Quantification of nuclear cardiac images: the Yale approach

Deep learning-based multi-frequency denoising for myocardial perfusion SPECT

BACKGROUND

Deep learning (DL)-based denoising has been proven to improve image quality and quantitation accuracy of low dose (LD) SPECT. However, conventional DL-based methods used SPECT images with mixed frequency components. This work aims to develop an integrated multi-frequency denoising network to further enhance LD myocardial perfusion (MP) SPECT denoising.

METHODS

Fifty anonymized patients who underwent routine 99mTc-sestamibi stress SPECT/CT scans were retrospectively recruited. Three LD datasets were obtained by reducing the 10 s acquisition time of full dose (FD) SPECT to be 5, 2 and 1 s per projection based on the list mode data for a total of 60 projections. FD and LD projections were Fourier transformed to magnitude and phase images, which were then separated into two or three frequency bands. Each frequency band was then inversed Fourier transformed back to the image domain. We proposed a 3D integrated attention-guided multi-frequency conditional generative adversarial network (AttMFGAN) and compared with AttGAN, and separate AttGAN for multi-frequency bands denoising (AttGAN-MF).The multi-frequency FD and LD projections of 35, 5 and 10 patients were paired for training, validation and testing. The LD projections to be tested were separated to multi-frequency components and input to corresponding networks to get the denoised components, which were summed to get the final denoised projections. Voxel-based error indices were measured on the cardiac region on the reconstructed images. The perfusion defect size (PDS) was also analyzed.

RESULTS

AttGAN-MF and AttMFGAN have superior performance on all physical and clinical indices as compared to conventional AttGAN. The integrated AttMFGAN is better than AttGAN-MF. Multi-frequency denoising with two frequency bands have generally better results than corresponding three-frequency bands methods.

CONCLUSIONS

AttGAN-MF and AttMFGAN are promising to further improve LD MP SPECT denoising.

Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT

BACKGROUND

Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon).

METHODS

Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and 99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with 99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images.

RESULTS

cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality.

CONCLUSIONS

Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.

Deep-learning-based estimation of attenuation map improves attenuation correction performance over direct attenuation estimation for myocardial perfusion SPECT

BACKGROUND

Deep learning (DL)-based attenuation correction (AC) is promising to improve myocardial perfusion (MP) SPECT. We aimed to optimize and compare the DL-based direct and indirect AC methods, with and without SPECT and CT mismatch.

METHODS

One hundred patients with different 99mTc-sestamibi activity distributions and anatomical variations were simulated by a population of XCAT phantoms. Additionally, 34 patients 99mTc-sestamibi stress/rest SPECT/CT scans were retrospectively recruited. Projections were reconstructed by OS-EM method with or without AC. Mismatch between SPECT and CT images was modeled. A 3D conditional generative adversarial network (cGAN) was optimized for two DL-based AC methods: (i) indirect approach, i.e., non-attenuation corrected (NAC) SPECT paired with the corresponding attenuation map for training. The projections were reconstructed with the DL-generated attenuation map for AC; (ii) direct approach, i.e., NAC SPECT paired with the corresponding AC SPECT for training to perform direct AC.

RESULTS

Mismatch between SPECT and CT degraded DL-based AC performance. The indirect approach is superior to direct approach for various physical and clinical indices, even with mismatch modeled.

CONCLUSION

DL-based estimation of attenuation map for AC is superior and more robust to direct generation of AC SPECT.

Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network

Purpose

Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images.

Methods

Fifty patients who underwent 1184 MBq ^99mTc-sestamibi stress SPECT/CT scan were retrospectively recruited. Sixty projections were acquired over 180° and the acquisition time was 10 s/view for the full time (FT) mode. Fast MP-SPECT projection images (1 s to 7 s) were generated from the FT list mode data. We further incorporated binary patient defect information (0 = without defect, 1 = with defect) into AttGAN (AttGAN-def). AttGAN, AttGAN-def, cGAN, and Unet were implemented using Tensorflow with the Adam optimizer running up to 400 epochs. FT and fast MP-SPECT projection pairs of 35 patients were used for training the networks for each acquisition time, while 5 and 10 patients were applied for validation and testing. Five-fold cross-validation was performed and data for all 50 patients were tested. Voxel-based error indices, joint histogram, linear regression, and perfusion defect size (PDS) were analyzed.

Results

All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet.

Conclusion

Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.

Cross-vender, cross-tracer, and cross-protocol deep transfer learning for attenuation map generation of cardiac SPECT

It has been proved feasible to generate attenuation maps (μ-maps) from cardiac SPECT using deep learning. However, this assumed that the training and testing datasets were acquired using the same scanner, tracer, and protocol. We investigated a robust generation of CT-derived μ-maps from cardiac SPECT acquired by different scanners, tracers, and protocols from the training data. We first pre-trained a network using 120 studies injected with 99mTc-tetrofosmin acquired from a GE 850 SPECT/CT with 360-degree gantry rotation, which was then fine-tuned and tested using 80 studies injected with 99mTc-sestamibi acquired from a Philips BrightView SPECT/CT with 180-degree gantry rotation. The error between ground-truth and predicted μ-maps by transfer learning was 5.13 ± 7.02%, as compared to 8.24 ± 5.01% by direct transition without fine-tuning and 6.45 ± 5.75% by limited-sample training. The error between ground-truth and reconstructed images with predicted μ-maps by transfer learning was 1.11 ± 1.57%, as compared to 1.72 ± 1.63% by direct transition and 1.68 ± 1.21% by limited-sample training. It is feasible to apply a network pre-trained by a large amount of data from one scanner to data acquired by another scanner using different tracers and protocols, with proper transfer learning.

Direct and indirect strategies of deep-learning-based attenuation correction for general purpose and dedicated cardiac SPECT

PURPOSE

Deep-learning-based attenuation correction (AC) for SPECT includes both indirect and direct approaches. Indirect approaches generate attenuation maps (μ-maps) from emission images, while direct approaches predict AC images directly from non-attenuation-corrected (NAC) images without μ-maps. For dedicated cardiac SPECT scanners with CZT detectors, indirect approaches are challenging due to the limited field-of-view (FOV). In this work, we aim to 1) first develop novel indirect approaches to improve the AC performance for dedicated SPECT; and 2) compare the AC performance between direct and indirect approaches for both general purpose and dedicated SPECT.

METHODS

For dedicated SPECT, we developed strategies to predict truncated μ-maps from NAC images reconstructed with a small matrix, or full μ-maps from NAC images reconstructed with a large matrix using 270 anonymized clinical studies scanned on a GE Discovery NM/CT 570c SPECT/CT. For general purpose SPECT, we implemented direct and indirect approaches using 400 anonymized clinical studies scanned on a GE NM/CT 850c SPECT/CT. NAC images in both photopeak and scatter windows were input to predict μ-maps or AC images.

RESULTS

For dedicated SPECT, the averaged normalized mean square error (NMSE) using our proposed strategies with full μ-maps was 1.20 ± 0.72% as compared to 2.21 ± 1.17% using the previous direct approaches. The polar map absolute percent error (APE) using our approaches was 3.24 ± 2.79% (R² = 0.9499) as compared to 4.77 ± 3.96% (R² = 0.9213) using direct approaches. For general purpose SPECT, the averaged NMSE of the predicted AC images using the direct approaches was 2.57 ± 1.06% as compared to 1.37 ± 1.16% using the indirect approaches.

CONCLUSIONS

We developed strategies of generating μ-maps for dedicated cardiac SPECT with small FOV. For both general purpose and dedicated SPECT, indirect approaches showed superior performance of AC than direct approaches.

Anger recall mental stress decreases 123I-metaiodobenzylguanidine (123I-MIBG) uptake and increases heterogeneity of cardiac sympathetic activity in the myocardium in patients with ischemic cardiomyopathy

BACKGROUND

Acute psychological stressors such as anger can precipitate ventricular arrhythmias, but the mechanism is incompletely understood. Quantification of regional myocardial sympathetic activity with 123I-metaiodobenzylguanidine (123I-mIBG) SPECT imaging in conjunction with perfusion imaging during mental stress may identify a mismatch between perfusion and sympathetic activity that may exacerbate a mismatch between perfusion and sympathetic activity that could create a milieu of increased vulnerability to ventricular arrhythmia.

METHODS

Five men with ischemic cardiomyopathy (ICM), and five age-matched healthy male controls underwent serial 123I-mIBG and 99mTc-Tetrofosmin SPECT/CT imaging during an anger recall mental stress task and dual isotope imaging was repeated approximately 1 week later during rest. Images were reconstructed using an iterative reconstruction algorithm with CT-based attenuation correction. The mismatch of left ventricular myocardial 123I-mIBG and 99mTc-Tetrofosmin was assessed along with radiotracer heterogeneity and the 123I-mIBG heart-to-mediastinal ratios (HMR) were calculated using custom software developed at Yale.

RESULTS

The hemodynamic response to mental stress was similar in both groups. The resting-HMR was greater in healthy control subjects (3.67 ± 0.95) than those with ICM (3.18 ± 0.68, P = .04). Anger recall significantly decreased the HMR in ICM patients (2.62 ± 0.3, P = .04), but not in normal subjects. The heterogeneity of 123I-mIBG uptake in the myocardium was significantly increased in ICM patients during mental stress (26% ± 8.23% vs. rest: 19.62% ± 9.56%; P = .01), whereas the 99mTc-Tetrofosmin uptake pattern was unchanged.

CONCLUSION

Mental stress decreased the 123I-mIBG HMR, increased mismatch between sympathetic activity and myocardial perfusion, and increased the heterogeneity of 123I-mIBG uptake in ICM patients, while there was no significant change in myocardial defect size or the heterogeneity of 99mTc-Tetrofosmin perfusion. The changes observed in this proof-of-concept study may provide valuable information about the trigger-substrate interaction and the potential vulnerability for ventricular arrhythmias.

Quantification of myocardial blood flow (MBF) and reserve (MFR) incorporated with a novel segmentation approach: Assessments of quantitative precision and the lower limit of normal MBF and MFR in patients

BACKGROUND

Quantification of myocardial blood flow (MBF) and myocardial flow reserve (MFR) has shown diagnostic and prognostic values for the assessment of coronary artery disease (CAD). This study aimed to evaluate in patients a highly automatic Yale-MQ (myocardial blood flow quantification) software incorporated with a novel image segmentation approach for quantification of global and regional MBF and MFR from dynamic 82Rb cardiac positron emission tomography (PET).

METHODS

Global and regional MBFs and MFRs were quantified in 80 patients (18 normal and 62 CAD subjects) by two different observers using the Yale-MQ software. Lower limits of normal (LLN) values and intra- and inter-observer variabilities of MBFs and MFRs were calculated for the assessment of quantitative precision. The Yale-MQ was compared with a commercially available software (Corridor 4DM) being used as a reference.

RESULTS

The Yale-MQ method provided precise assessments of LLNs of MBF and MFR. The global and regional MBFs and MFR quantified via Yale-MQ were correlated strongly with those via Corridor4DM (R ≥ 0.867). The intra- and inter-observer variabilities of MBFs and MFRs quantified via Yale-MQ were small (≤ 7.7% for MBFs and ≤ 10.0% for MFRs) with excellent correlations (R ≥ 0.980 for MBFs and R ≥ 0.976 for MFRs).

CONCLUSIONS

The new Yale-MQ software associated with the automatic processing scheme provides a highly reproducible clinical tool for precise quantification of MBF and MFR in patients with reliable LLN values.

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning

PURPOSE

Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design and evaluate a deep learning (DL) approach to automatic diagnosis of myocardial perfusion abnormalities from stress-only MPI.

METHODS

The new DL approach developed for this study was compared to a conventional quantitative perfusion defect size (DS) method. A total of 37,243 patients (51.5% males) undergone stress ^99mTc-Tetrofosmin or ^99mTc-Sestamibi MPI were selected retrospectively from Yale New Haven Hospital. Patients were dichotomized as studies with normal (75.4%) or abnormal (24.6%) myocardial perfusion based on final diagnoses of clinical nuclear cardiologists. Stress myocardial perfusion defect size was calculated using Yale quantitative analytic software. A deep CNN was trained using the circumferential count profile maps derived from SPECT MPI and was evaluated for the diagnosis of perfusion abnormality with a 5-fold cross-validation approach. In each fold, 27,933, 1862 and 7448 patients were used as training, validation and testing datasets, respectively. The area under the receiver-operating characteristic curve (AUC) was calculated and analyzed for all patients as well as for the eight sub-groups classified based on patient genders, quantitative algorithms, radioactive tracers and SPECT cameras.

RESULTS

The AUC value resulted from the DL method was significantly higher than that from the DS method (0.872 ± 0.002 vs. 0.838 ± 0.003, p < 0.01). Across the eight sub-groups, the DL method provided more consistent AUC values in terms of smaller standard deviation and higher diagnostic accuracy and specificity, but slightly lower sensitivity than the DS method (AUC: 0.865 ± 0.010 vs. 0.838 ± 0.019, Accuracy: 82.7% ± 2.5% vs. 78.5% ± 3.6%, Specificity: 84.9% ± 3.7% vs. 77.5% ± 6.5%, Sensitivity: 74.4% ± 4.2% vs. 79.8% ± 5.8%).

CONCLUSIONS

The incorporation of deep learning for stress-only MPI has a considerable potential to improve the diagnostic accuracy and consistency in the detection of myocardial perfusion abnormalities.

SPECT/CT imaging of lower extremity perfusion reserve: A non-invasive correlate to exercise tolerance and cardiovascular fitness in patients undergoing clinically indicated myocardial perfusion imaging

BACKGROUND

Although exercise is often prescribed for the management of cardiovascular diseases, a non-invasive imaging approach that quantifies skeletal muscle physiology and correlates with patients' functional capacity and cardiovascular fitness has been absent. Therefore, we evaluated the potential of lower extremity single photon emission computed tomography (SPECT)/CT perfusion imaging as a non-invasive correlate to exercise tolerance and cardiovascular fitness.

METHODS

Patients (n = 31) undergoing SPECT/CT myocardial perfusion imaging underwent additional stress/rest SPECT/CT imaging of the lower extremities. CT-based image segmentation was used for regional quantification of perfusion reserve within the tibialis anterior, soleus, and gastrocnemius muscles. Metabolic equivalents (METs) at peak exercise and heart rate recovery (HRR) after exercise were recorded.

RESULTS

Peak METs were significantly associated with perfusion reserve of tibialis anterior (p = 0.02), soleus (p = 0.01) and gastrocnemius (p = 0.01). HRR was significantly associated with perfusion reserve of the soleus (p = 0.02) and gastrocnemius (p = 0.04) muscles. Perfusion reserve of the tibialis anterior (40.6 ± 20.2%), soleus (35.4 ± 16.7%), and gastrocnemius (29.7 ± 19.1%) all significantly differed from each other.

CONCLUSIONS

SPECT/CT imaging provides regional quantification of skeletal muscle perfusion reserve which is significantly associated with exercise tolerance and cardiovascular fitness. Future application of SPECT/CT may elucidate the underlying skeletal muscle adapations to exercise therapy in patients with cardiovascular diseases.

Relationship between Semi-Quantitative Parameters of Thallium-201 Myocardial Perfusion Imaging and Coronary Artery Disease

This study aimed to investigate the diagnostic performance of semi-quantitative parameters of thallium-201 myocardial perfusion imaging (MPI) for coronary artery disease (CAD). From January to December 2017, patients were enrolled who had undergone Tl-201 MPI and received cardiac catheterization for coronary artery disease within three months of MPI. Receiver operating characteristics (ROC) analysis was used to determine the optimal cutoff values of semi-quantitative parameters. A comparison of the sensitivity and specificity of these parameters based on different subgroupings was further performed. A total of 130 patients were enrolled for further analysis. Among the collected parameters, the stress total perfusion deficit (sTPD) had the highest value of the area under curve (0.813) under the optimal cutoff value of 3.5%, with a sensitivity and specificity of 73.5% and 74.5%, respectively (p = 0.0000), for the diagnosis of CAD. With further subgrouping analysis based on history of diabetes or dyslipidemia, the sensitivity and specificity showed similar results. Based on the currently collected data and image acquisition conditions, the sTPD parameter has a clinical role for the diagnosis of CAD with a cutoff value of 3.5%.

Serial Assessment of Coronary Flow Reserve by Rubidium-82 Positron Emission Tomography Predicts Mortality in Heart Transplant Recipients

OBJECTIVES

This study aimed to evaluate the long-term prognostic value of serial assessment of coronary flow reserve (CFR) by rubidium Rb 82 (82Rb) positron emission tomography (PET) in heart transplantation (HT) patients.

BACKGROUND

Cardiac allograft vasculopathy is a major determinant of late mortality in HT recipients. The long-term prognostic value of serial CFR quantification by PET imaging in HT patients is unknown.

METHODS

A total of 89 patients with history of HT (71% men, 7.0 ± 5.7 years post-HT, age 57 ± 11 years) scheduled for dynamic rest and stress (dipyridamole) 82Rb PET between March 1, 2008 and July 31, 2009 (PET-1) were prospectively enrolled in a single-center study. PET myocardial perfusion studies were reprocessed using U.S. Food and Drug Administration-approved software (Corridor 4DM, version 2017) for calculation of CFR. Follow-up PET (PET-2) imaging was performed in 69 patients at 1.9 ± 0.3 years following PET-1. Patients were categorized based on CFR values considering CFR ≤1.5 as low and CFR >1.5 as high CFR.

RESULTS

Forty deaths occurred during the median follow-up time of 8.6 years. Low CFR at PET-1 was associated with a 2.77-fold increase in all-cause mortality (95% confidence interval [CI]: 1.34 to 5.74; p = 0.004). CFR decreased over time in patients with follow-up imaging (PET-1: 2.11 ± 0.74 vs. PET-2: 1.81 ± 0.61; p = 0.003). Twenty-five patients were reclassified based on PET-1 and PET-2 (high to low CFR: n = 18, low to high CFR: n = 7). Overall survival was similar in patients reclassified from high to low as patients with low to low CFR, whereas patients reclassified from low to high had similar survival as patients with high to high CFR. In multivariate Cox regression of patients with PET-2, higher baseline CFR (hazard ratio [HR] for a 0.73 unit (one SD) increase: 0.36, 95% CI: 0.16 to 0.82) and reduction in CFR from PET-1 to PET-2 (HR for a 0.79 unit (one SD) decrease: 1.50 to 7.84) were independent predictors of all-cause mortality.

CONCLUSIONS

Serial assessment of CFR by 82Rb PET independently predicts long-term mortality in HT patients.

New Trends in Quantitative Nuclear Cardiology Methods

Purpose of review

The use of quantitative analysis in single photon emission computed tomography (SPECT) and positron emission tomography (PET) has become an integral part of current clinical practice and plays a crucial role in the detection and risk stratification of coronary artery disease. Emerging technologies, new protocols, and new quantification methods have had a significant impact on the diagnostic performance and prognostic value of nuclear cardiology imaging, while reducing the need for clinician oversight. In this review, we aim to describe recent advances in automation and quantitative analysis in nuclear cardiology.

Recent Findings

Recent publications have shown that fully automatic processing is feasible, limiting human input to specific cases where aberrancies are detected by the quality control software. Furthermore, there is evidence indicating that fully quantitative analysis of myocardial perfusion imaging is feasible and can achieve at least similar diagnostic accuracy as visual interpretation by an expert clinician. In addition, the use of fully automated quantification in combination with machine learning algorithms can provide incremental diagnostic and prognostic value over the traditional method of expert visual interpretation.

Summary

Emerging technologies in nuclear cardiology focus on automation and the use of artificial intelligence as part of the interpretation process. This review highlights the benefits and limitations of these applications, and outlines future directions in the field.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

INTRODUCTION

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Novel SPECT Technologies and Approaches in Cardiac Imaging

Recent novel approaches in myocardial perfusion single photon emission CT (SPECT) have been facilitated by new dedicated high-efficiency hardware with solid-state detectors and optimized collimators. New protocols include very low-dose (1 mSv) stress-only, two-position imaging to mitigate attenuation artifacts, and simultaneous dual-isotope imaging. Attenuation correction can be performed by specialized low-dose systems or by previously obtained CT coronary calcium scans. Hybrid protocols using CT angiography have been proposed. Image quality improvements have been demonstrated by novel reconstructions and motion correction. Fast SPECT acquisition facilitates dynamic flow and early function measurements. Image processing algorithms have become automated with virtually unsupervised extraction of quantitative imaging variables. This automation facilitates integration with clinical variables derived by machine learning to predict patient outcome or diagnosis. In this review, we describe new imaging protocols made possible by the new hardware developments. We also discuss several novel software approaches for the quantification and interpretation of myocardial perfusion SPECT scans.

Automatic Valve Plane Localization in Myocardial Perfusion SPECT/CT by Machine Learning: Anatomic and Clinical Validation

Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing ^99mTc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (P < 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7-0.87]; AUC, 0.81 [95% CI, 0.73-0.89]) and the SVM (0.82 [0.74-0.9]) for AC data were the same (P = NS) and were higher than those for the unadjusted VP (0.63 [0.53-0.73]) (P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.87]) were the same (P = NS) and were higher than those for the unadjusted VP (0.65 [0.56-0.75]) (P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.

Normal Databases for the Relative Quantification of Myocardial Perfusion

PURPOSE OF REVIEW

Myocardial perfusion imaging (MPI) with SPECT is performed clinically worldwide to detect and monitor coronary artery disease (CAD). MPI allows an objective quantification of myocardial perfusion at stress and rest. This established technique relies on normal databases to compare patient scans against reference normal limits. In this review, we aim to introduce the process of MPI quantification with normal databases and describe the associated perfusion quantitative measures that are used.

RECENT FINDINGS

New equipment and new software reconstruction algorithms have been introduced which require the development of new normal limits. The appearance and regional count variations of normal MPI scan may differ between these new scanners and standard Anger cameras. Therefore, these new systems may require the determination of new normal limits to achieve optimal accuracy in relative myocardial perfusion quantification. Accurate diagnostic and prognostic results rivaling those obtained by expert readers can be obtained by this widely used technique.

SUMMARY

Throughout this review, we emphasize the importance of the different normal databases and the need for specific databases relative to distinct imaging procedures. use of appropriate normal limits allows optimal quantification of MPI by taking into account subtle image differences due to the hardware and software used, and the population studied.

High-Sensitivity and High-Resolution SPECT/CT Systems Provide Substantial Dose Reduction Without Compromising Quantitative Precision for Assessment of Myocardial Perfusion and Function

There is increasing concern about radiation exposure from myocardial perfusion SPECT (MPS). γ-cameras with solid-state cadmium-zinc-telluride (CZT) detectors have better count sensitivity and spatial resolution than conventional sodium iodine detectors, allowing for significant reductions in radiotracer dose or acquisition time. This study aimed to demonstrate the capability of a hybrid CZT SPECT/64-slice CT system for dose reduction and to determine the maximal reduction possible without compromising image quality or the quantification precision of clinical MPS.

METHODS

The imaging data of patients with normal myocardial perfusion and 30 patients with mid-sized to large perfusion defects who had undergone stress (99m)Tc-tetrofosmin MPS were postprocessed. Low-dose (361 ± 60 MBq) and high-dose (725 ± 142 MBq) (99m)Tc-tetrofosmin scans were included, with 6-min and 4-min scanning times, respectively. List-mode SPECT data were reconstructed with CT-based attenuation correction and with full as well as 50% and 75% reductions in acquisition time to simulate the corresponding relative dose reductions. The reconstructed SPECT images were analyzed to calculate global MPS defect size and regional defect size for 3 coronary artery territories-left anterior descending, left circumflex, and right-as well as left ventricular (LV) volume and ejection fraction.

RESULTS

For patients with normal MPS results, there were no differences in defect size, LV volume, or ejection fraction, regardless of whether 50% or 75% reduction was used. For patients with abnormal MPS results, at a 50% reduction there was a significant difference in global defect size but not in regional defect size in the left anterior descending, left circumflex, and right coronary artery territories, whereas at a 75% reduction the difference was statistically significant in all territories, including the difference in global defect size. Nonetheless, differences in the defect size were minimal. The LV end-diastolic and end-systolic volumes and LV ejection fraction were not significantly different, regardless of whether 50% or 75% dose reduction was used.

CONCLUSION

Ultra-low-dose (<190 MBq) MPS even with short imaging times (<6 min) is feasible using a hybrid CZT SPECT/CT camera without compromising image quality or significantly altering quantification of myocardial perfusion or LV function. We demonstrated that an additional 50% reduction in the current low-dose recommendations from the American Society of Nuclear Cardiology guidelines for (99m)Tc-labeled MPS is highly feasible while retaining short imaging protocols.

Automated Quantitative Nuclear Cardiology Methods

Quantitative analysis of SPECT and PET has become a major part of nuclear cardiology practice. Current software tools can automatically segment the left ventricle, quantify function, establish myocardial perfusion maps, and estimate global and local measures of stress/rest perfusion, all with minimal user input. State-of-the-art automated techniques have been shown to offer high diagnostic accuracy for detecting coronary artery disease, as well as predict prognostic outcomes. This article briefly reviews these techniques, highlights several challenges, and discusses the latest developments.

Clinical decision support systems in myocardial perfusion imaging

Diagnostic imaging is becoming more complicated, physicians are also required to master an ever-expanding knowledge base and take into account an ever increasing amount of patient-specific clinical information while the time available to master this knowledge base, assemble the relevant clinical data, and apply it to specific tasks is steadily shrinking. Compounding these problems, there is an ever increasing number of aging "Baby Boomers" who are becoming patients coupled with a declining number of cardiac diagnosticians experienced in interpreting these studies. Hence, it is crucial that decision support tools be developed and implemented to assist physicians in interpreting studies at a faster rate and at the highest level of up-to-date expertise. Such tools will minimize subjectivity and intra- and inter-observer variation in image interpretation, help achieve a standardized high level of performance, and reduce healthcare costs. Presently, there are many decision support systems and approaches being developed and implemented to provide greater automation and to further objectify and standardize analysis, display, integration, interpretation, and reporting of myocardial perfusion SPECT and PET studies. This review focuses on these systems and approaches.

Quantitative analysis of perfusion studies: strengths and pitfalls

Tools for automated quantification of myocardial perfusion are available to nuclear cardiology practitioners and researchers. These methods have demonstrated superior reproducibility with comparable diagnostic and prognostic performance, when compared with segmental visual scoring by expert observers. A particularly useful application of the quantitative analysis can be in the detection of subtle changes or in precise determination of ischemia. Some challenges remain in the routine application of perfusion quantification. Multiple quantitative parameters may need to be reconciled by the expert reader for the final diagnosis. Computer analysis may be sensitive to imaging artifacts, resulting in false positive scans. Perfusion quantification may require site specific normal limits and some degree of manual interaction. New software improvements have been proposed to address some of these challenges.

Toward a practical template-based approach to semiquantitative SPECT myocardial perfusion imaging

PURPOSE

Our template-based quantitative perfusion single photon emission computed tomography (SPECT) method (T-QPS) performs semiquantitative analysis for myocardial perfusion imaging (MPI) without the use of normal databases. However, in its current form, T-QPS requires extensive calculations, which limits its clinical application. In the interest of clinical feasibility, the authors examine the trade-off between accuracy and processing time as the method is simplified.

METHODS

The T-QPS method uses the reconstructed SPECT image of the patient to create a 3D digital template of his∕her healthy heart. This template is then projected, reconstructed, and sampled into the bulls-eye map domain. A ratio of the patient and template images produces a final corrected image in which a threshold is applied to identify perfusion defects. In principle, the template should be constructed with the heart and all extracardiac activity, and the projection step should include primary and scatter components; however, this leads to lengthy calculations. In an attempt to shorten the processing time, the authors analyzed the performance of four template (T) generation methods: T(P-HRT), T(PS-HRT), T(P-HRTBKG), and T(PS-HRTBKG), where P and S represent primary and scattered photons included in the projection step, respectively; and HRT and HRTBKG represent template constructed with the heart only and the heart with background activity, respectively. Forty-eight thorax phantoms and 21 randomly selected patient studies were analyzed using each approach. All studies used GE's Infinia Hawkeye SPECT∕CT system and followed a standard cardiac acquisition protocol.

RESULTS

Approximate processing times for the T(P-HRT), T(PS-HRT), T(P-HRTBKG), and T(PS-HRTBKG) methods were less than a minute, 2-3 h, less than a minute and 3-4 h, respectively. In both the simulation and patient studies, a significant reduction in the quality of perfusion defect definition was exhibited by the T(P-HRT) method relative to the other three methods. The optimal method with respect to perfusion defect definition and processing time was T(P-HRTBKG) with a sensitivity, specificity, and accuracy in spatially defining the perfusion defects (simulation study) of 80%, 84%, and 83%, respectively.

CONCLUSIONS

The T-QPS method using T(P-HRTBKG) leads to accurate and fast semiquantitative analysis of SPECT MPI, without the use of normal databases.

A template-based approach to semi-quantitative SPECT myocardial perfusion imaging: Independent of normal databases

PURPOSE

Normal patient databases (NPDs) are used to distinguish between normal and abnormal perfusion in SPECT myocardial perfusion imaging (MPI) and have gained wide acceptance in the clinical environment, yet there are limitations to this approach. This study introduces a template-based method for semi-quantitative MPI, which attempts to overcome some of the NPD limitations.

METHODS

Our approach involves the construction of a 3D digital healthy heart template from the delineation of the patient's left ventricle in the SPECT image. This patient-specific template of the heart, filled with uniform activity, is then analytically projected and reconstructed using the same algorithm as the original image. Subsequent to generating bulls-eye maps for the patient image (PB) and the template image (TB), a ratio (PB/TB) is calculated, which produces a reconstruction-artifact corrected image (CB). Finally, a threshold is used to define defects within CB enabling measurements of the perfusion defect extent (EXT). The SPECT-based template (Ts) measurements were compared to those of a CT-based "ideal" template (TI). Twenty digital phantoms were simulated: male and female, each with one healthy heart and nine hearts with various defects. Four physical phantom studies were performed modeling a healthy heart and three hearts with different defects. The phantom represented a thorax with spine, lung, and left ventricle inserts. Images were acquired on General Electric's (GE) Infinia Hawkeye SPECT/CT camera using standard clinical MPI protocol. Finally, our method was applied to 14 patient MPI rest/stress studies acquired on the GE Infinia Hawkeye SPECT/CT camera and compared to the results obtained from Cedars-Sinai's QPS software.

RESULT

In the simulation studies, the true EXT correlated well with the TI (slope= 1.08; offset = -0.40%; r = 0.99) and Ts (slope = 0.90; offset = 0.27%; r = 0.99) methods with no significant differences between them. Similarly, strong correlations were measured for EXT obtained from QPS and the template method for patient studies (slope =0.91; offset = 0.45%; r = 0.98). Mean errors in extent for the Ts method using simulation, physical phantom, and patient data were 2.7% +/- 2.4%, 0.9% +/- 0.5%, 2.0% +/- 2.7%, respectively.

CONCLUSIONS

The authors introduced a method for semi-quantitative SPECT MPI, which offers a patient-specific approach to define the perfusion defect regions within the heart, as opposed to the patient-averaged NPD methodology.

SPECT myocardial perfusion imaging for the assessment of left ventricular mechanical dyssynchrony

Phase analysis of gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is an evolving technique for measuring LV mechanical dyssynchrony. Since its inception in 2005, it has undergone considerable technical development and clinical evaluation. This article reviews the background, the technical and clinical characteristics, and evolving clinical applications of phase analysis of gated SPECT MPI in patients requiring cardiac resynchronization therapy or implantable cardioverter defibrillator therapy and in assessing LV diastolic dyssynchrony.