Improved waveform fidelity using local HYPR reconstruction (HYPR LR)

Abstract

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 signalto-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 P t i , i ϭ 1…N p , where N p is the number of projections acquired at each timeframe. Each of these Radon projections is then normalized by the corresponding Radon projections P c i , i ϭ 1…N p , of the composite image I c 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 I w . The individual timeframe weighting images provide dynamic information. The final HYPR images I H are obtained by multiplication of the individual timeframe weighting images with the composite image, and can be described as: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 con...

Undersampled radial MR acquisition and highly constrained back projection (HYPR) reconstruction: Potential medical imaging applications in the post‐Nyquist era

“…The synergistic combination of HYPR with VIPR further diminishes the effects of these artifacts and restores a substantial amount of the SNR lost as a result of the shortened acquisition times. When reasonable choices of processing parameters are chosen, this can be done with good preservation of temporal resolution, especially when the most recent HYPR versions are employed (38).…”

Renal Perfusion and Hemodynamics: Accurate in Vivo Determination at CT with a 10-Fold Decrease in Radiation Dose and HYPR Noise Reduction

“…In HYPR, individual time frames are obtained by unfiltered back projection of normalized sinograms using anatomic constraints provided by a composite image. A modified algorithm-HYPR-local reconstruction (LR)-permits the use of a longer temporal window in the formation of the composite image, resulting in increased signal-to-noise ratio (SNR) and quantitative reconstruction accuracy (9).…”

“…The SNR was improved to 3.83 with the use of a 10 ϫ 10 convolution kernel. However, further increasing the kernel size is not very helpful because the SNR is primarily determined by the number of frames used in the composite image (9). In addition, there is also a risk of crosstalk from different anatomic areas if a larger kernel is used (9).…”

Improved kinetic analysis of dynamic PET data with optimized HYPR‐LR

“…When images are sparse, as they are in angiography, so long as two objects with different temporal behaviors do not overlap in the filtering process, they will be perfectly reproduced. 2 Unfortunately, in non sparse images, such as those in PET, regions with different temporal behavior will invariably overlap in the filtering process, potentially creating a bias. This bias will manifest itself at high-frequency boundaries since the weighting image is formed by a lowfrequency comparison.…”

“…[1][2][3] The central idea of HYPR is to estimate individual frames in a dynamic study by weighting a temporally summed composite image. In the ideal case, individual frames will take on the noise properties of the composite image.…”