Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography.

PURPOSE: The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA). MATERIALS AND METHODS: Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels. RESULTS: A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83-0.94) at the patient level and 0.92 (95 % CI: 0.89-0.94) at the vessel level. CONCLUSION: Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.

Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

PURPOSE: This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners. MATERIAL AND METHODS: We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD (≥ 50% diameter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significant stenosis (FFR ≤ 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. RESULTS: The AI model demonstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, particularly for significant CAD (25.0%), indicating a higher incidence of false positives. CONCLUSION: AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.

An operando spectroscopic investigation of the Re2O7.2H2O adduct obtained by controlled hydration of Re2O7.

We provide here a comprehensive investigation spectroscopic of the controlled hydration of Re2O7 using Raman, FTIR and XAS techniques in complement with ab initio modelling for confirming the spectral assignments. Hence, the Raman signature of Re2O7.2H2O was obtained, and the evolution kinetics was investigated to provide a detailed description of the hydration process.

Theoretical study of the catalytic hydrodeoxygenation of furan, methylfuran and benzofurane on MoS2.

Herein, we have studied the direct deoxygenation (DDO) (without prior hydrogenation) of furan, 2-methylfuran and benzofuran on the metal edge of MoS2 with a vacancy created under pressure of dihydrogen. For the three molecules, we found that the desorption of the water molecule for the regeneration of the vacancy is the most endothermic. Based on the thermodynamic and kinetic aspects, the reactivity order of the oxygenated compounds is furan ≈ 2-methylfuran > benzofuran, which is in agreement with literature. We present the key stages of the mechanisms and highlight the effects of substituents.

Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.

Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.

3D printing in anatomical lung segmentectomies: A randomized pilot trial.

Objective: This pilot study evaluated the impact of using a 3D printed model of the patient's bronchovascular lung anatomy on the mental workload and fatigue of surgeons during full thoracoscopic segmentectomy. Design: We performed a feasibility pilot study of a prospective randomized controlled trial with 2 parallel arms. All included patients underwent digital 3D visual reconstruction of their bronchovascular anatomy and were randomized into the following two groups: Digital arm (only a virtual 3D model was available) and Digital + Object arm (both virtual and printed 3D models were available). The primary end-point was the surgeons' mental workload measured using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) score. Setting: Between October 28, 2020 and October 05, 2021, we successively investigated all anatomic segmentectomies performed via thoracoscopy in the Thoracic Department of the Montsouris Mutualiste Institute, except for S6 segmentectomies and S4+5 left bi-segmentectomies. Participants: We assessed 102 patients for anatomical segmentectomy. Among the, 40 were randomly assigned, and 34 were deemed analysable, with 17 patients included in each arm. Results: Comparison of the two groups, each comprising 17 patients, revealed no statistically significant difference in primary or secondary end-points. The consultation of the visual digital model was significantly less frequent when a 3D printed model was available (6 versus 54 consultations, p = 0.001). Notably, both arms exhibited high NASA-TLX scores, particularly in terms of mental demand, temporal demand, and effort scores. Conclusion: In our pilot study, 3D printed models and digital 3D reconstructions for pre-operative planning had an equivalent effect on thoracoscopic anatomic segmentectomy for experienced surgeons. The originality of this study lies in its focus on the impact of 3D printing of bronchovascular anatomy on surgeons, rather than solely on the surgical procedure.

Diagnostic performance of deep learning to exclude coronary stenosis on CT angiography in TAVI patients.

We evaluated the diagnostic performance of a deep-learning model (DLM) (CorEx®, Spimed-AI, Paris, France) designed to automatically detect > 50% coronary stenosis on coronary computed tomography angiography (CCTA) images. We studied inter-observer variability as an additional aim. CCTA images obtained before transcatheter aortic valve implantation (TAVI) were assessed by two radiologists and the DLM, and the results were compared to those of invasive coronary angiography (ICA) used as the reference standard. 165 consecutive patients underwent both CCTA and ICA as part of their TAVI work-up. We excluded the 42 (25.5%) patients with a history of stenting or bypass grafting and the 23 (13.9%) patients with low-quality images. We retrospectively subjected the CCTA images from the remaining 100 patients to evaluation by the DLM and compared the DLM and ICA results. All 25 patients with > 50% stenosis by ICA also had > 50% stenosis by DLM evaluation of CCTA: thus, the DLM had 100% sensitivity and 100% negative predictive value. False-positive DLM results were common, yielding a positive predictive value of only 39% (95% CI, 27-51%). Two radiologists with 3 and 25 years' experience, respectively, performed similarly to the DLM in evaluating the CCTA images; thus, accuracy did not differ significantly between each reader and the DLM (p = 0.625 and p = 0.375, respectively). The DLM had 100% negative predictive value for > 50% stenosis and performed similarly to experienced radiologists. This tool may hold promise for identifying the up to one-third of patients who do not require ICA before TAVI.

Coronary artery disease evaluation during transcatheter aortic valve replacement work-up using photon-counting CT and artificial intelligence.

PURPOSE: The purpose of this study was to evaluate the capabilities of photon-counting (PC) CT combined with artificial intelligence-derived coronary computed tomography angiography (PC-CCTA) stenosis quantification and fractional flow reserve prediction (FFRai) for the assessment of coronary artery disease (CAD) in transcatheter aortic valve replacement (TAVR) work-up. MATERIALS AND METHODS: Consecutive patients with severe symptomatic aortic valve stenosis referred for pre-TAVR work-up between October 2021 and June 2023 were included in this retrospective tertiary single-center study. All patients underwent both PC-CCTA and ICA within three months for reference standard diagnosis. PC-CCTA stenosis quantification (at 50% level) and FFRai (at 0.8 level) were predicted using two deep learning models (CorEx, Spimed-AI). Diagnostic performance for global CAD evaluation (at least one significant stenosis ≥ 50% or FFRai ≤ 0.8) was assessed. RESULTS: A total of 260 patients (138 men, 122 women) with a mean age of 78.7 ± 8.1 (standard deviation) years (age range: 51-93 years) were evaluated. Significant CAD on ICA was present in 126/260 patients (48.5%). Per-patient sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 96.0% (95% confidence interval [CI]: 91.0-98.7), 68.7% (95% CI: 60.1-76.4), 74.3 % (95% CI: 69.1-78.8), 94.8% (95% CI: 88.5-97.8), and 81.9% (95% CI: 76.7-86.4) for PC-CCTA, and 96.8% (95% CI: 92.1-99.1), 87.3% (95% CI: 80.5-92.4), 87.8% (95% CI: 82.2-91.8), 96.7% (95% CI: 91.7-98.7), and 91.9% (95% CI: 87.9-94.9) for FFRai. Area under the curve of FFRai was 0.92 (95% CI: 0.88-0.95) compared to 0.82 for PC-CCTA (95% CI: 0.77-0.87) (P < 0.001). FFRai-guidance could have prevented the need for ICA in 121 out of 260 patients (46.5%) vs. 97 out of 260 (37.3%) using PC-CCTA alone (P < 0.001). CONCLUSION: Deep learning-based photon-counting FFRai evaluation improves the accuracy of PC-CCTA ≥ 50% stenosis detection, reduces the need for ICA, and may be incorporated into the clinical TAVR work-up for the assessment of CAD.

Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain.

Aims: To evaluate a deep-learning model (DLM) for detecting coronary stenoses in emergency room patients with acute chest pain (ACP) explored with electrocardiogram-gated aortic computed tomography angiography (CTA) to rule out aortic dissection. Methods and results: This retrospective study included 217 emergency room patients (41% female, mean age 67.2 years) presenting with ACP and evaluated by aortic CTA at our institution. Computed tomography angiography was assessed by two readers, who rated the coronary arteries as 1 (no stenosis), 2 (<50% stenosis), or 3 (≥50% stenosis). Computed tomography angiography was categorized as high quality (HQ), if all three main coronary arteries were analysable and low quality (LQ) otherwise. Curvilinear coronary images were rated by a DLM using the same system. Per-patient and per-vessel analyses were conducted. One hundred and twenty-one patients had HQ and 96 LQ CTA. Sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of the DLM in patients with high-quality image for detecting ≥50% stenoses were 100, 62, 59, 100, and 75% at the patient level and 98, 79, 57, 99, and 84% at the vessel level, respectively. Sensitivity was lower (79%) for detecting ≥50% stenoses at the vessel level in patients with low-quality image. Diagnostic accuracy was 84% in both groups. All 12 patients with acute coronary syndrome (ACS) and stenoses by invasive coronary angiography (ICA) were rated 3 by the DLM. Conclusion: A DLM demonstrated high NPV for significant coronary artery stenosis in patients with ACP. All patients with ACS and stenoses by ICA were identified by the DLM.

[Benefits and drawbacks of CT scan as a triple rule-out exam in acute chest pain to exclude acute coronary syndrome, pulmonary embolism and aortic dissection]. / Le scanner thoracique injecté « triple-rule-out ¼ a-t-il un intérêt dans le bilan des douleurs thoraciques aiguës pour éliminer le syndrome coronaire aigu, l'embolie pulmonaire et la dissection aortique ?

Chest pain is one of the major causes for admission in the Emergency Room in most countries and one of the principal reasons for urgent consultation with a cardiologist or a general practitioner. After clinical examination and initial biological measurements, substantial patients require further explorations. CT scan allows the search for pulmonary embolism in the early stage of pulmonary arteries iodine contrast exploration. During the same exam at the systemic arterial phase, the search for aortic dissection or coronary artery disease is possible while exploring the later contrast in the aortic artery. This triple rule-out exam allows correct diagnosis in case of acute chest pain with suspected pulmonary embolism, aortic dissection and other acute aortic syndromes or acute coronary syndrome. But X-rays are substantially increased as well as iodine contrast agent quantity while exam quality is globally decreased. Artificial intelligence may play an important role in the development of this protocol.

Identification of a Ubiquinone-Ubiquinol Quinhydrone Complex in Bacterial Photosynthetic Membranes and Isolated Reaction Centers by Time-Resolved Infrared Spectroscopy.

Ubiquinone redox chemistry is of fundamental importance in biochemistry, notably in bioenergetics. The bi-electronic reduction of ubiquinone to ubiquinol has been widely studied, including by Fourier transform infrared (FTIR) difference spectroscopy, in several systems. In this paper, we have recorded static and time-resolved FTIR difference spectra reflecting light-induced ubiquinone reduction to ubiquinol in bacterial photosynthetic membranes and in detergent-isolated photosynthetic bacterial reaction centers. We found compelling evidence that in both systems under strong light illumination-and also in detergent-isolated reaction centers after two saturating flashes-a ubiquinone-ubiquinol charge-transfer quinhydrone complex, characterized by a characteristic band at ~1565 cm-1, can be formed. Quantum chemistry calculations confirmed that such a band is due to formation of a quinhydrone complex. We propose that the formation of such a complex takes place when Q and QH2 are forced, by spatial constraints, to share a common limited space as, for instance, in detergent micelles, or when an incoming quinone from the pool meets, in the channel for quinone/quinol exchange at the QB site, a quinol coming out. This latter situation can take place both in isolated and membrane bound reaction centers Possible consequences of the formation of this charge-transfer complex under physiological conditions are discussed.

Capture and immobilization of gaseous ruthenium tetroxide RuO4 in the UiO-66-NH2 metal-organic framework.

106Ru is a radioactive isotope usually generated by the nuclear industry within power plant reactors. During a nuclear accident, 106Ru reacts with oxygen, leading to the production of highly volatile ruthenium tetroxide RuO4. The combination of volatility and radioactivity makes 106RuO4, one of the most radiotoxic species and justifies the development of a specific setup for its capture and immobilization. In this study, we report for the first time the capture and immobilization of gaseous RuO4 within a porous metal-organic framework (UiO-66-NH2). We used specific installation for the production of gaseous RuO4 as well as for the quantification of this gas trapped within the filtering medium. We proved that UiO-66-NH2 has remarkable affinity for RuO4 capture, as this MOF exhibited the worldwide highest RuO4 decontamination factor (DF of 5745), hundreds of times higher than the DF values of sorbents daily used by the nuclear industry (zeolites or activated charcoal). The efficiency of UiO-66-NH2 can be explained by its pore diameters well adapted to the capture and immobilization of RuO4 as well as its conversion into stable RuO2 within the pores. This conversion corresponds to the reactivity of RuO4 with the MOF organic sub-network, leading to the oxidation of terephthalate ligands. As proved by powder X-ray diffraction and NMR techniques, these modifications did not decompose the MOF structure.

[Heart imaging by CT-scan and MRI in cardiac tumors]. / L'imagerie en coupe dans le bilan des tumeurs intra-cardiaques.

The etiology of cardiac masses is often oncological or thrombotic, rarely inflammatory. Among heart tumors, the vast majority are metastatic. We describe the most frequent benign primary cardiac tumors and the most frequent malignant primary cardiac tumors and give information about the advantages of using a multi-modality approach for the accurate diagnosis of a cardiac mass using Computed Tomography Scanner and Magnetic Resonance Investigation.

Grading mitral regurgitation using 4D flow CMR: Comparison to transthoracic echocardiography.

OBJECTIVES: To determine the 4D Flow Cardiac Magnetic Resonance (CMR) thresholds that achieve the best agreement with transthoracic echocardiography (TTE) for grading mitral regurgitation (MR). METHODS: We conducted a single-center prospective study of patients evaluated for chronic primary MR in 2016-2020. MR was evaluated blindly by TTE and 4D Flow CMR, respectively by two cardiologists and two radiologists with decades of experience. MR was graded with both methods as mild, moderate, or severe. 4D Flow CMR measurements included MR regurgitant volume per beat (RV) and mitral anterograde flow per beat (MF). RF was obtained as the ratio RV/MF. Additionally, MF was compared to left ventricular stroke volume (LVSV) by cine-CMR. RESULTS: We included 33 patients in the initial cohort and 33 in the validation cohort. Inter-observer agreement was excellent for 4D Flow CMR ICC = .94 (95% CI, .86-.97, p < 0.0001). Using recommended TTE thresholds (30 ml, 60 ml, 30%, 50%), agreement was moderate for RV and RF. The best agreement between 4D Flow CMR and TTE was obtained with CMR thresholds of 20 and 40 ml for RV (κ = .93; 95% CI, .8-1) and 20% and 37% for RF (κ = .90; 95% CI, .7-.9). In the validation cohort, agreement between TTE and 4D Flow CMR was good with the optimal thresholds (κ = .78; 95% CI, .61-.94). CONCLUSION: We propose CMR thresholds that provide a good agreement between TTE and CMR for grading MR. Further studies are needed to fully validate 4D-Flow CMR accuracy for primary MR quantification.

Characterizing the ZrC(111)/c-ZrO2(111) Hetero-Ceramic Interface: First Principles DFT and Atomistic Thermodynamic Modeling.

The mechanical and physical properties of zirconium carbide (ZrC) are limited to its ability to deteriorate in oxidizing environments. Low refractory oxides are typically formed as layers on ZrC surfaces when exposed to the slightest concentrations of oxygen. However, this carbide has a wide range of applications in nuclear reactor lines and nozzle flaps in the aerospace industry, just to name a few. To develop mechanically strong and oxygen-resistant ZrC materials, the need for studying and characterizing the oxidized layers, with emphasis on the interfacial structure between ZrC and the oxidized phases, cannot be understated. In this paper, the ZrC(111)//c-ZrO2 (111) interface was studied by both finite temperature molecular dynamic simulation and DFT. The interfacial mechanical properties were characterized by the work of adhesion which revealed a Zr|OO|Zr|OO//ZrC(111) interface model as the most stable with an oxygen layer from ZrO2 being deposited on the ZrC(111) surface. Further structural analysis at the interface showed a crack in the first ZrO2 layer at the interfacial region. Investigations of the electronic structure using the density of state calculations and Bader charge analysis revealed the interfacial properties as local effects with no significant impacts in the bulk regions of the interface slab.

Capture of Gaseous Iodine in Isoreticular Zirconium-Based UiO-n Metal-Organic Frameworks: Influence of Amino Functionalization, DFT Calculations, Raman and EPR Spectroscopic Investigation.

A series of Zr-based UiO-n MOF materials (n=66, 67, 68) have been studied for iodine capture. Gaseous iodine adsorption was collected kinetically from a home-made set-up allowing the continuous measurement of iodine content trapped within UiO-n compounds, with organic functionalities (-H, -CH3 , -Cl, -Br, -(OH)2 , -NO2 , -NH2 , (-NH2 )2 , -CH2 NH2 ) by in-situ UV-Vis spectroscopy. This study emphasizes the role of the amino groups attached to the aromatic rings of the ligands connecting the {Zr6 O4 (OH)4 } brick. In particular, the preferential interaction of iodine with lone-pair groups, such as amino functions, has been experimentally observed and is also based on DFT calculations. Indeed, higher iodine contents were systematically measured for amino-functionalized UiO-66 or UiO-67, compared to the pristine material (up to 1211 mg/g for UiO-67-(NH2 )2 ). However, DFT calculations revealed the highest computed interaction energies for alkylamine groups (-CH2 NH2 ) in UiO-67 (-128.5 kJ/mol for the octahedral cavity), and pointed out the influence of this specific functionality compared with that of an aromatic amine. The encapsulation of iodine within the pore system of UiO-n materials and their amino-derivatives has been analyzed by UV-Vis and Raman spectroscopy. We showed that a systematic conversion of molecular iodine (I2 ) species into anionic I- ones, stabilized as I- ⋅⋅⋅I2 or I3 - complexes within the MOF cavities, occurs when I2 @UiO-n samples are left in ambient light.

Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection.

PURPOSE: The purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS). MATERIALS AND METHODS: The DLM was trained with 10,800 curved multiplanar reformatted (cMPR) CCTA images classified by an expert radiologist using the CAD-RADS. For each of the three main coronary arteries, nine cMPR images 40° apart acquired around each arterial circumference were then classified by the DLM using the highest probability. For the validation set composed of 159 arteries from 53 consecutive patients, the images were read by two senior and two junior readers; consensus of the two seniors was the reference standard. With the DLM, the majority vote for the nine images was used to classify each artery. Three groups (CAD-RADS 0, 1-2, or 3-4-5) and 2 groups CAD-RADS 0-1-2 or 3-4-5 (<50% vs. ≥50% stenosis) were used for comparisons with readers and consensus. Performance of the model and readers was compared to the consensus reading using the intraclass coefficient (ICC) and Cohen's kappa coefficient at the artery and patient levels. RESULTS: With the three groups at the artery level, the ICC of the DLM was 0.82 (95% CI: 0.75-0.88) and not significantly different from those of 3/4 readers; accuracy was 81%. With the binary classification, Cohen kappa coefficient of the DLM was 0.85 (95% CI: 0.69-0.94) and not significantly different from that of 3/4 readers; accuracy was 96%. At the patient level, sensitivity and specificity were 93% and 97% respectively, and the negative predictive value was 97%. CONCLUSION: The DLM detected ≥50% stenoses with performances similar to those achieved by senior radiologists.

Aortic Root Anatomy Is Related to the Bicuspid Aortic Valve Phenotype.

BACKGROUND: Bicuspid aortic valve (BAV) is associated with an asymmetric (not circular) aortic root, resulting in variability in the aortic root diameter measurements obtained using different techniques. The objective of this study was to describe aortic root asymmetry, including its orientation in the thorax, in relation to the various phenotypes of BAV and its impact on aortic root diameter measurements obtained using transthoracic echocardiography. METHODS: Aortic root asymmetry, orientation of the largest root diameter, and orientation of the valve opening were studied using computed tomographic scans of patients with BAV without significant aortic valve dysfunction referred for evaluation of a thoracic aortic aneurysm. Eighty-five patients with BAV were evaluated; BAV with fusion of the left and the right coronary cusps (L-R BAV), with or without raphe (n = 63), was compared with BAV with fusion of the right coronary and noncoronary cusps (N-R BAV), with or without raphe (n = 22). RESULTS: Asymmetry of the aortic root and its orientation in the thorax can be predicted from BAV phenotype: orientation of the valve opening differed from orientation of the largest root diameter by nearly 75° in both groups. The angle of the largest root diameter with the reference sagittal plane was 64.3° in the L-R BAV group versus 143.1° in the N-R BAV group (P < .0001). Therefore, using the parasternal long-axis view on transthoracic echocardiography, in N-R BAV, the ultrasound beam is roughly parallel to the valve opening orientation and almost orthogonal to the maximum diameter of the root. On the contrary, in L-R BAV, the ultrasound beam is roughly perpendicular to the valve opening orientation and almost parallel to the maximum diameter of the root. Consequently, the parasternal long-axis view on transthoracic echocardiography significantly underestimates maximal aortic root diameter in N-R BAV and modestly underestimates root diameter in L-R BAV (-6.1 ± 0.96 vs -2.3 ± 0.47 mm, P = .0008). CONCLUSIONS: Aortic root morphology in patients with BAV can be predicted by BAV phenotype: the largest root diameter is roughly perpendicular to the orientation of the valve opening. Therefore, echocardiographic measurements according to present recommendations (parasternal long-axis view) underestimate maximal diameter in patients with N-R BAV.