A Framework for generalized reference image reconstruction methods including HYPR-LR, PR-FOCUSS, and k-t FOCUSS

Abstract

To investigate the relationships among highly constrained back projection (HYPR)-LR, projection reconstruction focal underdetermined system solver (PR-FOCUSS), and k-t FOCUSS by showing how each method relates to a generalized reference image reconstruction method. That is, the generalized series model employs a fixed reference image and multiplicative corrections-that model is extended here to consider reference images more broadly, both in image space and in transform spaces (x-t and x-f spaces), and that can evolve with iteration. Theoretical relationships between the methods were derived. Computer simulations were done to compare HYPR-LR to one iteration of PR-FOCUSS. The generalized reference approaches applied in the x-t or x-f domain were compared using computer simulation, five cardiac cine imaging datasets, and six myocardial perfusion datasets. PR-FOCUSS and HYPR-LR gave comparable errors, with PR-FOCUSS slightly outperforming HYPR-LR. The baseline image is important to the performance of k-t FOCUSS and x-t FOCUSS, as demonstrated by results from cardiac cine imaging. For cardiac perfusion reconstructions with the use of a temporal average image as the baseline image, k-t FOCUSS gave lower errors than x-t FOCUSS. HYPR-LR and PR-FOCUSS are closely related: both work for radial sampling and use reference images in the x-t domain; HYPR-LR is an approximate implementation of the generalized reference framework, while PR-FOCUSS is a conjugate gradient implementation of the generalized reference framework. The superiority of generalized reference approaches applied in the x-t or x-f domain was sensitive to the characteristics of the acquired data and to the baseline image used.