Berthomier C, Herment A, Giovannelli J F, Guidi G, Pourcelot L, Diebold B
U494 INSERM, CHU Pitié-Salpêtrière, 91 Bld de l'Hôpital, Paris, France.
Ultrasound Med Biol. 2001 Nov;27(11):1515-23. doi: 10.1016/s0301-5629(01)00446-x.
Autoregressive (AR) modelling has already been proposed as an alternative to fast Fourier transform to process ultrasound (US) Doppler signals. Previous works introduced long AR models, set up under a regularization framework. The latter may be in 1-D (frequency) or 2-D (frequency and space or time). This study generalizes the spectrum regularization in the three dimensions frequency, space and time. The problem of the penalization function is addressed, and a new convex solution is proposed, taking into account possible nonstationarity of the Doppler signal. The parameter tuning is based on simulations using a standard Doppler signal model. The first results show that this processing improves the spectral estimation, and is well suited to flow interpretation.
自回归(AR)建模已被提议作为快速傅里叶变换的替代方法来处理超声(US)多普勒信号。先前的工作引入了在正则化框架下建立的长AR模型。后者可以是一维(频率)或二维(频率和空间或时间)的。本研究将频谱正则化推广到频率、空间和时间三个维度。解决了惩罚函数的问题,并提出了一种新的凸解,同时考虑了多普勒信号可能的非平稳性。参数调整基于使用标准多普勒信号模型的模拟。初步结果表明,这种处理方法改善了频谱估计,并且非常适合于血流解释。