An algorithm for noise suppression in dual energy CT material density images

Abstract

Dual-energy material density images obtained by prereconstruction-basis material decomposition techniques offer specific tissue information, but they exhibit relatively high pixel noise. It is shown that noise in the material density images is negatively correlated and that this can be exploited for noise reduction in the two-basis material density images. The algorithm minimizes noise-related differences between pixels and their local mean values, with the constraint that monoenergetic CT values, which can be calculated from the density images, remain unchanged. Applied to the material density images, a noise reduction by factors of 2 to 5 is achieved. While quantitative results for regions of interest remain unchanged, edge effects can occur in the processed images. To suppress these, locally adaptive algorithms are presented and discussed. Results are documented by both phantom measurements and clinical examples.

Brown Adipose Tissue in Humans

“…The simplest method for combining the dual energy image data for the purpose of material differentiation is to perform a linearly weighted image subtraction. The low tube potential images (typically 80 kV) are multiplied by a weighting factor and subtracted from the high voltage images (140 kV) to suppress or enhance a specific material (Kalender, Klotz and Kostaridou 1988). Reconstructed CT images acquired using the two different tube potentials can be processed with a three--material decomposition algorithm (Johnson et al 2007).…”

Dual-Energy CT with Fast-kVp Switching and Its Applications in Orthopedics

“…It has been proven that the noises in the two resulting basis material density images are negatively correlated [2,8]. Taking advantage of this property, several noise suppression algorithms have been developed [8,25].…”

“…It has been proven that the noises in the two resulting basis material density images are negatively correlated [2,8]. Taking advantage of this property, several noise suppression algorithms have been developed [8,25]. These algorithms subtract a weighted high-pass filtered version of the first basis material density image (e.g., water) to noise reduce the complimentary basis material density image (e.g., iodine).…”

“…Dual-energy CT is an imaging technique that has been known and extensively studied for many decades [1][2][3][4][5][6][7][8]. However, due to various technical challenges, only recently has dual-energy CT imaging become a reality.…”

Noise suppression for dual-energy CT via penalized weighted least-square optimization with similarity-based regularization

“…22,23 The problem can be alleviated by general imageprocessing algorithms, such as filtering/smoothing based methods, 24 with degraded spatial resolution. DECT-specific algorithms of noise suppression have been previously developed as well.…”