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Automated assessment of low contrast sensitivity for CT systems using a model observer
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Image of FIG. 1.

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FIG. 1.

A constructed 38 mm CT slice of the CTP515 module. Each contrast group and the low contrast object diameters have been tagged in the figure for the supraslice region.

Image of FIG. 2.

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FIG. 2.

Mask consisting of templates with respect to each low contrast object in the Catphan module.

Image of FIG. 3.

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FIG. 3.

Detectability index d′ and proportion correct PC (left and right column, respectively) as a function of low contrast circle diameters of the Catphan phantom for the 1% contrast group for different kV (A, D), mAs (B, E), and reconstruction filters (C, F). The parameters kept constant in each case appear as a legend on the figures to identify them as image series in Table I. The black line on PC graphs (D, E, F) represents the visibility threshold criterion applied (PC = 75%).

Image of FIG. 4.

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FIG. 4.

Smallest object diameters visible (with a PC = 75%), λ, obtained with the psychometric fitting for all contrast groups for different kV values. Lines are a mere data connector in the graph.

Image of FIG. 5.

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FIG. 5.

Smallest object diameters visible (with a PC = 75%), λ, obtained with the psychometric fitting for all contrast groups for different mAs values. Lines are a mere data connector in the graph.

Image of FIG. 6.

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FIG. 6.

Smallest object diameters visible (with a PC = 75%), λ, obtained with the psychometric fitting for all contrast groups for different reconstruction filters. Lines are a mere data connector in the graph.

Image of FIG. 7.

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FIG. 7.

Psychometric fitting functions of PC as a function of object size for different mAs and 1% contrast group. Fitting parameters, λ and f, have been included in the legend for each image series.

Image of FIG. 8.

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FIG. 8.

PC curves normalized to the reference dose (16.9 mGy) for different tube charge per rotation values. The black line represents the global psychometric fit for all data.

Image of FIG. 9.

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FIG. 9.

PC curves as a function of object size for the average expert (lines, Exp) and nonexpert (dots, NE) observers for varying kV (A), mAs (B), and reconstruction filter (FC12 and FC50, soft body and lung, respectively, (C). Observers were not able to score images related to the sharp filters (FC81, FC53). Note that PC values run from 0 to 1.

Image of FIG. 10.

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FIG. 10.

PC curves as a function of kV (left column) and mAs (right column) for different object sizes for the average expert observer (first row, (A) and (D), 1% contrast series) and the LCD software (second and third row, 1% and 0.5% contrast series, respectively). Note that PC values run from 0 to 1 for human observer (first row) and from 0.5 to 1 for the software (second and third row).


Generic image for table

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Overview of the acquisition and reconstruction parameters.

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Values of D1 (largest diameter object) and its corresponding PC value (PC1) which fail the PC ≥ 75% criteria to be considered visible, for the three contrast groups and all image series.

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Summary of the results of the individual psychometric fittings for all contrast groups and acquisition conditions considered. Parameter λ represents the smallest object diameter which reached PC ≥ 75% and so, considered visible. The error associated with this parameter is also shown as a (%).

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Lambda values (λNorm) obtained performing the psychometric fits taking as f the value obtained with the psychometric fit of the normalized data to the reference dose (CTDIvol ref = 16.9 mGy) for all contrast groups and different mAs values. The relative differences (ɛrelative) between lambda values obtained for the individual fits (λIndiv.Fit) for each series (with lambda and f unbound) and this method, are also shown.

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Measured and nominal contrast values (%) and contrast-to-noise ratio (CNR) for the low contrast module of the Catphan phantom.


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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.


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.


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.


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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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Automated assessment of low contrast sensitivity for CT systems using a model observer