Application of the Karhunen–Loeve transform temporal image filter to reduce noise in real-time cardiac cine MRI

Abstract

Real-time dynamic magnetic resonance imaging (MRI) typically sacrifices the signal-to-noise ratio (SNR) to achieve higher spatial and temporal resolution. Spatial and/or temporal filtering (e.g., low-pass filtering or averaging) of dynamic images improves the SNR at the expense of edge sharpness. We describe the application of a temporal filter for dynamic MR image series based on the Karhunen-Loeve transform (KLT) to remove random noise without blurring stationary or moving edges and requiring no training data. In this paper, we present several properties of this filter and their effects on filter performance, and propose an automatic way to find the filter cutoff based on the autocorrelation of the eigenimages. Numerical simulation and in vivo real-time cardiac cine MR image series spanning multiple cardiac cycles acquired using multi-channel sensitivity-encoded MRI, i.e., parallel imaging, are used to validate and demonstrate these properties. We found that in this application, the noise standard deviation was reduced to 42% of the original with no apparent image blurring by using the proposed filter cutoff. Greater noise reduction can be achieved by increasing the length of the image series. This advantage of KLT filtering provides flexibility in the form of another scan parameter to trade for SNR.

Non-rigid registration and KLT filter to improve SNR and CNR in GRE-EPI myocardial perfusion imaging

“…Previous experience with this filter in dynamic cine imaging showed a large reduction of temporally random noise without degradation of image sharpness or introduction of artifacts [9]. In this scenario, no motion correction was needed and the temporal correlation was sufficient for the success of KLT filtering, probably due to the repetitive nature of the cardiac cycle and a consistent image contrast [20].…”

“…Only eigenimages that contain significant spatially coherent structures are kept. It has been shown previously that the noise only eigenimages can be automatically selected based on the width of the central peak of the 2D autocorrelation function of each eigenimage—a measure of the spatially coherent structure in the image [9]. The width of the central peak can be described by its full width at half maximum (FWHM).…”

“…The Karhunen-Loeve Transform (KLT) filter has been shown to significantly improve SNR in dynamic MRI [9]. The KLT filter can be viewed as a “smart averaging” approach and its performance is highly dependent on the temporal correlation between images in a dynamic series [10].…”

Signal-to-Noise Ratio Improvement in Dynamic Contrast-enhanced CT and MR Imaging with Automated Principal Component Analysis Filtering

“…Moreover, setting an optimal number of PCA components with these methods does not yield an indication on information conservation. To our knowledge, PCA dimensioning based on an independent criterion was proposed in only one study ( 25 ). In the Ding et al study, the optimal number of components was determined by subsequent analysis of images corresponding to data projection on the fi rst-ordered PCA components.…”

“…PCA method.-PCA is a linear transformation that is commonly used in the compression of redundant data and in signal fi ltering in medical applications, such as signal noise reduction in magnetoencephalography ( 16 ), dynamic image fi ltering ( 25 ), and parametric mapping for data interpretation ( 17,19,20,26 ). Similar to Fourier transformation, PCA yields a data description on a new basis.…”