Detection Of Perfusion Defects Research Articles

Cardiac motion correction with a deep learning network for perfusion defect assessment in SPECT myocardial perfusion imaging

BackgroundIn myocardial perfusion imaging (MPI) with SPECT ungated studies are used for evaluation of perfusion defects despite motion blur. We investigate the potential benefit of motion correction using a deep-learning (DL) network for evaluating perfusion defects. MethodsWe employed a DL network for cardiac motion correction in ECG-gated SPECT-MPI images, wherein the image data from different cardiac phases are combined with respect to a reference gate to reduce motion blur. For training the DL network 197 cases were used. Given the variability of gated images during the cardiac cycle, we investigated the detectability of perfusion defects in two distinct reference gates. To assess perfusion defect detection, we performed receiver-operating-characteristic (ROC) analyses on the motion-corrected images using a separate test dataset of clinical 194 subjects, in which studies were created from actual patient data with inserted simulated-lesions as ground truth. The reconstructed images were assessed by the quantitative-perfusion SPECT (QPS) software. We also evaluated the performance on reduced-count studies (by two and four folds). ResultsThe quantitative results, measured by area-under-the-ROC curve (AUC), demonstrated that DL motion correction improves the detectability of perfusion defects significantly on both standard- and reduced-count studies, and that the detectability can vary with reference cardiac phases. A joint assessment from two reference-phases achieved AUC=0.841 on the quarter-count data, higher than with ungated full-count data (AUC=0.795, p-value=0.0054). ConclusionsDL motion correction can benefit assessment of perfusion defects in standard- and reduced-count SPECT-MPI studies. It can also be beneficial to evaluate perfusion images over multiple cardiac phases.

ECHO-MPS, a dual modality strategy of cardiac imaging to identify myocardial ischemia.

We aimed to evaluate an approach with resting echocardiography (TTE) and stress myocardial perfusion scintigraphy (MPS) compared to standard MPS in patients with stable angina and normal left ventricle (LV). We hypothesized that normal LV on TTE may allow for the elimination of rest MPS without compromising accuracy and offering an efficient diagnostic pathway with reduced radiation exposure. In a prospective, non-randomized study TTE was performed prior to MPS in patients (pts) referred for assessment of coronary artery disease (CAD). In pts with normal LV assessment was performed using the hybrid and the standard approach. TTE and MPS were interpreted by two TTE readers (ER1-2) and two MPS readers (NR1-2). ECHO-MPS was compared with standard MPS for diagnostic accuracy. 103 patients, mean age 61 ± 12 year, (63M, 40W) were recruited. Standard MPS were normal in 75 patients and abnormal in 28 patients, with the hybrid approach 79 studies were reported as normal and 24 studies as abnormal. Kappa values were 0.580, (p < 0.001) for large, 0.394, (p < 0.001) for medium, and 0.298 (p = 0.002) for small defects. With standard MPS as a reference, sensitivity for detection of perfusion defects by ECHO-MPS was 75% (95% CI 0.67-0.83) [NR2] and 78% (95% CI 0.70-0.86)[NR1]. Specificity was 95% (95% CI 0.90-0.99) [NR2] and 95% (CI 95%CI 0.90-0.99) [NR1]. ECHO-MPS protocol provides similar diagnostic accuracy as standard stress-rest MPS. In patients with normal systolic LV function in TTE, performing only stress MPS provides similar information as standard rest and stress MPS.

Characterization of myocardial perfusion imaging systems - an extension of quality metrics

PurposeThe aim of this study was to evaluate the performance of myocardial perfusion imaging (MPI) systems in detecting perfusion defects (PDs). The defect perfusion index (DPI) was introduced to extend and further advance the current MPI quality metrics. MethodsAn anthropomorphic phantom simulating normal and pathological myocardial perfusion conditions was imaged by various NaI-crystal detector systems with and without corrections for scatter (SC) and attenuation (AC) (Symbia, Symbia + SC, Symbia IQ + SCAC, Symbia IQ), and cadmium-zinc-telluride detector systems without corrections (DSPECT, D530c). The extent of PD and the summed score (SS) were obtained by comparing polar maps with ad hoc normal databases created for each MPI system by using phantom polar maps with normal perfusion. The segmental uptake (SU) and the global uniformity (GU) were evaluated. The DPI was calculated on segments included in the PD to minimize attenuation artifacts outside the PD. The 17 segmental model was used. ResultsThe highest level of uniformity of polar map was obtained for Symbia IQ + SCAC. D530c showed the highest extent of PD and dependence of the extent on the PD position. It showed in general the lowest SU values and the highest GU due to attenuation artifacts. Nevertheless, D530c outperforms other MPI systems in terms of PD detection, showing the highest DPI value. DSPECT system showed the lowest SS value, and DPI values comparable to NaI-crystal detector systems. ConclusionThe DPI can be evaluated to investigate the intrinsic ability of MPI systems to detect PDs, whatever the quantitative post-processing software used.

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.

Myocardial perfusion in patients with diabetes mellitus according to stress CMR with adenosine triphosphate

Aim. To evaluate the features of myocardial perfusion according to stress CMR with adenosine triphosphate (ATP) in patients with type 2 diabetes mellitus (DM) and obstructive and non-obstructive coronary arteries (CA).Materials and Methods. Stress СMR with ATP was provided in 55 patients with a suspected or known ischemic heart disease. 39 patients of them had DM type 2. All patients were provided with invasive coronary angiography or non-invasive CT angiogram to evaluate lesions of CA. Study protocol included providing myocardial perfusion in a rest and stress with ATP in dose 160 mkg/ kg/min during 3-6 min.Results. The detection of stress-induced perfusion defects was statistically significantly associated with the obstructive CA lesions of more than 50% (p&lt;0,001). With non-obstructive CA lesions less than 50%, perfusion defects were found more often in patients with DM (36.4%) than in patients without DM (16.7%). Furthermore, the detection of stress-induced myocardial ischemia of patients with DM was not statistically significantly associated with obstructive CA (p=0,071) In case of positive result of stress CMR, patients with DM had a more widespread zone of stress-induced ischemia in the form of increase involved segments (6,45±3,78 (4,78-8,13) than patients without DM (3,17±1,60 (1,49-4,85), p=0,050).Conclusion. Patients with DM and non-obstructive CA are more likely to have a myocardial perfusion disorder than patients without DM due to coronary microvascular dysfunction.

Multicenter Standardization of Phase-Resolved Functional Lung MRI in Patients With Suspected Chronic Thromboembolic Pulmonary Hypertension.

Detection of pulmonary perfusion defects is the recommended approach for diagnosing chronic thromboembolic pulmonary hypertension (CTEPH). This is currently achieved in a clinical setting using scintigraphy. Phase-resolved functional lung (PREFUL) magnetic resonance imaging (MRI) is an alternative technique for evaluating regional ventilation and perfusion without the use of ionizing radiation or contrast media. To assess the feasibility and image quality of PREFUL-MRI in a multicenter setting in suspected CTEPH. This is a prospective cohort sub-study. Forty-five patients (64 ± 16 years old) with suspected CTEPH from nine study centers. 1.5 T and 3 T/2D spoiled gradient echo/bSSFP/T2 HASTE/3D MR angiography (TWIST). Lung signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between study centers with different MRI machines. The contrast between normally and poorly perfused lung areas was examined on PREFUL images. The perfusion defect percentage calculated using PREFUL-MRI (QDPPREFUL) was compared to QDP from the established dynamic contrast-enhanced MRI technique (QDPDCE). Furthermore, QDPPREFUL was compared between a patient subgroup with confirmed CTEPH or chronic thromboembolic disease (CTED) to other clinical subgroups. t-Test, one-way analysis of variance (ANOVA), Pearson's correlation. Significance level was 5%. Significant differences in lung SNR and CNR were present between study centers. However, PREFUL perfusion images showed a significant contrast between normally and poorly perfused lung areas (mean delta of normalized perfusion -4.2% SD 3.3) with no differences between study sites (ANOVA: P = 0.065). QDPPREFUL was significantly correlated with QDPDCE (r = 0.66), and was significantly higher in 18 patients with confirmed CTEPH or CTED (57.9 ± 12.2%) compared to subgroups with other causes of PH or with excluded PH (in total 27 patients with mean ± SD QDPPREFUL = 33.9 ± 17.2%). PREFUL-MRI could be considered as a non-invasive method for imaging regional lung perfusion in multicenter studies. 3 TECHNICAL EFFICACY: Stage 1.

Diagnostic accuracy of subendocardial vs. transmural myocardial perfusion defect for the detection of in-stent restenosis or progression of coronary artery disease after percutaneous coronary intervention

BackgroundThe ADVANTAGE study demonstrated in a cohort of stented patients a diagnostic accuracy of stress myocardial CT perfusion (CTP) significantly higher than that of coronary CT angiography (CCTA) for the detection of in-stent restenosis (ISR) or CAD progression vs. quantitative coronary angiography (QCA). This is a pre-defined subanalysis of the ADVANTAGE aimed at assessing the difference in terms of diagnostic accuracy vs. QCA of a subendocardial vs. a transmural perfusion defect using static stress CTP. MethodsWe enrolled consecutive patients who previously underwent coronary stenting and were referred for QCA. All patients underwent stress CTP and rest CTP ​+ ​CCTA. The diagnostic accuracy of CCTA and CTP were evaluated in territory-based and patient-based analyses. We compared the diagnostic accuracy of “subendocardial” perfusion defect, defined as hypo-enhancement encompassing >25% but <50% of the transmural myocardial thickness within a specific coronary territory vs. “transmural” perfusion defect, defined as hypo-enhancement encompassing >50% of the transmural thickness. ResultsIn 150 patients (132 men, mean age 65.1 ​± ​9.1 years), the diagnostic accuracy of subendocardial vs. transmural perfusion defect in a vessel-based analysis was 93.5% vs. 87.7%, respectively (p ​< ​0.0001). The sensitivity and specificity of subendocardial vs. transmural defect were 87.9% vs. 46.9% (p ​< ​0.001) and 94.9% vs. 97.9% (p ​= ​0.004), respectively. In a patient-based analysis, the diagnostic accuracy of the subendocardial vs. transmural approach was 86.6% vs. 68% (p ​< ​0.0001). ConclusionsThis study shows that detection of a subendocardial perfusion defect as compared to a transmural defect is significantly more accurate to identify coronary territories with ISR or CAD progression.

Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.

Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been the use of deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as root mean squared error (RMSE) and structural similarity index measure (SSIM). However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to: (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; and (3) demonstrate the utility of virtual imaging trials (VITs) to evaluate DL-based methods. A VIT to evaluate a DL-based method for denoising myocardial perfusion SPECT (MPS) images was conducted. To conduct this evaluation study, we followed the recently published best practices for the evaluation of AI algorithms for nuclear medicine (the RELAINCE guidelines). An anthropomorphic patient population modeling clinically relevant variability was simulated. Projection data for this patient population at normal and low-dose count levels (20%, 15%, 10%, 5%) were generated using well-validated Monte Carlo-based simulations. The images were reconstructed using a 3-D ordered-subsets expectation maximization-based approach. Next, the low-dose images were denoised using a commonly used convolutional neural network-based approach. The impact of DL-based denoising was evaluated using both fidelity-based FoMs and area under the receiver operating characteristic curve (AUC), which quantified performance on the clinical task of detecting perfusion defects in MPS images as obtained using a model observer with anthropomorphic channels. We then provide a mathematical treatment to probe the impact of post-processing operations on signal-detection tasks and use this treatment to analyze the findings of this study. Based on fidelity-based FoMs, denoising using the considered DL-based method led to significantly superior performance. However, based on ROC analysis, denoising did not improve, and in fact, often degraded detection-task performance. This discordance between fidelity-based FoMs and task-based evaluation was observed at all the low-dose levels and for different cardiac-defect types. Our theoretical analysis revealed that the major reason for this degraded performance was that the denoising method reduced the difference in the means of the reconstructed images and of the channel operator-extracted feature vectors between the defect-absent and defect-present cases. The results show the discrepancy between the evaluation of DL-based methods with fidelity-based metrics versus the evaluation on clinical tasks. This motivates the need for objective task-based evaluation of DL-based denoising approaches. Further, this study shows how VITs provide a mechanism to conduct such evaluations computationally, in a time and resource-efficient setting, and avoid risks such as radiation dose to the patient. Finally, our theoretical treatment reveals insights into the reasons for the limited performance of the denoising approach and may be used to probe the effect of other post-processing operations on signal-detection tasks.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images. Further, many SPECT systems do not include a CT component. To address these issues, we developed a Scatter-window projection and deep Learning-based AC (SLAC) method to perform AC without a separate transmission scan. To investigate the clinical efficacy of this method, we then objectively evaluated the performance of this method on the clinical task of detecting perfusion defects on MPI in a retrospective study with anonymized clinical SPECT/CT stress MPI images. The proposed method was compared with CT-based AC (CTAC) and no-AC (NAC) methods. Our results showed that the SLAC method yielded an almost overlapping receiver operating characteristic (ROC) plot and a similar area under the ROC (AUC) to the CTAC method on this task. These results demonstrate the capability of the SLAC method for transmission-less AC in SPECT and motivate further clinical evaluation.

Automated identification of myocardial perfusion defects in dynamic cardiac computed tomography using deep learning

PurposeThe purpose of this study was to develop and evaluate deep convolutional neural network (CNN) models for quantifying myocardial blood flow (MBF) as well as for identifying myocardial perfusion defects in dynamic cardiac computed tomography (CT) images. MethodsAdenosine stress cardiac CT perfusion data acquired from 156 patients having or being suspected with coronary artery disease were considered for model development and validation. U-net-based deep CNN models were developed to segment the aorta and myocardium and to localize anatomical landmarks. Color-coded MBF maps were obtained in short-axis slices from the apex to the base level and were used to train a deep CNN classifier. Three binary classification models were built for the detection of perfusion defect in the left anterior descending artery (LAD), the right coronary artery (RCA), and the left circumflex artery (LCX) territories. ResultsMean Dice scores were 0.94 (±0.07) and 0.86 (±0.06) for the aorta and myocardial deep learning-based segmentations, respectively. With the localization U-net, mean distance errors were 3.5 (±3.5) mm and 3.8 (±2.4) mm for the basal and apical center points, respectively. The classification models identified perfusion defects with the accuracy of mean area under the receiver operating curve (AUROC) values of 0.959 (±0.023) for LAD, 0.949 (±0.016) for RCA, and 0.957 (±0.021) for LCX. ConclusionThe presented method has the potential to fully automate the quantification of MBF and subsequently identify the main coronary artery territories with myocardial perfusion defects in dynamic cardiac CT perfusion.

Beyond greyscale: improved detection of myocardial perfusion defects in spectral CT aortic angiography.

A 27-year-old female presented with acute-onset left-sided radiating chest pain, dizziness, and nausea triggered by recreational cycling and aggravated by exertion. Personal and family medical history was negative for heart disease. Nicotine and cannabis abuse stood out as the only cardiovascular risk factors. ECG showed T-wave inversions in V2 and V3 and cardiac biomarkers were elevated (peak hs-troponine I 17 900 ng/L, creatine kinase 1000 U/L). Thoracoabdominal ECG-gated CT angiography was performed on a second-generation spectral CT scanner (CT 7500, Philips Healthcare, the Netherlands) to rule out aortic dissection. While conventional images (A) appeared inconspicuous, the iodine density map (B) showed markedly reduced iodine uptake of the left anterolateral and inferolateral basal to midventricular myocardium (arrowhead). Emergency coronary angiography (C) confirmed type 2 spontaneous coronary dissection (SCAD) of the mid and distal left circumflex artery and hypokinesia of the lateral wall. SCAD was treated conservatively since coronary blood flow was preserved and the patient exhibited only mild symptoms at that time. In the absence of other risk factors for SCAD such as fibromuscular dysplasia or connective tissue disorders, cannabis use stands out as a possible provoking factor in our patient. This case illustrates the value of spectral CT as it allows for on-demand multi-energy analysis of routine scans without prior selection of a dedicated protocol and can improve the detection of perfusion-related pathologies. The area of reduced iodine uptake closely matched late gadolinium enhancement on follow-up cardiac MRI (D) performed 6 weeks later.

Comparison of deep learning-based denoising methods in cardiac SPECT

BackgroundMyocardial 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.MethodsConvolution 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.ResultsAll 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.ConclusionsDL-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.

Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising

BackgroundWe previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dose clinical acquisitions. MethodsTo quantify perfusion-defect detection accuracy, we conducted a receiver-operating characteristic (ROC) analysis on reconstructed images with and without processing by the DL network using a set of clinical SPECT-MPI data from 190 subjects. For perfusion-defect detection hybrid studies were used as ground truth, which were created from clinically normal studies with simulated realistic lesions inserted. We considered ordered-subset expectation-maximization (OSEM) reconstruction with corrections for attenuation, resolution, and scatter and with 3D Gaussian post-filtering. Total perfusion deficit (TPD) scores, computed by Quantitative Perfusion SPECT (QPS) software, were used to evaluate the reconstructed images. ResultsCompared to reconstruction with optimal Gaussian post-filtering (sigma = 1.2 voxels), further DL denoising increased the area under the ROC curve (AUC) from 0.80 to 0.88 (P-value < 10−4). For reconstruction with less Gaussian post-filtering (sigma = 0.8 voxels), thus better spatial resolution, DL denoising increased the AUC value from 0.78 to 0.86 (P-value < 10−4) and achieved better spatial resolution in reconstruction. ConclusionsDL denoising can effectively improve the detection of abnormal defects in standard-dose SPECT-MPI images over conventional reconstruction.

Prognostic role of advanced retinopathy of vascular etiology in coronary artery disease in very long-term follow-up

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Although an association between some forms of vascular retinopathy and cardiovascular disease has been described, its predictive role in ischemic heart disease has not been sufficiently clarified. Methods Prospective cohort of 107 patients with medical indication for coronary angiography who underwent funduscopic examination to assess the presence of retinopathy. We recorded the occurrence of new acute coronary syndromes, the detection of perfusion defects in ischemia tests, the need for new catheterization and revascularization, and hospital readmission for cardiovascular causes with a median follow-up of 10 years. Results The median age was 67 years. A total of 39.3% were women, and 91.2% presented some cardiovascular risk factor, the most frequent being arterial hypertension in 78.5%, followed by dyslipidemia in 57% and diabetes in 32.7%. At the start of follow-up, 84.1% had some type of vascular retinopathy, with 46.3% of patients having high-grade retinopathy. In coronary angiography, up to 55% presented significant coronary artery disease, and angioplasty was performed in 39% of cases. During follow-up, only 2.9% of patients suffered ACS, 15% presented pathological myocardial SPECT, up to 14% underwent a new coronary angiography and 6.5% required new myocardial revascularization. The presence of advanced vascular retinopathy was associated with a higher rate of defects on Tc99 perfusion scintigraphy (p=0.002); a higher rate of coronary angiography (p=0.001) and myocardial revascularization (p=0.045). In addition, in the survival analysis, ischemia development was observed in earlier SPECT (103.13±4 vs 116.3±1.9 p=0.01), as well as earlier myocardial revascularization (109.72±3.5 vs 118.8±0.1 p=0.03) than in the rest of patients. Conclusions High-grade vascular retinopathy is associated with more and earlier inducible ischemia as well as more need for revascularization. The performance of fundus imaging in patients with suspected ischemic heart disease may be useful for long-term stratification.

Ideal-Observer Computation with anthropomorphic phantoms using Markov chain Monte Carlo.

In medical imaging, it is widely recognized that image quality should be objectively evaluated based on performance in clinical tasks. To evaluate performance in signal-detection tasks, the ideal observer (IO) is optimal but also challenging to compute in clinically realistic settings. Markov Chain Monte Carlo (MCMC)-based strategies have demonstrated the ability to compute the IO using pre-computed projections of an anatomical database. To evaluate image quality in clinically realistic scenarios, the observer performance should be measured for a realistic patient distribution. This implies that the anatomical database should also be derived from a realistic population. In this manuscript, we propose to advance the MCMC-based approach towards achieving these goals. We then use the proposed approach to study the effect of anatomical database size on IO computation for the task of detecting perfusion defects in simulated myocardial perfusion SPECT images. Our preliminary results provide evidence that the size of the anatomical database affects the computation of the IO.

Spectral imaging with dual-layer spectral detector computed tomography for the detection of perfusion defects in acute coronary syndrome.

To evaluate the feasibility of spectral imaging with dual-layer spectral detector computed tomography (CT) for the diagnosis of acute coronary syndrome. We identified 30 consecutive patients who underwent cardiac CT using dual-layer spectral detector CT and were diagnosed with acute ischemic syndrome by an invasive coronary angiography. We reconstructed 120kVp images and generated virtual monochromatic images (VMIs; 40-200keV in 10keV increments), iodine concentration maps, and effective atomic number (Z) maps. We calculated the contrast and contrast-to-noise ratio (CNR) between myocardial normal and hypo-perfusion and chose the VMIs with the best CNR for quantitative analysis. We compared the image noise, contrast, and CNR of 120kVp images and the best VMIs, CT value, iodine concentration, and effective Z between myocardial normal and hypo-perfusion with the paired t test. As the X-ray energy decreased, venous attenuation, contrast, and CNR gradually increased. The 40keV image yielded the best CNR. The contrast and CNR between myocardial normal and hypo-perfusion were significantly higher in 40keV images than those in 120kVp images.The iodine concentration and the effective Z were significantly higher in normal myocardium than those in hypo-perfused myocardium. Spectral imaging with dual-layer spectral detector CT is a feasible technique to detect the hypo-perfused area of acute ischemic syndrome.

Evaluation Of a New Reconstruction Technique for Dual-Energy (DECT) Lung Perfusion: Preliminary Experience In 58 Patients

To compare dual-energy (DE) lung perfused blood volume generated by subtraction of virtual monoenergetic images (Lung Mono) with images obtained by three-compartment decomposition (Lung PBV). The study included 58 patients (28 patients with and 30 patients without PE) with reconstruction of Lung PBV images (i.e., the reference standard) and Lung Mono images. The inter-technique comparison was undertaken at a patient and segment level. The distribution of scores of subjective image noise (patient level) significantly differed between the two reconstructions (p<0.0001), with mild noise in 58.6% (34/58) of Lung Mono images vs 25.9% (15/58) of Lung PBV images. Detection of perfusion defects (segment level) was concordant in 1104 segments (no defect: n=968; defects present: n=138) and discordant in 2 segments with a PE-related defect only depicted on Lung Mono images. Among the 28 PE patients, the distribution of gradient of attenuation between perfused areas and defects was significantly higher on Lung Mono images compared to Lung PBV (median= 73.5 HU (QI=65.0; Q3=86.0) vs 24.5 HU (22.0; 30.0); p<0.0001). In all patients, fissures were precisely identified in 77.6% of patients (45/58) on Lung Mono images while blurred (30/58; 51.7%) or not detectable (28/58; 48.3%) on Lung PBV images. Lung Mono perfusion imaging allows significant improvement in the overall image quality and improved detectability of PE-type perfusion defects.

Diagnostic efficiency of myocardial perfusion computed tomography with transesophageal pacing in patients with an initial diagnosis of acute coronary syndrome

Introduction. Computed tomography angiography (CTA) is widely used to detect atherosclerotic changes in coronary arteries (CA). However, the method is limited by the impossibility to perform functional assessment of detected stenoses. Perfusion computed tomography of the myocardium (PCT) can be used for this purpose.Aim of the study. To assess diagnostic accuracy of PCT of the myocardium with transesophageal electrocardiostimulation (TEES) and stress-echocardiography (stress-echoCG) with veloergometry (VEM) in detection of transient ischemia in patients with initial diagnosis of “acute coronary syndrome” (ACS) with borderline stenoses (50–75%) in CA in relation to measurements of fractional flow reserve (FFR).Materials and Methods. The study included 30 patients with the initial diagnosis of ACS with borderline (50–75%) stenoses in CA according to CTA or coronary angiography (CAG). Subsequently, they underwent myocardial PCT with 320-row detector with TEES, as well as stress-echoCG with VEM. Invasive FFR measurement was used as a reference method. FFR value &lt; 0.8 indicated hemodynamic significance of stenosis. Myocardial perfusion was assessed visually. Regional myocardial contractility of the left ventricle was assessed by stress-echoCG.Results. All patients were examined according to the stated protocol. PCT with TEES revealed FFR-significant stenoses with sensitivity, specificity, prognostic value of positive result and prognostic value of negative result 56, 93, 90, 65% respectively, stress-echoCG with VEM 62, 93, 91, 68% respectively.Conclusion. Myocardial PCT with TEES enables to detect perfusion defects associated with transient ischemia, and the diagnostic accuracy of the method in comparison with FFR is comparable with the already well-proven stress-echoCG. The use of PCT with TEES in combination with CTA can be considered as a promising diagnostic tool in patients without known coronary anatomy and with suspected ACS.

Contemporary Role of Cardiac Magnetic Resonance in the Management of Patients with Suspected or Known Coronary Artery Disease.

Cardiac magnetic resonance imaging (CMR) is a useful non-invasive radiation-free imaging modality for the management of patients with coronary artery disease (CAD). CMR cine imaging provides the “gold standard” assessment of ventricular function, late gadolinium enhancement (LGE) provides useful data for the diagnosis and extent of myocardial scar and viability, while stress imaging is an established technique for the detection of myocardial perfusion defects indicating ischemia. Beyond its role in the diagnosis of CAD, CMR allows accurate risk stratification of patients with established CAD. This review aims to summarize the data regarding the role of CMR in the contemporary management of patients with suspected or known coronary artery disease.

Retrospective fractional dose reduction in Tc-99m cardiac perfusion SPECT/CT patients: A human and model observer study

BackgroundIn the ongoing efforts to reduce cardiac perfusion dose (injected radioactivity) for conventional SPECT/CT systems, we performed a human observer study to confirm our clinical model observer findings that iterative reconstruction employing OSEM (ordered-subset expectation-maximization) at 25% of the full dose (quarter-dose) has a similar performance for detection of hybrid cardiac perfusion defects as FBP at full dose. MethodsOne hundred and sixty-six patients, who underwent routine rest-stress Tc-99m sestamibi cardiac perfusion SPECT/CT imaging and clinically read as normally perfused, were included in the study. Ground truth was established by the normal read and the insertion of hybrid defects. In addition to the reconstruction of the 25% of full-dose data using OSEM with attenuation (AC), scatter (SC), and spatial resolution correction (RC), FBP and OSEM (with AC, SC, and RC) both at full dose (100%) were done. Both human observer and clinical model observer confidence scores were obtained to generate receiver operating characteristics (ROC) curves in a task-based image quality assessment. ResultsAverage human observer AUC (area under the ROC curve) values of 0.725, 0.876, and 0.890 were obtained for FBP at full dose, OSEM at 25% of full dose, and OSEM at full dose, respectively. Both OSEM strategies were significantly better than FBP with P values of 0.003 and 0.01 respectively, while no significant difference was recorded between OSEM methods (P = 0.48). The clinical model observer results were 0.791, 0.822, and 0.879, respectively, for the same patient cases and processing strategies used in the human observer study. ConclusionsCardiac perfusion SPECT/CT using OSEM reconstruction at 25% of full dose has AUCs larger than FBP and closer to those of full-dose OSEM when read by human observers, potentially replacing the higher dose studies during clinical reading.