- Poster presentation
- Open Access
Circumferential Compression Encoding (CIRCOME) using Polar K-space
© Golshani and Nasiraei-Moghaddam 2016
- Published: 27 January 2016
- Regional Myocardial Function
- Radial Spoke
- Radial Acquisition
- Circumferential Compression
- Full Cardiac Cycle
Circumferential strain of the Left Ventricle gives valuable insights into regional myocardial function. This strain can be calculated from the density of radial taglines estimated by CIRCOME method , which benefits from direct use of tagline information in the frequency domain. CIRCOME exploits circular bandpass filters in order to selectively separate various frequencies in this region and reconstruct compression encoded images. A series of 2D filtering over a Cartesian grid is computationally expensive and also results in round-off errors. Therefore, direct extraction of the effective frequencies in k-space has a potential advantage of producing density maps faster and with higher accuracy. The purpose of this study was to investigate the feasibility of an efficient polar approach for CIRCOME method applicable on radial data acquisition in combination with an adapted Polar Fourier Transform .
Mid-ventricular short axis myocardial images with radial tag pattern of one healthy volunteer were acquired using a 2D segmented radial k-space trajectory on a 1.5T Siemens TIM Avanto scanner with 88, 64, and 40 radial spokes. All datasets consisted of 19 frames in a full cardiac cycle and took 11.7, 8.5 and 5.3 seconds, respectively. The radial raw data was transposed to generate the circular rings format. Each distinct circle in the annular sub-region of k-space, indicating the specific frequency of tagging modulation, was then individually used through a Hankel-based algorithm, to reconstruct an image. These images were then 2D cross-correlated in the polar coordinate system with the image obtained from the full k-space data and finally the compression encoded density maps were estimated directly in the polar domain.
We have shown that the analysis of radially tagged images can become more efficient and simpler if the data is sampled through radial acquisitions. Our proposed approach will provide efficient and accurate myocardial strain assessment with high reliability.
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