Magnetic resonance myocardial first‐pass perfusion imaging: Parameter optimization for signal response and cardiac coverage

Bertschinger, K.; Nanz, Daniel; Buechi, Martin; Luescher, Thomas F; Marinček, Borut; Schulthess, Gustav K. von; Schwitter, Juerg

doi:10.1002/jmri.1219

Cited by 65 publications

(48 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This is the K trans parameter in Eqs. [1]- [3]. The division of the tissue into regions is done on a single frame and the same regions are used for all of the frames.…”

Section: Discussionmentioning

confidence: 99%

“…[3] are chosen such that the chi-squared error between the fit and the original curves is minimized. This form without the time delay term (17) is much faster than standard nonlinear least squares.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…[3] is solved rapidly with a matrix inversion to obtain the three parameters K trans , k ep , and v p , while the time delay is held fixed. A first approximation of the time delay is obtained by using a coarse one-dimensional search grid and recomputing the three fit parameters K trans , k ep , and v p at each delay value.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…The input function C input used in Eq. [3] to generate the model images is obtained in the same way as for the short-axis slices, but from a region chosen in the LV blood pool of the heart. Figure 4 represents the model images for a typical long-axis data set.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…DYNAMIC MR EVALUATION of myocardial perfusion is becoming a powerful tool for the detection of coronary artery disease (1)(2)(3). The standard approach is to rapidly acquire T1-weighted images to track the uptake and washout of the contrast agent Gd-DTPA.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Model‐based registration for dynamic cardiac perfusion MRI

Adluru

DiBella

Schabel

2006

Magnetic Resonance Imaging

View full text Add to dashboard Cite

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...

show abstract

“…This is the K trans parameter in Eqs. [1]- [3]. The division of the tissue into regions is done on a single frame and the same regions are used for all of the frames.…”

Section: Discussionmentioning

confidence: 99%

Section: Model-based Methodsmentioning

confidence: 99%

Section: Model-based Methodsmentioning

confidence: 99%

Section: Model-based Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations