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arXiv 1504.00344math.FA
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锥变换的几个反演公式

Some Inversion Formulas for the Cone Transform

Fatma Terzioglu

摘要:

几个新的成像应用程序最近导致了各种各样的Radon型变换,其中积分是在一个家庭的锥面。我们称之为\emph{cone transforms}(在2D中,它们也称为\emph{V-line}或\emph{broken ray}变换)。最突出的是,它们存在于所谓的康普顿照相机成像中,这种成像出现在医疗诊断、天文学以及最近的国土安全应用中。锥变换的几个具体体现已被分别考虑。本文讨论了最一般的超定锥变换,得到了$\mathbb{R}^n$中锥变换与Radon变换之间的积分关系,以及各种反演公式.在许多应用中(例如,在国土安全中),信噪比非常低。因此,如果收集了超定数据(如康普顿成像的情况),尝试降低维度可能会导致信号的基本消除。因此,我们主要集中在获得公式涉及超定数据。

英文摘要:

Several novel imaging applications have lead recently to a variety of Radon type transforms, where integration is done over a family of conical surfaces. We call them \emph{cone transforms} (in 2D they are also called \emph{V-line} or \emph{broken ray} transforms). Most prominently, they are present in the so called Compton camera imaging that arises in medical diagnostics, astronomy, and lately in homeland security applications. Several specific incarnations of the cone transform have been considered separately. In this paper, we address the most general (and overdetermined) cone transform, obtain integral relations between cone and Radon transforms in $\mathbb{R}^n$, and a variety of inversion formulas. In many applications (e.g., in homeland security), the signal to noise ratio is very low. So, if overdetermined data is collected (as in the case of Compton imaging), attempts to reduce the dimensionality might lead to essential elimination of the signal. Thus, our main concentration is on obtaining formulas involving overdetermined data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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