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This paper deals with the problem of tracking cardiac motion and deformation using velocity-encoded magnetic resonance imaging. We expand upon an earlier described method and fit a spatiotemporal motion model to measured velocity data. We investigate several different spatial elements both qualitatively and quantitatively using phantom measurements and data from human subjects. In addition, we also use optical flow estimation by the Horn-Schunk method as complementary data in regions where the velocity measurements are noisy. Our results show that it is possible to obtain good motion tracking accuracy in phantoms with relatively few spatial elements, if the type of element is properly chosen. The use of optical flow can correct some measurement artifacts but may give an underestimation of the magnitude of the deformation. In human subjects the different spatial elements perform quantitatively in a similar way but qualitative differences exists, as shown by a semiquantitative visual scoring of the different methods.

Background Quantitative blood flow and aspects of regional myocardial function such as myocardial displacement and strain can be measured using phase-contrast cardiovascular magnetic resonance (PC-CMR). Since a gadolinium-based contrast agent is often used to measure myocardial infarct size, we sought to determine whether the contrast agent affects measurements of aortic flow and myocardial displacement and strain. Phase-contrast data pre and post contrast agent was acquired during free breathing using 1.5T PC-CMR. Results For aortic flow and regional myocardial function 12 and 17 patients were analysed, respectively. The difference pre and post contrast agent was 0.03 ± 0.16 l/min for cardiac output, and 0.1 ± 0.5 mm for myocardial displacement. Linear regression for myocardial displacement (MD) after and before contrast agent (CA) showed MDpostCA = 0.95MDpreCA+0.05 (r = 0.95, p < 0.001). For regional myocardial function, the contrast-to-noise ratios for left ventricular myocardial wall versus left ventricular lumen were pre and post contrast agent administration 7.4 ± 3.3 and 4.4 ± 8.9, respectively (p < 0.001). The contrast-to-noise ratios for left ventricular myocardial wall versus surrounding tissue were pre and post contrast agent administration -16.9 ± 22 and -0.2 ± 6.3, respectively (p < 0.0001). Conclusions Quantitative measurements of aortic flow yield equal results both in the absence and presence of gadolinium contrast agent. The total examination time may thereby be reduced when assessing both viability and quantitative flow using PC-CMR, by assessing aortic flow post contrast agent administration. Phase-contrast information for myocardial displacement is also assessable both in the absence and presence of contrast agent. However, delineation of the myocardium may be difficult or impossible post contrast agent due to the lower image contrast. Acquisition of myocardial displacement should therefore be performed pre contrast agent using current PC-CMR sequences.

This paper deals with model based regularization of velocity encoded cardiac magnetic resonance images (MRI). We extend upon an existing spatiotemporal model of cardiac kinematics by considering data certainty and regularity of the model in order to improve its performance. The method was evaluated using a computer simulated phantom and using in vivo gridtag MRI as gold standard. We show, both quantitatively and qualitatively, that our modified model performs better than the original one.

Purpose: To develop a fast and highly automated method for calculating two-dimensional myocardial motion and deformation using velocity encoded magnetic resonance imaging. Materials and Methods: Two-dimensional phase contrast magnetic resonance imaging was used to acquire time resolved velocity maps of the myocardium. Cardiac motion was calculated by an iterative integration-regularization scheme of low computational cost. Image segmentation was performed using active appearance models. Results: Validation of motion tracking was performed in N = 47 subjects using saturation grid-tagging and closely followed "tag-lines." Image segmentation was validated vs. manual delineation. Conclusion: The speed and limited user interaction gives the method good potential for use in clinical practice. (Less)

This paper presents a post processing strategy for myocardial velocity fields obtained by phase contrast magnetic resonance imaging. Such data can be used to track cardiac motion and to calculate strain. The method combines data regularization with optical flow estimation to overcome the partial volume effect in the image acquisition. Validation is performed both in vitro and in vivo and it is shown that the method improves the accuracy of cardiac motion tracking

Background Regional myocardial function is typically evaluated by visual assessment by experienced users, or by methods requiring substantial post processing time. Visual assessment is subjective and not quantitative. Therefore, the purpose of this study is to develop and validate a simple method to derive quantitative measures of regional wall function from velocity encoded Cardiovascular Magnetic Resonance (CMR), and provide associated normal values for longitudinal strain. Method Both fast field echo (FFE) and turbo field echo (TFE) velocity encoded CMR images were acquired in three long axis planes in 36 healthy volunteers (13 women, 23 men), age 35±12 years. Strain was also quantified in 10 patients within one week after myocardial infarction. The user manually delineated myocardium in one time frame and strain was calculated as the myocardium was tracked throughout the cardiac cycle using an optimization formulation and mechanical a priori assumptions. A phantom experiment was performed to validate the method with optical tracking of deformation as an independent gold standard. Results There was an excellent agreement between longitudinal strain measured by optical tracking and longitudinal strain measured with TFE velocity encoding. Difference between the two methods was 0.0025 ± 0.085 (ns). Mean global longitudinal strain in the 36 healthy volunteers was −0.18 ± 0.10 (TFE imaging). Intra-observer variability for all segments was 0.00 ± 0.06. Inter-observer variability was −0.02 ± 0.07 (TFE imaging). The intra-observer variability for radial strain was high limiting the applicability of radial strain. Mean longitudinal strain in patients was significantly lower (−0.15± 0.12) compared to healthy volunteers (p Conclusion In conclusion, we have developed and validated a robust and clinically applicable technique that can quantify longitudinal strain and regional myocardial wall function and present the associated normal values for longitudinal strain.

In the above titled paper (ibid., vol. 27, no. 8, pp. 1045-1053, Aug. 08), the equation on p. 1052 should be corrected as presented here.