Optimizing spectral CT parameters for material classification tasks.

Abstract:

:In this work, we propose a framework for optimizing spectral CT imaging parameters and hardware design with regard to material classification tasks. Compared with conventional CT, many more parameters must be considered when designing spectral CT systems and protocols. These choices will impact material classification performance in a non-obvious, task-dependent way with direct implications for radiation dose reduction. In light of this, we adapt Hotelling Observer formalisms typically applied to signal detection tasks to the spectral CT, material-classification problem. The result is a rapidly computable metric that makes it possible to sweep out many system configurations, generating parameter optimization curves (POC's) that can be used to select optimal settings. The proposed model avoids restrictive assumptions about the basis-material decomposition (e.g. linearity) and incorporates signal uncertainty with a stochastic object model. This technique is demonstrated on dual-kVp and photon-counting systems for two different, clinically motivated material classification tasks (kidney stone classification and plaque removal). We show that the POC's predicted with the proposed analytic model agree well with those derived from computationally intensive numerical simulation studies.

journal_name

Phys Med Biol

authors

Rigie DS,La Rivière PJ

doi

10.1088/0031-9155/61/12/4599

subject

Has Abstract

pub_date

2016-06-21 00:00:00

pages

4599-622

issue

12

eissn

0031-9155

issn

1361-6560

journal_volume

61

pub_type

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