Improved waveform fidelity using local HYPR reconstruction (HYPR LR).

Dynamic MR imaging applications often require compromises in spatial and/or temporal resolution when standard reconstruction schemes are used. Acquisition windows are limited by the passage of contrast agents, as with hyperpolarized nuclei and contrast enhanced angiography, and/or clinical feasibility, as in 3D cine flow imaging. Recently, several alternative sampling and reconstruction methods have been introduced that explore data redundancies in such applications. These methods include model-based reconstructions (1-3) that rely on a priori information and compressed sensing methods (4,5), which aim to reduce the number of k-space points to represent a given object. Recently, HighlY constrained backPRojection (HYPR) (3) reconstruction has been used in conjunction with undersampled radial acquisitions to permit radial undersampling factors of up to 80 in 2D and 1000 in 3D (6-8) in selected time-resolved applications in which the images are sparse and have a high degree of spatiotemporal correlation. Unlike other acceleration methods, where signal-to-noise ratio (SNR) tends to decrease in proportion to the square root of the acceleration factor, HYPR maintains SNR from the composite image used to constrain the unfiltered backprojection process. While originally formulated for angiography, HYPR has been applied to a wide range of imaging methods including hyperpolarized gas imaging, cerebral diffusion, and cine phase contrast, all of which have temporal information that is spatially correlated. In the original HYPR method, a series of radial acquisitions with interleaved k-space projection sets is acquired. Using 1D discrete Fourier transform, we obtain image space profiles Pti, i = 1…Np, where Np is the number of projections acquired at each timeframe. Each of these Radon projections is then normalized by the corresponding Radon projections Pci, i = 1…Np, of the composite image Ic that is reconstructed by conventional methods from the projections in several or all of the acquired timeframes. An unfiltered backprojection operator B is applied to each normalized projection. The average of all the backprojected information for each timeframe may be regarded as a weighting image Iw. The individual timeframe weighting images provide dynamic information. The final HYPR images IH are obtained by multiplication of the individual timeframe weighting images with the composite image, and can be described as: IH(t)=Ic⋅Iw(t)=Ic⋅1NpΣi=1NpB(PtiPci) [1] In the limit of extremely sparse images or images with complete spatiotemporal correlation the HYPR algorithm provides near exact reconstruction. However, as the sparsity and spatiotemporal correlation deteriorate, there can be crosstalk of signals from different portions of the imaging volume. This crosstalk has generally forced the use of narrow sliding window composites to improve waveform fidelity. Since the sliding window composite has fewer projections, it has more artifact than a full-length composite would. A HYPR-based method presented here uses the concept of local reconstruction (HYPR LR) by constraining the unfiltered backprojected information to local regions in order to reduce the crosstalk and improve waveform fidelity. Simulations were performed to compare the new HYPR LR method to the original HYPR method and determine properties of the new method.