Visual-search observers for assessing tomographic x-ray image quality

Abstract

Purpose: Mathematical model observers commonly used for diagnostic image-quality assessments in x-ray imaging research are generally constrained to relatively simple detection tasks due to their need for statistical prior information. Visual-search (VS) model observers that employ morphological features in sequential search and analysis stages have less need for such information and fewer task constraints. The authors compared four VS observers against human observers and an existing scanning model observer in a pilot study that quantified how mass detection and localization in simulated digital breast tomosynthesis (DBT) can be affected by the number P of acquired projections.

Methods: Digital breast phantoms with embedded spherical masses provided single-target cases for a localization receiver operating characteristic (LROC) study. DBT projection sets based on an acquisition arc of 60° were generated for values of P between 3 and 51. DBT volumes were reconstructed using filtered backprojection with a constant 3D Butterworth postfilter; extracted 2D slices were used as test images. Three imaging physicists participated as observers. A scanning channelized nonprewhitening (CNPW) observer had knowledge of the mean lesion-absent images. The VS observers computed an initial single-feature search statistic that identified candidate locations as local maxima of either a template matched-filter (MF) image or a gradient-template MF (GMF) image. Search inefficiencies that modified the statistic were also considered. Subsequent VS candidate analyses were carried out with (i) the CNPW statistical discriminant and (ii) the discriminant computed from GMF training images. These location-invariant discriminants did not utilize covariance information. All observers read 36 training images and 108 study images per P value. Performance was scored in terms of area under the LROC curve.

Results: Average human-observer performance was stable for P between 7 and 35. In the absence of search inefficiencies, the VS models based on the GMF analysis provided the best correlation (Pearson ρ ≥ 0.62) with the human results. The CNPW-based VS observers deviated from the humans primarily at lower values of P. In this limited study, search inefficiencies allowed for good quantitative agreement with the humans for most of the VS observers.

Conclusions: The computationally efficient training requirements for the VS observer are suitable for high-resolution imaging, indicating that the observer framework has the potential to overcome important task limitations of current model observers for x-ray applications.

FIG. 1.

Comparison of mean breast densities used in the study. From top to bottom are example slices from a low-density phantom (25% VGP), a medium-density phantom (50% VGP), and a high-density phantom (75% VGP). Within the breast regions, adipose tissue is white and glandular tissue is black.

FIG. 2.

Example study images showing the impact of phantom VGP. From top to bottom, the left-hand column shows abnormal cases with 25%, 50%, and 75% VGP, respectively. The lesion in each image is indicated by the arrow. The corresponding normal images are shown at right. All images were generated from acquisitions with P = 25.

FIG. 3.

Example study images showing the effects of P. Shown is a same-slice comparison from FBP reconstructions generated from (a) P = 3, (b) 7, (c) 25, and (d) 51. This was a lesion-present case with 50% VGP. The lesion location is indicated by the arrow in the bottom left-hand image (P = 25).

FIG. 4.

Example of search region used by model observers. Top: a test image; bottom: same image with fibroglandular search region highlighted.

FIG. 5.

Correlation-map calculations for the VS observers. (a) A lesion-present, mid-density test image. The lesion is indicated by the arrow. (b) CNPW correlation map; (c) MF correlation map; (d) GMF correlation map; (e) Watershed segmentation of the MF map; (f) Watershed segmentation of the GMF map. The dark regions in the segmentation images represent the watershed supports and the white lines are the watershed boundaries.

FIG. 6.

Comparison of performances from the scanning-CNPW and human observers.

FIG. 7.

Mean number of VS candidates per test image as a function of acquisition strategy (P), search feature and average phantom VGP. Data from the low-, mid-, and high-VGP phantoms are distinguished by the line style. With both search types, the low-density phantoms consistently yielded the fewest candidates. The mid-density phantoms consistently produced the most candidates, slightly more than with the high-density phantoms.

FIG. 8.

Comparison of observer performances from the VS and human observers. (a) The MF–CNPW observer; (b) the GMF–CNPW observer; (c) the MF–GMF observer; and (d) the GMF–GMF observer. The search thresholding and noise processes were not implemented with the VS models for these plots.

FIG. 9.

Comparison of VS and human observer performances as a function of phantom density. The densities are denoted in the plot as low (L), medium (M) and high (H). The average 2D search area in the images increased with density.

FIG. 10.

Comparison of VS and human observer performances when the models included the search thresholding and noise processes controlled by p_t and σ_t, respectively. (a) The MF–CNPW observer; (b) the GMF–CNPW observer; (c) the MF–GMF observer; and (d) the GMF–GMF observer. The model-observer error bars for the nonzero σ_t examples indicate the standard deviation in A_L from ten study realizations.

FIG. 11.

Human and scanning-GMF performances. Compare with the GMF–GMF observer results in Fig. 8(d).