Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.

BACKGROUND: The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A deep learning method is also proposed to generate synthetic dense-view image volumes from few-view counterparts. METHODS: By moving the detector array, a total of four projection angle sets were acquired and combined for image reconstructions. A deep neural network is proposed to generate synthetic four-angle images with 76 ([Formula: see text]) projections from corresponding one-angle images with 19 projections. Simulated data, pig, physical phantom, and human studies were used for network training and evaluation. Reconstruction results were quantitatively evaluated using representative image metrics. The myocardial perfusion defect size of different subjects was quantified using an FDA-cleared clinical software. RESULTS: Multi-angle reconstructions and network results have higher image resolution, improved uniformity on normal myocardium, more accurate defect quantification, and superior quantitative values on all the testing data. As validated against cardiac catheterization and diagnostic results, deep learning results showed improved image quality with better defect contrast on human studies. CONCLUSION: Increasing angular sampling can substantially improve image quality on DNM, and deep learning can be implemented to improve reconstruction quality in case of stationary imaging.

A novel deep-learning-based approach for automatic reorientation of 3D cardiac SPECT images.

PURPOSE: Reconstructed transaxial cardiac SPECT images need to be reoriented into standard short-axis slices for subsequent accurate processing and analysis. We proposed a novel deep-learning-based method for fully automatic reorientation of cardiac SPECT images and evaluated its performance on data from two clinical centers. METHODS: We used a convolutional neural network to predict the 6 rigid-body transformation parameters and a spatial transformation network was then implemented to apply these parameters on the input images for image reorientation. A novel compound loss function which balanced the parametric similarity and penalized discrepancy of the prediction and training dataset was utilized in the training stage. Data from a set of 322 patients underwent data augmentation to 6440 groups of images for the network training, and a dataset of 52 patients from the same center and 23 patients from another center were used for evaluation. Similarity of the 6 parameters was analyzed between the proposed and the manual methods. Polar maps were generated from the output images and the averaged count values of the 17 segments were computed from polar maps to evaluate the quantitative accuracy of the proposed method. RESULTS: All the testing patients achieved automatic reorientation successfully. Linear regression results showed the 6 predicted rigid parameters and the average count value of the 17 segments having good agreement with the reference manual method. No significant difference by paired t-test was noticed between the rigid parameters of our method and the manual method (p > 0.05). Average count values of the 17 segments show a smaller difference of the proposed and manual methods than those between the existing and manual methods. CONCLUSION: The results strongly indicate the feasibility of our method in accurate automatic cardiac SPECT reorientation. This deep-learning-based reorientation method has great promise for clinical application and warrants further investigation.

Optimization of a simultaneous dual-isotope 201Tl/123I-MIBG myocardial SPECT imaging protocol with a CZT camera for trigger zone assessment after myocardial infarction for routine clinical settings: Are delayed acquisition and scatter correction necessary?

BACKGROUND: Dual-isotope 201Tl/123I-MIBG SPECT can assess trigger zones (dysfunctions in the autonomic nervous system located in areas of viable myocardium) that are substrate for ventricular arrhythmias after STEMI. This study evaluated the necessity of delayed acquisition and scatter correction for dual-isotope 201Tl/123I-MIBG SPECT studies with a CZT camera to identify trigger zones after revascularization in patients with STEMI in routine clinical settings. METHODS: Sixty-nine patients were prospectively enrolled after revascularization to undergo 201Tl/123I-MIBG SPECT using a CZT camera (Discovery NM 530c, GE). The first acquisition was a single thallium study (before MIBG administration); the second and the third were early and late dual-isotope studies. We compared the scatter-uncorrected and scatter-corrected (TEW method) thallium studies with the results of magnetic resonance imaging or transthoracic echography (reference standard) to diagnose myocardial necrosis. RESULTS: Summed rest scores (SRS) were significantly higher in the delayed MIBG studies than the early MIBG studies. SRS and necrosis surface were significantly higher in the delayed thallium studies with scatter correction than without scatter correction, leading to less trigger zone diagnosis for the scatter-corrected studies. Compared with the scatter-uncorrected studies, the late thallium scatter-corrected studies provided the best diagnostic values for myocardial necrosis assessment. CONCLUSIONS: Delayed acquisitions and scatter-corrected dual-isotope 201Tl/123I-MIBG SPECT acquisitions provide an improved evaluation of trigger zones in routine clinical settings after revascularization for STEMI.

Respiratory signal estimation for cardiac perfusion SPECT using deep learning.

BACKGROUND: Respiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting. PURPOSE: We aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation. METHODS: A modified U-Net was implemented that takes temporally finely sampled SPECT sub-projection data (100 ms) as input. These sub-projections are obtained by reframing the 20-s list-mode data, resulting in 200 sub-projections, at each projection angle for each SPECT camera head. The network outputs a 200-time-point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo-camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data-driven approach based on the center-of-mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub-projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation. RESULTS: The Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de-blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12 mm, compared to 2.6% for the CoM method and 8.5% for no correction. CONCLUSION: We demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo-camera VTS signals. This may enable fully automatic data-driven respiratory motion correction without relying on external motion tracking devices.

Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.

OBJECTIVE: Deep learning approaches have attracted attention for improving the scoring accuracy in computed tomography-less single photon emission computed tomography (SPECT). In this study, we proposed a novel deep learning approach referring to positron emission tomography (PET). The aims of this study were to analyze the agreement of representative voxel values and perfusion scores of SPECT-to-PET translation model-generated SPECT (SPECTSPT) against PET in 17 segments according to the American Heart Association (AHA). METHODS: This retrospective study evaluated the patient-to-patient stress, resting SPECT, and PET datasets of 71 patients. The SPECTSPT generation model was trained (stress: 979 image pairs, rest: 987 image pairs) and validated (stress: 421 image pairs, rest: 425 image pairs) using 31 cases of SPECT and PET image pairs using an image-to-image translation network. Forty of 71 cases of left ventricular base-to-apex short-axis images were translated to SPECTSPT in the stress and resting state (stress: 1830 images, rest: 1856 images). Representative voxel values of SPECT and SPECTSPT in the 17 AHA segments against PET were compared. The stress, resting, and difference scores of 40 cases of SPECT and SPECTSPT were also compared in each of the 17 segments. RESULTS: For AHA 17-segment-wise analysis, stressed SPECT but not SPECTSPT voxel values showed significant error from PET at basal anterior regions (segments #1, #6), and at mid inferoseptal regions (segments #8, #9, and #10). SPECT, but not SPECTSPT, voxel values at resting state showed significant error at basal anterior regions (segments #1, #2, and #6), and at mid inferior regions (segments #8, #9, and #11). Significant SPECT overscoring was observed against PET in basal-to-apical inferior regions (segments #4, #10, and #15) during stress. No significant overscoring was observed in SPECTSPT at stress, and only moderate over and underscoring in the basal inferior region (segment #4) was found in the resting and difference states. CONCLUSIONS: Our PET-supervised deep learning model is a new approach to correct well-known inferior wall attenuation in SPECT myocardial perfusion imaging. As standalone SPECT systems are used worldwide, the SPECTSPT generation model may be applied as a low-cost and practical clinical tool that provides powerful auxiliary information for the diagnosis of myocardial blood flow.

Comparison of deep learning-based denoising methods in cardiac SPECT.

BACKGROUND: Myocardial perfusion SPECT (MPS) images often suffer from artefacts caused by low-count statistics. Poor-quality images can lead to misinterpretations of perfusion defects. Deep learning (DL)-based methods have been proposed to overcome the noise artefacts. The aim of this study was to investigate the differences among several DL denoising models. METHODS: Convolution neural network (CNN), residual neural network (RES), UNET and conditional generative adversarial neural network (cGAN) were generated and trained using ordered subsets expectation maximization (OSEM) reconstructed MPS studies acquired with full, half, three-eighths and quarter acquisition time. All DL methods were compared against each other and also against images without DL-based denoising. Comparisons were made using half and quarter time acquisition data. The methods were evaluated in terms of noise level (coefficient of variation of counts, CoV), structural similarity index measure (SSIM) in the myocardium of normal patients and receiver operating characteristic (ROC) analysis of realistic artificial perfusion defects inserted into normal MPS scans. Total perfusion deficit scores were used as observer rating for the presence of a perfusion defect. RESULTS: All the DL denoising methods tested provided statistically significantly lower noise level than OSEM without DL-based denoising with the same acquisition time. CoV of the myocardium counts with the different DL noising methods was on average 7% (CNN), 8% (RES), 7% (UNET) and 14% (cGAN) lower than with OSEM. All DL methods also outperformed full time OSEM without DL-based denoising in terms of noise level with both half and quarter acquisition time, but this difference was not statistically significant. cGAN had the lowest CoV of the DL methods at all noise levels. Image quality and polar map uniformity of DL-denoised images were also better than reduced acquisition time OSEM's. SSIM of the reduced acquisition time OSEM was overall higher than with the DL methods. The defect detection performance of full time OSEM measured as area under the ROC curve (AUC) was on average 0.97. Half time OSEM, CNN, RES and UNET provided equal or nearly equal AUC. However, with quarter time data CNN, RES and UNET had an average AUC of 0.93, which was lower than full time OSEM's AUC, but equal to quarter acquisition time OSEM. cGAN did not achieve the defect detection performance of the other DL methods. Its average AUC with half time data was 0.94 and 0.91 with quarter time data. CONCLUSIONS: DL-based denoising effectively improved noise level with slightly lower perfusion defect detection performance than full time reconstruction. cGAN achieved the lowest noise level, but at the same time the poorest defect detection performance among the studied DL methods.

DuDoSS: Deep-learning-based dual-domain sinogram synthesis from sparsely sampled projections of cardiac SPECT.

PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission-computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. In clinical practice, the long scanning procedures and acquisition time might induce patient anxiety and discomfort, motion artifacts, and misalignments between SPECT and computed tomography (CT). Reducing the number of projection angles provides a solution that results in a shorter scanning time. However, fewer projection angles might cause lower reconstruction accuracy, higher noise level, and reconstruction artifacts due to reduced angular sampling. We developed a deep-learning-based approach for high-quality SPECT image reconstruction using sparsely sampled projections. METHODS: We proposed a novel deep-learning-based dual-domain sinogram synthesis (DuDoSS) method to recover full-view projections from sparsely sampled projections of cardiac SPECT. DuDoSS utilized the SPECT images predicted in the image domain as guidance to generate synthetic full-view projections in the sinogram domain. The synthetic projections were then reconstructed into non-attenuation-corrected and attenuation-corrected (AC) SPECT images for voxel-wise and segment-wise quantitative evaluations in terms of normalized mean square error (NMSE) and absolute percent error (APE). Previous deep-learning-based approaches, including direct sinogram generation (Direct Sino2Sino) and direct image prediction (Direct Img2Img), were tested in this study for comparison. The dataset used in this study included a total of 500 anonymized clinical stress-state MPI studies acquired on a GE NM/CT 850 scanner with 60 projection angles following the injection of 99m Tc-tetrofosmin. RESULTS: Our proposed DuDoSS generated more consistent synthetic projections and SPECT images with the ground truth than other approaches. The average voxel-wise NMSE between the synthetic projections by DuDoSS and the ground-truth full-view projections was 2.08% ± 0.81%, as compared to 2.21% ± 0.86% (p < 0.001) by Direct Sino2Sino. The averaged voxel-wise NMSE between the AC SPECT images by DuDoSS and the ground-truth AC SPECT images was 1.63% ± 0.72%, as compared to 1.84% ± 0.79% (p < 0.001) by Direct Sino2Sino and 1.90% ± 0.66% (p < 0.001) by Direct Img2Img. The averaged segment-wise APE between the AC SPECT images by DuDoSS and the ground-truth AC SPECT images was 3.87% ± 3.23%, as compared to 3.95% ± 3.21% (p = 0.023) by Direct Img2Img and 4.46% ± 3.58% (p < 0.001) by Direct Sino2Sino. CONCLUSIONS: Our proposed DuDoSS is feasible to generate accurate synthetic full-view projections from sparsely sampled projections for cardiac SPECT. The synthetic projections and reconstructed SPECT images generated from DuDoSS are more consistent with the ground-truth full-view projections and SPECT images than other approaches. DuDoSS can potentially enable fast data acquisition of cardiac SPECT.

Comparison of post reconstruction- and reconstruction-based deep learning denoising methods in cardiac SPECT.

Objective. The quality of myocardial perfusion SPECT (MPS) images is often hampered by low count statistics. Poor image quality might hinder reporting the studies and in the worst case lead to erroneous diagnosis. Deep learning (DL)-based methods can be used to improve the quality of the low count studies. DL can be applied in several different methods, which might affect the outcome. The aim of this study was to investigate the differences between post reconstruction- and reconstruction-based denoising methods.Approach. A UNET-type network was trained using ordered subsets expectation maximization (OSEM) reconstructed MPS studies acquired with half, quarter and eighth of full-activity. The trained network was applied as a post reconstruction denoiser (OSEM+DL) and it was incorporated into a regularized reconstruction algorithm as a deep learning penalty (DLP). OSEM+DL and DLP were compared against each other and against OSEM images without DL denoising in terms of noise level, myocardium-ventricle contrast and defect detection performance with signal-to-noise ratio of a non-prewhitening matched filter (NPWMF-SNR) applied to artificial perfusion defects inserted into defect-free clinical MPS scans. Comparisons were made using half-, quarter- and eighth-activity data.Main results. OSEM+DL provided lower noise level at all activities than other methods. DLP's noise level was also always lower than matching activity OSEM's. In addition, OSEM+DL and DLP outperformed OSEM in defect detection performance, but contrary to noise level ranking DLP had higher NPWMF-SNR overall than OSEM+DL. The myocardium-ventricle contrast was highest with DLP and lowest with OSEM+DL. Both OSEM+DL and DLP offered better image quality than OSEM, but visually perfusion defects were deeper in OSEM images at low activities.Significance. Both post reconstruction- and reconstruction-based DL denoising methods have great potential for MPS. The preference between these methods is a trade-off between smoother images and better defect detection performance.

Efficacity of Deep Inspiration Breath Hold and Intensity-Modulated Radiotherapy in Preventing Perfusion Defect for Left Sided Breast Cancer (EDIPE): A Prospective Cohort Study Protocol.

Breast radiotherapy can lead to radiation-induced cardiac disease, particularly in left breast cancers. Recent studies have shown that subclinical cardiac lesions, such as myocardial perfusion deficits, may occur early after radiotherapy. The primary method for irradiating breast cancer, known as opposite tangential field radiotherapy, can cause the anterior interventricular coronary artery to receive a high dose of radiation during left breast irradiation. To explore alternative approaches that could reduce the risk of myocardial perfusion defects in patients with left breast cancer, we plan to conduct a prospective single-center study using a combination of deep inspiration breath hold radiotherapy and intensity modulated radiation therapy. The study will use stress and, if necessary, resting myocardial scintigraphy to assess myocardial perfusion. The trial aims to show that reducing the cardiac dose with these techniques can prevent the appearance of early (3-month) and medium-term (6- and 12-month) perfusion disorders.

DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.

Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases. Attenuation maps (µ-maps) derived from computed tomography (CT) are utilized for attenuation correction (AC) to improve the diagnostic accuracy of cardiac SPECT. However, in clinical practice, SPECT and CT scans are acquired sequentially, potentially inducing misregistration between the two images and further producing AC artifacts. Conventional intensity-based registration methods show poor performance in the cross-modality registration of SPECT and CT-derived µ-maps since the two imaging modalities might present totally different intensity patterns. Deep learning has shown great potential in medical imaging registration. However, existing deep learning strategies for medical image registration encoded the input images by simply concatenating the feature maps of different convolutional layers, which might not fully extract or fuse the input information. In addition, deep-learning-based cross-modality registration of cardiac SPECT and CT-derived µ-maps has not been investigated before. In this paper, we propose a novel Dual-Channel Squeeze-Fusion-Excitation (DuSFE) co-attention module for the cross-modality rigid registration of cardiac SPECT and CT-derived µ-maps. DuSFE is designed based on the co-attention mechanism of two cross-connected input data streams. The channel-wise or spatial features of SPECT and µ-maps are jointly encoded, fused, and recalibrated in the DuSFE module. DuSFE can be flexibly embedded at multiple convolutional layers to enable gradual feature fusion in different spatial dimensions. Our studies using clinical patient MPI studies demonstrated that the DuSFE-embedded neural network generated significantly lower registration errors and more accurate AC SPECT images than existing methods. We also showed that the DuSFE-embedded network did not over-correct or degrade the registration performance of motion-free cases. The source code of this work is available at https://github.com/XiongchaoChen/DuSFE_CrossRegistration.

Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer.

Convolutional neural networks (CNNs) have been extremely successful in various medical imaging tasks. However, because the size of the convolutional kernel used in a CNN is much smaller than the image size, CNN has a strong spatial inductive bias and lacks a global understanding of the input images. Vision Transformer, a recently emerged network structure in computer vision, can potentially overcome the limitations of CNNs for image-reconstruction tasks. In this work, we proposed a slice-by-slice Transformer network (SSTrans-3D) to reconstruct cardiac SPECT images from 3D few-angle data. To be specific, the network reconstructs the whole 3D volume using a slice-by-slice scheme. By doing so, SSTrans-3D alleviates the memory burden required by 3D reconstructions using Transformer. The network can still obtain a global understanding of the image volume with the Transformer attention blocks. Lastly, already reconstructed slices are used as the input to the network so that SSTrans-3D can potentially obtain more informative features from these slices. Validated on porcine, phantom, and human studies acquired using a GE dedicated cardiac SPECT scanner, the proposed method produced images with clearer heart cavity, higher cardiac defect contrast, and more accurate quantitative measurements on the testing data as compared with a deep U-net.

Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification.

PURPOSE: Myocardial perfusion (MP) stress single-photon emission computed tomography (SPECT) is an established diagnostic test for patients suspected of coronary artery disease (CAD). Meanwhile, coronary artery calcification (CAC) scoring obtained from diagnostic CT is a highly sensitive test, offering incremental diagnostic information in identifying patients with significant CAD yet normal MP stress SPECT (MPSS) scans. However, after decades of wide utilization of MPSS, CAC is not commonly reimbursed (e.g. by the CMS), nor widely deployed in community settings. We studied the potential of complementary information deduced from the radiomics analysis of normal MPSS scans in predicting the CAC score. METHODS: We collected data from 428 patients with normal (non-ischemic) MPSS (99mTc-sestamibi; consensus reading). A nuclear medicine physician verified iteratively reconstructed images (attenuation-corrected) to be free from fixed perfusion defects and artifactual attenuation. Three-dimensional images were automatically segmented into four regions of interest (ROIs), including myocardium and three vascular segments (left anterior descending [LAD]-left circumference [LCX]-right coronary artery [RCA]). We used our software package, standardized environment for radiomics analysis (SERA), to extract 487 radiomic features in compliance with the image biomarker standardization initiative (IBSI). Isotropic cubic voxels were discretized using fixed bin-number discretization (eight schemes). We first performed blind-to-outcome feature selection focusing on a priori usefulness, dynamic range, and redundancy of features. Subsequently, we performed univariate and multivariate machine learning analyses to predict CAC scores from i) selected radiomic features, ii) 10 clinical features, and iii) combined radiomics + clinical features. Univariate analysis invoked Spearman correlation with Benjamini-Hotchberg false-discovery correction. The multivariate analysis incorporated stepwise linear regression, where we randomly selected a 15% test set and divided the other 85% of data into 70% training and 30% validation sets. Training started from a constant (intercept) model, iteratively adding/removing features (stepwise regression), invoking the Akaike information criterion (AIC) to discourage overfitting. Validation was run similarly, except that the training output model was used as the initial model. We randomized training/validation sets 20 times, selecting the best model using log-likelihood for evaluation in the test set. Assessment in the test set was performed thoroughly by running the entire operation 50 times, subsequently employing Fisher's method to verify the significance of independent tests. RESULTS: Unsupervised feature selection significantly reduced 8×487 features to 56. In univariate analysis, no feature survived the false-discovery rate (FDR) to directly correlate with CAC scores. Applying Fisher's method to the multivariate regression results demonstrated combining radiomics with the clinical features to enhance the significance of the prediction model across all cardiac segments.  Conclusions: Our standardized and statistically robust multivariate analysis demonstrated significant prediction of the CAC score for all cardiac segments when combining MPSS radiomic features with clinical features, suggesting radiomics analysis can add diagnostic or prognostic value to standard MPSS for wide clinical usage.

Improvement of Performance of Cardiac SPECT Camera Using Curved Detectors With Pinholes.

SPECT is primarily used in the clinic for cardiac myocardial perfusion imaging. However, for SPECT, sensitivity is impaired due to the need for collimation. System resolution FWHM is poor as well (~ 1 cm). In this work the resolution of a curved detector was theoretically derived. The advantage of a curved detector over a flat detector with pinhole collimation was demonstrated for cardiac applications using theoretical derivations as well as a ray-tracing voxel-based forward projector. For the flat detector using parameters close to what was expected for the new multi-pinhole GE Discovery system, it is shown that using a paraboloid detector one may obtain a better system resolution (about 29% better on the average), keeping same pinhole opening. Alternately, sensitivity gains of as much as 2.25 may be obtained, for similar resolutions as a flat detector by just using a different pinhole with higher hole-diameter.

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study.

Dedicated cardiac SPECT scanners with cadmium-zinc-telluride cameras have shown capabilities for shortened scan times or reduced radiation doses, as well as improved image quality. Since most dedicated scanners do not have integrated CT, image quantification with attenuation correction (AC) is challenging and artifacts are routinely encountered in daily clinical practice. In this work, we demonstrated a direct AC technique using deep learning (DL) for myocardial perfusion imaging (MPI). Methods: In an institutional review board-approved retrospective study, 100 cardiac SPECT/CT datasets with 99mTc-tetrofosmin, obtained using a scanner specifically with a small field of view, were collected at the Yale New Haven Hospital. A convolutional neural network was used for generating DL-based attenuation-corrected SPECT (SPECTDL) directly from noncorrected SPECT (SPECTNC) without undergoing an additional image reconstruction step. The accuracy of SPECTDL was evaluated by voxelwise and segmentwise analyses against the reference, CT-based AC (SPECTCTAC), using the 17-segment myocardial model of the American Heart Association. Polar maps of representative (best, median, and worst) cases were visually compared to illustrate potential benefits and pitfalls of the DL approach. Results: The voxelwise correlations with SPECTCTAC were 92.2% ± 3.7% (slope, 0.87; R2 = 0.81) and 97.7% ± 1.8% (slope, 0.94; R2 = 0.91) for SPECTNC and SPECTDL, respectively. The segmental errors of SPECTNC scattered from -35% to 21% (P < 0.001), whereas the errors of SPECTDL stayed mostly within ±10% (P < 0.001). The average segmental errors (mean ± SD) were -6.11% ± 8.06% and 0.49% ± 4.35% for SPECTNC and SPECTDL, respectively. The average absolute segmental errors were 7.96% ± 6.23% and 3.31% ± 2.87% for SPECTNC and SPECTDL, respectively. Review of polar maps revealed successful reduction of attenuation artifacts; however, the performance of SPECTDL was not consistent for all subjects, likely because of different amounts of attenuation and different uptake patterns. Conclusion: We demonstrated the feasibility of direct AC using DL for SPECT MPI. Overall, our DL approach reduced attenuation artifacts substantially compared with SPECTNC, justifying further studies to establish safety and consistency for clinical applications in stand-alone SPECT systems suffering from attenuation artifacts.

Development of a new Python-based cardiac phantom for myocardial SPECT imaging.

PURPOSE: The aim of this work was to develop a digital dynamic cardiac phantom able to mimic gated myocardial perfusion single photon emission computed tomography (SPECT) images. METHODS: A software code package was written to construct a cardiac digital phantom based on mathematical ellipsoidal model utilizing powerful numerical and mathematic libraries of python programing language. An ellipsoidal mathematical model was adopted to create the left ventricle geometrical volume including myocardial boundaries, left ventricular cavity, with incorporation of myocardial wall thickening and motion. Realistic myocardial count density from true patient studies was used to simulate statistical intensity variation during myocardial contraction. A combination of different levels of defect extent and severity were precisely modeled taking into consideration defect size variation during cardiac contraction. Wall thickening was also modeled taking into account the effect of partial volume. RESULTS: It has been successful to build a python-based software code that is able to model gated myocardial perfusion SPECT images with variable left ventricular volumes and ejection fraction. The recent flexibility of python programming enabled us to manipulate the shape and control the functional parameters in addition to creating variable sized-defects, extents and severities in different locations. Furthermore, the phantom code also provides different levels of image filtration mimicking those filters used in image reconstruction and their influence on image quality. Defect extent and severity were found to impact functional parameter estimation in consistence to clinical examinations. CONCLUSION: A python-based gated myocardial perfusion SPECT phantom has been successfully developed. The phantom proved to be reliable to assess cardiac software analysis tools in terms of perfusion and functional parameters. The software code is under further development and refinement so that more functionalities and features can be added.

Clinical evaluation of three respiratory gating schemes for different respiratory patterns on cardiac SPECT.

PURPOSE: Respiratory gating reduces respiratory blur in cardiac single photon emission computed tomography (SPECT). It can be implemented as three gating schemes: (a) equal amplitude-based gating (AG); (b) phase or time-based gating (TG); or (c) equal count-based gating (CG), that is, a variant of amplitude-based method. The goal of this study is to evaluate the effectiveness of these respiratory gating methods for patients with different respiratory patterns in myocardial perfusion SPECT. METHODS: We reviewed 1274 anonymized patient respiratory traces obtained via the Vicon motion-tracking system during their 99m Tc-sestamibi SPECT scans and grouped them into four breathing categories: (a) regular respiration (RR); (b) periodic respiration (PR); (c) respiration with apnea (AR); and (d) unclassified respiration (UR). For each respiratory pattern, 15 patients were randomly selected and their list-mode data were rebinned using the three gating schemes. A preliminary reconstruction was performed for each gate with the heart region segmented and registered to a reference gate to estimate the respiratory motion. A final reconstruction incorporating respiratory motion correction was done to get a final image set. The estimated respiratory motion, the full-width-at-half-maxima (FWHM) measured across the image intensity profile of the left ventricle wall, as well as the normalized standard deviation measured in a uniform cuboid region of the thorax were analyzed. RESULTS: There are 47.1%, 24.3%, 13.5%, and 15.1% RR, PR, AR, and UR patients, respectively, among the 1274 patients in this study. The differences among the three gating schemes in RR were smaller than other respiratory patterns. The AG and CG methods showed statistically larger motion estimation than TG particularly in the AR and PR patterns. Noise of AG varied more in different gates, especially for AR and UR patterns. CONCLUSION: More than half of the patients reviewed exhibited nonregular breathing patterns. Amplitude-based gating, that is, AG and CG, is a preferred gating method for such patterns and is a robust respiratory gating implementation method given the respiratory pattern of the patients is unknown before data acquisition. Phase gating is also a feasible option for regular respiratory pattern.

Comparison of coronary CT angiography versus functional imaging for CABG patients: A resource utilization analysis.

AIMS: The impact of anatomical versus functional testing in patients with prior coronary artery bypass surgery (CABG) is poorly defined. We therefore sought to determine the rates of downstream investigations and the attendant healthcare costs in CABG patients undergoing CCTA versus SPECT. METHODS AND RESULTS: 2754 consecutive CABG patients were imaged by SPECT (2163) or CCTA (591). 425 patients (15.4%) underwent downstream testing which was more common in those imaged with CCTA versus SPECT (23.18% vs 13.31% respectively, p < 0.01). When a propensity score adjustment was made for differences in baseline characteristics, the findings in downstream testing persisted (p < 0.01). When patients who subsequently underwent repeat revascularization (arguably the highest risk patients) were removed from the analysis, downstream testing remained more frequent in CCTA (12.7%) versus SPECT imaged patients (8.8%) (p = 0.01). Costs of downstream tests per patient were two-fold greater in the CCTA group in comparison to the SPECT group ($366.79 ± 29.59 vs $167.35 ± 10.12 respectively, p < 0.01). Conversely, total costs which included the index costs were less in the CCTA group, $764.66 ± 29.59 versus $1396.73 ± 1012 for the SPECT cohort, p < 0.0001). CONCLUSIONS: Index imaging with SPECT versus CCTA in CABG patients was associated with fewer downstream tests, less ICA, less repeat revascularization but greater expense. Cost however is only part of the decision making process that determines an optimal index test. Until CCTA demonstrates improved risk stratification over SPECT in CABG patients it is likely SPECT will remain the preferred first imaging test.

Performance analysis of a high-sensitivity multi-pinhole cardiac SPECT system with hemi-ellipsoid detectors.

PURPOSE: Single-photon emission computed tomography (SPECT) is a noninvasive imaging modality, used in myocardial perfusion imaging. The challenges facing the majority of clinical SPECT systems are low sensitivity, poor resolution, and the relatively high radiation dose to the patient. New generation systems (GE Discovery, DSPECT) dedicated to cardiac imaging improve sensitivity by a factor of 5-8. This improvement can be used to decrease acquisition time and/or dose. However, in the case of ultra-low dose (~3 mCi) injections, acquisition times are still significantly long, taking 10-12 min. The purpose of this work is to investigate a new gamma camera design with 21 hemi-ellipsoid detectors each with a pinhole collimator for cardiac SPECT for further improvement in sensitivity and resolution and reduced patient exposures and imaging times. METHODS: To evaluate the resolution of our hemi-ellipsoid system, GATE Monte-Carlo simulations were performed on point-sources, rod-sources, and NCAT phantoms. For average full-width-half-maximum (FWHM) equivalence with base flat-detector, the pinhole-diameter for the curved hemi-ellipsoid detector was found to be 8.68 mm, an operating pinhole-diameter nominally expected to be ~3 times more sensitive than state-of-the-art systems. Rod-sources equally spaced within the region of interest were acquired with a 21-detector system and reconstructed with our multi-pinhole (MPH) iterative OSEM algorithm with collimator resolution recovery. The results were compared with the results of a state-of-the-art system (GE Discovery) available in the literature. The system was also evaluated using the mathematical anthropomorphic NCAT (NURBS-based Cardiac Torso; Segars et al. IEEE Trans Nucl Sci. 1999;46:503-506) phantom with a full (clinical)-dose acquisition (25 mCi) for 2 min and an ultra-low dose acquisition of 3 mCi for 5.44 min. The estimated left ventricle (LV) counts were compared with the available literature on a state-of-the-art system (DSPECT). FWHM of the LV wall on MPH-OSEM-reconstructed images with collimator resolution recovery was estimated. RESULTS: On acquired rod-sources, the average resolution (FWHM) after reconstruction with resolution recovery in the entire region of interest (ROI) for cardiac imaging was on the average 4.44 mm (±2.84), compared to 6.9 mm (±1 mm) reported for GE Discovery (Kennedy et al., J Nucl Cardiol. 2014:21:443-452). For NCAT studies, improved sensitivity allowed a full-dose (25 mCi) 2-min acquisition (Ell8.68mmFD) which yielded 3.79 M LV counts. This is ~3.35 times higher compared to 1.13 M LV counts acquired in 2 min for clinical full dose for state-of-the-art DSPECT. The increased sensitivity also allowed an ultra-low dose acquisition protocol (Ell8.68 mmULD), 3 mCi (eight times less injected dose) in 5.44 min. This ultra-low dose protocol yielded ~1.23 M LV counts which was comparable to the full-dose 2-min acquisition for DSPECT. The estimated NCAT average FWHM at the LV wall after 12 iterations of the OSEM reconstruction was 4.95 and 5.66 mm around the mid-short-axis slices for Ell8.68mmFD and Ell8.68mmULD, respectively. CONCLUSION: Our Monte-Carlo simulation studies and reconstruction suggest using (inverted wineglass sized) hemi-ellipsoid detectors with pinhole collimators can increase the sensitivity ~3.35 times over the new generation of dedicated cardiac SPECT systems, while also improving the reconstructed resolution for rod-sources with an average of 4.44 mm in region of interest. The extra sensitivity may be used for ultra-low dose imaging (3 mCi) at ~5.44 min for comparable clinical counts as state-of-the-art systems.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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%. CONCLUSION: 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.

EANM procedural guidelines for myocardial perfusion scintigraphy using cardiac-centered gamma cameras.

An increasing number of Nuclear Medicine sites in Europe are using cardiac-centered gamma cameras for myocardial perfusion scintigraphy (MPS). Three cardiac-centered gamma cameras are currently the most frequently used in Europe: the D-SPECT (Spectrum Dynamics), the Alcyone (Discovery NM 530c and Discovery NM/CT 570c; General Electric Medical Systems), and the IQ-SPECT (Siemens Healthcare). The increased myocardial count sensitivity of these three cardiac-centered systems has allowed for a decrease in the activities of radiopharmaceuticals injected to patients for myocardial perfusion imaging and, consequently, radiation exposure of patients. When setting up protocols for MPS, the overall objective should be to maintain high diagnostic accuracy of MPS, while injecting the lowest activities reasonably achievable to reduce the level of radiation exposure of patient and staff. These guidelines aim at providing recommendations for acquisition protocols and image interpretation using cardiac-centered cameras. As each imaging system has specific design and features for image acquisition and analysis, these guidelines have been separated into three sections for each gamma camera system. These recommendations have been written by the members of the Cardiovascular Committee of EANM and were based on their own experience with each of these systems and on the existing literature.