DCEUS‐based multiparametric perfusion imaging using pulse‐inversion Bubblet decorrelation

Abstract

As a multiparametric functional imaging technique, these improvements in the proposed scheme can be beneficial to accurately quantify and depict the hemodynamic perfusion features and details of tumor angiogenesis, and further can also assist clinicians in making a confirmed diagnosis.

A fast scheme for renal microvascular perfusion functional imaging: Assessed by an imaging quality evaluation model

“…Our group proposed a bubble‐echo deconvolution strategy to enhance the spatial resolution of DCEUS and then improved PFIs of intratumoral angiogenesis to single‐pixel resolution, respectively. Additionally, influences of various contrast and microvascular discriminability in DCEUS techniques on renal PFIs are also illustrated in our previous studies . The average contrast and information entropy of renal PFIs are significantly improved by a novel PFIs using pulse‐inversion Bubblet decorrelation with high contrast and microvascular discriminability .…”

“…The scanner fixed in the sagittal and coronal planes to record the renal 3D DCEUS sequences in the corresponding imaging planes. These DCEUS sequences were obtained with a frame rate of 25 Hz according to the rules of pulse‐inversion harmonic decorrelation technique . After clipping the pre‐ and postinjection DCEUS images and selecting the renal regions, the renal 3D DCEUS sequences (n = 17) with the whole bolus kinetics were collected with a size of 480 × 320 pixels and a time scale of 875 frames.…”

“…(a), which included the TIC processing and parametric imaging at the following steps. (a) TICs were extracted by computing the intensity within windows with a neighborhood smoothing of 3 × 3 pixels in DCEUS sequences at each time point . A 3D TIC matrix was then built.…”

“…A multiparametric model was further built to objectively evaluate the modified fast PFI scheme from multiple perspectives, as the following equation:where Y is taken as a systematic and comprehensive coefficient of imaging quality (CIQ). S = [ S 1 S 4 ] is a evaluation matrix containing four terms of the average information entropy (S 1 ), average contrast (S 2 ), average gray (S 3 ), and average noise coefficient ( S 4 ) of DCEUS‐based PFI. C is the coefficient matrix of S .…”

Bubble‐echo based deconvolution of contrast‐enhanced ultrasound imaging: Simulation and experimental validations

“…The bubble‐echo based PSF built on a multiple microbubble model that considers microbubble interactions and nonlinear propagation as microbubbles will be introduced to BED in our future work. In addition, the improvements of both resolution and CTR in BED also distinctly enhanced the detection sensitivity of microbubbles in CEUS, which has the potential applications in microvascular detection and imaging with low flow . However, the stronger microbubbles attenuation effects with the increasing imaging depth would limit its clinical application in the deeper microvessels .…”