Search

For the detection of very small objects, high resolution detectors are expected to provide higher dose efficiency. We assessed this impact of increased resolution on a clinical photon counting detector CT (PCD-CT) by comparing its detectability in high resolution and standard resolution (with 2×2 binning and larger focal spot) modes. A 50μm-thin metal wire was placed in a thorax phantom and scanned in both modes at three exposure levels (12, 15, and 18 mAs); acquired data were reconstructed with three reconstruction kernels (Br40, Br68, and Br76, from smooth to sharp). A scanning nonprewhitening model observer searched for the wire location within each slice independently. Detection performance was quantified as area under the exponential transform of the free response ROC curve. The high-resolution mode had the mean AUCs at 18 mAs of 0.45, 0.49, and 0.65 for Br40, Br68, and Br76, respectively, which were 2 times, 3.6 times, and 4.6 times those of the standard resolution mode. The high-resolution mode achieved greater AUC at 12 mAs than the standard resolution mode at 18 mAs for every reconstruction kernel, but improvements were larger at sharper kernels. The results are consistent with the greater suppression of noise aliasing expected at higher frequencies with high resolution CT. This work illustrates that PCD-CT can provide large dose efficiency gains for detection tasks of small, high contrast lesions.

Detection of low contrast liver metastases varies between radiologists. Training may improve performance for lower-performing readers and reduce inter-radiologist variability. We recruited 31 radiologists (15 trainees, 8 non-abdominal staff, and 8 abdominal staff) to participate in four separate reading sessions: pre-test, search training, classification training, and post-test. In the pre-test, each radiologist interpreted 40 liver CT exams containing 91 metastases, circumscribed suspected hepatic metastases while under eye tracker observation, and rated confidence. In search training, radiologists interpreted a separate set of 30 liver CT exams while receiving eye tracker feedback and after coaching to increase use of coronal reformations, interpretation time, and use of liver windows. In classification training, radiologists interpreted up to 100 liver CT image patches, most with benign or malignant lesions, and compared their annotations to ground truth. Post-test was identical to pre-test. Between pre- and post-test, sensitivity increased by 2.8% (p = 0.01) but AUC did not change significantly. Missed metastases were classified as search errors (2 seconds gaze time) using the eye tracker. Out of 2775 possible detections, search errors decreased (10.8% to 8.1%; p < 0.01) but classification errors were unchanged (5.7% vs 5.7%). When stratified by difficulty, easier metastases showed larger reductions in search errors: for metastases with average sensitivity of 0–50%, 50–90%, and 90–100%, reductions in search errors were 16%, 35%, and 58%, respectively. The training program studied here may be able to improve radiologist performance by reducing errors but not classification errors.

The Rose criterion, stating that an object is detectable if it is five standard deviations above background, has been used as a rule of thumb for decades but its applicability is limited in computed tomography. Recent denoising algorithms, powered by convolutional neural networks, promise to reveal objects that were previously obscured by noise, but any denoising algorithm is fundamentally limited by the statistics of the sinogram. In this work, we estimate the minimum SNR necessary for detecting one of a set of objects in the projection domain. We assume there is a set of objects O for which detection is desired, and we study an ideal observer that sequentially compares each member of O to the null hypothesis. This comparison can be reduced to the classic one-dimensional signal detection problem between two Gaussians with different mean values, and from this we define a quantity, the projection SNR. We use simulations to estimate the minimum projection SNR necessary to achieve a sensitivity of 80% and specificity of 80%. We find that when we model a search task of a circular 6 mm lesion in a region of interest that is 60 mm by 60 mm by 10 slices, the minimum projection SNR is 5.1. This required SNR is reminiscent of the Rose criterion but is derived with entirely different assumptions, including the application of the ideal observer in the projection domain.