<i>k‐t</i> PCA: Temporally constrained <i>k‐t</i> BLAST reconstruction using principal component analysis

TLDR

k‑t BLAST accelerates dynamic MRI by undersampling k‑space over time, but its adaptive filtering from low‑resolution covariance often increases reconstruction error or limits acceleration, posing challenges for applications with broad temporal frequencies such as free‑breathing myocardial perfusion imaging. The study aims to demonstrate that PCA‑derived temporal basis functions can constrain k‑t BLAST reconstruction to enhance temporal resolution. The authors applied principal component analysis to training data to derive temporal basis functions that constrain the k‑t BLAST reconstruction. The resulting constrained reconstruction method is named k‑t PCA. Published in Magn Reson Med 2009; © 2009 Wiley‑Liss, Inc.

Abstract

Abstract The k‐t broad‐use linear acquisition speed‐up technique (BLAST) has become widespread for reducing image acquisition time in dynamic MRI. In its basic form k‐t BLAST speeds up the data acquisition by undersampling k ‐space over time (referred to as k‐t space). The resulting aliasing is resolved in the Fourier reciprocal x‐f space ( x = spatial position, f = temporal frequency) using an adaptive filter derived from a low‐resolution estimate of the signal covariance. However, this filtering process tends to increase the reconstruction error or lower the achievable acceleration factor. This is problematic in applications exhibiting a broad range of temporal frequencies such as free‐breathing myocardial perfusion imaging. We show that temporal basis functions calculated by subjecting the training data to principal component analysis (PCA) can be used to constrain the reconstruction such that the temporal resolution is improved. The presented method is called k‐t PCA. Magn Reson Med, 2009. © 2009 Wiley‐Liss, Inc.