The 80 kV and 80 kV partial angle protocols demonstrated the greatest reduction to breast and lung dose, however, the subsequent increase in image noise may be deemed clinically unacceptable. Tube output for these protocols can be adjusted to achieve a more desirable noise level with lesser breast dose savings. Breast shielding increased breast and lung dose when maintaining equivalent ÂFE. The results demonstrated that comparisons of dose performance depend on both the image quality metric and the specific task, and that CNR may not be a reliable metric of signal detectability.

The proposed database and calculation method enable the estimation of organ dose for coronary angiography and brain perfusion CT scans utilizing any spectral shape and angular tube current modulation scheme by taking advantage of the precalculated Monte Carlo simulation results. The database can be used in conjunction with image quality studies to develop optimized acquisition techniques and may be particularly beneficial for optimizing dual kVp acquisitions for which numerous kV, mA, and filtration combinations may be investigated.

Purpose: Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index (d 0 gen ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.Methods: This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index (d 0 gen ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d 0 gen metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between d 0 gen and noise standard deviation. The performance of the proposed d 0 gen -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images. Results: The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the d 0 gen predicted by the lookup table and d 0 measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the d 0 gen -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the d 0 gen -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P > 0.35). The d 0 gen -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans. Conclusions: A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed d 0 gen -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.

Fractal dimension correlated with the NPS-peak frequency and was independent of noise magnitude, suggesting that the scalar metric of fractal dimension can be used to quantify the change in noise texture across reconstruction approaches. Results demonstrated that fractal dimension can be estimated from four, 64 × 64-pixel ROIs or one 128 × 128 ROI within a head CT image, which may make it amenable for quantifying noise texture within clinical images.

Recent advances in energy-resolved CT can potentially improve contrast-to-noise ratio (CNR), which could subsequently reduce dose in conventional and dedicated breast CT. Two methods have been proposed for optimal energy weighting: weighting the energy-bin data prior to log normalization (projection-based weighting) and weighting the energy-bin data after log normalization (image-based weighting). Previous studies suggested that optimal projection-based and image-based energy weighting provide similar CNR improvements for energyresolved CT compared to photon-counting or conventional energy-integrating CT. This study experimentally investigated the improvement in CNR of projection-based and image-based weighted images relative to photoncounting for six different energy-bin combinations using a bench top system with a CZT detector. The results showed CNR values ranged between 0.85 and 1.01 for the projection-based weighted images and between 0.91 and 1.43 for the image-based weighted images, relative to the CNR for the photon-counting image. The range of CNR values demonstrates the effects of energy-bin selection on CNR for a particular energy weighting scheme. The non-ideal spectral response of the CZT detector caused spectral tailing, which appears to generally reduce the CNR for the projection-based weighted images. Image-based weighting increased CNR in five of the six bin combinations despite the non-ideal spectral effects.