Myocardial blood flow quantification with MRI by model‐independent deconvolution

Jerosch‐Herold, Michael; Swingen, Cory; Seethamraju, Ravi

doi:10.1118/1.1473135

Cited by 246 publications

(245 citation statements)

References 38 publications

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“…Quantitative perfusion analysis was performed as previously described9; briefly, MBF was determined independently at baseline and under adenosine stress by model‐independent deconvolution of signal intensity curves with an arterial input function measured in the LV blood pool, with explicit accounting for any delay in the arrival of the tracer. Fitting quality of MBF was assessed in a blinded fashion for each myocardial segment; MBF values derived from segments with good fitting were averaged to derive a global per‐subject MBF value, which was used for all analyses.…”

Section: Methodsmentioning

confidence: 99%

“…Cardiac magnetic resonance (CMR) is a validated, noninvasive, and accurate method for detecting myocardial hypoperfusion8 and can also quantify myocardial blood flow (MBF) 9. We hypothesized that CMR would demonstrate microcirculatory dysfunction and impaired myocardial perfusion in patients with paroxysmal or persistent AF who have no significant epicardial coronary artery disease or diabetes mellitus and that these findings would relate to both left atrial (LA) dysfunction and the reduction in left ventricular (LV) function that is present even when the ventricular rate is well controlled 10, 11.…”

Section: Introductionmentioning

confidence: 99%

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Myocardial Perfusion Is Impaired and Relates to Cardiac Dysfunction in Patients With Atrial Fibrillation Both Before and After Successful Catheter Ablation

Wijesurendra

Liu

Notaristefano

et al. 2018

JAHA

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BackgroundAtrial fibrillation (AF) is associated with myocardial infarction, and patients with AF and no obstructive coronary artery disease can present with symptoms and evidence of cardiac ischemia. We hypothesized that microvascular coronary dysfunction underlies these observations.Methods and ResultsMyocardial blood flow (MBF) at baseline and during adenosine stress and left ventricular and left atrial function were evaluated by magnetic resonance in 49 patients with AF (25 paroxysmal, 24 persistent) with no history of epicardial coronary artery disease or diabetes mellitus, before and 6 to 9 months after ablation. Findings were compared with those obtained in matched controls in sinus rhythm (n=25). Before ablation, patients with AF had impaired left atrial function and left ventricular ejection fraction and strain indices (all P<0.05 versus controls). MBF was impaired in patients both under baseline conditions (1.21±0.24 mL/min per g·[mm Hg·bpm/104]−1 versus 1.34±0.28 mL/min per g·[mm Hg·bpm/104]−1 in controls, P=0.044) and during adenosine stress (2.29±0.48 mL/min per g versus 2.73±0.37 mL/min per g in controls, P<0.001). Under baseline conditions, MBF correlated with left ventricular strain and left atrial function (all P≤0.001), so that cardiac function was most impaired in patients with the lowest MBF. Baseline and stress MBF remained unchanged postablation (both P=ns), and baseline MBF showed similar correlations with functional indices to those present preablation (all P≤0.001).ConclusionsBaseline and stress MBF are significantly impaired in patients with AF but no epicardial coronary artery disease. Reduction in MBF is proportional to severity of left ventricular and left atrial dysfunction, even after successful ablation. Coronary microvascular dysfunction may be a relevant pathophysiological mechanism in patients with a history of AF.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Myocardial Perfusion Is Impaired and Relates to Cardiac Dysfunction in Patients With Atrial Fibrillation Both Before and After Successful Catheter Ablation

Wijesurendra

Liu

Notaristefano

et al. 2018

JAHA

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“…Various models have been proposed, and the first-order difference operator (L 1 , a bidiagonal matrix (see Eq. [7] below)) is commonly used to reduce the oscillations in r (14,15). After an initial comparison between the solutions obtained with different-order difference operators (L i ) for the R lor (t) model (note that since the derivatives of an exponential function are proportional to the function, all of the L i matrices give similar results for the R exp (t) model), the first-order difference operator L 1 was chosen because it produced the closest solution to the simulated model (data not shown).…”

Section: Tikhonov Regularizationmentioning

confidence: 99%

“…In fact, the commonly used SVD approach introduced by Østergaard et al (8) corresponds to the regularization method known as truncated SVD (TSVD) (14). Different regularization methods have been used, with various degrees of success, for the quantification of CBF (8,10), and myocardial blood flow (15). The present work describes the implementation of Tikhonov regularization with the L-curve criterion (14) to quantify CBF and obtain a better characterization of R(t).…”

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confidence: 99%

Quantification of bolus‐tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization

Calamante

Gadian

Connelly

2003

Magnetic Resonance in Med

123

129

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Quantification of cerebral blood flow (CBF) and the tissue residue function (R) using bolus-tracking MRI requires deconvolution of the arterial input function (AIF). Currently, the most commonly used deconvolution method is singular value decomposition (SVD), which has been shown to produce accurate estimations of CBF. However, this method introduces unwanted oscillations in the time course of R, and there are situations in which the actual shape is of interest (e.g., in calculating flow heterogeneity and assessing bolus dispersion). In such cases, the conventional SVD method may no longer be suitable, and an alternative approach may be required. This work describes the implementation of Tikhonov regularization with the L-curve criterion to quantify CBF and obtain a better characterization of R. The methodology is tested on simulated and patient data, and the results are compared to those found using the conventional SVD approach. Although both methods produce similar CBF values, the deconvolved R shape obtained using SVD is dominated by oscillations and fails to characterize the shape in the presence of dispersion. On the other hand, the use of the proposed regularization method improves the char- Key words: perfusion; dynamic susceptibility contrast MRI; deconvolution; singular value decomposition; arterial input function Bolus-tracking MRI, also known as dynamic susceptibility contrast MRI, is becoming a well-established technique for measuring cerebral blood flow (CBF) (1). The most important clinical application by far is in the investigation of cerebral ischemia. Although some limitations have been reported (2,3), this technique is playing an increasingly important role in the diagnosis, assessment, and management of acute stroke patients (4 -7).The fundamental equation for CBF quantification is (8):where C(t) is the tissue concentration time curve, C a (t) is the arterial input function (AIF; i.e., the concentration of contrast entering the tissue of interest at time t), R(t) is the tissue residue function (i.e., the fraction of contrast agent concentration at time t for the case of an ideal instantaneous bolus injected at t ϭ 0), and the symbol V indicates the convolution operation (1). Bolus-tracking MRI relies on the deconvolution of the AIF to obtain the product function CBF ⅐ R(t), and the calculation of CBF from the initial (or maximum) value of this function (8). Currently, the most common deconvolution method is singular value decomposition (SVD), which has been shown to produce fairly accurate estimations of CBF (8). However, this method introduces unwanted oscillations 1 in the shape of R(t) (9,10), and there are situations in which the actual shape of R(t)-not just its initial value-is of interest. For example, the determination of flow heterogeneity and oxygen delivery relies on characterization of the whole residue function (11). In addition, the presence of bolus dispersion has been shown to be a significant source of error in CBF quantification (11,12), and accurate determination of the sha...

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“…Other models, such as the B-splines in Ref. 15, could possibly be used instead. The basic form of the two-compartment model is given by …”

Section: Model-based Methodsmentioning

confidence: 99%

Model‐based registration for dynamic cardiac perfusion MRI

Adluru

DiBella

Schabel

2006

Magnetic Resonance Imaging

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Purpose:To assess the accuracy of a model-based approach for registration of myocardial dynamic contrastenhanced (DCE)-MRI corrupted by respiratory motion. Materials and Methods:Ten patients were scanned for myocardial perfusion on 3T or 1.5T scanners, and short-and long-axis slices were acquired. Interframe registration was done using an iterative model-based method in conjunction with a mean square difference metric. The method was tested by comparing the absolute motion before and after registration, as determined from manually registered images. Regional flow indices of myocardium calculated from the manually registered data were compared with those obtained with the model-based registration technique. Results:The mean absolute motion of the heart for the short-axis data sets over all the time frames decreased from 5.3 Ϯ 5.2 mm (3.3 Ϯ 3.1 pixels) to 0.8 Ϯ 1.3 mm (0.5 Ϯ 0.7 pixels) in the vertical direction, and from 3.0 Ϯ 3.7 mm (1.7 Ϯ 2.1 pixels) to 0.9 Ϯ 1.2 mm (0.5 Ϯ 0.7 pixels) in the horizontal direction. A mean absolute improvement of 77% over all the data sets was observed in the estimation of the regional perfusion flow indices of the tissue as compared to those obtained from manual registration. Similar results were obtained with two-chamber-view long-axis data sets. Conclusion:The model-based registration method for DCE cardiac data is comparable to manual registration and offers a unique registration method that reduces errors in the quantification of myocardial perfusion parameters as compared to those obtained from manual registration. DYNAMIC MR EVALUATION of myocardial perfusion is becoming a powerful tool for the detection of coronary artery disease (1-3). The standard approach is to rapidly acquire T1-weighted images to track the uptake and washout of the contrast agent Gd-DTPA. Multiple 2D images are acquired each heartbeat. The slices are acquired using ECG-gated sequences so that a given slice is always acquired during the same phase of the cardiac cycle. The time-series data are analyzed visually or with semiquantitative or quantitative models (4,5). Depending on the analysis technique used, at least 20 -40 seconds of data are needed to determine regional perfusion values in the left ventricular (LV) myocardium. Many patients cannot hold their breath long enough to provide a motion-free study. A breathhold of any appreciable length becomes more difficult when pharmacological stress is induced by vasodilating agents, which is necessary for detection of coronary artery disease. Respiration causes motion of the heart, which makes qualitative visual analysis difficult and can cause incorrect estimation of semiquantitative and quantitative parameters.A number of registration methods have been proposed to correct the motion of the heart in dynamic contrast MRI acquisitions (6 -10). In the method of Bidaut and Vallee (6), the mean squared difference between images in the perfusion sequence and a reference image in a region defined by a cardiac mask is minimized. The initial reference image is c...

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Myocardial blood flow quantification with MRI by model‐independent deconvolution

Cited by 246 publications

References 38 publications

Myocardial Perfusion Is Impaired and Relates to Cardiac Dysfunction in Patients With Atrial Fibrillation Both Before and After Successful Catheter Ablation

Myocardial Perfusion Is Impaired and Relates to Cardiac Dysfunction in Patients With Atrial Fibrillation Both Before and After Successful Catheter Ablation

Quantification of bolus‐tracking MRI: Improved characterization of the tissue residue function using Tikhonov regularization

Model‐based registration for dynamic cardiac perfusion MRI

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