Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones.

Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and/or ureteral stones were included. All patients had undergone abdominal CT between August 2019 and October 2019. CT-images were reconstructed using the following three methods: filtered back-projection, iterative reconstruction, and PixelShine (DL-software) with both sharp and soft kernels. Stone size, CT attenuation, and objective image quality (signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) were evaluated and compared using Bonferroni-corrected Friedman tests. Objective image quality was measured in six regions-of-interest. Stone size ranged between 4.4 × 3.1−4.4 × 3.2 mm (sharp kernel) and 5.1 × 3.8−5.6 × 4.2 mm (soft kernel). Mean attenuation ranged between 704−717 Hounsfield Units (HU) (soft kernel) and 915−1047 HU (sharp kernel). Differences in measured stone sizes were ≤1.3 mm. DL-processed images resulted in significantly higher CNR and SNR values (p < 0.001) by decreasing image noise significantly (p < 0.001). DL-software significantly improved objective image quality while maintaining both correct stone size and CT-attenuation values. Therefore, the clinical impact of stone assessment in denoised image data sets remains unchanged. Through the relevant noise suppression, the software additionally offers the potential to further reduce radiation exposure.

Implementation of Institutional Size-Specific Diagnostic Reference Levels for CT Angiography.

RATIONALE AND OBJECTIVES: To generate institutional size-specific diagnostic reference levels (DRLs) for computed tomography angiography (CTA) examinations and assess the potential for dose optimization compared to size-independent DRLs. MATERIALS AND METHODS: CTA examinations of the aorta, the pulmonary arteries and of the pelvis/lower extremity performed between January 2016 and January 2017 were included in our retrospective study. Water equivalent diameter (Dw) was automatically calculated for each patient. The relationship between Dw and computed tomography dose index (CTDIvol) was analyzed and the 75th percentile was chosen as the upper limit for institutional DRLs. Size-specific institutional DRLs were compared to national size-independent DRLs from Germany and the UK. RESULTS: A total of 1344 examinations were included in our study (n = 733 aortic CTA, n = 406 pulmonary CTA, n = 205 pelvic/lower extremity CTA). Mean Dw was 26 ± 9 cm and mean CTDIvol was 7.0 ± 4.6 mGy. For all CTA protocols, there was a linear progression of CTDIvol with increasing Dw with an R²â€¯= 0.95 in aortic CTA, R²â€¯= 0.94 in pulmonary CTA and R²â€¯= 0.93 in pelvic/lower extremity CTA. Median CTDIvol increased by 0.57 mGy per additional cm Dw in aortic CTA, by 1.1 mGy in pulmonary CTA and by 0.31 mGy in pelvic/lower extremity CTA. Institutional DRLs were lower than national DRLs for average size patients (aortic CTA: Dw 28.2 cm, CTDIvol 7.6 mGy; pulmonary CTA, Dw 27.9 cm, CTDIvol 11.8 mGy; pelvic/lower extremity CTA, Dw 20.0 cm, CTDIvol 6.4 mGy). More dose outliers in small patients were detected with size-specific DRLs compared to national size-independent DRLs (56.4% vs 16.2%). CONCLUSION: We implemented institutional size-specific DRLs for CTA examinations which enabled a more precise analysis compared to national sizeindependent DRLs.

Potential of a machine-learning model for dose optimization in CT quality assurance.

OBJECTIVES: To evaluate machine learning (ML) to detect chest CT examinations with dose optimization potential for quality assurance in a retrospective, cross-sectional study. METHODS: Three thousand one hundred ninety-nine CT chest examinations were used for training and testing of the feed-forward, single hidden layer neural network (January 2016-December 2017, 60% male, 62 ± 15 years, 80/20 split). The model was optimized and trained to predict the volumetric computed tomography dose index (CTDIvol) based on scan patient metrics (scanner, study description, protocol, patient age, sex, and water-equivalent diameter (DW)). The root mean-squared error (RMSE) was calculated as performance measurement. One hundred separate, consecutive chest CTs were used for validation (January 2018, 60% male, 63 ± 16 years), independently reviewed by two blinded radiologists with regard to dose optimization, and used to define an optimal cutoff for the model. RESULTS: RMSE was 1.71, 1.45, and 1.52 for the training, test, and validation dataset, respectively. The scanner and DW were the most important features. The radiologists found dose optimization potential in 7/100 of the validation cases. A percentage deviation of 18.3% between predicted and actual CTDIvol was found to be the optimal cutoff: 8/100 cases were flagged as suboptimal by the model (range 18.3-53.2%). All of the cases found by the radiologists were identified. One examination was flagged only by the model. CONCLUSIONS: ML can comprehensively detect CT examinations with dose optimization potential. It may be a helpful tool to simplify CT quality assurance. CT scanner and DW were most important. Final human review remains necessary. A threshold of 18.3% between the predicted and actual CTDIvol seems adequate for CT quality assurance. KEY POINTS: • Machine learning can be integrated into CT quality assurance to improve retrospective analysis of CT dose data. • Machine learning may help to comprehensively detect dose optimization potential in chest CT, but an individual review of the results by an experienced radiologist or radiation physicist is required to exclude false-positive findings.

Tailoring CT Dose to Patient Size: Implementation of the Updated 2017 ACR Size-specific Diagnostic Reference Levels.

RATIONALE AND OBJECTIVES: To use an automatic computed tomography (CT) dose monitoring system to analyze the institutional chest and abdominopelvic CT dose data as regards the updated 2017 American College of Radiology (ACR) diagnostic reference levels (DRLs) based on water-equivalent diameter (Dw) and size-specific dose estimates (SSDE) to detect patient-size subgroups in which CT dose can be optimized. MATERIALS AND METHODS: All chest CT examinations performed between July 2016 and April 2017 with and without contrast material, CT of the pulmonary arteries, and abdominopelvic CT with and without contrast material were included in this retrospective study. Dw and SSDE were automatically calculated for all scans using a previously validated in-house developed Matlab software and stored into our CT dose monitoring system. CT dose data were analyzed as regards the updated ACR DRLs (size groups: 21-25 cm, 25-29 cm, 29-33 cm, 33-37 cm, 37-41 cm). SSDE and volumetric computed tomography dose index (CTDIvol) were used as CT dose parameter. RESULTS: Overall, 30,002 CT examinations were performed in the study period, 3860 of which were included in the analysis (mean age 62.1 ± 16.4 years, Dw 29.0 ± 3.3 cm; n = 577 chest CT without contrast material, n = 628 chest CT with contrast material, n = 346 CT of chest pulmonary, n = 563 abdominopelvic CT without contrast material, n = 1746 abdominopelvic CT with contrast material). Mean SSDE and CTDIvol relative to the updated DRLs were 43.3 ± 26.4 and 45.1 ± 27.9% for noncontrast chest CT, 52.3 ± 23.1 and 52.0 ± 23.1% for contrast-enhanced chest CT, 68.8 ± 29.5 and 70.0 ± 31.0% for CT of pulmonary arteries, 41.9 ± 29.2 and 43.3 ± 31.3% for noncontrast abdominopelvic CT, and 56.8 ± 22.2 and 58.8 ± 24.4% for contrast-enhanced abdominopelvic CT. Lowest dose compared to the DRLs was found for the Dw group of 21-25 cm in noncontrast abdominopelvic CT (SSDE 30.4 ± 21.8%, CTDIvol 30.8 ± 21.4%). Solely the group of patients with a Dw of 37-41 cm undergoing noncontrast abdominopelvic CT exceeded the ACR DRL (SSDE 100.3 ± 59.0%, CTDIvol 107.1 ± 63.5%). CONCLUSIONS: On average, mean SSDE and CTDIvol of our institutional chest and abdominopelvic CT protocols were lower than the updated 2017 ACR DRLs. Size-specific subgroup analysis revealed a wide variability of SSDE and CTDIvol across CT protocols and patient size groups with a transgression of DRLs in noncontrast abdominopelvic CT of large patients (Dw 37-41 cm).