Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance

Villa, Adriana; Corsinovi, Laura; Ntalas, Ioannis; Milidonis, Xenios; Scannell, Cian M.; Giovine, Gabriella Di; Child, Nicholas; Ferreira, Catarina; Nazir, Muhummad Sohaib; Karády, Júlia; Eshja, Esmeralda; Viola, Francesco; Bettencourt, Nuno; Schuster, Andreas; Ismail, Tevfik F; Razavi, Reza; Chiribiri, Amedeo

doi:10.1186/s12968-018-0493-4

Cited by 39 publications

(30 citation statements)

References 41 publications

(54 reference statements)

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“…Myocardial perfusion can be assessed using dynamic MRI during first-pass of a contrast agent 1 . While the images are routinely reviewed visually in the clinic, quantification is desirable as it is user-independent 2 . Quantification of myocardial perfusion can be challenging mainly due to the non-linearity between the signal intensity and contrast agent concentration at concentrations necessary to observe potential perfusion abnormalities within the myocardium 3 .…”

Section: Introductionmentioning

confidence: 99%

“…However, the true AIF is affected by the non-linear response of the saturation recovery signal and T2*-related losses at high contrast agent concentrations in the blood pool. To ameliorate these effects, the dual-bolus 21 and dual-sequence 2 imaging strategies have been proposed. The dual-bolus method uses a low dose bolus to measure the AIF and a high dose bolus for myocardial analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The mDixon MRI acquisitions also provide fat-free diagnostic images, thereby avoiding the need for a fat-suppression pulse to null the signal from (epicardial) fat. In myocardial perfusion MRI fat suppression is important 2,20 to minimise potential partial volume effects at the myocardial-epicardial border and to improve the accuracy of myocardial blood flow quantification. Moreover, accurate myocardial perfusion quantification depends on the accurate measurement of the arterial input function (AIF).…”

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confidence: 99%

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Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Scannell

Correia

Villa

et al. 2020

Sci Rep

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Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. in this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. the Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. this is followed by a second non-linear correction step using the Dixon water images to remove residual motion. the proposed Dixon motion correction technique was compared to the stateof-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images. Myocardial perfusion can be assessed using dynamic MRI during first-pass of a contrast agent 1. While the images are routinely reviewed visually in the clinic, quantification is desirable as it is user-independent 2. Quantification of myocardial perfusion can be challenging mainly due to the non-linearity between the signal intensity and contrast agent concentration at concentrations necessary to observe potential perfusion abnormalities within the myocardium 3. Furthermore, respiratory motion makes the quantification of perfusion difficult, and may even preclude it. Although most commonly used to mitigate respiratory motion, breath-holding for at least 40 s (to cover the full first pass of the contrast bolus) is challenging for many patients, particularly under stress conditions. Moreover, long breath-holds can lead to changes in heart rate, resulting in images being acquired at slightly different cardiac phases thus introducing cardiac motion. Conversely, if patients are allowed to breathe normally, respiratory motion tends to be more regular and shallower when compared to the large gasps that may occur when the patient can no longer hold their breath. Therefore, acquisitions in free-breathing are easier to correct for respiratory motion using image registration compared to breath-holding with intermittent breathing. The problem of registering myocardial perfusion MR images is, however, challenging due to the rapid change in signal intensity during the contrast bolus transit. The dynamic contrast-enhancement invalidate...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Scannell

Correia

Villa

et al. 2020

Sci Rep

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“…assessed visually; however, this requires extensive training and the diagnostic accuracy depends strongly on the operator [4]. As was first suggested more than 20 years ago, it is possible to quantitatively analyse myocardial perfusion in units of • −1 • −1 using CMR [5], [6] through the application of the indicator-dilution theory [7], [8].…”

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confidence: 99%

Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data

Scannell

Villa

Lee

et al. 2019

IEEE Trans. Med. Imaging

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“…Underperfused regions in the myocardium are reliable indicators of the presence of significant coronary artery disease, making first-pass CMR a widely used tool in the diagnosis, management, and prognosis of cardiovascular disease [33]. While qualitative first-pass contrast-enhanced CMR is a well-established, noninvasive technique used in the assessment of myocardial perfusion, quantitative estimation of myocardial perfusion is challenging and therefore has not been established in clinical routine but is the subject of active, ongoing research [25,24,1,17,57,42,29,21,60]. A quantitative approach allows for observer-independent and reproducible evaluation of the perfusion [60].…”

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confidence: 99%

Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging

Lehnert¹,

Kolbitsch²,

Wübbeler³

et al. 2019

SIAM J. Imaging Sci.

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We develop a hierarchical Bayesian approach for the inference of large-scale spatial-temporal regression as often encountered in the analysis of imaging data. For each spatial location a linear temporal Gaussian regression model is considered. Large-scale refers to the large number of spatially distributed regression parameters to be inferred. The spatial distribution of the sought regression parameters, which typically represent physical quantities, is assumed to be smooth and bounded from below. Truncated, intrinsic Gaussian Markov random field priors are employed to express this prior knowledge. The dimensionality of the spatially distributed parameters is high, which challenges the calculation of results using standard Markov chain Monte Carlo procedures. A calculation scheme is developed that utilizes an approximate analytical expression for the marginal posterior of the amount of smoothness and the strength of the noise in the data, in conjunction with a conditional high-dimensional truncated Gaussian distribution for the spatial distribution of the regression parameters. We prove propriety of the posterior and explore the existence of its moments. The proposed approach is applicable to high-dimensional imaging problems arising in the use of Gaussian Markov random field priors for inferring spatially distributed parameters from spatial or spatial-temporal data. We demonstrate its application for the quantification of myocardial perfusion imaging from first-pass contrast-enhanced cardiovascular magnetic resonance data. The Bayesian approach allows for a quantification of perfusion, including a complete characterization of uncertainties which is desirable in diagnostics. It therefore provides not just a perfusion estimate but also a measure for how reliable this estimate is.

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Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance

Cited by 39 publications

References 41 publications

Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging

Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data

Large-Scale Bayesian Spatial-Temporal Regression with Application to Cardiac MR-Perfusion Imaging

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