This study is an initial investigation into methods to harmonize quantitative imaging (QI) feature values across CT scanners based on image quality metrics. To assess the impact of harmonization on QI features, we: (1) scanned an image quality assessment phantom on three scanners over a wide range of acquisition and reconstruction conditions; (2) from those scans, assessed image quality for each scanner at each acquisition and reconstruction condition; (3) from these assessments, identified a set of parameters for each scanner that yielded similar image quality values (“harmonized condition”); (4) scanned a second phantom with texture (i.e., local variations in attenuation) under the same set of conditions; and (5) extracted QI features and compared values between non-harmonized and harmonized image quality conditions. Quantitative image quality assessments provided contrast to noise ratio (CNR) and modulation transfer function frequency at 50% (MTF f50) values for each scanner and each condition used. A set of harmonized conditions was identified across three CT scanners based on the similarity of CNR and MTF f50. To provide a comparison, several non-harmonized condition sets were identified. From the texture phantom, the standard deviation of the QI feature values (intensity mean and variance, GLCM autocorrelation and cluster tendency, GLDM high and low gray level emphasis) across the three CT systems decreased between 72.8% and 81.1% between the unharmonized and harmonized groups (with exception of intensity mean which showed little difference across scanners). These initial results suggest that selecting protocols that produce similar quantitative image quality metric values across different CT systems can reduce the variance of QI feature values across those systems.
PurposeTo integrate and evaluate an artificial intelligence (AI) system that assists in checking endotracheal tube (ETT) placement on chest x-rays (CXRs) in clinical practice.ApproachIn clinical use over 17 months, 214 CXR images were ordered to check ETT placement with AI assistance by intensive care unit (ICU) physicians. The system was built on the SimpleMind Cognitive AI platform and integrated into a clinical workflow. It automatically identified the ETT and checked its placement relative to the trachea and carina. The ETT overlay and misplacement alert messages generated by the AI system were compared with radiology reports as the reference. A survey study was also conducted to evaluate usefulness of the AI system in clinical practice.ResultsThe alert messages indicating that either the ETT was misplaced or not detected had a positive predictive value of 42% (21/50) and negative predictive value of 98% (161/164) based on the radiology reports. In the survey, radiologist and ICU physician users indicated that they agreed with the AI outputs and that they were useful.ConclusionsThe AI system performance in real-world clinical use was comparable to that seen in previous experiments. Based on this and physician survey results, the system can be deployed more widely at our institution, using insights gained from this evaluation to make further algorithm improvements and quality assurance of the AI system.
This study investigates the effect of radiation dose reduction of a renal perfusion CT protocol on quantitative imaging features for patients of different sizes. Our findings indicate that the impact of dose reduction is significantly different between patients of different sizes for standard deviation, entropy, and GLCM joint average at all dose levels evaluated, and for mean at the lowest dose level evaluated (p < .001). These results suggest that a size-based scanning protocol may be needed to provide quantitative results that are robust with respect to patient size.
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