RATIONALE AND OBJECTIVES:To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the deep learning image reconstruction (DLIR) for participants with high body mass index (BMI). MATERIALS AND METHODS:This prospective study involved 180 participants (BMI of ≥ 25 kg/m2). Participants were randomly assigned to three groups: the standard-dose (SD) group in 100 kVp with adaptive statistical iterative reconstruction (ASIR-V 50%) using contrast media (CM) administration at an iodine load of 245 mgI/kg (Iohexol, 350 mgI/mL) with a 10 s injection time; the experimental group underwent imaging with 80 kVp and DLIR-H, using an iodine load of 128 mgI/kg with a 7 s injection time, representing the triple-low (TL) with 350 mgI/mL Iohexol and quadruple-low (QL) groups with 320 mgI/mL Iodixanol. Quantitative image quality evaluations included vascular attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), while qualitative image quality and diagnostic performance were also assessed. RESULTS:Compared to the SD group, the TL group achieved reductions of 29.4% in radiation dose, 47.5% in CM volume, and 23.5% in CM injection rate, while the QL group achieved reductions of 32.5% in radiation dose, 41.5% in CM volume, 14.5% in CM injection rate, and 9% in CM concentration. The image noise in the QL and TL groups were significantly lower than in the SD group (all p < 0.001). Overall, the SNR, CNR, and qualitative image quality of the TL and QL groups were superior to those of the SD group (p < 0.05). The QL group maintained the stenosis diagnostic performance (all p > 0.05). CONCLUSION:TL and QL CCTA protocols showed equivalent or superior image quality and diagnostic accuracy compared to the SD protocol and may serve as effective alternatives for reducing radiation dose and contrast media use in patients with high BMI.

Background Reducing the radiation dose without compromising image quality or diagnostic accuracy is essential for repeat CT monitoring in emphysema. Purpose To evaluate the accuracy of ultra-low-dose (ULD) photon-counting CT (PCCT) for both visually and quantitatively assessing emphysema, and to compare this accuracy with that of low-dose (LD) PCCT. Materials and Methods Participants with emphysema who underwent same-day LD and ULD PCCT between November 2024 and February 2025 were prospectively included. Two radiologists independently evaluated the image quality parameters (overall image quality, sharpness, artifacts, and noise) and visually assessed emphysema (subtype and severity). Automated emphysema quantification was performed using low-attenuation volume (LAV) analysis. For centrilobular emphysema (CLE), automated LAV measurements were converted to severity grades and compared with visual severity grades. Paired t tests, Cohen κ analysis, and intraclass correlation coefficients were used to evaluate differences and agreement in findings at LD and ULD PCCT. Results In 152 participants (median age, 68 years [IQR, 60-72 years]; 139 male participants), the ULD protocol reduced radiation exposure by 87% compared with the LD protocol (mean effective dose, 0.20 mSv ± 0.03 [SD] vs 1.58 mSv ± 0.39; P < .001). There was no evidence of a difference between the two protocols in overall image quality (median score, 4 [IQR, 4-5] for both; P = .16), sharpness (median score, 4 [IQR, 4-5] for both; P = .08), or artifacts (median score, 4 [IQR, 3-4] for both; P = .39), but ULD images had lower noise scores (ie, more noise) than LD images (median score, 3 [IQR, 3-3] vs 4 [IQR, 4-4]; P < .001). There was excellent agreement between the two protocols for grading of visual CLE severity (weighted κ = 0.98) and paraseptal emphysema severity (κ = 0.96). The two protocols exhibited excellent agreement in LAV measurements across the lungs and for individual lung lobes (intraclass correlation coefficient range, 0.96-0.98). Visual CLE severity grades demonstrated good agreement with LAV measurements for both the LD (weighted κ = 0.73) and ULD (weighted κ = 0.75) protocols. Conclusion PCCT enables accurate visual and automated assessments of emphysema at a radiation dose equivalent to that of two chest radiographs without compromising image quality. © RSNA, 2026 Supplemental material is available for this article.

Background: The bolus tracking technique has been used for decades, yet still faces the challenging task of determining the optimal scanning time for individuals. Our study aimed to assess the feasibility of a novel bolus tracking method with a personalized post-trigger delay (PTD) to optimize scanning time and achieve optimized enhancement and contrast homogeneity in aortic computed tomography angiography (CTA). Methods: Participants undergoing aortic CTA with bolus tracking were prospectively assigned to two different groups: Group A with a fixed 6-second PTD and Group B with a personalized PTD. A reader assessed objective image quality and evaluated enhancement level and contrast homogeneity; two readers rated subjective image quality. Student's t-test or Mann-Whitney U test was used to determine quantitative data, whereas the Chi-square test compared categorical variables between the two groups. Results: Group A comprised 70 participants [13 female; mean +/- standard deviation (SD) age 58 +/- 11 years], whereas Group B included 70 participants (18 female, mean +/- SD age 59 +/- 12 years) with the personalized PTD ranging from 7.8 to 14.1 seconds (mean +/- SD, 11.2 +/- 1.5 seconds). Group B demonstrated improved mean attenuation and contrast-to-noise ratio (CNR) of aortoiliac artery [417.55 +/- 71.55 vs. 345.71 +/- 60.41 Hounsfield units (HU), 16 vs. 13, both P<0.001, respectively]. Enhancement level (78.6% vs. 37.1%, P<0.001), contrast homogeneity (94.3% vs. 64.3%, P<0.001), and subjective ratings (scores greater than or equal to 4, 91.4% vs. 68.6%, P<0.001) were superior in Group B compared to Group A. Enhancement level of abdominal aortic branches in aortic dissection or aortic aneurysm patients was optimized in Group B (74.5% vs. 23.3%, P<0.001). Conclusions: Bolus tracking with a personalized PTD can improve enhancement level and contrast homogeneity in aortic CTA due to reliable scan timing.

PurposeCurrent guidelines provide a recognized yet broad framework for stratifying recurrence risk in differentiated thyroid cancer (DTC) patients. More precise tools are needed for intermediate- and high-risk groups. This study aims to identify recurrence-associated risk factors and develop a machine learning-based predictive model.MethodsIn this retrospective analysis, 2,388 DTC patients were randomly assigned to a training group (1,910 cases) and a validation group (478 cases). Predictive factors were identified using univariate and multivariate analyses. Six machine learning models were trained and validated, with performance evaluated through accuracy, area under the curve, and clinical utility via decision curve analysis.ResultsIndependent risk factors for recurrence included intraglandular dissemination, total tumor size, bilateral cervical lymph node involvement, and Hashimoto’s thyroiditis, while normal/elevated TSH and multifocal nodules were protective. The random forest model demonstrated the best performance (training accuracy: 0.801; validation accuracy: 0.808). A random forest-based online calculator was developed to facilitate individualized risk assessment in clinical settings.ConclusionsThe random forest model effectively predicts DTC recurrence, offering a practical tool for individualized risk assessment and aiding clinical decision-making.

Purpose To evaluate the efficacy and safety of ultrasound-guided percutaneous transluminal angioplasty for hemodialysis-associated venous hypertension syndrome caused by central venous stenosis or occlusion. Patients and methods The clinical data of 24 treatment instances with color duplex ultrasound-guided percutaneous transluminal angioplasty from January 2021 to January 2023 were retrospectively analyzed. The primary endpoint of the study was clinical success rate, while the secondary endpoints were 6- or 12-month vascular patency rates. Results Of the 24 treatment instances enrolled, 20 were primary onset and treated initially with percutaneous transluminal angioplasty under color duplex ultrasound guidance. Of these 20 patients, 17 were treated successfully (85%), while 3 procedures failed, requiring reoperation. Six patients showed recurrence within 1 year, of which four received repeated ultrasound-guided percutaneous transluminal angioplasty. Three of these procedures were successful (75.0%). Therefore, the total success rate was 83.3%. The patency rates were 76.5% at the 6-month follow-up and 64.7% at the 12-month follow-up. No patient developed complications such as dissection or perforation of the target vessel wall. Conclusion Color duplex ultrasound-guided percutaneous transluminal angioplasty is a feasible technique for hemodialysis-associated venous hypertension syndrome.

RATIONALE AND OBJECTIVES:This study evaluated the image quality of photon-counting detector computed tomography (PCD-CT) for head and neck vascular imaging using the "Quadruple Low" strategy (ultra-low contrast dose, low contrast concentration, low injection rate, and low radiation dose). The results were compared with those from conventional energy-integrating detector computed tomography (EID-CT) performed using routine scanning parameters. MATERIALS AND METHODS:This retrospective cohort study included 50 patients with cerebrovascular disease who underwent EID-CT with a standard contrast protocol (contrast dose: body weight × 0.6 mL/kg), serving as the control group (CG). Two prospective cohorts, each comprising 50 patients, underwent PCD-CT with an optimized contrast protocol (contrast dose: body weight × 0.4 mL/kg; injection rate: contrast dose/[delay time + scan duration]), forming the experimental groups (EG1 and EG2). In EG2, the radiation dose was further reduced relative to that in EG1. Objective image quality was assessed by measuring vascular attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Subjective image quality was evaluated using a 5-point Likert scale. RESULTS:This study included 150 patients (86 men; mean age, 56.03 ± 11.10 years; mean body mass index[BMI], 24.28 ± 3.03 kg/m²). Compared with the CG, the optimized contrast protocol in EG1 and EG2 reduced the median contrast volume by >30%, decreased the contrast concentration by 9%, and lowered the injection flow rate by 10% (all p < 0.001). The effective radiation dose in EG2 was reduced by 28% (p < 0.001) while maintaining image quality comparable to that of other groups (p > 0.05). EG2 exhibited a greater potential for radiation dose reduction. EG1 and EG2 exhibited significant improvements in vascular attenuation, reduced image noise, and enhanced SNR and CNR. Subjective image quality was consistently rated as high across all groups, with a median Likert score of 4 (IQR: 4-5), and excellent interobserver agreement (κ = 0.814-0.934), indicating consistently high diagnostic confidence. CONCLUSION:PCD-CT using the "Quadruple Low" strategy enables neurovascular imaging with substantially reduced contrast media and radiation exposure, while maintaining image quality comparable to conventional EID-CT. This protocol may serve as a safer alternative in routine cerebrovascular evaluation.

This study aims to assess the feasibility of “double-low,” low radiation dosage and low contrast media dosage, CT pulmonary angiography (CTPA) based on deep-learning image reconstruction (DLIR) algorithms. One hundred consecutive patients (41 females; average age 60.9 years, range 18–90) were prospectively scanned on multi-detector CT systems. Fifty patients in the conventional-dose group (CD group) underwent CTPA with 100 kV protocol using the traditional iterative reconstruction algorithm, and 50 patients in the low-dose group (LD group) underwent CTPA with a 70 kVp DLIR protocol. Radiation and contrast agent doses were recorded and compared between groups. Objective parameters were measured and compared. Two radiologists evaluated images for overall image quality, artifacts, and image contrast separately on a 5-point scale. The furthest visible branches were compared between groups. Compared to the control group, the study group reduced the dose-length product by 80.3

This study aimed to validate bolus tracking using a patient-tailored post-trigger delay (PTD) in run-off computed tomography angiography (CTA) and to compare the resulting image quality and diagnostic confidence with those obtained using a fixed PTD. Participants were prospectively assigned to either fixed (10 s) or patient-tailored cohorts. We measured attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for each vascular segment. Two radiologists evaluated qualitative image quality and diagnostic confidence. Differences in quantitative variables between cohorts were assessed using Student’s t-test or Mann–Whitney U test, while categorical variables were assessed using chi-square test. Generalized least squares linear regression was used to evaluate the effects of different cohorts, anatomical locations, and their interactions on attenuation. The patient-tailored cohort showed significantly higher attenuation from the popliteal artery to the foot artery (all p < 0.001), with a patient-tailored PTD ranging from 8.10 to 14.90 s. Generalized least squares linear regression revealed more uniform attenuation in patient-tailored cohort and decreasing attenuation in fixed cohort (decrease of 89.06 HU in foot artery, p < 0.001). There was no significant difference in image noise between cohorts (p = 0.060), while SNR and CNR were higher in the patient-tailored cohort at most measurement locations (all p < 0.05). Both radiologists rated the patient-tailored cohort as having better qualitative image quality and more segment with unrestricted diagnostic confidence. Bolus tracking with patient-tailored PTD provides reliable scan timing, resulting in optimized vessel opacification, improved image quality, and enhanced diagnostic confidence in run-off CTA. Question Is this novel bolus tracking algorithm for run-off CT angiography feasible for improving image quality by automatically predicting patient-tailored post-trigger delay (PTD) through real-time monitoring. Findings Patient-tailored PTD improved image quality and distal artery opacification through reliable scan timing, thereby enhancing diagnostic confidence. Clinical relevance Patient-tailored PTD improved image quality, not only demonstrating the potential for reducing contrast media volume but also eliminating the need for compensatory scans from the feet to the knee, thus minimizing additional radiation exposure.

Background:The adjacent bones and vessels in complex anatomic regions presented diagnostic challenges, especially in head and neck computed tomography angiography (CTA). It requires more accurate removal of bones for clinical routine diagnosis, which couldn't be fully satisfied with conventional bone removal techniques. This study aims to evaluate the performance of a novel deep learning-based bone removal algorithm compared to a conventional method in head and neck CTA, focusing on image quality and radiation dose optimization across two tube voltage settings (100 and 120 kVp). Methods:In a single-center randomized controlled trial (RCT) (February to March 2024), 119 consecutive patients [median age, 57 years; interquartile range (IQR), 50-65 years] suspected of cerebrovascular disease underwent head and neck CTA on a dual-source computed tomography (CT) scanner. Patients were randomized to 100 kVp (n=58) or 120 kVp (n=61) groups. Images were processed using a conventional threshold-based algorithm and a convolutional neural network (CNN)-based deep learning algorithm, which was trained on a dataset of 1,014 annotated CTA images. Two blinded radiologists assessed image quality (bone removal effectiveness, vessel branch completeness, whole vessel completeness) with a 5-point Likert scale. Radiation dose was recorded with CT dose index volume (CTDIvol) and dose-length product (DLP). Statistical analysis was performed with Wilcoxon signed-rank tests, Mann-Whitney U tests, and multivariate regression, and P<0.05 was regarded as significant. Results:The image quality of deep learning algorithm significantly outperformed that of conventional method across all metrics (P<0.001), with large effect sizes (Cohen's d, 0.886-1.028). Bone removal scores were higher at 100 kVp (median, 4.50; IQR, 4.50-4.50) than those at 120 kVp (median, 4.50; IQR, 3.50-4.50; P=0.002) with the deep learning algorithm, despite lower radiation doses at 100 kVp (CTDIvol, 8.4±0.9 vs. 12.5±1.2 mGy, P<0.001). Age negatively influenced whole vessel completeness (β=-0.0114, P=0.015), while body mass index (BMI) and hypertension showed no effect (P>0.05). Conclusions:The CNN-based bone removal algorithm enhances vascular visualization in head and neck CTA, particularly at 100 kVp, offering superior segmentation accuracy even at lower radiation dose. These findings advocate for its integration into clinical workflows to improve cerebrovascular diagnostics.

Background:Contrast-enhanced computed tomography (CT) is essential for tumor assessment, but the detection of low-contrast liver lesions remains challenging. Reducing the radiation dose increases image noise, compromising image quality and diagnostic accuracy. Iterative reconstruction (IR) algorithms can reduce noise; however, they can also alter image texture and limit lesion detection. Deep-learning image reconstruction (DLIR) represents a promising alternative, but its efficacy in ultra-low-dose (ULD) hepatic CT for detecting small, low-contrast lesions remains underexplored. Thus, this study aimed to evaluate a novel real-time DLIR algorithm in ULD hepatic CT, focusing on image quality and lesion detection. Methods:In total, 65 patients with hepatic lesions underwent both standard-dose and ULD abdominal CT scans during the portal venous phase. The standard-dose protocol (group A) used 120 kV with a signal-to-noise ratio (SNR) of 1.0, and the images were reconstructed using 50% IR. The ULD protocol (group B) used 120 kV with an SNR of 0.5, and the images were reconstructed using 50% IR and 50% DLIR (groups B1 and B2, respectively). The quantitative and qualitative image quality parameters were assessed. The lesion detection rates were evaluated by lesion type and size using the metrics of detection rate, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results:Group B showed a 73.3% reduction in the radiation dose compared to group A (1.5±0.8 vs. 6.9±2.0 mSv, P<0.001). Image noise differed significantly across the protocols: group B1 had the highest noise [10.05±2.94 Hounsfield units (HU)], followed by groups A (8.29±2.82 HU) and B2 (8.04±2.71 HU; all pairwise P<0.05 except group A vs. group B2: P=0.625). The CT values and contrast-to-noise ratios (CNRs) were comparable between groups B2 and A (all P>0.05), while group B2 had a 29.9-42.2% higher CNR than group B1 (all P<0.001). The qualitative assessments confirmed that the image quality and diagnostic acceptability of groups B2 (100%) and A (all P>0.05) were comparable, while the images of group B1 were diagnostically unacceptable (all scores <3). Overall, lesion detection was comparable in groups B2 (90.5%, 133/147) and A (98.0%, 144/147; P>0.05). However, group B2 had a significantly lower detection rate for small lesions (<0.5 cm: 77.8%, 42/54) compared to group A (P<0.05), but outperformed group B1 (57.4%, 31/54; P<0.05). Group B2 also had a significantly improved lesion detection rate and sensitivity for low-contrast lesions (87.2%, 95/109) compared to group B1 (75.2%, 82/109; P<0.05). The novel DLIR algorithm achieved a reconstruction speed of 60 images per second (ips), which was significantly faster than that of other DLIR approaches, while maintaining comparable performance to the IR algorithm. Conclusions:The combination of tube current reduction with a novel real-time DLIR algorithm enabled ULD abdominal CT to achieve a 73.3% reduction in the radiation dose while maintaining image quality and diagnostic performance for detecting hepatic lesions larger than 0.5 cm.

Pulse oximetry–triggered coronary CT angiography (CCTA) on a 16-cm z-axis coverage CT with motion correction and deep learning image reconstruction algorithms showed comparable image quality at equivalent radiation and contrast doses as conventional CCTA while significantly reducing examination time.

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To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0.6mL/kg (n = 70); Group C, 0.5mL/kg (n = 70). All patients were examined via a prospective ECG-triggered scan protocol within one heartbeat. A high level DLIR (DLIR-H) algorithm was used for image reconstruction with a thickness and interval of 0.625mm. The CT values of ascending aorta (AA), descending aorta (DA), three main coronary arteries, pulmonary artery (PA), and superior vena cava (SVC) were measured and analyzed for objective assessment. Two radiologists assessed the image quality and diagnostic confidence using a 5-point Likert scale. The CM doses were 46.81 ± 6.41mL, 41.96 ± 7.51mL and 34.65 ± 5.38mL for Group A, B and C, respectively. The objective assessments on AA, DA and the three main coronary arteries and the overall subjective scoring showed no significant difference among the three groups (all p > 0.05). The subjective assessment proved that excellent CCTA images can be obtained from the three different contrast media protocols. There were no significant differences in intracoronary attenuation values between the higher HR subgroup and the lower HR subgroup among three groups. CCTA reconstructed with DLIR could be realized with adequate enhancement in coronary arteries, excellent image quality and diagnostic confidence at low contrast dose of a 0.5mL/kg. The use of lower tube voltages may further reduce the contrast dose requirement.

Background: Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. Methods: This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50keV), 50 keV ), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) 50 k eV ) and 40 keV (LDDL40 40 keV), ), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 50 keV, e V , LD-DL50 50 keV, e V , and LD-DL40 40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Results: Compared to SD-IR50 50 keV VMIs, LD-DL50 50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL 40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL 50 keV and LD-DL 40 keV were consistent with those of SD-IR50 50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR 50 keV vs. LD-DL 50 keV vs. LD-DL40 keV: 40 k eV : 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions ( <= 0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. Conclusions: VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

ObjectiveThis study aims to investigate the image quality of a high-resolution, low-dose coronary CT angiography (CCTA) with deep learning image reconstruction (DLIR) and second-generation motion correction algorithms, namely, SnapShot Freeze 2 (SSF2) algorithm, and its diagnostic accuracy for in-stent restenosis (ISR) in patients after percutaneous coronary intervention (PCI), in comparison with standard-dose CCTA with high-definition mode reconstructed by adaptive statistical iterative reconstruction Veo algorithm (ASIR-V) and the first-generation motion correction algorithm, namely, SnapShot Freeze 1 (SSF1).MethodsPatients after PCI and suspected of having ISR scheduled for high-resolution CCTA (randomly for 100 kVp low-dose CCTA or 120 kVp standard-dose) and invasive coronary angiography (ICA) were prospectively enrolled in this study. After the basic information pairing, a total of 105 patients were divided into the LD group (60 patients underwent 100 kVp low-dose CCTA reconstructed with DLIR and SSF2) and the SD group (45 patients underwent 120 kVp standard-dose CCTA reconstructed with ASIR-V and SSF1). Radiation and contrast medium doses, objective image quality including CT value, image noise (standard deviation), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for the aorta, left main artery (LMA), left ascending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) of the two groups were compared. A five-point scoring system was used for the overall image quality and stent appearance evaluation. Binary ISR was defined as an in-stent neointimal proliferation with diameter stenosis ≥50% to assess the diagnostic performance between the LD group and SD group with ICA as the standard reference.ResultsThe LD group achieved better objective and subjective image quality than that of the SD group even with 39.1% radiation dose reduction and 28.0% contrast media reduction. The LD group improved the diagnostic accuracy for coronary ISR to 94.2% from the 83.8% of the SD group on the stent level and decreased the ratio of false-positive cases by 19.2%.ConclusionCompared with standard-dose CCTA with ASIR-V and SSF1, the high-resolution, low-dose CCTA with DLIR and SSF2 reconstruction algorithms further improves the image quality and diagnostic performance for coronary ISR at 39.1% radiation dose reduction and 28.0% contrast dose reduction.

PURPOSE:The aim of the study is to investigate the feasibility of using dual-source computed tomography (CT) combined with low flow rate and low tube voltage for postchemotherapy image assessment in cancer patients. METHODS:Ninety patients undergoing contrast-enhanced CT scans of the upper abdomen were prospectively enrolled and randomly assigned to groups A, B, and C (n = 30 each). In group A, patients underwent scans at 120 kVp with 448 mgI/kg. Patients in group B underwent scans at 100 kVp with 336 mgI/kg. Patient in group C underwent scans at 70 kVp with of 224 mgI/kg. Quantitative measurements including the CT number, standard deviation of CT number, signal-to-noise ratio, contrast-to-noise ratio, subjective reader scores, and the volume and flow rate of contrast agent were evaluated for each group. RESULTS:There was no statistically significant difference in the subjective image scores within the three groups except for the kidney (all P > 0.05). Group C showed significantly higher CT values, lower noise levels, and higher signal-to-noise ratio and contrast-to-noise ratio values in the majority of the regions of interest compared to the other groups ( P < 0.05). In group C, the contrast agent dose was decreased by 46% compared to group A (79.48 ± 12.24 vs 42.7 ± 8.6, P < 0.01), and the contrast agent injection rate was reduced by 22% (2.7 ± 0.41 vs 2.1 ± 0.4, P < 0.01). CONCLUSIONS:The use of 70 kVp tube voltage combined with low iodine flow rates prove to be a more effective approach in solving the challenge of compromised blood vessels in postchemotherapy tumor patients, without reducing image quality and diagnostic confidence.

OBJECTIVE:To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorithm (ClearInfinity, DLIR-CI). METHODS:Iodine attenuation curves obtained from a phantom study informed the administration of CM protocols. Non-obese participants undergoing aortic CTA were prospectively allocated into two groups and then obtained three reconstruction groups. The conventional group (100kVp-CV group) underwent imaging at 100kVp and received 210 mg iodine/kg in combination with a hybrid iterative reconstruction algorithm (ClearView, HIR-CV). The experimental group was imaged at 60kVp with 105 mg iodine/kg, while images were reconstructed with HIR-CV (60kVp-CV group) and with DLIR-CI (60kVp-CI group). Student's t-test was used to compare differences in CM protocol and radiation dose. One-way ANOVA compared CT attenuation, image noise, SNR, and CNR among the three reconstruction groups, while the Kruskal-Wallis H test assessed subjective image quality scores. Post hoc analysis was performed with Bonferroni correction for multiple comparisons, and consistency analysis conducted in subjective image quality assessment was measured using Cohen's kappa. RESULTS:The radiation dose (1.12 ± 0.23mSv vs. 2.03 ± 0.82mSv) and CM dosage (19.04 ± 3.03mL vs. 38.11 ± 6.47mL) provided the reduction of 45% and 50% in the experimental group compared to the conventional group. The CT attenuation, SNR, and CNR of 60kVp-CI were superior to or equal to those of 100kVp-CV. Compared to the 60kVp-CV group, images in 60kVp-CI showed higher SNR and CNR (all P < 0.001). There was no difference between the 60kVp-CI and 100kVp-CV group in terms of the subjective image quality of the aorta in various locations (all P > 0.05), with 60kVp-CI images were deemed diagnostically sufficient across all vascular segments. CONCLUSION:For non-obese patients, the combined use of 60kVp and DLIR-CI algorithm can be preserving image quality while enabling radiation dose and contrast medium savings for aortic CTA compared to 100kVp using HIR-CV.

OBJECTIVES:To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). METHODS:In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. RESULTS:Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. CONCLUSION:DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. CLINICAL RELEVANCE STATEMENT:Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. KEY POINTS:• The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

Objectives: To validate the peak enhancement timing of a patient-specific post-trigger delay (PTD) in Coronary CT angiography (CCTA) and compare its image quality against a fixed PTD.Methods: In this prospective study, 204 consecutive participants were randomly divided into two groups to perform CCTA in bolus tracking with either a fixed 5-second PTD (Group A) or a patient-specific PTD (Group B). Test bolus was also performed in Group B to determine the reference peak enhancement timing. One reader evaluated objective image quality, while two readers rated subjective image quality. The predicted PTD was validated through correlation and agreement analysis with the reference measurement. Objective image quality was compared between groups via two-sample t-test and linear regression, while the subjective ratings were compared with chi-square analysis.Results: The two groups each had 102 participants with comparable characteristics (52.9 +/- 11.3 versus 52.1 +/- 11.3 years of age, and 53 versus 52 males). The scan timing from patient-specific PTD demonstrated strong correlation (R = 0.77) and consistency (ICC = 0.618) with the reference peak timing. Both readers rated better subjective image quality for the Group B (p < 0.001). The mean vessel enhancement was significantly higher in Group B in all coronary vessels (all p < 0.05). After adjusting for the participant variation, the patient-specific PTD strategy was associated with an average of 33.5 HU higher enhancement compared to the fixed PTD.Conclusions: Patient-specific delay could achieve reliable scan timing, optimize vessel opacification and obtain better image quality in CCTA.