Myocardial Perfusion Cardiovascular Magnetic Resonance: Advanced Techniques

k-t Undersampling Techniques

The k-t broad linear acquisition speed-up technique ( k-t BLAST) is a prior-knowledge spatiotemporal (k-t) undersampling technique that has gained common use. In k-t BLAST, undersampling is applied along k -space and time, while a low spatial resolution image (“training data”) is obtained in an interleaved fashion during the acquisition. A non­aliased, full image series is then reconstructed using prior knowledge derived from the training data ( Fig. 5.4 ). The signal aliasing is resolved by an adaptive filtering process in which the aliased signal is distributed according to a low-resolution estimate of the signal covariance, as derived from the training images. A particular advantage of this approach is that it allows some overlapping of the aliased object, thereby permitting higher acceleration factors. However, the inherent temporal filtering reduces temporal fidelity, which can lead to reconstruction errors or limit the reduction in data acquisition actually achieved.

A further limitation of k-t BLAST is that the reconstruction problem is intrinsically underdetermined (i.e., there are fewer equations than unknowns), which is a reflection of its dependence on the estimated signal covariance from training data. One method of improving this is to incorporate sensitivity encoding information into the reconstruction—this is the k-t SENSE method. A further solution, which allows the use of higher acceleration factors, is to constrain the reconstruction using a standard data compression technique, called principal component analysis (PCA)—this is a commonly used mathematical algorithm that reduces highly dimensional datasets to lower dimensionality by extracting and exploiting relevant correlations within the data. The extension of k-t BLAST or k-t SENSE methods using PCA in the reconstruction is referred to as k-t PCA.

In the k-t PCA method, the adaptive filter used to remove aliasing is improved by applying PCA to the training data from which the filter is derived. Effectively, PCA is used to transform the images into a new mathematical domain of temporal “basis function” more suitable for reconstruction. The advantage of this new mathematical domain is that it is sparser, even in cases of nonperiodic motion such as respiration or misgating, and therefore the core perfusion data is contained within a few principal components and the rest can be discarded. This mathematical transformation process facilitates the easier separation of overlapping signals before they are converted back into images and accounts for the greater temporal fidelity and relative robustness of k-t PCA to motion.

Non-Cartesian Techniques

Alternative approaches to acceleration involve data acquisition along non-Cartesian patterns such as radial trajectories through the center of k -space ( Fig. 5.5 ). Spatial and temporal redundancy are exploited serially and the altered geometry of k -space coverage facilitates greater efficiency compared with conventional Cartesian techniques by collecting more data with each RF excitation. Non-Cartesian techniques can also be combined with parallel imaging techniques and hold potential for large gains in spatial–temporal resolution.

In the highly constrained back-projection reconstruction (HYPR) method and other radial acquisition variants, k -space data is acquired with undersampled radial projections and overall rotation of the undersampling pattern at different time points. In image reconstruction, a fully sampled composite image is formed by populating missing data from neighboring time frames. This very low temporal resolution composite is then used to constrain back projection of the undersampled data acquired for each individual time frame.

To date, perhaps because other approaches are already well established, radial imaging has not been widely applied to two-dimensional (2D) perfusion CMR, other than for specific research purposes such as multiple samples through the center of k -space to calculate the arterial input function (AIF) or to capitalize on its inherent motion robustness for developing free-breathing techniques. Radial sampling does, however, lend itself to combination with compressed sensing methods (see later) and may therefore see greater future application in 3D perfusion CMR techniques requiring particularly high acceleration factors.

Spiral imaging is another non-Cartesian technique in which data is acquired spiraling outward from the central raw data through k -space. Spiral pulse sequences have some inherent efficiency and SNR advantages over the radial technique, but are also more sensitive to off-resonance effects. However, careful selection of readout duration, flip angle strategy, and other sequence characteristics have been shown to compensate for spiral-related artifacts to produce high-quality high-resolution perfusion images. Most commonly, for 3D data acquisition, a stack of spiral planes with Fourier encoding in the third direction is used to achieve whole-heart coverage in a cylindrical distribution ( Fig. 5.6 ).