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PurposeTo assess the dose performance in terms of image quality of filtered back projection (FBP) and two generations of iterative reconstruction (IR) algorithms developed by the most common CT vendors.Materials and methodsWe used four CT systems equipped with a hybrid/statistical IR (H/SIR) and a full/partial/advanced model-based IR (MBIR) algorithms. Acquisitions were performed on an ACR phantom at five dose levels. Raw data were reconstructed using a standard soft tissue kernel for FBP and one iterative level of the two IR algorithm generations. The noise power spectrum (NPS) and the task-based transfer function (TTF) were computed. A detectability index (d′) was computed to model the detection task of a large mass in the liver (large feature; 120 HU and 25-mm diameter) and a small calcification (small feature; 500 HU and 1.5-mm diameter).ResultsWith H/SIR, the highest values of d′ for both features were found for Siemens, then for Canon and the lowest values for Philips and GE. For the large feature, potential dose reductions with MBIR compared with H/SIR were − 35% for GE, − 62% for Philips, and − 13% for Siemens; for the small feature, corresponding reductions were − 45%, − 78%, and − 14%, respectively. With the Canon system, a potential dose reduction of − 32% was observed only for the small feature with MBIR compared with the H/SIR algorithm. For the large feature, the dose increased by 100%.ConclusionThis multivendor comparison of several versions of IR algorithms allowed to compare the different evolution within each vendor. The use of d′ is highly adapted and robust for an optimization process.Key Points• The performance of four CT systems was evaluated by using imQuest software to assess noise characteristic, spatial resolution, and lesion detection.• Two task functions were defined to model the detection task of a large mass in the liver and a small calcification.• The advantage of task-based image quality assessment for radiologists is that it does not include only complicated metrics, but also clinically meaningful image quality.

The current version (ver. 7.3) of the popular quantitative coronary analysis system QAngio XA (Medis Medical Imaging System BV, Leiden, the Netherlands) is widely used without evaluating the agreement between the current and older versions in relation to a change of algorithms. The purpose of this study was to assess the equivalence of averages between QAngio XA versions 7.3 and 6.0. Based on the calculated sample size, angiographic images of 100 patients who underwent percutaneous coronary intervention of a single target lesion were randomly selected from two published studies (OUCH-TL: 154 lesions; OUCH-PRO: 160 lesions). The primary endpoint was the minimum lumen diameter (MLD), and the secondary endpoints were the reference diameter (RefD) and length of the stenotic lesion (LL). Two independent analysts measured the same frame using both previous and current versions of QAngio XA. Version-order for each lesion was randomly determined per coronary locations targeted. Data were analysed by using a mixed model that includes random lesion effects and fixed rater effects and reading-order effects. A Bland–Altman plot of parameters showed no large differences between the versions. Differences in parameters were estimated by the mixed model, and the 95% confidence interval of the MLD, RefD, and LL estimates was from −0.045 to −0.0001 mm, from −0.040 to 0.006 mm, and from −1.08 to 0.46 mm, respectively, compared with the predefined non-inferiority margin of ±0.2 mm. Measurements of MLD and RefD using QAngio XA showed no major systematic differences between versions.

Objectives To compare the image quality and hepatic metastasis detection of low-dose deep learning image reconstruction (DLIR) with full-dose filtered back projection (FBP)/iterative reconstruction (IR). Methods A contrast-detail phantom consisting of low-contrast objects was scanned at five CT dose index levels (10, 6, 3, 2, and 1 mGy). A total of 154 participants with 305 hepatic lesions who underwent abdominal CT were enrolled in a prospective non-inferiority trial with a three-arm design based on phantom results. Data sets with full dosage (13.6 mGy) and low dosages (9.5, 6.8, or 4.1 mGy) were acquired from two consecutive portal venous acquisitions, respectively. All images were reconstructed with FBP (reference), IR (control), and DLIR (test). Eleven readers evaluated phantom data sets for object detectability using a two-alternative forced-choice approach. Non-inferiority analyses were performed to interpret the differences in image quality and metastasis detection of low-dose DLIR relative to full-dose FBP/IR. Results The phantom experiment showed the dose reduction potential from DLIR was up to 57% based on the reference FBP dose index. Radiation decreases of 30% and 50% resulted in non-inferior image quality and hepatic metastasis detection with DLIR compared to full-dose FBP/IR. Radiation reduction of 70% by DLIR performed inferiorly in detecting small metastases (< 1 cm) compared to full-dose FBP (difference: −0.112; 95% confidence interval [CI]: −0.178 to 0.047) and full-dose IR (difference: −0.123; 95% CI: −0.182 to 0.053) ( p < 0.001). Conclusion DLIR enables a 50% dose reduction for detecting low-contrast hepatic metastases while maintaining comparable image quality to full-dose FBP and IR. Key Points • Non-inferiority study showed that deep learning image reconstruction (DLIR) can reduce the dose to oncological patients with low-contrast lesions without compromising the diagnostic information. • Radiation dose levels for DLIR can be reduced to 50% of full-dose FBP and IR for detecting low-contrast hepatic metastases, while maintaining comparable image quality. • The reduction of radiation by 70% by DLIR is clinically acceptable but insufficient for detecting small low-contrast hepatic metastases (< 1 cm).

Objectives To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26 kg/m 2 , in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V). Methods A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group ( n  = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group ( n  = 50) with 120 kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal–noise-ratio (SNR) for vessel) were compared between the two groups. Results Low-dose group used significantly reduced contrast dose (23.82 ± 3.69 mL, 50.6% reduction) and radiation dose (0.75 ± 0.14 mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38 mL and 1.65 ± 0.66 mSv, respectively) (all p  < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p  < 0.001) compared to the conventional group. Conclusions The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V. Key Points •  The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol . •  CCTA of normal size (BMI < 26 kg/m 2 ) patients acquired at sub-mSv radiation dose and 24 mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement . •  DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination .

Purpose To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). Methods Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. Results Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p  < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p  < 0.0001); and (c) a higher level of objective noise ( p  < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ ( p  = 0.05) with less subjective image noise in Group 2 ( p  = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups ( p  < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 ( n  = 60; 84.5% vs n  = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups ( p  < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 ( n  = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts ( n  = 52; 73.2%). Conclusion PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. Clinical relevance statement The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. Key Points The speed of data acquisition with PCD-CT accounts for mild motion artifacts . Sharply delineated fissures are depicted on PCD-CT perfusion images . High-quality perfusion imaging was obtained with a 52% dose reduction .

Objectives To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. Methods This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n  = 74) or 80-kVp protocol (360 mgL/kg, n  = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale. Results SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p  < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p  < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p  < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p  < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR ( p  ≥ 0.38). Conclusion Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT. Clinical relevance statement Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT. Key Points Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.

Objective To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC). Methods Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis. Results Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p  = .003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p  < .001) than the SD protocol. The comparative analysis demonstrated that CNR ( p  < .001) and portal vein conspicuity ( p  = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p  = .140) and HCCs (75.7% vs. 70.4%, p  = .644) between the SD protocol and DLD-DL. Conclusions DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC. Key Points • Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.

Objectives To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. Methods We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets ( n = 100) and the validation datasets ( n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. Results The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K -values > 0.80, p < 0.05). Conclusions The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. Key Points • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.

Objectives To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of coronary artery disease (CAD). Materials and methods Adults with indications for CCTA were prospectively and consecutively enrolled at two hospitals and randomly assigned to either FAAI-based or semi-automated image processing using equipment workstations. Outcome measures were workflow efficiency, diagnostic accuracy for obstructive CAD (≥ 50% stenosis), and cardiovascular events at 2-year follow-up. The endpoints included major adverse cardiovascular events, hospitalization for unstable angina, and recurrence of cardiac symptoms. The non-inferiority margin was 3 percentage difference in diagnostic accuracy and C-index. Results In total, 1801 subjects (62.7 ± 11.1 years) were included, of whom 893 and 908 were assigned to the FAAI-based and semi-automated modes, respectively. Image processing times were 121.0 ± 18.6 and 433.5 ± 68.4 s, respectively ( p  <0.001). Scan-to-report release times were 6.4 ± 2.7 and 10.5 ± 3.8 h, respectively ( p  < 0.001). Of all subjects, 152 and 159 in the FAAI-based and semi-automated modes, respectively, subsequently underwent invasive coronary angiography. The diagnostic accuracies for obstructive CAD were 94.7% (89.9–97.7%) and 94.3% (89.5–97.4%), respectively (difference 0.4%). Of all subjects, 779 and 784 in the FAAI-based and semi-automated modes were followed for 589 ± 182 days, respectively, and the C-statistic for cardiovascular events were 0.75 (0.67 to 0.83) and 0.74 (0.66 to 0.82) (difference 1%). Conclusions FAAI-based CCTA image processing significantly improves workflow efficiency than semi-automated mode, and is non-inferior in diagnosing obstructive CAD and risk stratification for cardiovascular events. Clinical relevance statement Conventional coronary CT angiography image processing is semi-automated. This observation shows that fully automated artificial intelligence-based image processing greatly improves efficiency, and maintains high diagnostic accuracy and the effectiveness in stratifying patients for cardiovascular events. Key Points • Coronary CT angiography (CCTA) relies heavily on high-quality and fast image processing. • Full-automation CCTA image processing is clinically non-inferior to the semi-automated mode. • Full automation can facilitate the application of CCTA in early detection of coronary artery disease.

Chest radiography is the most common diagnostic imaging examination performed in emergency departments (EDs). Augmenting clinicians with automated preliminary read assistants could help expedite their workflows, improve accuracy, and reduce the cost of care. To assess the performance of artificial intelligence (AI) algorithms in realistic radiology workflows by performing an objective comparative evaluation of the preliminary reads of anteroposterior (AP) frontal chest radiographs performed by an AI algorithm and radiology residents. This diagnostic study included a set of 72 findings assembled by clinical experts to constitute a full-fledged preliminary read of AP frontal chest radiographs. A novel deep learning architecture was designed for an AI algorithm to estimate the findings per image. The AI algorithm was trained using a multihospital training data set of 342 126 frontal chest radiographs captured in ED and urgent care settings. The training data were labeled from their associated reports. Image-based F1 score was chosen to optimize the operating point on the receiver operating characteristics (ROC) curve so as to minimize the number of missed findings and overcalls per image read. The performance of the model was compared with that of 5 radiology residents recruited from multiple institutions in the US in an objective study in which a separate data set of 1998 AP frontal chest radiographs was drawn from a hospital source representative of realistic preliminary reads in inpatient and ED settings. A triple consensus with adjudication process was used to derive the ground truth labels for the study data set. The performance of AI algorithm and radiology residents was assessed by comparing their reads with ground truth findings. All studies were conducted through a web-based clinical study application system. The triple consensus data set was collected between February and October 2018. The comparison study was preformed between January and October 2019. Data were analyzed from October to February 2020. After the first round of reviews, further analysis of the data was performed from March to July 2020. The learning performance of the AI algorithm was judged using the conventional ROC curve and the area under the curve (AUC) during training and field testing on the study data set. For the AI algorithm and radiology residents, the individual finding label performance was measured using the conventional measures of label-based sensitivity, specificity, and positive predictive value (PPV). In addition, the agreement with the ground truth on the assignment of findings to images was measured using the pooled κ statistic. The preliminary read performance was recorded for AI algorithm and radiology residents using new measures of mean image-based sensitivity, specificity, and PPV designed for recording the fraction of misses and overcalls on a per image basis. The 1-sided analysis of variance test was used to compare the means of each group (AI algorithm vs radiology residents) using the F distribution, and the null hypothesis was that the groups would have similar means. The trained AI algorithm achieved a mean AUC across labels of 0.807 (weighted mean AUC, 0.841) after training. On the study data set, which had a different prevalence distribution, the mean AUC achieved was 0.772 (weighted mean AUC, 0.865). The interrater agreement with ground truth finding labels for AI algorithm predictions had pooled κ value of 0.544, and the pooled κ for radiology residents was 0.585. For the preliminary read performance, the analysis of variance test was used to compare the distributions of AI algorithm and radiology residents' mean image-based sensitivity, PPV, and specificity. The mean image-based sensitivity for AI algorithm was 0.716 (95% CI, 0.704-0.729) and for radiology residents was 0.720 (95% CI, 0.709-0.732) (P = .66), while the PPV was 0.730 (95% CI, 0.718-0.742) for the AI algorithm and 0.682 (95% CI, 0.670-0.694) for the radiology residents (P < .001), and specificity was 0.980 (95% CI, 0.980-0.981) for the AI algorithm and 0.973 (95% CI, 0.971-0.974) for the radiology residents (P < .001). These findings suggest that it is possible to build AI algorithms that reach and exceed the mean level of performance of third-year radiology residents for full-fledged preliminary read of AP frontal chest radiographs. This diagnostic study also found that while the more complex findings would still benefit from expert overreads, the performance of AI algorithms was associated with the amount of data available for training rather than the level of difficulty of interpretation of the finding. Integrating such AI systems in radiology workflows for preliminary interpretations has the potential to expedite existing radiology workflows and address resource scarcity while improving overall accuracy and reducing the cost of care.