How Real Are Computed Tomography Low Dose Simulations? An Investigational In-Vivo Large Animal Study.

OBJECTIVES: CT low-dose simulation methods have gained significant traction in protocol development, as they lack the risk of increased patient exposure. However, in-vivo validations of low-dose simulations are as uncommon as prospective low-dose image acquisition itself. Therefore, we investigated the extent to which simulated low-dose CT datasets resemble their real-dose counterparts. MATERIALS AND METHODS: Fourteen veterinarian-sedated alive pigs underwent three CT scans on the same third generation dual-source scanner with 2 months between each scan. At each time, three additional scans ensued, with mAs reduced to 50%, 25%, and 10%. All scans were reconstructed using wFBP and ADMIRE levels 1-5. Matching low-dose datasets were generated from the 100% scans using reconstruction-based and DICOM-based simulations. Objective image quality (CT numbers stability, noise, and signal-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (-1=inferior, 0=equal, 1=superior). The points were averaged for a semiquantitative score, and inter-rater-agreement was measured using Spearman's correlation coefficient. A structural similarity index (SSIM) analyzed the voxel-wise similarity of the volumes. Adequately corrected mixed-effects analysis compared objective and subjective image quality. Multiple linear regression with three-way interactions measured the contribution of dose, reconstruction mode, simulation method, and rater to subjective image quality. RESULTS: There were no significant differences between objective and subjective image quality of reconstruction-based and DICOM-based simulation on all dose levels (p≥0.137). However, both simulation methods produced significantly lower objective image quality than real-dose images below 25% mAs due to noise overestimation (p<0.001; SSIM≤89±3). Overall, inter-rater-agreement was strong (r≥0.68, mean 0.93±0.05, 95% CI 0.92-0.94; each p<0.001). In regression analysis, significant decreases in subjective image quality were observed for lower radiation doses (b ≤ -0.387, 95%CI -0.399 to -0.358; p<0.001) but not for reconstruction modes, simulation methods, raters, or three-way interactions (p≥0.103). CONCLUSION: Simulated low-dose CT datasets are subjectively and objectively indistinguishable from their real-dose counterparts down to 25% mAs, making them an invaluable tool for efficient low-dose protocol development.

Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort.

RATIONAL AND OBJECTIVES: Comparison of radiation dose and image quality in routine abdominal and pelvic contrast-enhanced computed tomography (CECT) between a photon-counting detector CT (PCD-CT) and a dual energy dual source CT (DSCT). MATERIALS AND METHODS: 70 oncologic patients (mean age 66 ± 12 years, 29 females) were prospectively enrolled between November 2021 and February 2022. Abdominal CECT were clinically indicated and performed first on a 2nd-generation DSCT and at follow-up on a 1st-generation dual-source PCD-CT. The same contrast media (Imeron 350, Bracco imaging) and pump protocol was used for both scans. For both scanners, polychromatic images were reconstructed with 3mm slice thickness and comparable kernel (I30f[DSCT] and Br40f[PCD-CT]); for PCD-CT data from all counted events above the lowest energy threshold at 20 keV ("T3D") were used. Results were compared in terms of radiation dose metrics of CT dose index (CTDIvol), dose length product (DLP) and size-specific dose estimation (SSDE), objective and subjective measurements of image quality were scored by two emergency radiologists including lesion conspicuity. RESULTS: Median time interval between the scans was 4 months (IQR: 3-6). CNRvessel and SNRvessel of T3D reconstructions from PCD-CT were significantly higher than those of DSCT (all, p < 0.05). Qualitative image noise analysis from PCD-CT and DSCT yielded a mean of 4 each. Lesion conspicuity was rated significantly higher in PCD-CT (Q3 strength) compared to DSCT images. CTDI, DLP and SSDE mean values for PCD-CT and DSCT were 7.98 ± 2.56 mGy vs. 14.11 ± 2.92 mGy, 393.13 ± 153.55 mGy*cm vs. 693.61 ± 185.76 mGy*cm and 9.98 ± 2.41 vs. 14.63 ± 1.63, respectively, translating to a dose reduction of around 32% (SSDE). CONCLUSION: PCD-CT enables oncologic abdominal CT with a significantly reduced dose while keeping image quality similar to 2nd-generation DSCT.

Effects of different virtual monoenergetic CT image data on chest wall post-processing "unfolded ribs" and proposal of an algorithm improvement.

PURPOSE: To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. MATERIAL AND METHODS: Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120 keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. RESULTS: Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120 keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. CONCLUSION: The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80 keV.

Reduced versus standard dose contrast volume for contrast-enhanced abdominal CT in overweight and obese patients using photon counting detector technology vs. second-generation dual-source energy integrating detector CT.

PURPOSE: To compare image quality of contrast-enhanced abdominal-CT using 1st-generation Dual Source Photon-Counting Detector CT (DS-PCD-CT) versus 2nd-generation Dual-Source Energy Integrating-Detector CT (DS-EID-CT) in patients with BMI ≥ 25, applying two different contrast agent volumes, vendor proposed protocols and different virtual monoenergetic images (VMI). METHOD: 68 overweight (BMI ≥ 25 kgm2) patients (median age: 65 years; median BMI 33.3 kgm2) who underwent clinically indicated, portal-venous contrast-enhanced abdominal-CT on a commercially available 1st-generation DS-PCD-CT were prospectively included if they already have had a pre-exam on 2nd-generation DS-EID-CT using a standardized exam protocol. Obesity were defined by BMI-calculation (overweight: 25-29.9, obesity grade I: 30-34.9; obesity grade II: 35-39.9; obesity grade III: > 40) and by the absolute weight value. Body weight adapted contrast volume (targeted volume of 1.2 mL/kg for the 1st study and 0.8 mL/kg for the 2nd study) was applied in both groups. Dual Energy mode was used for both the DS-PCD-CT and the DS-EID-CT. Polychromatic images and VMI (40 keV and 70 keV) were reconstructed for both the DS-EID-CT and the DS-PCD-CT data (termed T3D). Two radiologists assessed subjective image quality using a 5-point Likert-scale. Each reader drew ROIs within parenchymatous organs and vascular structures to analyze image noise, contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). RESULTS: Median time interval between scans was 12 months (Min: 6 months; Max: 36 months). BMI classification included overweight (n = 10, 14.7 %), obesity grade I (n = 38, 55.9 %), grade II (n = 13, 19.1 %) and grade III (n = 7, 10.3 %). The SNR achieved with DS-PCD-CT at QIR level 3was 12.61 vs. 11.47 (QIR 2) vs. 10.53 (DS-EID-CT), irrespective of parenchymatous organs. For vessels, the SNR were 16.73 vs. 14.20 (QIR 2) vs. 12.07 (DS-EID-CT). Moreover, the obtained median noise at QIR level 3 was as low as that of the DS-EID-CT (8.65 vs. 8.65). Both radiologists rated the image quality higher for DS-PCD-CT data sets (p < 0.05). The highest CNR was achieved at 40 keV for both scanners. T3D demonstrated significantly higher SNR and lower noise level compared to 40 keV and 70 keV. Median CTDIvol and DLP values for DS-PCD-CT and DS-EID-CT were 10.90 mGy (IQR: 9.31 - 12.50 mGy) vs. 16.55 mGy (IQR: 15.45 - 18.17 mGy) and 589.50 mGy * cm (IQR: 498.50 - 708.25 mGy * cm) vs. 848.75 mGy * cm (IQR: 753.43 - 969.58 mGy * cm) (p < 0.001). CONCLUSION: Image quality can be maintained while significantly reducing the contrast volume and the radiation dose (27% and 34% lower DLP and 31% lower CDTIvol) for abdominal contrast-enhanced CT using a 1st-generation DS-PCD-CT. Moreover, polychromatic reconstruction T3D on a DS-PCD-CT enables sufficient diagnostic image quality for oncological imaging.

AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans.

(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman's correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 ± 9.62 vs. 43.4 ± 4.45 vs. 34.8 ± 3.27 HU; each p < 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4−5) vs. 3 (4−5) vs. 3 (2−4), each p < 0.001) with good inter-rater agreement (r ≥ 0.790; p ≤ 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r ≥ 0.764; p < 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 ± 1.56 vs. 2.45 ± 1.90 vs. 2.66 ± 2.31 min; F (1.000; 99.00) = 268.1; p < 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially.

Image quality and dose exposure of contrast-enhanced abdominal CT on a 1st generation clinical dual-source photon-counting detector CT in obese patients vs. a 2nd generation dual-source dual energy integrating detector CT.

PURPOSE: To compare the radiation dose as well as the image quality of contrast-enhanced abdominal 1st-generation Photon-Counting Detector CT (PCD-CT) to a 2nd-generation Dual-Source Dual-Energy-Integrating-Detector CT (DSCT) in obese patients. METHOD: 51 overweight (BMI ≥ 25 kgm2) patients (median age: 67.00 years; IQR: 59.00-73.00, median BMI 32.15 kgm2; IQR: 28.70-35.76) who underwent clinically indicated, contrast-enhanced abdominal-CT in portal-venous phase on both 2nd-generation DSCT and on a commercially available 1st-generation PCD-CT were prospectively included the degree of obesity was defined by BMI-calculation (overweight, obesity grade I/30-34.9; obesity grade II/35-39.9; obesity grade III > 40) and by the absolute weight value. The same contrast media and pump protocol were used for both scans. PCD-CT was performed in Quantumplus mode at 120 kVp whereas DSCT used also 120 kVp in single energy mode. Comparable convolution algorithm between DSCT and PCD-CT were set. For both scanners, polychromatic images were reconstructed; for PCD-CT data from all counted events above the lowest energy threshold at 20 keV (termed T3D) were used. Two independent radiologists assessed subjective image quality using a 5-point Likert-scale and quantified the contrast-to-noise ratio of parenchymatous organs and vascular structures. RESULTS: Median time interval between the scans was 4 months (IQR 3-7 months). BMI was classified overweight (n = 18, 35.3%), grade I (n = 19, 37.3%), II (n = 9, 17.6%), III (n = 5, 9.8%). Mean CNRrenal_cortex (12.35 ± 3.77 vs. 14.16 ± 3.55) as well as median CNRvessels (9.88 vs. 12.40) and median CNRpancreas (2.81 vs. 4.04) of PCD-CT were significantly higher than those at DSCT (p < 0.05). The inter-reader agreement for all subjective image quality readings was moderate to substantial. Both radiologists independently rated the image quality higher for PCD-CT data sets (p < 0.05). Median CTDI and DLP values for PCD-CT and DSCT were 12.00 mGy (IQR: 10.20-13.50 mGy) vs. 16.05 mGy (IQR: 14.81-17.98) and 608 mGy * cm (IQR: 521.00-748.00 mGy * cm) vs. and 821.90 mGy * cm (IQR: 709.30-954.00 mGy * cm) (p < 0.001). CONCLUSION: Significant dose reduction by similar or even improved image quality was obtained with abdominal contrast-enhanced CT using PCD-CT in obese patients as compared to 2nd-generation DSCT.

AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging.

(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of "Radiation Dose", "Scanner", "Mode", "Rater", and "Timepoint" to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good-excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74-0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80-0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78-0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good-excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63-0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72-3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.

Predicting 1-, 3- and 5-year outcomes in patients with coronary artery disease: A comparison of available risk assessment scores.

BACKGROUND AND AIMS: Thromboischemic and bleeding events are rare but life-threatening complications after percutaneous coronary intervention (PCI). Various risk assessment models have been established to predict short- and long-term adverse events in patients with chronic and acute coronary syndromes (CCS, ACS). The aim of the present study was to compare available risk assessment systems based on their performance in identifying high-risk patients with symptomatic coronary artery disease (CAD). METHODS: We enrolled 1565 consecutive patients with symptomatic CAD (n = 821 CCS, n = 744 ACS). CALIBER, DAPT, GRACE 2.0, PARIS-CTE, PARIS-MB, PRECISE-DAPT and PREDICT-STABLE scores were calculated in appropriate patient subgroups. All patients were followed-up for 1, 3 and 5 years for all-cause death (ACD), myocardial infarction (MI), ischemic stroke (IS) and bleeding. The primary combined ischemic endpoint (CE) consisted of ACD, MI and/or IS. Secondary endpoints were defined as single occurrence of either ACD, MI, IS, or bleeding. RESULTS: GRACE 2.0 score showed good discrimination performance (AUC>0.7) for CE in a 3- and 5-year follow-up. CALIBER, GRACE 2.0 and PARIS-CTE showed best performance (AUC>0.7) in predicting ACD throughout the follow-up, whereas IS was best predicted by PARIS-CTE and CALIBER scores. None of the scores performed well (AUC>0.7) in predicting MI or bleeding. CONCLUSIONS: In a consecutive German CAD cohort, CALIBER, GRACE 2.0 and PARIS-CTE scores performed best in predicting CE, ACD and/or IS whereas none of the selected scores could predict MI and bleeding efficiently.