Identifying if participants with differing diagnostic accuracy and visual search behavior during radiologic tasks also differ in nonradiologic tasks is investigated. Four clinician groups with different radiologic experience were used: a reference expert group of five consultant radiologists, four radiology registrars, five senior house officers, and six interns. Each of the four clinician groups is known to have significantly different performance in the identification of pneumothoraces in chest x-ray. Each of the 20 participants was shown 6 nonradiologic images (3 maps and 3 sets of geometric shapes) and was asked to perform search tasks. Eye movements were recorded with a Tobii TX300 (Tobii Technology, Stockholm, Sweden) eye tracker. Four eye-tracking metrics were analyzed. Variables were compared to identify any differences among the groups. All data were compared by using nonparametric methods of analysis. The average number of targets identified in the maps did not change among groups [mean=5.8 of 6 targets (range 5.6 to 6 p=0.861)]. None of the four eye-tracking metrics investigated varied with experience in either search task (p>0.5). Despite clear differences in radiologic experience, these clinician groups showed no difference in nonradiologic search pattern behavior or skill across complex images. This is another viewpoint adding to the evidence that radiologic image interpretation is a learned skill and is task specific.
This study’s purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients’ CT images (i.e., in vivo). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient’s CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with −15 HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model. This method was applied to CT data from a previous study in which diagnostic performance of 16 readers was measured for the task of detecting subtle, hypoattenuating liver lesions (N=105), using a two-alternative-forced-choice (2AFC) method, over six dose levels and two reconstruction algorithms. In vivo detectability indices were estimated and compared to the human readers’ binary 2AFC outcomes using a generalized linear mixed-effects statistical model. The results of this modeling showed that the in vivo detectability indices were strongly related to 2AFC outcomes (p<0.05). Linear comparison between human-detection accuracy and model-predicted detection accuracy (for like conditions) resulted in Pearson and Spearman correlation coefficients exceeding 0.84. These results suggest the potential utility of using in vivo estimates of a detectability index for an automated image quality tracking system that could be implemented clinically.
Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming. A mathematical estimability model was used to assess the quantification precision of CT nodule volumetry in terms of an index (e′), incorporating image noise and resolution, nodule properties, and segmentation software. The noise and resolution were characterized in terms of noise power spectrum and task transfer function. The nodule properties and segmentation algorithm were modeled in terms of a task function and a template function, respectively. The e′ values were benchmarked against experimentally acquired precision values from an anthropomorphic chest phantom across 54 acquisition protocols, 2 nodule sizes, and 2 volume segmentation softwares. e′ exhibited correlation with experimental precision across nodule sizes and acquisition protocols but dependence on segmentation software. Compared to the assessment of empirical precision, which required ∼300 h to perform the segmentation, the e′ method required ∼3 h from data collection to mathematical computation. A mathematical modeling of volume quantification provides efficient prediction of quantitative performance. It establishes a method to verify quantitative compliance and to optimize clinical protocols for chest CT volumetry.