157 research outputs found

    Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients

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    Purpose: Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF) extraction. Failure to accurately identify the left ventricle (LV) prevents AIF estimation required for quantification, therefore high detection accuracy is required. This study presents a robust LV detection method using the convolutional neural network (CNN). Methods: CNN models were trained by assembling 25,027 scans (N = 12,984 patients) from three hospitals, seven scanners. Performance was evaluated using a hold‐out test set of 5721 scans (N = 2805 patients). Model inputs were a time series of AIF images (2D+T). Two variations were investigated: (1) two classes (2CS) for background and foreground (LV mask), and (2) three classes (3CS) for background, LV, and RV. The final model was deployed on MRI scanners using the Gadgetron reconstruction software framework. Results: Model loading on the MRI scanner took ~340 ms and applying the model took ~180 ms. The 3CS model successfully detected the LV in 99.98% of all test cases (1 failure out of 5721). The mean Dice ratio for 3CS was 0.87 ± 0.08 with 92.0% of all cases having Dice >0.75. The 2CS model gave a lower Dice ratio of 0.82 ± 0.22 (P .2) comparing automatically extracted AIF signals with signals from manually drawn contours. Conclusions: A CNN‐based solution to detect the LV blood pool from the arterial input function image series was developed, validated, and deployed. A high LV detection accuracy of 99.98% was achieved

    Non-invasive Ischaemia Testing in Patients With Prior Coronary Artery Bypass Graft Surgery: Technical Challenges, Limitations, and Future Directions

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    Coronary artery bypass graft (CABG) surgery effectively relieves symptoms and improves outcomes. However, patients undergoing CABG surgery typically have advanced coronary atherosclerotic disease and remain at high risk for symptom recurrence and adverse events. Functional non-invasive testing for ischaemia is commonly used as a gatekeeper for invasive coronary and graft angiography, and for guiding subsequent revascularisation decisions. However, performing and interpreting non-invasive ischaemia testing in patients post CABG is challenging, irrespective of the imaging modality used. Multiple factors including advanced multi-vessel native vessel disease, variability in coronary hemodynamics post-surgery, differences in graft lengths and vasomotor properties, and complex myocardial scar morphology are only some of the pathophysiological mechanisms that complicate ischaemia evaluation in this patient population. Systematic assessment of the impact of these challenges in relation to each imaging modality may help optimize diagnostic test selection by incorporating clinical information and individual patient characteristics. At the same time, recent technological advances in cardiac imaging including improvements in image quality, wider availability of quantitative techniques for measuring myocardial blood flow and the introduction of artificial intelligence-based approaches for image analysis offer the opportunity to re-evaluate the value of ischaemia testing, providing new insights into the pathophysiological processes that determine outcomes in this patient population

    A comparison of standard and high dose adenosine protocols in routine vasodilator stress cardiovascular magnetic resonance: dosage affects hyperaemic myocardial blood flow in patients with severe left ventricular systolic impairment

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    Background: Adenosine stress perfusion cardiovascular magnetic resonance (CMR) is commonly used in the assessment of patients with suspected ischaemia. Accepted protocols recommend administration of adenosine at a dose of 140 µg/kg/min increased up to 210 µg/kg/min if required. Conventionally, adequate stress has been assessed using change in heart rate, however, recent studies have suggested that these peripheral measurements may not reflect hyperaemia and can be blunted, in particular, in patients with heart failure. This study looked to compare stress myocardial blood flow (MBF) and haemodynamic response with different dosing regimens of adenosine during stress perfusion CMR in patients and healthy controls. Methods: 20 healthy adult subjects were recruited as controls to compare 3 adenosine perfusion protocols: standard dose (140 µg/kg/min for 4 min), high dose (210 µg/kg/min for 4 min) and long dose (140 µg/kg/min for 8 min). 60 patients with either known or suspected coronary artery disease (CAD) or with heart failure and different degrees of left ventricular (LV) dysfunction underwent adenosine stress with standard and high dose adenosine within the same scan. All studies were carried out on a 3 T CMR scanner. Quantitative global myocardial perfusion and haemodynamic response were compared between doses. Results: In healthy controls, no significant difference was seen in stress MBF between the 3 protocols. In patients with known or suspected CAD, and those with heart failure and mild systolic impairment (LV ejection fraction (LVEF) ≥ 40%) no significant difference was seen in stress MBF between standard and high dose adenosine. In those with LVEF < 40%, there was a significantly higher stress MBF following high dose adenosine compared to standard dose (1.33 ± 0.46 vs 1.10 ± 0.47 ml/g/min, p = 0.004). Non-responders to standard dose adenosine (defined by an increase in heart rate (HR) < 10 bpm) had a significantly higher stress HR following high dose (75 ± 12 vs 70 ± 14 bpm, p = 0.034), but showed no significant difference in stress MBF. Conclusions: Increasing adenosine dose from 140 to 210 µg/kg/min leads to increased stress MBF in patients with significantly impaired LV systolic function. Adenosine dose in clinical perfusion assessment may need to be increased in these patients

    Measurement of T1 Mapping in Patients With Cardiac Devices: Off-Resonance Error Extends Beyond Visual Artifact but Can Be Quantified and Corrected

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    Background: Measurement of myocardial T1 is increasingly incorporated into standard cardiovascular magnetic resonance (CMR) protocols, however accuracy may be reduced in patients with metallic cardiovascular implants. Measurement is feasible in segments free from visual artifact, but there may still be off-resonance induced error. Aim: To quantify off-resonance induced T1 error in patients with metallic cardiovascular implants, and validate a method for error correction for a conventional MOLLI pulse sequence. Methods: Twenty-four patients with cardiac implantable electronic devices (CIEDs: 46% permanent pacemakers, PPMs; 33% implantable loop recorders, ILRs; and 21% implantable cardioverter-defibrillators, ICDs); and 31 patients with aortic valve replacement (AVR) (45% metallic) were studied. Paired mid-myocardial short-axis MOLLI and single breath-hold off-resonance field maps were acquired at 1.5 T. T1 values were measured by AHA segment, and segments with visual artifact were excluded. T1 correction was applied using a published relationship between off-resonance and T1. The accuracy of the correction was assessed in 10 healthy volunteers by measuring T1 before and after external placement of an ICD generator next to the chest to generate off-resonance. Results: T1 values in healthy volunteers with an ICD were underestimated compared to without (967 ± 52 vs. 997 ± 26 ms respectively, p = 0.0001), but were similar after correction (p = 0.57, residual difference 2 ± 27 ms). Artifact was visible in 4 ± 12, 42 ± 31, and 53 ± 27% of AHA segments in patients with ILRs, PPMs, and ICDs, respectively. In segments without artifact, T1 was underestimated by 63 ms (interquartile range: 7–143) per patient. The greatest error for patients with ILRs, PPMs and ICDs were 79, 146, and 191 ms, respectively. The presence of an AVR did not generate T1 error. Conclusion: Even when there is no visual artifact, there is error in T1 in patients with CIEDs, but not AVRs. Off-resonance field map acquisition can detect error in measured T1, and a correction can be applied to quantify T1 MOLLI accurately

    Quantitative cardiovascular magnetic resonance myocardial perfusion mapping to assess hyperaemic response to adenosine stress

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    AIMS: Assessment of hyperaemia during adenosine stress cardiovascular magnetic resonance (CMR) remains a clinical challenge with lack of a gold-standard non-invasive clinical marker to confirm hyperaemic response. This study aimed to validate maximum stress myocardial blood flow (SMBF) measured using quantitative perfusion mapping for assessment of hyperaemic response and compare this to current clinical markers of adenosine stress. METHODS AND RESULTS: Two hundred and eighteen subjects underwent adenosine stress CMR. A derivation cohort (22 volunteers) was used to identify a SMBF threshold value for hyperaemia. This was tested in a validation cohort (37 patients with suspected coronary artery disease) who underwent invasive coronary physiology assessment on the same day as CMR. A clinical cohort (159 patients) was used to compare SMBF to other physiological markers of hyperaemia [splenic switch-off (SSO), heart rate response (HRR), and blood pressure (BP) fall]. A minimum SMBF threshold of 1.43 mL/g/min was derived from volunteer scans. All patients in the coronary physiology cohort demonstrated regional maximum SMBF (SMBFmax) >1.43 mL/g/min and invasive evidence of hyperaemia. Of the clinical cohort, 93% had hyperaemia defined by perfusion mapping compared to 71% using SSO and 81% using HRR. There was no difference in SMBFmax in those with or without SSO (2.58 ± 0.89 vs. 2.54 ± 1.04 mL/g/min, P = 0.84) but those with HRR had significantly higher SMBFmax (2.66 1.86 mL/g/min, P 15 bpm was superior to SSO in predicting adequate increase in SMBF (AUC 0.87 vs. 0.62, P < 0.001). CONCLUSION: Adenosine-induced increase in myocardial blood flow is accurate for confirmation of hyperaemia during stress CMR studies and is superior to traditional, clinically used markers of adequate stress such as SSO and BP response

    Two-Minute k-Space and Time–accelerated Aortic Four-dimensional Flow MRI: Dual-Center Study of Feasibility and Impact on Velocity and Wall Shear Stress Quantification

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    PURPOSE: To investigate the two-center feasibility of highly k-space and time (k-t)–accelerated 2-minute aortic four-dimensional (4D) flow MRI and to evaluate its performance for the quantification of velocities and wall shear stress (WSS). MATERIALS AND METHODS: This cross-sectional study prospectively included 68 participants (center 1, 11 healthy volunteers [mean age ± standard deviation, 61 years ± 15] and 16 patients with aortic disease [mean age, 60 years ± 10]; center 2, 14 healthy volunteers [mean age, 38 years ± 13] and 27 patients with aortic or cardiac disease [mean age, 78 years ± 18]). Each participant underwent highly accelerated 4D flow MRI (k-t acceleration, acceleration factor of 5) of the thoracic aorta. For comparison, conventional 4D flow MRI (acceleration factor of 2) was acquired in the participants at center 1 (n = 27). Regional aortic peak systolic velocities and three-dimensional WSS were quantified. RESULTS: k-t–accelerated scan times (center 1, 2:03 minutes ± 0:29; center 2, 2:06 minutes ± 0:20) were significantly reduced compared with conventional 4D flow MRI (center 1, 12:38 minutes ± 2:25; P < .0001). Overall good agreement was found between the two techniques (absolute differences ≤15%), but proximal aortic WSS was significantly underestimated in patients by using k-t–accelerated 4D flow when compared with conventional 4D flow (P ≤ .03). k-t–accelerated 4D flow MRI was reproducible (intra- and interobserver intraclass correlation coefficient ≥0.98) and identified significantly increased peak velocities and WSS in patients with stenotic (P ≤ .003) or bicuspid (P ≤ .04) aortic valves compared with healthy volunteers. In addition, k-t–accelerated 4D flow MRI–derived velocities and WSS were inversely related to age (r ≥−0.53; P ≤ .03) over all healthy volunteers. CONCLUSION: k-t–accelerated aortic 4D flow MRI providing 2-minute scan times was feasible and reproducible at two centers. Although consistent healthy aging- and disease-related changes in aortic hemodynamics were observed, care should be taken when considering WSS, which can be underestimated in patients

    Assessment of Multivessel Coronary Artery Disease Using Cardiovascular Magnetic Resonance Pixelwise Quantitative Perfusion Mapping

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    OBJECTIVES: The authors sought to compare the diagnostic accuracy of quantitative perfusion maps to visual assessment (VA) of first-pass perfusion images for the detection of multivessel coronary artery disease (MVCAD). BACKGROUND: VA of first-pass stress perfusion cardiac magnetic resonance (CMR) may underestimate ischemia in MVCAD. Pixelwise perfusion mapping allows quantitative measurement of regional myocardial blood flow, which may improve ischemia detection in MVCAD. METHODS: One hundred fifty-one subjects recruited at 2 centers underwent stress perfusion CMR with myocardial perfusion mapping, and invasive coronary angiography with coronary physiology assessment. Ischemic burden was assessed by VA of first-pass images and by quantitative measurement of stress myocardial blood flow using perfusion maps. RESULTS: In patients with MVCAD (2-vessel [2VD] or 3-vessel disease [3VD]; n = 95), perfusion mapping identified significantly more segments with perfusion defects (median segments per patient 12 [interquartile range (IQR): 9 to 16] by mapping vs. 8 [IQR: 5 to 9.5] by VA; p < 0.001). Ischemic burden (IB) measured using mapping was higher in MVCAD compared with IB measured using VA (3VD mapping 100 % (75% to 100%) vs. first-pass 56% (38% to 81%) ; 2VD mapping 63% (50% to 75%) vs. first-pass 41% (31% to 50%); both p < 0.001), but there was no difference in single-vessel disease (mapping 25% (13% to 44%) vs. 25% (13% to 31%). Perfusion mapping was superior to VA for the correct identification of extent of coronary disease (78% vs. 58%; p < 0.001) due to better identification of 3VD (87% vs. 40%) and 2VD (71% vs. 48%). CONCLUSIONS: VA of first-pass stress perfusion underestimates ischemic burden in MVCAD. Pixelwise quantitative perfusion mapping increases the accuracy of CMR in correctly identifying extent of coronary disease. This has important implications for assessment of ischemia and therapeutic decision-making
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