of short-time SPECT. MIP images were constructed using SPECT data, but the Az values of MIP images were not higher than those of planar images. As to diagnosis, beginners tended to interprete most of the accumulations as metastatic lesions. Short-time SPECT can be helpful to some degree, but to provide greater benefit, the observers require considerable exercise and experience.