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Purpose To determine the accuracy of CT number and calcium score of a kV‐independent technique based on an artificial 120 kV reconstruction, and its potential to reduce radiation dose. Methods Anthropomorphic chest phantoms were scanned on a third‐generation dual‐source CT system equipped with the artificial 120 kV reconstruction. First, a phantom module containing a 20‐mm diameter hydroxyapatite (HA) insert was scanned inside the chest phantoms at different tube potentials (70–140 kV) to evaluate calcium CT number accuracy. Next, three small HA inserts (diameter/length = 5 mm) were inserted into a pork steak and scanned inside the phantoms to evaluate calcium score accuracy at different kVs. Finally, the same setup was scanned using automatic exposure control (AEC) at 120 kV, and then with automatic kV selection (auto‐kV). Phantoms were also scanned at 120 kV using a size‐dependent mA chart. CT numbers of soft tissue and calcium were measured from different kV images. Calcium score of each small HA insert was measured using commercial software. Results The CT number difference from 120 kV was small with tube potentials from 90 to 140 kV for both soft tissue and calcium (maximal difference of 4/5 HU, respectively). Consistent calcium scores were obtained from images of different kVs compared to 120 kV, with a relative difference <8%. Auto‐kV provided a 25–34% dose reduction compared to AEC alone. Conclusion A kV‐independent calcium scoring technique can produce artificial 120 kV images with consistent soft tissue and calcium CT numbers compared to standard 120 kV examinations. When coupled with auto‐kV, this technique can reduce radiation by 25–34% compared to that with AEC alone, while providing consistent calcium scores as that of standard 120 kV examinations.

Imaging-based measurements form the basis of surgical decision making in patients with aortic aneurysm. Unfortunately, manual measurement suffer from suboptimal temporal reproducibility, which can lead to delayed or unnecessary intervention. We tested the hypothesis that deep learning could improve upon the temporal reproducibility of CT angiography-derived thoracic aortic measurements in the setting of imperfect ground-truth training data. To this end, we trained a standard deep learning segmentation model from which measurements of aortic volume and diameter could be extracted. First, three blinded cardiothoracic radiologists visually confirmed non-inferiority of deep learning segmentation maps with respect to manual segmentation on a 50-patient hold-out test cohort, demonstrating a slight preference for the deep learning method (p < 1e-5). Next, reproducibility was assessed by evaluating measured change (coefficient of reproducibility and standard deviation) in volume and diameter values extracted from segmentation maps in patients for whom multiple scans were available and whose aortas had been deemed stable over time by visual assessment (n = 57 patients, 206 scans). Deep learning temporal reproducibility was superior for measures of both volume (p < 0.008) and diameter (p < 1e-5) and reproducibility metrics compared favorably with previously reported values of manual inter-rater variability. Our work motivates future efforts to apply deep learning to aortic evaluation.

Use of a narrow display WW significantly improved technologists' performance in dQC for detecting subtle but clinically relevant artifacts as compared to that using a 100 HU display WW.

Purpose: Frequent review of CT scanner protocols has been recognized as an important component to any CT quality control (QC) program, yet manual review is extremely labor‐intensive and prone to human errors. The purpose of this study was to develop, implement and evaluate a software system to automatically monitor protocols on our CT scanners. Methods: A software system written in Matlab (MathWorks) and Excel Visual Basic (Microsoft) was developed to automatically check scanner CT protocols and notify designated users of changes on a weekly basis. The development team included medical physicists and lead technologists. Abdomen and pelvis protocols were monitored between December 2012 and February 2013 on two representative CT scanners for adult and pediatric, single‐and dual‐energy protocols. A total of 78 and 89 protocols were monitored on scanners 1 and 2, respectively. The overall workflow and number of errors identified were evaluated. Results: During the 2+ months monitoring period, 89 scanning protocols were modified on scanner 1, of which 20 were identified to be inappropriate (22%). 40 protocols were modified on scanner 2, of which 5 were identified to be inappropriate (13%). While the most frequent inappropriate modification was to the series description, errors were also found in changes to mAs, kernel, and reconstruction direction, increment and slice width. Conclusion: In routine clinical practice, CT protocols were found to be changed quite frequently. Thus, an automated protocol QC program is essential to ensure protocol accuracy and consistency. Use of access controls to limit the number of users able to modify protocols may help reduce the number of changes, but does not guarantee the accuracy of changes by authorized users. We successfully developed and implemented a software system to automatically monitor CT protocols that can be easily applied to a wide range of practices with negligible impact on workload.