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Automated assessment of low contrast sensitivity for CT systems using a model observer
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/content/aapm/journal/medphys/38/S1/10.1118/1.3577757
2011-07-20
2014-12-20

Abstract

Purpose:

Low contrast sensitivity of CT scanners is regularly assessed by subjective scoring of low contrast detectability within phantom CTimages. Since in these phantoms low contrast objects are arranged in known fixed patterns, subjective rating of low contrastvisibility might be biased. The purpose of this study was to develop and validate a software for automated objective low contrast detectability based on a model observer.

Methods:

Images of the low contrast module of the Catphan 600 phantom were used for the evaluation of the software. This module contains two subregions: the supraslice region with three groups of low contrast objects (each consisting of nine circular objects with diameter 2–15 mm and contrast 0.3, 0.5, and 1.0%, respectively) and the subslice region with three groups of four circular objects each (diameter 3–9 mm; contrast 1.0%). The software method offered automated determination of low contrast detectability using a NPWE (nonprewhitening matched filter with an eye filter) model observer for the supraslice region. The model observer correlated templates of the low contrast objects with the acquired images of the Catphan phantom and a discrimination indexd′ was calculated. This index was transformed into a proportion correct (PC) value. In the two-alternative forced choice (2-AFC) experiments used in this study, a PC ≥ 75% was proposed as a threshold to decide whether objects were visible. As a proof of concept, influence of kVp (between 80 and 135 kV), mAs (25–200 mAs range) and reconstruction filter (four filters, two soft and two sharp) on low contrast detectability was investigated. To validate the outcome of the software in a qualitative way, a human observer study was performed.

Results:

The expected influence of kV, mAs and reconstruction filter on image quality are consistent with the results of the proposed automated model. Higher values ford′ (or PC) are found with increasing mAs or kV values and for the soft reconstruction filters. For the highest contrast group (1%), PC values were fairly above 75% for all object diameters >2 mm, for all conditions. For the 0.5% contrast group, the same behavior was observed for object diameters >3 mm for all conditions. For the 0.3% contrast group, PC values were higher than 75% for object diameters >6 mm except for the series acquired at the lowest dose (25 mAs), which gave lower PC values. In the human observer study similar trends were found.

Conclusions:

We have developed an automated method to objectively investigate image quality using the NPWE model in combination with images of the Catphan phantom low contrast module. As a first step, low contrast detectability as a function of both acquisition and reconstruction parameter settings was successfully investigated with the software. In future work, this method could play a role in image reconstruction algorithms evaluation, dose reduction strategies or novel CT technologies, and other model observers may be implemented as well.

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Scitation: Automated assessment of low contrast sensitivity for CT systems using a model observer
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/S1/10.1118/1.3577757
10.1118/1.3577757
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