Non-invasive fractional flow reserve (FFRCT) in the evaluation of acute chest pain ? Concepts and first experiences

Objective: To evaluate 30 day rate of major adverse cardiac events (MACE) utilizing cCTA and FFRCT for evaluation of patients presenting to the Emergency Department (ED) with acute chest pain. Materials and methods: Patients between the ages of 18?95 years who underwent clinically indicated cCTA and FFRCT in the evaluation of acute chest pain in the emergency department were retrospectively evaluated for 30 day MACE, repeat presentation/admission for chest pain, revascularization, and additional testing. Results: A total of 59 patients underwent CCTA and subsequent FFRCT for the evaluation of acute chest pain in the ED over the enrollment period. 32 out of 59 patients (54 %) had negative FFRCT (>0.80) out of whom 18 patients (55 %) were discharged from the ED. Out of the 32 patients without functionally significant CAD by FFRCT, 32 patients (100 %) underwent no revascularization and 32 patients (100 %) had no MACE at the 30-day follow-up period. Conclusion: In this limited retrospective study, patients presenting to the ED with acute chest pain and with CCTA with subsequent FFRCT of >0.8 had no MACE at 30 days; however, for many of these patients results were not available at time of clinical decision making by the ED physician

Differentiation of acute and four-week old myocardial infarct with Gd(ABE-DTTA)-enhanced CMR

<p>Abstract</p> <p>Background</p> <p>Standard extracellular cardiovascular magnetic resonance (CMR) contrast agents (CA) do not provide differentiation between acute and older myocardial infarcts (MI). The purpose of this study was to develop a method for differentiation between acute and older myocardial infarct using myocardial late-enhancement (LE) CMR by a new, low molecular weight contrast agent.</p> <p>Dogs (n = 6) were studied in a closed-chest, reperfused, double myocardial infarct model. Myocardial infarcts were generated by occluding the Left Anterior Descending (LAD) coronary artery with an angioplasty balloon for 180 min, and four weeks later occluding the Left Circumflex (LCx) coronary artery for 180 min. LE images were obtained on day 3 and day 4 after second myocardial infarct, using Gd(DTPA) (standard extracellular contrast agent) and Gd(ABE-DTTA) (new, low molecular weight contrast agent), respectively. Triphenyltetrazolium chloride (TTC) histomorphometry validated existence and location of infarcts. Hematoxylin-eosin and Masson's trichrome staining provided histologic evaluation of infarcts.</p> <p>Results</p> <p>Gd(ABE-DTTA) or Gd(DTPA) highlighted the acute infarct, whereas the four-week old infarct was visualized by Gd(DTPA), but not by Gd(ABE-DTTA). With Gd(ABE-DTTA), the mean ± SD signal intensity enhancement (SIE) was 366 ± 166% and 24 ± 59% in the acute infarct and the four-week old infarct, respectively (P < 0.05). The latter did not differ significantly from signal intensity in healthy myocardium (P = NS). Gd(DTPA) produced signal intensity enhancements which were similar in acute (431 ± 124%) and four-week old infarcts (400 ± 124%, P = NS), and not statistically different from the Gd(ABE-DTTA)-induced SIE in acute infarct. The existence and localization of both infarcts were confirmed by triphenyltetrazolium chloride (TTC). Histologic evaluation demonstrated coagulation necrosis, inflammation, and multiple foci of calcification in the four day old infarct, while the late subacute infarct showed granulation tissue and early collagen deposition.</p> <p>Conclusions</p> <p>Late enhancement CMR with separate administrations of standard extracellular contrast agent, Gd(DTPA), and the new low molecular weight contrast agent, Gd(ABE-DTTA), differentiates between acute and late subacute infarct in a reperfused, double infarct, canine model.</p

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions

Quantitative analysis of dynamic computed tomography angiography for the detection of endoleaks after abdominal aorta aneurysm endovascular repair:A feasibility study

ObjectivesTo assess the feasibility of quantitative analysis of dynamic computed tomography angiography (dCTA) for the detection of endoleaks in patients who underwent endovascular repair of abdominal aortic aneurysms (EVAR).Material and methodsTwenty patients scheduled for contrast-enhanced CT angiography (CTA) of the abdominal aorta post-EVAR were prospectively enrolled. All patients received a standard triphasic CTA protocol, followed by an additional dCTA. The dCTA acquisition enabled reconstruction of color-coded maps depicting blood perfusion and a dCTA dataset of the aneurysm sac. Observers assessed the dCTA and dynamic CT perfusion (dCTP) images for the detection of endoleaks, establishing diagnostic confidence based on a modified 5-point Likert scale. An index was calculated for the ratio between the endoleak and aneurysm sac using blood flow for dCTP and Hounsfield units (HU) for dCTA. The Wilcoxon test compared the endoleak index and the diagnostic confidence of the observers.ResultsIn total, 19 patients (18 males, median age 74 years [70.5-75.7]) were included for analysis. Nine endoleaks were detected in 7 patients using triphasic CTA as the reference standard. There was complete agreement for endoleak detection between the two techniques on a per-patient basis. Both dCTA and dCTP identified an additional endoleak in one patient. The diagnostic confidence using dCTP for detection of endoleaks was not significantly superior to dCTA (5.0 [5-5] vs. 4.5 [4-5], respectively; p = 0.11); however, dCTP demonstrated superior diagnostic confidence for endoleak exclusion compared to dCTA (1.0 [1-1] vs 1.5 [1.5-1.5], respectively; p ConclusionsQuantitative analysis of dCTP imaging can aid in the detection of endoleaks and demonstrates a higher endoleak detection rate than triphasic CTA, as well as a strong correlation with visual assessment of dCTA images

Predictive Value of Cardiac CTA, Cardiac MRI, and Transthoracic Echocardiography for Cardioembolic Stroke Recurrence

Background: Transthoracic echocardiography (TTE) is the standard of care for initial evaluation of patients with suspected cardioembolic stroke. While TTE is useful for assessing certain sources of cardiac emboli, its diagnostic capability is limited in the detection of other sources, including left atrial thrombus and aortic plaques. Objectives: To investigate sensitivity, specificity and predictive value of cardiac CT angigography (cCTA), cardiac MRI (CMR), and TTE for recurrence in patients with suspected cardioembolic stroke. Methods: We retrospectively included 151 patients with suspected cardioembolic stroke who underwent TTE and either CMR (n=75) or cCTA (n=76) between January 2013 and May 2017. We evaluated for presence of left atrial thrombus, left ventricular thrombus, vulnerable aortic plaque, cardiac tumors, and valvular vegetation as causes of cardioembolic stroke. The end-point was stroke recurrence. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for recurrent stroke were calculated; the diagnostic accuracy of CMR, cCTA, and TTE was compared between and within groups using area under the curves (AUCs). Results: Twelve and 14 recurrent strokes occurred in the cCTA and CMR groups, respectively. Sensitivity, specificity, PPV and NPV were: 33.3%, 93.7%, 50.0%, and 88.2% for cCTA; 14.3%, 80.3%, 14.3%, and 80.3% for CMR; 14.3%, 83.6%, 16.7%, 80.9% for TTE in the CMR group, and 8.3%, 93.7%, 20.0% and 84.5% for TTE in the cCTA group. Accuracy was not different (p&gt;0.05) between cCTA (0.63, 95% CI [0.49, 0.77]), CMR (0.53, [0.42, 0.63]), TTE in CMR group (0.51, [0.40, 0.61], and TTE in cCTA group (0.51, [0.42, 0.59]). In cCTA group, atrial and ventricular thrombus were detected by cCTA in 3 patients and TTE in 1 patient; in CMR group, thrombus was detected by CMR in 1 patient and TTE in 2 patients. Conclusion: cCTA, CMR, and TTE showed comparably high specificity and NPV for cardioembolic stroke recurrence. cCTA and CMR may be valid alternatives to TTE. cCTA may be preferred given potentially better detection of atrial and ventricular thrombus. Clinical impact: cCTA and CMR have similar clinical performance as TTE for predicting cardioembolic stroke recurrence. This observation may be especially important when TTE provides equivocal findings

The Feasibility, Tolerability, Safety, and Accuracy of Low-radiation Dynamic Computed Tomography Myocardial Perfusion Imaging With Regadenoson Compared With Single-photon Emission Computed Tomography

Objectives: Computed tomography (CT) myocardial perfusion imaging (CT-MPI) with hyperemia induced by regadenoson was evaluated for the detection of myocardial ischemia, safety, relative radiation exposure, and patient experience compared with single-photon emission computed tomography (SPECT) imaging. Materials and Methods: Twenty-four patients (66.5 y, 29% male) who had undergone clinically indicated SPECT imaging and provided written informed consent were included in this phase II, IRB-approved, and FDA-approved clinical trial. All patients underwent coronary CT angiography and CT-MPI with hyperemia induced by the intravenous administration of regadenoson (0.4 mg/5 mL). Patient experience and findings on CT-MPI images were compared to SPECT imaging. Results: Patient experience and safety were similar between CT-MPI and SPECT procedures and no serious adverse events due to the administration of regadenoson occurred. SPECT resulted in a higher number of mild adverse events than CT-MPI. Patient radiation exposure was similar during the combined coronary computed tomography angiography and CT-MPI (4.4 [2.7] mSv) and SPECT imaging (5.6 [1.7] mSv) (P-value 0.401) procedures. Using SPECT as the reference standard, CT-MPI analysis showed a sensitivity of 58.3% (95% confidence interval [CI]: 27.7-84.8), a specificity of 100% (95% CI: 73.5-100), and an accuracy of 79.1% (95% CI: 57.9-92.87). Low apparent sensitivity occurred when the SPECT defects were small and highly suspicious for artifacts. Conclusions: This study demonstrated that CT-MPI is safe, well tolerated, and can be performed with comparable radiation exposure to SPECT. CT-MPI has the benefit of providing both complete anatomic coronary evaluation and assessment of myocardial perfusion

Accuracy of an Artificial Intelligence Deep Learning Algorithm Implementing a Recurrent Neural Network With Long Short-term Memory for the Automated Detection of Calcified Plaques From Coronary Computed Tomography Angiography

Purpose: The purpose of this study was to evaluate the accuracy of a novel fully automated deep learning (DL) algorithm implementing a recurrent neural network (RNN) with long short-term memory (LSTM) for the detection of coronary artery calcium (CAC) from coronary computed tomography angiography (CCTA) data. Materials and Methods: Under an IRB waiver and in HIPAA compliance, a total of 194 patients who had undergone CCTA were retrospectively included. Two observers independently evaluated the image quality and recorded the presence of CAC in the right (RCA), the combination of left main and left anterior descending (LM-LAD), and left circumflex (LCx) coronary arteries. Noncontrast CACS scans were allowed to be used in cases of uncertainty. Heart and coronary artery centerline detection and labeling were automatically performed. Presence of CAC was assessed by a RNN-LSTM. The algorithm's overall and per-vessel sensitivity, specificity, and diagnostic accuracy were calculated. Results: CAC was absent in 84 and present in 110 patients. As regards CCTA, the median subjective image quality, signal-to-noise ratio, and contrast-to-noise ratio were 3.0, 13.0, and 11.4. A total of 565 vessels were evaluated. On a per-vessel basis, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 93.1% (confidence interval [CI], 84.3%-96.7%), 82.76% (CI, 74.6%-89.4%), and 86.7% (CI, 76.8%-87.9%), respectively, for the RCA, 93.1% (CI, 86.4%-97.7%), 95.5% (CI, 88.77%-98.75%), and 94.2% (CI. 90.2%-94.6%), respectively, for the LM-LAD, and 89.9% (CI, 80.2%-95.8%), 90.0% (CI, 83.2%-94.7%), and 89.9% (CI, 85.0%-94.1%), respectively, for the LCx. The overall sensitivity, specificity, and diagnostic accuracy were 92.1% (CI, 92.1%-95.2%), 88.9% (CI. 84.9%-92.1%), and 90.3% (CI, 88.0%-90.0%), respectively. When accounting for image quality, the algorithm achieved a sensitivity, specificity, and diagnostic accuracy of 76.2%, 87.5%, and 82.2%, respectively, for poor-quality data sets and 93.3%, 89.2% and 90.9%, respectively, when data sets rated adequate or higher were combined. Conclusion: The proposed RNN-LSTM demonstrated high diagnostic accuracy for the detection of CAC from CCTA