- Poster presentation
- Open Access
Improved real-time exercise stress cardiac cine imaging using self consistent parallel imaging with temporal sensitivity estimation (TSPIRIT)
© Xue et al; licensee BioMed Central Ltd. 2012
- Published: 1 February 2012
- Spatial Regularization
- Myocardial Wall Motion
- Conjugate Gradient Solver
- Artifact Score
- Left Ventricular Thickness
To improve the image quality and increase the signal-noise-ratio of real-time exercise stress cardiac cine imaging, we extended the recently proposed SPIRIT image reconstruction by incorporating temporal coil sensitivity estimation and spatial regularization. The proposed method was tested on 10 volunteers and the average SNR gain was 38.2% without increasing ghosting artifacts.
Myocardial wall motion can now be assessed using CMR immediately following treadmill exercise [1, 2]. Given that stress-induced wall motion abnormalities rapidly fade after cessation of exercise, imaging must be completed as quickly as possible. Additionally, shortness of breath following exercise precludes the use of segmented k-space acquisitions making real-time imaging the only practical choice for post-exercise cine. While this eliminates the needs for ECG triggering and breath-holding, signal-noise-ratio (SNR) and temporal and spatial resolution are typically sacrificed. Image degradation can be more severe for post-exercise cine due to rapid heart rate and exaggerated breathing. To improve the quality of exercise stress cine, we propose to extend the SPIRIT reconstruction technique  by incorporating temporal sensitivity estimation (TSPIRIT) and spatial regularization.
10 healthy volunteers (6 males; age 23.1−41.1 yrs) with normal left ventricular thickness underwent free-breathing real-time exercise stress cine examinations after having given written consent. An MR compatible treadmill system was utilized  together with a 1.5T scanner (Avanto, Siemens) and a 32-channel coil (Rapid MRI). Three slices (one short-axis and two long-axis views) were acquired in each subject using the following sequence parameters: bSSFP, TR1.09/TE0.9ms, image matrix 160×80, flip angle 58°, resolution 2.44×2.44 mm2, bandwidth 1420Hz, acceleration rate 4 with time-interleaved sampling of k-space.
All temporally interleaved k-space frames were averaged to generate ACS lines and GRAPPA was first used to estimate full k-space for every frame. Karhunen-Loeve transform filtering was applied to improve sensitivity estimation, and SPIRIT calibration was performed for every frame. The under-sampled k-space and estimated kernels served as inputs to a non-linear solver. An LSQR matrix inversion solver was performed, and then a non-linear conjugate gradient solver was called with spatial regularization. Final images were generated after convergence and compared to images of conventional TGRAPPA reconstruction  of the same raw data. SNR was estimated  and ghosting artifacts caused by chest wall motion were quantified by computing the peak spatial cross-correlation ratio along the phase-encoding direction .
A new TSPRIT reconstruction scheme has been proposed by extending the SPIRIT method with temporal sensitivity estimation and spatial regularization. This technique was shown to significantly improve the quality of exercise stress real-time cine imaging. In vivo testing shows 38.2% SNR gain can be achieved without raising ghosting artifacts.
NIH grant R01 HL102450
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