DNA damage and repair in patients undergoing myocardial perfusion single-photon emission computed tomography.

As patient exposure to ionizing radiation from medical imaging and its risks are continuing issues, this study aimed to evaluate DNA damage and repair markers after myocardial perfusion single-photon emission computed tomography (MPS). Thirty-two patients undergoing Tc-99m sestamibi MPS were studied. Peripheral blood was collected before radiotracer injection at rest and 60-90 min after injection. The comet assay (single-cell gel electrophoresis) was performed with peripheral blood cells to detect DNA strand breaks. Three descriptors were evaluated: the percentage of DNA in the comet tail, tail length, and tail moment (the product of DNA tail percentage and tail length). Quantitative PCR (qPCR) was performed to evaluate the expression of five genes related to signaling pathways in response to DNA damage and repair (ATM, ATR, BRCA1, CDKN1A, and XPC). Mann-Whitney's test was employed for statistical analysis; p < 0.05 was considered significant. Mean Tc-99m sestamibi dose was 15.1 mCi. After radiotracer injection, comparing post-exposure to pre-exposure samples of each of the 32 patients, no statistically significant differences of the DNA percentage in the tail, tail length or tail moment were found. qPCR revealed increased expression of BRCA1 and XPC, without any significant difference regarding the other genes. No significant increase in DNA strand breaks was detected after a single radiotracer injection for MPS. There was activation of only two repair genes, which may indicate that, in the current patient sample, the effects of ionizing radiation on the DNA were not large enough to trigger intense repair responses, suggesting the absence of significant DNA damage.

A deep learning-based automated diagnosis system for SPECT myocardial perfusion imaging.

Images obtained from single-photon emission computed tomography for myocardial perfusion imaging (MPI SPECT) contain noises and artifacts, making cardiovascular disease diagnosis difficult. We developed a deep learning-based diagnosis support system using MPI SPECT images. Single-center datasets of MPI SPECT images (n = 5443) were obtained and labeled as healthy or coronary artery disease based on diagnosis reports. Three axes of four-dimensional datasets, resting, and stress conditions of three-dimensional reconstruction data, were reconstructed, and an AI model was trained to classify them. The trained convolutional neural network showed high performance [area under the curve (AUC) of the ROC curve: approximately 0.91; area under the recall precision curve: 0.87]. Additionally, using unsupervised learning and the Grad-CAM method, diseased lesions were successfully visualized. The AI-based automated diagnosis system had the highest performance (88%), followed by cardiologists with AI-guided diagnosis (80%) and cardiologists alone (65%). Furthermore, diagnosis time was shorter for AI-guided diagnosis (12 min) than for cardiologists alone (31 min). Our high-quality deep learning-based diagnosis support system may benefit cardiologists by improving diagnostic accuracy and reducing working hours.

Myocardial Blood Flow by Magnetic Resonance in Patients With Suspected Coronary Stenosis: Comparison to PET and Invasive Physiology.

BACKGROUND: Despite recent guideline recommendations, quantitative perfusion (QP) estimates of myocardial blood flow from cardiac magnetic resonance (CMR) have only been sparsely validated. Furthermore, the additional diagnostic value of utilizing QP in addition to the traditional visual expert interpretation of stress-perfusion CMR remains unknown. The aim was to investigate the correlation between myocardial blood flow measurements estimated by CMR, positron emission tomography, and invasive coronary thermodilution. The second aim is to investigate the diagnostic performance of CMR-QP to identify obstructive coronary artery disease (CAD). METHODS: Prospectively enrolled symptomatic patients with >50% diameter stenosis on computed tomography angiography underwent dual-bolus CMR and positron emission tomography with rest and adenosine-stress myocardial blood flow measurements. Subsequently, an invasive coronary angiography (ICA) with fractional flow reserve and thermodilution-based coronary flow reserve was performed. Obstructive CAD was defined as both anatomically severe (>70% diameter stenosis on quantitative coronary angiography) or hemodynamically obstructive (ICA with fractional flow reserve ≤0.80). RESULTS: About 359 patients completed all investigations. Myocardial blood flow and reserve measurements correlated weakly between estimates from CMR-QP, positron emission tomography, and ICA-coronary flow reserve (r<0.40 for all comparisons). In the diagnosis of anatomically severe CAD, the interpretation of CMR-QP by an expert reader improved the sensitivity in comparison to visual analysis alone (82% versus 88% [P=0.03]) without compromising specificity (77% versus 74% [P=0.28]). In the diagnosis of hemodynamically obstructive CAD, the accuracy was only moderate for a visual expert read and remained unchanged when additional CMR-QP measurements were interpreted. CONCLUSIONS: CMR-QP correlates weakly to myocardial blood flow measurements by other modalities but improves diagnosis of anatomically severe CAD. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03481712.

Impact of pretransplant T2DM on left ventricular deformation and myocardial perfusion in heart transplanted recipients: a 3.0 T cardiac magnetic resonance study.

BACKGROUND: Pretransplant type 2 diabetes mellitus (T2DM) is associated with increased cardiovascular and all-cause mortality after heart transplant (HT), but the underlying causes of this association remain unclear. The purpose of this research was to examine the impact of T2DM on left ventricular (LV) myocardial deformation and myocardial perfusion following heart transplantation using cardiovascular magnetic resonance imaging. METHODS: We investigated thirty-one HT recipients with pretransplant T2DM [HT(DM+)], thirty-four HT recipients without pretransplant T2DM [HT(DM-)] and thirty-six controls. LV myocardial strains, including the global longitudinal, radial, and circumferential strain (GLS, GRS and GCS, respectively), were calculated and compared among groups, as were resting myocardial perfusion indices, which included time to peak myocardial signal intensity (TTM), maximum signal intensity (MaxSI), and Upslope. The relationships between LV strain parameters or perfusion indices and biochemical indicators were determined through Spearman's analysis. The impact of T2DM on LV strains in HT recipients was assessed using multivariable linear regression analyses with backward stepwise selection. RESULTS: In the HT(DM+) group, the LV GLS, GRS, and GCS exhibited significantly lower magnitudes than those in both the HT(DM-) and control groups. TTM was higher in the HT(DM+) group than in both the HT(DM-) and control groups, while no significant differences were observed among the groups regarding Upslope and MaxSI. There was a negative correlation between glycated hemoglobin and the magnitude of strains (longitudinal, r = - 0.399; radial, r = - 0.362; circumferential, r = - 0.389) (all P < 0.05), and a positive correlation with TTM (r = 0.485, P < 0.001). Regression analyses that included both pretransplant T2DM and perfusion indices revealed that pretransplant T2DM, rather than perfusion indices, was an independent determinant of LV strain (ß = longitudinal, - 0.508; radial, - 0.370; circumferential, - 0.371) (all P < 0.05). CONCLUSION: In heart transplant recipients, pretransplant T2DM has a detrimental effect on subclinical left ventricular systolic function and could potentially impact myocardial microcirculation following HT.

Myocardial Blood Flow Reserve, Microvascular Coronary Health, and Myocardial Remodeling in Patients With Aortic Stenosis.

BACKGROUND: Coronary microvascular function and hemodynamics may play a role in coronary circulation and myocardial remodeling in patients with aortic stenosis (AS). We aimed to evaluate the relationship between myocardial blood flow and myocardial function in patients with AS, no AS, and aortic valve sclerosis. METHODS AND RESULTS: We included consecutive patients who had resting transthoracic echocardiography and clinically indicated positron emission tomography myocardial perfusion imaging to capture their left ventricular ejection fraction, global longitudinal strain (GLS), and myocardial flow reserve (MFR). The primary outcome was major adverse cardiovascular event (all-cause mortality, myocardial infarction, or late revascularization). There were 2778 patients (208 with aortic sclerosis, 39 with prosthetic aortic valve, 2406 with no AS, and 54, 49, and 22 with mild, moderate, and severe AS, respectively). Increasing AS severity was associated with impaired MFR (P<0.001) and GLS (P<0.001), even when perfusion was normal. Statistically significant associations were noted between MFR and GLS, MFR and left ventricular ejection fraction, and MFR and left ventricular ejection fraction reserve. After a median follow-up of 349 (interquartile range, 116-662) days, 4 (7.4%), 5 (10.2%), and 6 (27.3%) patients experienced a major adverse cardiovascular event in the mild, moderate, and severe AS groups, respectively. In a matched-control analysis, patients with mild-to-moderate AS had higher rates of impaired MFR (52.9% versus 39.9%; P=0.048) and major adverse cardiovascular event (11.8% versus 3.0%; P=0.002). CONCLUSIONS: Despite lack of ischemia, as severity of AS increased, MFR decreased and GLS worsened, reflecting worse coronary microvascular health and myocardial remodeling. Positron emission tomography-derived MFR showed a significant independent correlation with left ventricular ejection fraction and GLS. Patients with prosthetic aortic valve showed a high prevalence of impaired MFR.

Computed tomography myocardial perfusion imaging to detect myocardial ischemia in patients with anxiety and obstructive coronary heart disease post-exposure to mental stressors.

This study aims to measure myocardial blood flow (MBF) using dynamic CT- myocardial perfusion imaging (CT-MPI) combined with mental stressors in patients with obstructive coronary artery disease (OCAD) and in patients with anxiety and no obstructive coronary artery disease (ANOCAD). A total of 30 patients with OCAD with 30 patients with ANOCAD were included in this analysis. Using the 17-segment model, the rest and stress phase MBF of major coronary arteries in participants were recorded respectively. Compared with ANOCAD patients, OCAD patients were more likely to have localized reduction of MBF (p < 0.05). For patients with ANOCAD, both global MBF and MBF of the main coronary arteries in the stress phase were lower than those in the rest phase (all p < 0.05), but there was no significant difference in MBF among the main coronary arteries in the rest or stress phase (p = 0.25, p = 0.15). For patients with OCAD, the MBF of the target area was lower than that of the non-target area in both the rest and stress phase, and the MBF of the target area in the stress phase was lower than that in the rest phase (all p < 0.05). However, there was no significant difference in MBF between the rest or stress phase in the non-target area (p = 0.73). Under mental stress, the decrease in MBF in ANOCAD patients was diffuse, while the decrease in MBF in OCAD patients was localized. Dynamic CT-MPI combined with mental stressors can be used to detect MBF changes in anxiety patients.

Development of a Novel Nomogram to Predict Major Adverse Cardiac Events in Patients with Chronic Total Occlusion.

Objectives: To create a nomogram using single photon emission computed tomography (SPECT) myocardial perfusion imaging and 18F-FDG positron emissions tomography (PET) gated myocardial metabolism imaging to forecast major adverse cardiovascular events (MACE) in chronic total occlusion (CTO) patients treated with optimal medical therapy (OMT). Methods: A total of 257 patients who received OMT between January 2016 and December 2021 were included in this retrospective study. Patients were randomly divided into development (n=179) and validation (n=78) cohorts. A thorough evaluation was conducted, encompassing clinical features and imaging analysis, which involved assessing myocardial perfusion and metabolism. Independent risk factors were identified using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Calibration curves and decision curve analysis (DCA) were used to evaluate the clinical usefulness. Results: In the development cohort, 53 patients (29.6%) experienced MACE out of 179 patients, while in the validation cohort, MACE occurred in 23 (29.5%) patients out of 78. The PET-left ventricular end-systolic volume (P-ESV) (HR 1.01; 95% CI 1.003-1.017; p=0.003), hibernating myocardium / total perfusion defect (HM/TPD) (HR 1.053; 95% CI 1.038-1.069; p<0.001), PET-left ventricular ejection fraction (P-LVEF) (HR 0.862; 95% CI 0.788-0.943; p=0.001), and left anterior descending branch (LAD) (HR 2.303; 95% CI 1.086-4.884; p=0.03) were significantly associated with MACE and were used to develop the nomogram. The nomogram demonstrated excellent discrimination with C-indexes of 0.931 and 0.911 in the development and validation cohorts. DCA determined that the model exhibited a considerably superior net advantage in predicting MACE. Conclusion: A new nomogram integrating clinical factors and imaging features was created to predict the risk of MACE in patients with CTO.

Prognostic Value of Stress Perfusion Cardiac MRI in Cardiovascular Disease: A Systematic Review and Meta-Analysis of the Effects of the Scanner, Stress Agent, and Analysis Technique.

Purpose To perform a systematic review and meta-analysis to assess the prognostic value of stress perfusion cardiac MRI in predicting cardiovascular outcomes. Materials and Methods A systematic literature search from the inception of PubMed, Embase, Web of Science, and China National Knowledge Infrastructure until January 2023 was performed for articles that reported the prognosis of stress perfusion cardiac MRI in predicting cardiovascular outcomes. The quality of included studies was assessed using the Quality in Prognosis Studies tool. Reported hazard ratios (HRs) of univariable regression analyses with 95% CIs were pooled. Comparisons were performed across different analysis techniques (qualitative, semiquantitative, and fully quantitative), magnetic field strengths (1.5 T vs 3 T), and stress agents (dobutamine, adenosine, and dipyridamole). Results Thirty-eight studies with 58 774 patients with a mean follow-up time of 53 months were included. There were 1.9 all-cause deaths and 3.5 major adverse cardiovascular events (MACE) per 100 patient-years. Stress-inducible ischemia was associated with a higher risk of all-cause mortality (HR: 2.55 [95% CI: 1.89, 3.43]) and MACE (HR: 3.90 [95% CI: 2.69, 5.66]). For MACE, pooled HRs of qualitative, semiquantitative, and fully quantitative methods were 4.56 (95% CI: 2.88, 7.22), 3.22 (95% CI: 1.60, 6.48), and 1.78 (95% CI: 1.39, 2.28), respectively. For all-cause mortality, there was no evidence of a difference between qualitative and fully quantitative methods (P = .79). Abnormal stress perfusion cardiac MRI findings remained prognostic when subgrouped based on underlying disease, stress agent, and field strength, with HRs of 3.54, 2.20, and 3.38, respectively, for all-cause mortality and 3.98, 3.56, and 4.21, respectively, for MACE. There was no evidence of subgroup differences in prognosis between field strengths or stress agents. There was significant heterogeneity in effect size for MACE outcomes in the subgroups assessing qualitative versus quantitative stress perfusion analysis, underlying disease, and field strength. Conclusion Stress perfusion cardiac MRI is valuable for predicting cardiovascular outcomes, regardless of the analysis method, stress agent, or magnetic field strength used. Keywords: MR-Perfusion, MRI, Cardiac, Meta-Analysis, Stress Perfusion, Cardiac MR, Cardiovascular Disease, Prognosis, Quantitative © RSNA, 2024 Supplemental material is available for this article.

A novel method combining gated SPECT and vectorcardiography to guide left ventricular lead placement to improve response to cardiac resynchronization therapy: A proof of concept study.

BACKGROUND: The segment of the latest mechanical contraction (LMC) does not always overlap with the site of the latest electrical activation (LEA). By integrating both mechanical and electrical dyssynchrony, this proof-of-concept study aimed to propose a new method for recommending left ventricular (LV) lead placements, with the goal of enhancing response to cardiac resynchronization therapy (CRT). METHODS: The LMC segment was determined by single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) phase analysis. The LEA site was detected by vectorcardiogram. The recommended segments for LV lead placement were as follows: (1) the LMC viable segments that overlapped with the LEA site; (2) the LMC viable segments adjacent to the LEA site; (3) If no segment met either of the above, the LV lateral wall was recommended. The response was defined as ≥15% reduction in left ventricular end-systolic volume (LVESV) 6-months after CRT. Patients with LV lead located in the recommended site were assigned to the recommended group, and those located in the non-recommended site were assigned to the non-recommended group. RESULTS: The cohort comprised of 76 patients, including 54 (71.1%) in the recommended group and 22 (28.9%) in the non-recommended group. Among the recommended group, 74.1% of the patients responded to CRT, while 36.4% in the non-recommended group were responders (P = .002). Compared to pacing at the non-recommended segments, pacing at the recommended segments showed an independent association with an increased response by univariate and multivariable analysis (odds ratio 5.00, 95% confidence interval 1.73-14.44, P = .003; odds ratio 7.33, 95% confidence interval 1.53-35.14, P = .013). Kaplan-Meier curves showed that pacing at the recommended LV lead position demonstrated a better long-term prognosis. CONCLUSION: Our findings indicate that pacing at the recommended segments, by integrating of mechanical and electrical dyssynchrony, is significantly associated with an improved CRT response and better long-term prognosis.

TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.

Inter-frame motion in dynamic cardiac positron emission tomography (PET) using rubidium-82 (82Rb) myocardial perfusion imaging impacts myocardial blood flow (MBF) quantification and the diagnosis accuracy of coronary artery diseases. However, the high cross-frame distribution variation due to rapid tracer kinetics poses a considerable challenge for inter-frame motion correction, especially for early frames where intensity-based image registration techniques often fail. To address this issue, we propose a novel method called Temporally and Anatomically Informed Generative Adversarial Network (TAI-GAN) that utilizes an all-to-one mapping to convert early frames into those with tracer distribution similar to the last reference frame. The TAI-GAN consists of a feature-wise linear modulation layer that encodes channel-wise parameters generated from temporal information and rough cardiac segmentation masks with local shifts that serve as anatomical information. Our proposed method was evaluated on a clinical 82Rb PET dataset, and the results show that our TAI-GAN can produce converted early frames with high image quality, comparable to the real reference frames. After TAI-GAN conversion, the motion estimation accuracy and subsequent myocardial blood flow (MBF) quantification with both conventional and deep learning-based motion correction methods were improved compared to using the original frames. The code is available at https://github.com/gxq1998/TAI-GAN.

The additive prognostic value of end-systolic pressure-volume relation by stress CMR in patients with known or suspected coronary artery disease.

PURPOSE: The difference between rest and peak stress end-systolic pressure-volume relation (ΔESPVR) is an afterload-independent index of left ventricular (LV) contractility. We assessed the independent prognostic value of ΔESPVR index by dipyridamole stress-cardiovascular magnetic resonance (CMR) in patients with known/suspected coronary artery disease (CAD). METHODS: We considered 196 consecutive patients (62.74 ± 10.66 years, 49 females). Wall motion and perfusion abnormalities at rest and peak stress were analysed. Replacement myocardial fibrosis was detected by late gadolinium enhancement (LGE) technique. The ESPVR was evaluated at rest and peak stress from raw measurement of systolic arterial pressure and end-systolic volume by biplane Simpson's method. RESULTS: A reduced ΔESPVR index (≤ 0.02 mmHg/mL/m2) was found in 88 (44.9%) patients and it was associated with a lower LV ejection fraction (EF) and with a higher frequency of abnormal stress CMR and myocardial fibrosis. During a mean follow-up of 53.17 ± 28.21 months, 50 (25.5%) cardiac events were recorded: 5 cardiac deaths, 17 revascularizations, one myocardial infarction, 23 hospitalisations for heart failure or unstable angina, and 4 ventricular arrhythmias. According to Cox regression analysis, diabetes, family history, LVEF, abnormal stress CMR, myocardial fibrosis, and reduced ΔESPVR were significant univariate prognosticators. In the multivariate analysis the independent predictors were ΔESPVR index ≤ 0.02 mmHg/mL/m2 (hazard ratio-HR = 2.58, P = 0.007), myocardial fibrosis (HR = 2.13, P = 0.036), and diabetes (HR = 2.33, P = 0.012). CONCLUSION: ΔESPVR index by stress-CMR was independently associated with cardiac outcomes in patients with known/suspected CAD, in addition to replacement myocardial fibrosis and diabetes. Thus, the assessment of ΔESPVR index may be included into the standard stress-CMR exam to further stratify the patients.

Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T.

PURPOSE: To develop and evaluate a deep learning (DL) -based rapid image reconstruction and motion correction technique for high-resolution Cartesian first-pass myocardial perfusion imaging at 3T with whole-heart coverage for both single-slice (SS) and simultaneous multi-slice (SMS) acquisitions. METHODS: 3D physics-driven unrolled network architectures were utilized for the reconstruction of high-resolution Cartesian perfusion imaging. The SS and SMS multiband (MB) = 2 networks were trained from 135 slices from 20 subjects. Structural similarity index (SSIM), peak SNR (PSNR), and normalized RMS error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5, excellent; 1, poor). For respiratory motion correction, a 2D U-Net based motion corrected network was proposed, and the temporal fidelity and second-order derivative were calculated to assess the performance of the motion correction. RESULTS: Excellent performance was demonstrated in the proposed technique with high SSIM and PSNR, and low NRMSE. Image quality scores were (4.3 [4.3, 4.4], 4.5 [4.4, 4.6], 4.3 [4.3, 4.4], and 4.5 [4.3, 4.5]) for SS DL and SS L1-SENSE, MB = 2 DL and MB = 2 SMS-L1-SENSE, respectively, showing no statistically significant difference (p > 0.05 for SS and SMS) between (SMS)-L1-SENSE and the proposed DL technique. The network inference time was around 4 s per dynamic perfusion series with 40 frames while the time of (SMS)-L1-SENSE with GPU acceleration was approximately 30 min. CONCLUSION: The proposed DL-based image reconstruction and motion correction technique enabled rapid and high-quality reconstruction for SS and SMS MB = 2 high-resolution Cartesian first-pass perfusion imaging at 3T.

Pulmonary blood volume measured by 82Rb-PET in patients with chronic obstructive pulmonary disease: a retrospective cohort study.

In patients with chronic obstructive pulmonary disease (COPD), pulmonary vascular dysfunction and destruction are observable before the onset of detectable emphysema, but it is unknown whether this is associated with central hypovolemia. We investigated if patients with COPD have reduced pulmonary blood volume (PBV) evaluated by 82Rb-positron emission tomography (PET) at rest and during adenosine-induced hyperemia. This single-center retrospective cohort study assessed 6,301 82Rb-PET myocardial perfusion imaging (MPI) examinations performed over a 6-yr period. We compared 77 patients with COPD with 44 healthy kidney donors (controls). Cardiac output ([Formula: see text]) and mean 82Rb bolus transit time (MBTT) were used to calculate PBV. [Formula: see text] was similar at rest (COPD: 3,649 ± 120 mL vs. control: 3,891 ± 160 mL, P = 0.368) but lower in patients with COPD compared with controls during adenosine infusion (COPD: 5,432 ± 124 mL vs. control: 6,185 ± 161 mL, P < 0.050). MBTT was shorter in patients with COPD compared with controls at rest (COPD: 8.7 ± 0.28 s vs. control: 11.4 ± 0.37 s, P < 0.001) and during adenosine infusion (COPD: 9.2 ± 0.28 s vs. control: 10.2 ± 0.37 s, P < 0.014). PBV was lower in patients with COPD, even after adjustment for body surface area, sex, and age at rest [COPD: 530 (29) mL vs. 708 (38) mL, P < 0.001] and during adenosine infusion [COPD: 826 (29) mL vs. 1,044 (38) mL, P < 0.001]. In conclusion, patients with COPD show evidence of central hypovolemia, but it remains to be determined whether this has any diagnostic or prognostic impact.NEW & NOTEWORTHY The present study demonstrated that patients with chronic obstructive pulmonary disease (COPD) exhibit central hypovolemia compared with healthy controls. Pulmonary blood volume may thus be a relevant physiological and/or clinical outcome measure in future COPD studies.

Relationship between transient ischemic dilatation and changes in heart rate during gated SPECT acquisition in a low-risk population without perfusion defects.

OBJECTIVES: Transient ischemic dilatation (TID) in myocardial perfusion single photon emission computed tomography (SPECT) is considered a marker of poor prognosis. However, it has been suggested that some cases are due to apparent volumetric changes secondary to differences in heart rate (HR) at the time of acquisition. We assessed the correlation between transient dilatation and HR in low risk patients with no perfusion defects. METHODS: We retrospectively analyzed patients sent for 99mTc-MIBI SPECT using a 2-day protocol. We recorded the median HR during acquisition and the HR difference (HRD) between the rest and post-stress. We obtained the medium ventricular volume, end-diastolic volume (EDV), and end-systolic volume (ESV). We included patients in which TID using medium ventricular volume (TIDMV) was ≥1.2. TID was also calculated for the EDV and ESV (TIDEDV, TIDESV). We excluded patients with known coronary artery disease, perfusion defects, various ECG disorders, positive stress test, or ESV < 10 ml. RESULTS: From a total of 2006 patients, 63 (50 exercise, 13 dipyridamole) met the criteria for analysis (age 63.8 ±â€…9.7, 44 men). TIDMV was 1.29 ±â€…0.09 and HRD 9.8 beats per minute (BPM) (range -10 to 41). There was positive correlation between HRD and TIDMV ( r  = 0.51, P  < 0.001) and TIDEDV ( r  = 0.5, P  < 0.001), but not TIDESV ( r  = 0.23, P  = 0.07). Correlation was stronger when HRD was ≥10 BPM ( r  = 0.67, P  < 0.001). CONCLUSION: TID without perfusion defects should be interpreted with caution in the presence of HRD ≥ 10 BPM during post-stress acquisition.

Clinical implementation of a fully automated quantitative perfusion cardiovascular magnetic resonance imaging workflow with a simplified dual-bolus contrast administration scheme.

This study clinically implemented a ready-to-use quantitative perfusion (QP) cardiovascular magnetic resonance (QP CMR) workflow, encompassing a simplified dual-bolus gadolinium-based contrast agent (GBCA) administration scheme and fully automated QP image post-processing. Twenty-five patients with suspected obstructive coronary artery disease (CAD) underwent both adenosine stress perfusion CMR and an invasive coronary angiography or coronary computed tomography angiography. The dual-bolus protocol consisted of a pre-bolus (0.0075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) and a main bolus (0.075 mmol/kg GBCA at 0.5 mmol/ml concentration + 20 ml saline) at an infusion rate of 3 ml/s. The arterial input function curves showed excellent quality. Stress MBF ≤ 1.84 ml/g/min accurately detected obstructive CAD (area under the curve 0.79; 95% Confidence Interval: 0.66 to 0.89). Combined visual assessment of color pixel QP maps and conventional perfusion images yielded a diagnostic accuracy of 84%, sensitivity of 70% and specificity of 93%. The proposed easy-to-use dual-bolus QP CMR workflow provides good image quality and holds promise for high accuracy in diagnosis of obstructive CAD. Implementation of this approach has the potential to serve as an alternative to current methods thus increasing the accessibility to offer high-quality QP CMR imaging by a wide range of CMR laboratories.

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction.

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [ 82 Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction. We establish a general pipeline for AI model validation to assess and compare the performance of global (i.e. average of the entire MPI signal), regional (17 segments), radiomics and Convolutional Neural Network (CNN) models leveraging various MPI signals on a dataset of 234 patients. Results showed that all regional AI models significantly outperformed the global model ( p < 0.001 ), where the best AUC of 73.9% (CI 72.5-75.3) was obtained with a CNN model. A regional AI model based on MBF averages from 17 segments fed to a Logistic Regression (LR) constituted an excellent trade-off between model simplicity and performance, achieving an AUC of 73.4% (CI 72.3-74.7). A radiomics model based on intensity features revealed that the global average was the least important feature when compared to other aggregations of the MPI signal over the myocardium. We conclude that AI models can allow better personalized prognosis assessment for MACE.