Noise and dose reduction in CT brain perfusion acquisition by projecting time attenuation curves onto lower dimensional spaces

TL;DR

This paper introduces a dimension reduction technique for CT perfusion imaging that effectively reduces noise, enabling lower radiation doses or improved image quality in high-noise scenarios.

Contribution

The authors propose a novel noise reduction method using time attenuation curve projection onto lower-dimensional spaces, enhancing CT perfusion imaging quality.

Findings

Significant noise reduction in perfusion maps.

Preservation of critical perfusion information.

Potential for dose reduction in CT protocols.

Abstract

CT perfusion imaging (CTP) plays an important role in decision making for the treatment of acute ischemic stroke with large vessel occlusion. Since the CT perfusion scan time is approximately one minute, the patient is exposed to a non-negligible dose of ionizing radiation. However, further dose reduction increases the level of noise in the data and the resulting perfusion maps. We present a method for reducing noise in perfusion data based on dimension reduction of time attenuation curves. For dimension reduction, we use either the fit of the first five terms of the trigonometric polynomial or the first five terms of the SVD decomposition of the time attenuation profiles. CTP data from four patients with large vessel occlusion and three control subjects were studied. To compare the noise level in the perfusion maps, we use the wavelet estimation of the noise standard deviation implemented in the scikit-image package. We show that both methods significantly reduce noise in the data while preserving important information about the perfusion deficits. These methods can be used to further reduce the dose in CT perfusion protocols or in perfusion studies using C-arm CT, which are burdened by high noise levels.