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1.
J Thorac Imaging ; 2020 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-32205821

RESUMO

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.

2.
AJR Am J Roentgenol ; : 1-7, 2020 Mar 04.
Artigo em Inglês | MEDLINE | ID: mdl-32130041

RESUMO

OBJECTIVE. The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for fully automated quantification of emphysema on chest CT compared with pulmonary function testing (spirometry). MATERIALS AND METHODS. A total of 141 patients (72 women, mean age ± SD of 66.46 ± 9.7 years [range, 23-86 years]; 69 men, mean age of 66.72 ± 11.4 years [range, 27-91 years]) who underwent both chest CT acquisition and spirometry within 6 months were retrospectively included. The spirometry-based Tiffeneau index (TI; calculated as the ratio of forced expiratory volume in the first second to forced vital capacity) was used to measure emphysema severity; a value less than 0.7 was considered to indicate airway obstruction. Segmentation of the lung based on two different reconstruction methods was carried out by using a deep convolution image-to-image network. This multilayer convolutional neural network was combined with multilevel feature chaining and depth monitoring. To discriminate the output of the network from ground truth, an adversarial network was used during training. Emphysema was quantified using spatial filtering and attenuation-based thresholds. Emphysema quantification and TI were compared using the Spearman correlation coefficient. RESULTS. The mean TI for all patients was 0.57 ± 0.13. The mean percentages of emphysema using reconstruction methods 1 and 2 were 9.96% ± 11.87% and 8.04% ± 10.32%, respectively. AI-based emphysema quantification showed very strong correlation with TI (reconstruction method 1, ρ = -0.86; reconstruction method 2, ρ = -0.85; both p < 0.0001), indicating that AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology. CONCLUSION. AI-based, fully automated emphysema quantification shows good correlation with TI, potentially contributing to an image-based diagnosis and quantification of emphysema severity.

3.
J Thorac Imaging ; 2020 Mar 12.
Artigo em Inglês | MEDLINE | ID: mdl-32168163

RESUMO

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.

4.
J Thorac Imaging ; 2020 Feb 06.
Artigo em Inglês | MEDLINE | ID: mdl-32032251

RESUMO

PURPOSE: The purpose of this study was to evaluate the utilization of invasive and noninvasive tests and compare cost in patients presenting with chest pain to the emergency department (ED) who underwent either triple-rule-out computed tomography angiography (TRO-CTA) or standard of care. MATERIALS AND METHODS: We performed a retrospective single-center analysis of 2156 ED patients who presented with acute chest pain with a negative initial troponin and electrocardiogram for myocardial injury. Patient cohorts matched by patient characteristics who had undergone TRO-CTA as a primary imaging test (n=1139) or standard of care without initial CTA imaging (n=1017) were included in the study. ED visits, utilization of tests, and costs during the initial episode of hospital care were compared. RESULTS: No significant differences in the diagnosis of coronary artery disease, pulmonary embolism, or aortic dissection were observed. Median ED waiting time (4.5 vs. 7.0 h, P<0.001), median total length of hospital stay (5.0 vs. 32.0 h, P<0.001), hospital admission rate (12.6% vs. 54.2%, P<0.001), and ED return rate to our hospital within 30 days (3.5% vs. 14.6%, P<0.001) were significantly lower in the TRO-CTA group. Moreover, reduced rates of additional testing and invasive coronary angiography (4.9% vs. 22.7%, P<0.001), and ultimately lower total cost per patient (11,783$ vs. 19,073$, P<0.001) were observed in the TRO-CTA group. CONCLUSIONS: TRO-CTA as an initial imaging test in ED patients presenting with acute chest pain was associated with shorter ED and hospital length of stay, fewer return visits within 30 days, and ultimately lower ED and hospitalization costs.

5.
J Thorac Imaging ; 2020 Feb 19.
Artigo em Inglês | MEDLINE | ID: mdl-32079905

RESUMO

PURPOSE: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry (DEXA). MATERIALS AND METHODS: In this Institutional Review Board-approved study, we analyzed the data of 65 patients (57 female, mean age: 67.4 y) who underwent both DEXA and chest CT (mean time between scans: 1.31 y). From the DEXA studies, T-scores for L1-L4 (lumbar vertebrae 1 to 4) were recorded. Patients were then divided on the basis of their T-scores into normal control, osteopenic, or osteoporotic groups. An AI algorithm based on wavelet features, AdaBoost, and local geometry constraints independently localized thoracic vertebrae from chest CT studies and automatically computed average Hounsfield Unit (HU) values with kVp-dependent spectral correction. The Pearson correlation evaluated the correlation between the T-scores and HU values. Mann-Whitney U test was implemented to compare the HU values of normal control versus osteoporotic patients. RESULTS: Overall, the DEXA-determined T-scores and AI-derived HU values showed a moderate correlation (r=0.55; P<0.001). This 65-patient population was divided into 3 subgroups on the basis of their T-scores. The mean T-scores for the 3 subgroups (normal control, osteopenic, osteoporotic) were 0.77±1.50, -1.51±0.04, and -3.26±0.59, respectively. The mean DEXA-determined L1-L4 BMD measures were 1.13±0.16, 0.88±0.06, and 0.68±0.06 g/cm, respectively. The mean AI-derived attenuation values were 145±42.5, 136±31.82, and 103±16.28 HU, respectively. Using these AI-derived HU values, a significant difference was found between the normal control patients and osteoporotic group (P=0.045). CONCLUSION: Our results show that this AI prototype can successfully determine BMD in moderate correlation with DEXA. Combined with other AI algorithms directed at evaluating cardiac and lung diseases, this prototype may contribute to future comprehensive preventative care based on a single chest CT.

6.
Eur J Radiol ; 122: 108744, 2020 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-31790934

RESUMO

PURPOSE: The study aimed to compare morphological and anatomic plaque markers derived from coronary computed tomography angiography (cCTA) for the detection of lesion specific ischemia with invasive instantaneous wave free ratio (iFR®) as the reference standard. METHODS: In our prospective study, we enrolled patients with suspected coronary artery disease (CAD), who had undergone cCTA, using a low-dose third-generation dual-source CT and invasive coronary angiography (ICA) with iFR® measurement. Various plaque markers were assessed on cCTA. Discriminatory power of these markers for the detection of ischemia-inducing coronary artery disease was evaluated against invasive iFR®. RESULTS: Our study cohort included 39 patients (66.6 ± 12.0 years, 72 % male). Among 54 vessel-specific lesions, 15 lesions (28 %) were characterized as hemodynamically significant by iFR® ≤0.89. The area under the curve (AUC) of lesion length/ minimal luminal diameter4 (LL/MLD4) (0.84) was greater than the AUC of minimal luminal area (MLA) (0.82), MLD (0.81), the degree of luminal diameter stenosis (0.81), corrected coronary opacification (CCO) (0.79), remodeling index (RI) (0.75), and percentage aggregate plaque volume (%APV) (0.72). LL, vessel volume (VV), total plaque volume (TPV), calcified and non-calcified plaque volume (CPV and NCPV) did not reach statistical significance and were unable to discriminate between vessels with and without ischemia-inducing coronary stenosis. CONCLUSION: LL/MLD4, MLA, MLD, the degree of luminal diameter stenosis, CCO, RI, and %APV derived from cCTA can support the detection of hemodynamically significant coronary stenosis as compared with iFR®, with LL/MLD4 showing the greatest discriminatory power.

8.
Artigo em Inglês | MEDLINE | ID: mdl-31843523

RESUMO

PURPOSE: To investigate the accuracy of Agatston scoring and potential for radiation dose reduction of a coronary artery calcium scoring (CACS) CT protocol at 100 kV with tin filtration (Sn100kV) and kV-independent iterative reconstruction, compared to standard 120 kV acquisitions. MATERIALS AND METHODS: With IRB approval and in HIPAA compliance, 114 patients (61.8 ± 9.6 years; 66 men) underwent CACS using a standard 120 kV protocol and an additional Sn100kV CACS scan. The two datasets were reconstructed using a medium sharp convolution algorithm and in addition the Sn100kV scans were reconstructed iteratively based on a kV-independent algorithm. Agatston scores and radiation dose values were compared between the Sn100kV and the standard 120 kV protocol. RESULTS: Median Agatston scores derived from the Sn100kV protocol with the kV-independent algorithm and the standard 120 kV were 21.4 (IQR, 0-173.8) and 24.7 (IQR, 0-171.1) respectively, with no significant differences (p=0.18). Agatston scores derived from the two different protocols had an excellent correlation (r = 0.99). The dose-length-product was 11.5 ± 4.1 mGy × cm using Sn100kV and 50.4 ± 24.9 mGy × cm using the standard 120 kV protocol (p < 0.01), resulting in a significantly lower (77%) effective dose at Sn100kV (0.16 ± 0.06 mSv vs. 0.71 ± 0.35 mSv, p < 0.01). Additionally, 99% of the patients were classified into the same risk category (0, 1-10, 11-100, 101-400, or >400) using the Sn100kV protocol. CONCLUSION: CACS at Sn100kV using the kV-independent iterative algorithm is feasible and provides high accuracy when compared to standard 120 kV scanning. Furthermore, radiation dose can be significantly reduced for this screening application in a priori healthy individuals.

9.
Artigo em Inglês | MEDLINE | ID: mdl-31780142

RESUMO

BACKGROUND: Clinical and safety outcomes of the strategy employing coronary computed tomography angiography (CCTA) as the first-choice imaging test have recently been demonstrated in the recently published CAT-CAD randomized, prospective, single-center study. Based on prospectively collected data in this patient population, we aimed to perform an initial cost analysis of this approach. METHODS: 120 participants of the CAT-CAD trial (age:60.6 ±â€¯7.9 years, 35% female) were included in the analysis. We analyzed medical resource use during the diagnostic and therapeutic episode of care. We prospectively estimated the cumulative cost for each strategy by multiplying the number of resources by standardized costs in accordance to medical databases and the 2015 Procedural Reimbursement Payment Guide. RESULTS: The total cost of coronary artery disease (CAD) diagnosis was significantly lower in the CCTA group as compared to the direct invasive coronary angiography (ICA) group ($50,176 vs $137,032) with corresponding per-patient cost of $836 vs $2,284, respectively. Similarly, the entire diagnostic and therapeutic episode of care was significantly less expensive in the CCTA group ($227,622 vs $502,827) with corresponding per-patient cost of $4630 vs $8,380, respectively. Overall, the application of CCTA as a first-line diagnostic test in stable patients with indications to ICA resulted in a 63% reduction of CAD diagnosis costs and a 55% reduction composite of diagnosis and treatment costs during 90-days follow-up. CONCLUSIONS: Application of CCTA as the first-line anatomic test in patients with suspected significant CAD decreased the total costs of diagnosis. This is likely attributable to reduced numbers of invasive tests and hospitalisations. Initial cost analysis of the CAT-CAD randomized trial suggests that this approach may provide significant cost savings for the entire health system.

10.
Can J Cardiol ; 35(11): 1523-1533, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31679622

RESUMO

BACKGROUND: The diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in detecting ischemia in myocardial bridging (MB) has not been investigated to date. METHODS: This retrospective multicentre study included 104 patients with left anterior descending MBs. MB was classified as either superficial or deep, short, or long, whereas all MB vessels were further divided into <50%, 50% to 69%, and ≥70% groups, according to proximal lumen stenosis on invasive coronary angiography. Diagnostic performance and receiver operating characteristics (ROC) of CT-FFR to detect lesion-specific ischemia was assessed on a per-vessel level, using invasive FFR as reference standard. Intraclass correlation coefficient (ICC) and Bland-Altman plots were used for agreement measurement. RESULTS: Forty-eight MB vessels (46.2%) showed ischemia by invasive FFR (≤0.80). Sensitivity, specificity, and accuracy of CT-FFR to detect functional ischemia were 0.96 (0.85 to 0.99), 0.84 (0.71 to 0.92), and 0.89 (0.81 to 0.94), respectively, in all MB vessels. There were no differences in diagnostic performance between superficial and deep MB or between short and long MB (all P > 0.05). The accuracy of CT-FFR was 0.96 (0.85 to 0.99) in ≥70% stenosis, 0.82 (0.67 to 0.91) in 50% to 69% stenosis, and 0.89 (0.51 to 0.99) in <50% stenosis (P = 0.081). Bland-Altman analysis showed a slight mean difference between CT-FFR and invasive FFR of 0.014 (95% limit of agreement, -0.117 to 0.145). The ICC was 0.775 (95% confidence interval, 0.685-0.842, P < 0.001). CONCLUSIONS: CT-FFR demonstrated high diagnostic performance for identifying functional ischemia in vessels with MB and concomitant proximal atherosclerotic disease when compared with invasive FFR. However, the clinical use of CT-FFR in patients with MB needs further study for stronger and more robust results.

11.
Clin Res Cardiol ; 2019 Oct 29.
Artigo em Inglês | MEDLINE | ID: mdl-31664509

RESUMO

BACKGROUND: Fractional flow reserve based on coronary CT angiography (CT-FFR) is gaining importance for non-invasive hemodynamic assessment of coronary artery disease (CAD). We evaluated the on-site CT-FFR with a machine learning algorithm (CT-FFRML) for the detection of hemodynamically significant coronary artery stenosis in comparison to the invasive reference standard of instantaneous wave free ratio (iFR®). METHODS: This study evaluated patients with CAD who had a clinically indicated coronary computed tomography angiography (cCTA) and underwent invasive coronary angiography (ICA) with iFR®-measurements. Standard cCTA studies were acquired with third-generation dual-source computed tomography and analyzed with on-site prototype CT-FFRML software. RESULTS: We enrolled 40 patients (73% males, mean age 67 ± 12 years) who had iFR®-measurement and CT-FFRML calculation. The mean calculation time of CT-FFRML values was 11 ± 2 min. The CT-FFRML algorithm showed, on per-patient and per-lesion level, respectively, a sensitivity of 92% (95% CI 64-99%) and 87% (95% CI 59-98%), a specificity of 96% (95% CI 81-99%) and 95% (95% CI 84-99%), a positive predictive value of 92% (95% CI 64-99%), and 87% (95% CI 59-98%), and a negative predictive value of 96% (95% CI 81-99%) and 95% (95% CI 84-99%). The area under the receiver operating characteristic curve for CT-FFRML on per-lesion level was 0.97 (95% CI 0.91-1.00). Per lesion, the Pearson's correlation between the CT-FFRML and iFR® showed a strong correlation of r = 0.82 (p < 0.0001; 95% CI 0.715-0.920). CONCLUSION: On-site CT-FFRML correlated well with the invasive reference standard of iFR® and allowed for the non-invasive detection of hemodynamically significant coronary stenosis.

12.
Artigo em Inglês | MEDLINE | ID: mdl-31615736

RESUMO

OBJECTIVE: To evaluate the feasibility of dual-energy CT (DECT)-based iodine quantification to estimate myocardial extracellular volume (ECV) fraction in patients with and without cardiomyopathy (CM), as well as to assess its ability to distinguish healthy myocardial tissue from cardiomyopathic, with the goal of defining a threshold ECV value for disease detection. METHODS: Ten subjects free of heart disease and 60 patients with CM (mean age 66.4 ±â€¯9.4; 59 males and 11 females; 40 ischemic and 20 non-ischemic CM) underwent late iodine enhanced DECT imaging. Myocardial iodine maps were obtained using 3-material decomposition. ECV of the left ventricle was estimated from hematocrit levels and the iodine maps using the AHA 16-segment model. Receiver operating characteristic curve analysis was performed, with corresponding area under the curve, along with Youden's index assessment, to establish a threshold for CM detection. RESULTS: The median ECV for healthy myocardium, non-ischemic CM, and ischemic CM were 25.4% (22.9-27.3), 38.3% (33.7-43.0), and 36.9% (32.4-41.1), respectively. Healthy myocardium showed significantly lower ECV values compared to ischemic and non-ischemic CM (p < 0.001). From Youden's index analysis, an ECV>29.5% would indicate the presence of CM in the myocardium (sensitivity = 90.3; specificity = 90.3); the AUC for this criterion was 0.950 (p < 0.001). CONCLUSION: The findings of this study resulted in a statistically significant distinction between healthy myocardium and CM ECVs. This led to the establishment of a promising threshold ECV value that could facilitate the differentiation between healthy and diseased myocardium, and highlights the potential of this DECT methodology to detect cardiomyopathic tissue.

13.
Eur Radiol Exp ; 3(1): 37, 2019 Sep 23.
Artigo em Inglês | MEDLINE | ID: mdl-31549323

RESUMO

BACKGROUND: Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed to convert unstructured computed tomography pulmonary angiography (CTPA) reports into structured reports. METHODS: A self-supervised convolutional neural network-based algorithm was trained on a dataset of 475 manually structured CTPA reports. Labels for individual statements included "pulmonary arteries," "lungs and airways," "pleura," "mediastinum and lymph nodes," "cardiovascular," "soft tissues and bones," "upper abdomen," and "lines/tubes." The algorithm was applied to a test set of 400 unstructured CTPA reports, generating a predicted label for each statement, which was evaluated by two independent observers. Per-statement accuracy was calculated based on strict criteria (algorithm label counted as correct if the statement unequivocally contained content only related to that particular label) and a modified criteria, accounting for problematic statements, including typographical errors, statements that did not fit well into the classification scheme, statements containing content for multiple labels, etc. RESULTS: Of the 4,157 statements, 3,806 (91.6%) and 3,986 (95.9%) were correctly labeled by the algorithm using strict and modified criteria, respectively, while 274 (6.6%) were problematic for the manual observers to label, the majority of which (n = 173) were due to more than one section being included in one statement. CONCLUSION: This algorithm showed high accuracy in converting free-text findings into structured reports, which could improve communication between radiologists and clinicians without loss of productivity and provide more structured data for research/data mining applications.

14.
Radiology ; 293(2): 260-271, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-31502938

RESUMO

In this article, the authors discuss the technical background and summarize the current body of literature regarding virtual monoenergetic (VM) images derived from dual-energy CT data, which can be reconstructed between 40 and 200 keV. Substantially improved iodine attenuation at lower kiloelectron volt levels and reduced beam-hardening artifacts at higher kiloelectron volt levels have been demonstrated from all major manufacturers of dual-energy CT units. Improved contrast attenuation with VM imaging at lower kiloelectron volt levels enables better delineation and diagnostic accuracy in the detection of various vascular or oncologic abnormalities. Low-kiloelectron-volt VM imaging may be useful for salvaging CT studies with suboptimal contrast material delivery or providing additional information on the arterial vasculature obtained from venous phase acquisitions. For patients with renal impairment, substantial reductions in the use of iodinated contrast material can be achieved by using lower-energy VM imaging. The authors recommend routine reconstruction of VM images at 50 keV when using dual-energy CT to exploit the increased contrast properties. For reduction of beam-hardening artifacts, VM imaging at 120 keV is useful for the initial assessment.

15.
Eur J Radiol ; 119: 108657, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31521876

RESUMO

PURPOSE: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific ischemia. METHOD: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR ≤ 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. RESULTS: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007]. CONCLUSIONS: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.


Assuntos
Angiografia por Tomografia Computadorizada/normas , Estenose Coronária/diagnóstico por imagem , Isquemia Miocárdica/diagnóstico por imagem , Angiografia por Tomografia Computadorizada/métodos , Angiografia Coronária/métodos , Angiografia Coronária/normas , Estenose Coronária/fisiopatologia , Métodos Epidemiológicos , Feminino , Reserva Fracionada de Fluxo Miocárdico/fisiologia , Hemodinâmica/fisiologia , Humanos , Aprendizado de Máquina , Masculino , Pessoa de Meia-Idade , Isquemia Miocárdica/fisiopatologia , Fatores Sexuais , Tomografia Computadorizada Espiral/métodos , Tomografia Computadorizada Espiral/normas
16.
Am J Cardiol ; 124(9): 1340-1348, 2019 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-31481177

RESUMO

This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21%). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis ≥50% (OR 3.83, p 0.042), and CT-FFR ≤0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis ≥50%, plaque markers and CT-FFR ≤0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis ≥50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone.

18.
Eur Radiol Exp ; 3(1): 29, 2019 Jul 31.
Artigo em Inglês | MEDLINE | ID: mdl-31363865

RESUMO

BACKGROUND: Whole-heart magnetic resonance angiography (MRA) requires sophisticated methods accounting for respiratory motion. Our purpose was to evaluate the image quality of compressed sensing-based respiratory motion-resolved three-dimensional (3D) whole-heart MRA compared with self-navigated motion-corrected whole-heart MRA in patients with known thoracic aorta dilation. METHODS: Twenty-five patients were prospectively enrolled in this ethically approved study. Whole-heart 1.5-T MRA was acquired using a prototype 3D radial steady-state free-precession free-breathing sequence. The same data were reconstructed with a one-dimensional motion-correction algorithm (1D-MCA) and an extradimensional golden-angle radial sparse parallel reconstruction (XD-GRASP). Subjective image quality was scored and objective image quality was quantified (signal intensity ratio, SIR; vessel sharpness). Wilcoxon, McNemar, and paired t tests were used. RESULTS: Subjective image quality was significantly higher using XD-GRASP compared to 1D-MCA (median 4.5, interquartile range 4.5-5.0 versus 4.0 [2.25-4.75]; p < 0.001), as well as signal homogeneity (3.0 [3.0-3.0] versus 2.0 [2.0-3.0]; p = 0.003), and image sharpness (3.0 [2.0-3.0] vs 2.0 [1.25-3.0]; p < 0.001). SIR with the 1D-MCA and XD-GRASP was 6.1 ± 3.9 versus 7.4 ± 2.5, respectively (p < 0.001); while signal homogeneity was 274.2 ± 265.0 versus 199.8 ± 67.2 (p = 0.129). XD-GRASP provided a higher vessel sharpness (45.3 ± 10.7 versus 40.6 ± 101, p = 0.025). CONCLUSIONS: XD-GRASP-based motion-resolved reconstruction of free-breathing 3D whole-heart MRA datasets provides improved image contrast, sharpness, and signal homogeneity and seems to be a promising technique that overcomes some of the limitations of motion correction or respiratory navigator gating.

19.
Artigo em Inglês | MEDLINE | ID: mdl-31422138

RESUMO

OBJECTIVES: The aim of this study was to validate the feasibility of a novel structural and computational fluid dynamics-based fractional flow reserve (FFR) algorithm for coronary computed tomography angiography (CTA), using alternative boundary conditions to detect lesion-specific ischemia. BACKGROUND: A new model of computed tomographic (CT) FFR relying on boundary conditions derived from structural deformation of the coronary lumen and aorta with transluminal attenuation gradient and assumptions regarding microvascular resistance has been developed, but its accuracy has not yet been validated. METHODS: A total of 338 consecutive patients with 422 vessels from 9 Chinese medical centers undergoing CTA and invasive FFR were retrospectively analyzed. CT FFR values were obtained on a novel on-site computational fluid dynamics-based CT FFR (uCT-FFR [version 1.5, United-Imaging Healthcare, Shanghai, China]). Performance characteristics of uCT-FFR and CTA in detecting lesion-specific ischemia in all lesions, intermediate lesions (luminal stenosis 30% to 70%), and "gray zone" lesions (FFR 0.75 to 0.80) were calculated with invasive FFR as the reference standard. The effect of coronary calcification on uCT-FFR measurements was also assessed. RESULTS: Per vessel sensitivities, specificities, and accuracies of 0.89, 0.91, and 0.91 with uCT-FFR, 0.92, 0.34, and 0.55 with CTA, and 0.94, 0.37, and 0.58 with invasive coronary angiography, respectively, were found. There was higher specificity, accuracy, and AUC for uCT-FFR compared with CTA and qualitative invasive coronary angiography in all lesions, including intermediate lesions (p < 0.001 for all). No significant difference in diagnostic accuracy was observed in the "gray zone" range versus the other 2 lesion groups (FFR ≤0.75 and >0.80; p = 0.397) and in patients with "gray zone" versus FFR ≤0.75 (p = 0.633) and versus FFR >0.80 (p = 0.364), respectively. No significant difference in the diagnostic performance of uCT-FFR was found between patients with calcium scores ≥400 and <400 (p = 0.393). CONCLUSIONS: This novel computational fluid dynamics-based CT FFR approach demonstrates good performance in detecting lesion-specific ischemia. Additionally, it outperforms CTA and qualitative invasive coronary angiography, most notably in intermediate lesions, and may potentially have diagnostic power in gray zone and highly calcified lesions.

20.
Artigo em Inglês | MEDLINE | ID: mdl-31422141

RESUMO

OBJECTIVES: This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR). BACKGROUND: CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated. METHODS: Four hundred eighty-two vessels from 314 patients (62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard. RESULTS: The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥ 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57-0.85] vs. 0.85 [95% CI: 0.82-0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001). CONCLUSIONS: Machine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).

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