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Objective: We investigated the effect of low-tube-voltage CT angiography with a reduced volume of contrast agent on qualitative and quantitative parameters and the radiation dose in patients with peripheral arterial disease.Methods: Eighty consecutive patients were divided into two groups; the protocol was 120 kVp and 1.8 ml/kg contrast agent (300 mgI/ml) in one group and 80 kVp and 1.2 ml/kg in the other. For quantitative analysis we calculated the mean arterial attenuation, the mean difference between maximum and minimum attenuation values, contrast-to-noise ratio (CNR), and figure of merit (FOM). For qualitative evaluation, we used visual scores.Results: There were no significant intergroup differences in mean arterial attenuation (120 vs. 80 kVp: 331.6 ± 61.6 vs. 354.9 ± 61.9 HU) and in the mean difference between maximum and minimum attenuation (120 vs. 80 kVp: 52.2 ± 25.5 vs. 61.5 ± 27.5 HU). While the mean CNR was significantly higher at 120 than 80 kVp (38.4 ± 18.8 vs. 31.1 ± 15.3), the mean FOM was not significantly different (120 vs. 80 kVp: 1.3 ± 1.5 vs. 1.2 ± 1.2), and there was no significant intergroup difference in visual scores. The mean dose-length product was significantly lower at 80 than 120 kVp (1,024.3 ± 151.3 vs. 1,464.7 ± 208.7 mGy·cm).Conclusion: The 80-kVp protocol allows for reduction of the radiation dose by approximately 30% and the volume of contrast agent by more than 30% without deterioration of vascular enhancement and image quality. [ABSTRACT FROM AUTHOR]

Purpose: Using glass rod dosimeters we investigated the radiation dose to the operator performing interventional procedures in 43 patients with the aid of a monoplane flat detector-based angiography system.Materials and Methods: During the procedures we recorded the number of radiographic frames and the radiographic conditions. After treatment we recorded the fluoroscopy time and the fluoroscopic, radiographic, and total air kerma. To obtain the total operator exposure dose we took measurements at five sites: left orbital fossa, thyroid, left hand, left chest, and pubic symphysis.Results: The mean operator exposure dose to the left hand was higher than at the other sites we measured; it was 387.0, 209.6, 174.3, and 237.1 microGy for the stentgraft, percutaneous transluminal arteriography, transarterial chemoembolization, and hepatic infusion port placement procedures. There was a positive correlation between the fluoroscopic and radiographic air kerma value and the operator exposure dose at the left orbital fossa, thyroid, and left hand.Conclusion: The operator exposure dose correlated with the radiographic and fluoroscopic air kerma. Exposure of the operator's left hand was higher than at the other sites studied. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the detectability of simulated pulmonary nodules with different X-ray attenuation by flat-panel detector (FPD) chest radiography using a dual-exposure dual-energy subtraction (DES) technique.Materials and Methods: Using a FPD radiography system, we obtained 108 sets of chest radiographs of a chest phantom. They consisted of 54 sets each of chest radiographs with and without simulated nodules. Each data set contained a standard and a corresponding dual-energy subtracted chest radiograph (DES image). The diameters of the simulated nodules were 8, 10, and 12 mm, respectively; nodules of each size manifested attenuation of -450, -200, and 30 Hounsfield units (HU). We performed receiver operating characteristic (ROC) analysis to compare the observers' performance in detecting nodules.Results: For the -450 HU nodules the mean areas under the ROC curve (AUC) without and with DES images were 0.66 and 0.77, respectively; the difference was significant (paired t-test, P < 0.01). For nodules with -200 and 30 HU, there was no significant difference in the AUC value (0.79 vs. 0.77, P = 0.13; 0.92 vs. 0.94, P = 0.17, respectively).Conclusion: The addition of DES images to standard chest radiographs improved the performance of radiologists charged with detecting simulated nodules with an attenuation of -450 HU. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the diagnostic performance of 256-slice cardiac CT for the evaluation of the in-stent lumen by using a hybrid iterative reconstruction (HIR) algorithm combined with a high-resolution kernel. Methods: This study included 28 patients with 28 stents who underwent cardiac CT. Three different reconstruction images were obtained with: (1) a standard filtered back projection (FBP) algorithm with a standard cardiac kernel (CB), (2) an FBP algorithm with a high-resolution cardiac kernel (CD), and (3) an HIR algorithm with the CD kernel. We measured image noise and kurtosis and used receiver operating characteristics analysis to evaluate observer performance in the detection of in-stent stenosis. Results: Image noise with FBP plus the CD kernel (80.2 ± 15.5 HU) was significantly higher than with FBP plus the CB kernel (28.8 ± 4.6 HU) and HIR plus the CD kernel (36.1 ± 6.4 HU). There was no significant difference in the image noise between FBP plus the CB kernel and HIR plus the CD kernel. Kurtosis was significantly better with the CD- than the CB kernel. The kurtosis values obtained with the CD kernel were not significantly different between the FBP- and HIR reconstruction algorithms. The areas under the receiver operating characteristics curves with HIR plus the CD kernel were significantly higher than with FBP plus the CB- or the CD kernel. The difference between FBP plus the CB- or the CD kernel was not significant. The average sensitivity, specificity, and positive and negative predictive value for the detection of in-stent stenosis were 83.3, 50.0, 33.3, and 91.6% for FBP plus the CB kernel, 100, 29.6, 40.0, and 100% for FBP plus the CD kernel, and 100, 54.5, 40.0, and 100% for HIR plus the CD kernel. Conclusions: The HIR algorithm combined with the high-resolution kernel significantly improved diagnostic performance in the detection of in-stent stenosis. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data., Methods: We retrospectively analyzed scan data of adult patients who underwent body CT during two periods relative to DLR implementation at our facility: a 12-month pre-DLR phase (n = 5553) using hybrid iterative reconstruction and a 12-month post-DLR phase (n = 5494) with routine CT reconstruction transitioning to DLR. To ensure comparability between two groups, we employed propensity score matching 1:1 based on age, sex, and body mass index. Dose data were collected to estimate organ-specific equivalent doses and total effective doses. We assessed the average dose reduction post-DLR implementation and estimated the Lifetime Attributable Risk (LAR) for cancer per CT exam pre- and post-DLR implementation. The number of radiation-induced cancers before and after the implementation of DLR was also estimated., Results: After propensity score matching, 5247 cases from each group were included in the final analysis. Post-DLR, the total effective body CT dose significantly decreased to 15.5 ± 10.3 mSv from 28.1 ± 14.0 mSv pre-DLR (p < 0.001), a 45% reduction. This dose reduction significantly lowered the radiation-induced cancer risk, especially among younger women, with the estimated annual cancer incidence from 0.247% pre-DLR to 0.130% post-DLR., Conclusion: The implementation of DLR has the possibility to reduce radiation dose by 45% and the risk of radiation-induced cancer from 0.247 to 0.130% as compared with the iterative reconstruction., Key Points: Question Can implementing deep learning reconstruction (DLR) in routine CT scans significantly reduce radiation dose and the risk of radiation-induced cancer compared to hybrid iterative reconstruction? Findings DLR reduced the total effective body CT dose by 45% (from 28.1 ± 14.0 mSv to 15.5 ± 10.3 mSv) and decreased estimated cancer incidence from 0.247 to 0.130%. Clinical relevance Adopting DLR in clinical practice substantially lowers radiation exposure and cancer risk from CT exams, enhancing patient safety, especially for younger women, and underscores the importance of advanced imaging techniques., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Toshinori Hirai. Conflict of interest: T.H. has received research support from Canon Medical Systems. T.T. is an employee of Canon Medical Systems. Canon Medical Systems had no control over the interpretation, writing, or publication of this work. T.N. controlled the study data. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Informed consent was waived by the Institutional Review Board. Ethical approval: Institutional Review Board approval was obtained. Study subjects or cohorts overlap: Some study subjects or cohorts have not been previously reported. Methodology: Retrospective Observational Performed at one institution, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background: This study aimed to determine whether spectral imaging with dual-energy computed tomography (CT) can improve diagnostic performance for coronary plaque characterization., Methods and Results: We conducted a retrospective analysis of 30 patients with coronary plaques, using coronary CT angiography (dual-layer CT) and intravascular ultrasound (IVUS) studies. Based on IVUS findings, patients were diagnosed with either vulnerable or stable plaques. We computed 7 histogram parameters for plaque CT numbers in 120 kVp images and virtual monochromatic images ranging from 40 to 140 keV at 5-keV intervals. A predictive model was developed using histogram data of optimal energy, plaque volume or stenosis, and a combination of both. The model's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) using 5-fold cross-validation. Peak diagnostic performances for each histogram parameter were observed at various energy levels (40-110 keV) in the univariate logistic regression model. The histogram model demonstrated optimal diagnostic performance at 65 keV, with an AUC of 0.81. The combined model, incorporating histogram data and plaque volume, achieved an AUC of 0.85, which was similar to the performance of qualitative CT characteristics (AUC=0.88; P=0.70)., Conclusions: Spectral imaging with dual-energy CT can enhance the diagnostic performance of machine learning using CT histograms for coronary plaque characterization., Competing Interests: T.N. has received research support from Nemoto Kyorindo Co., Ltd. T.H. has received research support from Canon Medical Systems. The department of diagnostic imaging analysis, to which M.K. belongs, is an endowed chair supported by Philips Healthcare. The Nemoto Kyorindo Co., Ltd, Philips Healthcare and Canon Medical Systems had no control over the interpretation, writing, or publication of this work., (Copyright © 2024, THE JAPANESE CIRCULATION SOCIETY.)

Rationale and Objectives: To evaluate the performance of various multimodal large language models (LLMs) in the Japanese Diagnostic Radiology Board Examinations (JDRBE) both with and without images., Materials and Methods: Five multimodal LLMs-GPT-4o, Claude 3 Opus, GPT-4 Vision, Gemini Flash 1.5, and Gemini Pro 1.5-were tested using questions from the JDRBE from 2021 to 2023. The models' performances were assessed in two conditions: with images and without images. Accuracy rates were calculated for each model, both overall and within specific subspecialties, including Abdominal and Pelvic Radiology, Musculoskeletal and Breast Imaging, Neuroradiology and Head and Neck Imaging, Nuclear Medicine, and Thoracic and Cardiac Radiology., Results: The average accuracy rates of the LLMs ranged from 30.21% to 45.00%, with GPT-4o achieving the highest (45.00%). Claude 3 Opus performed best without images (45.83%), while the addition of images did not significantly improve accuracy for any model. Performance varied across subspecialties, with GPT-4o excelling in "Other" (65.63%) and Claude 3 Opus in Neuroradiology and Head and Neck Imaging (55.56%). Importantly, none of the models surpassed the passing threshold of 60%., Conclusion: Our findings demonstrate that multimodal LLMs exhibit a range of accuracy in JDRBE, with GPT-4o and Claude 3 Opus showing the highest overall performance. However, the addition of images did not significantly improve accuracy for any model., Summary: Multimodal LLMs are a very promising tool in the field of radiology. However, our study shows that while there are some promising results, their ability to evaluate radiological medical images is currently limited. Further development seems necessary before they can be used routinely., Key Points: Multimodal LLMs show varying accuracy (30.21-45.83%) on Japanese diagnostic radiology board examinations. Adding images did not significantly improve multimodal LLM performance, and significantly decreased accuracy for one model. Performances of multimodal LLMs varied considerably across radiology subspecialties., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Toshinori Hirai reports research support from Canon Medical Systems. Canon Medical Systems had no control over the interpretation, writing, or publication of this work., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

The air-gap method is a technique employed to control dose distribution and radiation scattering in medical imaging. By introducing a layer of air between the radiation source and the object, this method effectively reduces the impact of scattered radiation. The purpose of this study was to investigate the suitability of the air-gap method for radiation dose reduction in pediatric patients during computed tomography (CT) examinations. Only one type of neonate phantom is used with 64 detector-row CT scanner while helical scanning the chest. The distance between the CT table and the subject was 0 mm at the conventional method and 150 mm at the air-gap method. The values of the real-time skin dosimeter on the dorsal surface of the body, and on the left and right mammary glands and image noise are measured and compared for each method. Compared with the conventional method, it was possible to reduce the exposure dose and image noise by approximately 10% and 15%, respectively, using the air-gap method (p < 0.05). The air-gap method was useful for reducing the radiation dose during pediatric CT examinations compared with the conventional method., (© 2024. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.)

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., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Rationale and Objectives: This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths., Materials and Methods: A Catphan phantom equipped with an external body ring was used. CT images were reconstructed using filtered back-projection (FBP), HIR, NR-DLR, and SR-DLR across three noise reduction strengths: mild, standard, and strong. The noise power spectrum (NPS) was obtained from the FBP, HIR, NR-DLR, and SR-DLR images at various FOVs, radiation doses, and noise reduction strengths. The noise magnitude ratio (NMR) and central frequency ratio (CFR) were calculated from the HIR, NR-DLR, and SR-DLR images relative to the FBP images using NPS. The high-contrast value was obtained from the amplitude values of the peaks and valleys of profile curve and the task-based transfer function were also analyzed., Results: SR-DLR consistently demonstrated superior noise reduction capabilities, with NMR of 0.29-0.36 at reduced dose and 0.35-0.45 at standard dose, outperforming HIR and showing comparable efficiency to NR-DLR. The high-contrast values for SR-DLR were highest at mild and standard levels for both low and standard doses (0.610 and 0.726 at mild and 0.725 and 0.603 at standard levels). At the standard dose, the spatial resolution of SR-DLR was significantly improved, regardless of the noise reduction strength and FOV., Conclusion: SR-DLR images achieved more substantial noise reduction than HIR and similar noise reduction as NR-DLR reconstructions while also improving spatial resolution., Competing Interests: Declaration of Competing Interest Toshinori Hirai is a recipient of research grant from Canon Medical Systems Corporation. Yuya Ito, Yutaka Chiba are employees of Canon Medical Systems Corporation., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Introduction and Objectives: To compare the real time skin dosimeter values at lens between with the lens included in the scan range (orbitomeatal base line [OML]) or without the lens included in the scan range (superior orbitomeatal line [SOML]) at different tube voltages., Materials and Methods: We used three pediatric anthropomorphic phantoms with a 64 detector-row computed tomography (CT) scanner with the OML- or SOML-protocol at different tube voltages during the head CT. A real time skin dosimeter was inserted into the phantom center of the head, and surfaces of the lens. We compared the real time skin dosimeter values at lens between the OML- and SOML-protocol., Results: There were no significant differences in the real time skin dosimeter values for the head in the scan area for each phantom at different tube voltages between the OML- and SOML-protocol (p > 0.05 for all phantom). Compared with the OML protocol, it is possible to reduce the real time skin dosimeter values at lens by approximately 80% by using the SOML protocol (p < 0.05) at all tube voltages. Compared with the OML protocol, it is possible to reduce the real time skin dosimeter values at mammary gland by approximately 20% by using the SOML protocol (p < 0.05) at all tube voltages., Conclusions: During the pediatric head CT examination, SOML protocol was possible to reduce the real time skin dosimeter values at lens by approximately 80% compared with OML protocol at all tube voltages., (Copyright © 2023. Published by Elsevier España, S.L.U.)

Objectives: This study assessed whether patient-specific contrast enhancement optimizer simulation software (p-COP) can reduce the contrast material (CM) dose compared with the conventional body weight (BW)-tailored scan protocol during transcatheter aortic valve implantation-computed tomography angiography (TAVI-CTA) in patients with aortic stenosis., Methods: We used the CM injection protocol selected by the p-COP in group A (n = 30). p-COP uses an algorithm that concerns data on an individual patient's cardiac output. Group B (n = 30) was assigned to the conventional BW-tailored CM injection protocol group. We compared the CM dose, CM amount, injection rate, and computed tomography (CT) values in the abdominal aorta between the 2 groups and classified them as acceptable (>280 Hounsfield units (HU)) or unacceptable (<279 HU) based on the optimal CT value and visualization scores for TAVI-CTA. We used the Mann-Whitney U test to compare patient characteristics and assess the interpatient variability of subjects in both groups., Results: Group A received 56.2 mL CM and 2.6 mL/s of injection, whereas group B received 76.9 mL CM and 3.4 mL/s of injection ( P < 0.01). The CT value for the abdominal aorta at the celiac level was 287.0 HU in group A and 301.7HU in group B ( P = 0.46). The acceptable (>280 HU) and unacceptable (<280 HU) CT value rates were 22 and 8 patients in group A and 24 and 6 patients in group B, respectively ( P = 0.76). We observed no significant differences in the visualization scores between groups A and B (visualization score = 3, P = 0.71)., Conclusion: The utilization of p-COP may decrease the CM dosage and injection rate by approximately 30% in individuals with aortic stenosis compared with the body-weight-tailored scan protocol during TAVI-CTA., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

To predict endoleaks after thoracic endovascular aneurysm repair (TEVAR) we submitted patient characteristics and vessel features observed on pre- operative computed tomography angiography (CTA) to machine-learning. We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent TEVAR for the presence or absence of an endoleak. We evaluated the effect of machine learning of the patient age, sex, weight, and height, plus 22 vascular features on the ability to predict post-TEVAR endoleaks. The extreme Gradient Boosting (XGBoost) for ML system was trained on 14 patients with- and 131 without endoleaks. We calculated their importance by applying XGBoost to machine learning and compared our findings between with those of conventional vessel measurement-based methods such as the 22 vascular features by using the Pearson correlation coefficients. Pearson correlation coefficient and 95% confidence interval (CI) were r = 0.86 and 0.75 to 0.92 for the machine learning, r = - 0.44 and - 0.56 to - 0.29 for the vascular angle, and r = - 0.19 and - 0.34 to - 0.02 for the diameter between the subclavian artery and the aneurysm (Fig. 3a-c, all: p < 0.05). With machine-learning, the univariate analysis was significant higher compared with the vascular angle and in the diameter between the subclavian artery and the aneurysm such as the conventional methods (p < 0.05). To predict the risk for post-TEVAR endoleaks, machine learning was superior to the conventional vessel measurement method when factors such as patient characteristics, and vascular features (vessel length, diameter, and angle) were evaluated on pre-TEVAR thoracic CTA images., (© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)

To assess the diagnostic performance of unenhanced electrocardiogram (ECG)-gated cardiac computed tomography (CT) for detecting myocardial edema, using MRI T2 mapping as the reference standard. This retrospective study protocol was approved by our institutional review board, which waived the requirement for written informed consent. Between December 2017 to February 2019, consecutive patients who had undergone T2 mapping for myocardial tissue characterization were identified. We excluded patients who did not undergo unenhanced ECG-gated cardiac CT within 3 months from MRI T2 mapping or who had poor CT image quality. All patients underwent unenhanced ECG-gated cardiac CT with an axial scan using a third-generation, 320 × 0.5 mm detector-row CT unit. Two radiologists together drew regions of interest (ROIs) in the interventricular septum on the unenhanced ECG-gated cardiac CT images. Using T2 mapping as the reference standard, the diagnostic performance of unenhanced cardiac CT for detecting myocardial edema was evaluated by using the area under the receiver operating characteristic curve with sensitivity and specificity. Youden index was used to find an optimal sensitivity-specificity cutoff point. A cardiovascular radiologist independently performed the measurements, and interobserver reliability was assessed using intraclass correlation coefficients for CT value measurements. A P value of <.05 was considered statistically significant. We included 257 patients who had undergone MRI T2 mapping. Of the 257 patients, 35 patients underwent unenhanced ECG-gated cardiac CT. One patient was excluded from the study because of poor CT image quality. Finally, 34 patients (23 men; age 64.7 ± 14.6 years) comprised our study group. Using T2 mapping, we identified myocardial edema in 19 patients. Mean CT and T2 values for 34 patients were 46.3 ± 2.7 Hounsfield unit and 49.0 ± 4.9 ms, respectively. Mean CT values moderately correlated with mean T2 values (Rho = -0.41; P < .05). Mean CT values provided a sensitivity of 63.2% and a specificity of 93.3% for detecting myocardial edema, with a cutoff value of ≤45.0 Hounsfield unit (area under the receiver operating characteristic curve = 0.77; P < .01). Inter-observer reproducibility in measuring mean CT values was excellent (intraclass correlation coefficient = 0.93; [95% confidence interval: 0.86, 0.96]). Myocardial edema could be detected by CT value of myocardium in unenhanced ECG-gated cardiac CT., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography., Methods: We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.25-second intervals. We constructed logistic regression models using the IPIR ratio as the independent variable and extravasation occurrence as the dependent variable (IPIR ratio models) at 1, 2, 3, 4, 5, and 6 seconds after the start of contrast administration. Univariate logistic regression models in which injection pressure is used as an independent variable (injection pressure models) were also constructed as a reference baseline. The performance of the models was evaluated with the area under the receiver operating characteristic curves., Results: Of the 57,528 cases, 46,022 were assigned to the training group and 11,506 were assigned to the test group, which included 112 extravasation cases (0.24%) in the training group and 28 (0.24%) in the test group. The area under the receiver operating characteristic curves for the IPIR ratio models and injection pressure models were 0.555 versus 0.563 at t = 1 (P = 0.270), 0.712 versus 0.678 at t = 2 (P = 0.305), 0.758 versus 0.693 at t = 3 (P = 0.032), 0.776 versus 0.688 at t = 4 (P = 0.005), 0.810 versus 0.699 at t = 5 (P = 0.002), and 0.811 versus 0.706 at t = 6 (P = 0.002)., Conclusions: The IPIR ratio models perform better in detecting contrast extravasation at 3 to 6 seconds after the start of contrast administration than injection pressure models., Competing Interests: Toshinori Hirai has received research support from Canon Medical Systems and is a guarantor of this study. Takeshi Nakaura has received research support from Nemoto Kyorindo Co, Ltd. Nemoto Kyorindo Co, Ltd had no control over the interpretation, writing, or publication of this work. There are no conflicts of interest to report for the other authors., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA., Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale., Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively)., Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1204/coif). D.U. reports that he has received research grants from Canon Medical Systems paid to Department of Diagnostic Radiology at Yokohama City University Graduate School of Medicine. T.T. reports that he is an employee of Canon Medical Systems Corporation. The other authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

The study investigated radiation dose, vascular computed tomography (CT) enhancement and image quality of cardiac computed tomography angiography (CCTA) with and without bolus tracking (BT) methods in infants with congenital heart disease (CHD). The volume CT dose index (CTDIvol) and dose length product (DLP) were recorded for all CT scans, and the effective dose was obtained using a conversion factors. The CT number for the ascending aorta (AO) and pulmonary artery (PA), image noise of muscle tissue and contrast-to-noise ratio (CNR) were measured and calculated. The median values in the groups with and without BT were 2.20 mGy versus 0.44 mGy for CTDIvol, 8.10 mGy·cm versus 6.20 mGy·cm for DLP, and 0.66 mSv versus 0.51 mSv for effective dose (p < 0.001). There were no statistical differences in vascular CT enhancement, image noise, and CNR. CCTA without BT methods can reduce the radiation dose while maintaining vascular CT enhancement and image quality compared to CCTA with BT methods., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Purpose: In this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports., Methods: This retrospective study involved 28 patients who underwent computed tomography (CT) scans and had a diagnosed disease with typical imaging findings. Radiology reports were generated using GPT-2, GPT-3.5, and GPT-4 based on the patient's age, gender, disease site, and imaging findings. We calculated the top-1, top-5 accuracy, and mean average precision (MAP) of differential diagnoses for GPT-2, GPT-3.5, GPT-4, and radiologists. Two board-certified radiologists evaluated the grammar and readability, image findings, impression, differential diagnosis, and overall quality of all reports using a 4-point scale., Results: Top-1 and Top-5 accuracies for the different diagnoses were highest for radiologists, followed by GPT-4, GPT-3.5, and GPT-2, in that order (Top-1: 1.00, 0.54, 0.54, and 0.21, respectively; Top-5: 1.00, 0.96, 0.89, and 0.54, respectively). There were no significant differences in qualitative scores about grammar and readability, image findings, and overall quality between radiologists and GPT-3.5 or GPT-4 (p > 0.05). However, qualitative scores of the GPT series in impression and differential diagnosis scores were significantly lower than those of radiologists (p < 0.05)., Conclusions: Our preliminary study suggests that GPT-3.5 and GPT-4 have the possibility to generate radiology reports with high readability and reasonable image findings from very short keywords; however, concerns persist regarding the accuracy of impressions and differential diagnoses, thereby requiring verification by radiologists., (© 2023. The Author(s).)

The study aimed to compare the performance of photon-counting detector computed tomography (PCD CT) with high-resolution (HR)-plaque kernel with that of the energy-integrating detector CT (EID CT) in terms of the visualization of the lumen size and the in-stent stenotic portion at different coronary vessel angles. The lumen sizes in PCD CT and EID CT images were 2.13 and 1.80 mm at 0°, 2.20 and 1.77 mm at 45°, and 2.27 mm and 1.67 mm at 90°, respectively. The lumen sizes in PCD CT with HR-plaque kernel were wider than those in EID CT. The mean degree of the in-stent stenotic portion at 50% was 69.7% for PCD CT and 90.4% for EID CT. PCD CT images with HR-plaque kernel enable improved visualization of lumen size and accurate measurements of the in-stent stenotic portion compared to conventional EID CT images regardless of the stent direction., Competing Interests: There are no conflicts of interest., (Copyright: © 2024 Journal of Medical Physics.)

Purpose: This study aimed to predict contrast effects in cardiac computed tomography (CT) from CT localizer radiographs using a deep learning (DL) model and to compare the prediction performance of the DL model with that of conventional models based on patients' physical size., Methods: This retrospective study included 473 (256 men and 217 women) cardiac CT scans between May 2014 and August 2017. We developed and evaluated DL models that predict milligrams of iodine per enhancement of the aorta from CT localizer radiographs. To assess the model performance, we calculated and compared Pearson correlation coefficient ( r ) between the actual iodine dose that was necessary to obtain a contrast effect of 1 HU (iodine dose per contrast effect [IDCE]) and IDCE predicted by DL, body weight, lean body weight, and body surface area of patients., Results: The model was tested on 52 cases for the male group (mean [SD] age, 63.7 ± 11.4) and 44 cases for the female group (mean [SD] age, 69.8 ± 11.6). Correlation coefficients between the actual and predicted IDCE were 0.607 for the male group and 0.412 for the female group, which were higher than the correlation coefficients between the actual IDCE and body weight (0.539 for male, 0.290 for female), lean body weight (0.563 for male, 0.352 for female), and body surface area (0.587 for male, 0.349 for female)., Conclusions: The performance for predicting contrast effects by analyzing CT localizer radiographs with the DL model was at least comparable with conventional methods using the patient's body size, notwithstanding that no additional measurements other than CT localizer radiographs were required., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) on the image quality of coronary CT angiography (CCTA)., Methods: Forty-one patients who underwent CCTA using a 320-row scanner were retrospectively included. Images were reconstructed with hybrid (HIR), model-based iterative reconstruction (MBIR), normal-resolution deep-learning-based reconstruction (NR-DLR), and SR-DLR algorithms. For each image series, image noise, and contrast-to-noise ratio (CNR) at the left main trunk, right coronary artery, left anterior descending artery, and left circumflex artery were quantified. Blooming artifacts from calcified plaques were measured. Image sharpness, noise magnitude, noise texture, edge smoothness, overall quality, and delineation of the coronary wall, calcified and noncalcified plaques, cardiac muscle, and valves were subjectively ranked on a 4-point scale (1, worst; 4, best). The quantitative parameters and subjective scores were compared among the four reconstructions. Task-based image quality was assessed with a physical evaluation phantom. The detectability index for the objects simulating the coronary lumen, calcified plaques, and noncalcified plaques was calculated from the noise power spectrum (NPS) and task-based transfer function (TTF)., Results: SR-DLR yielded significantly lower image noise and blooming artifacts with higher CNR than HIR, MBIR, and NR-DLR (all p < 0.001). The best subjective scores for all the evaluation criteria were attained with SR-DLR, with significant differences from all other reconstructions (p < 0.001). In the phantom study, SR-DLR provided the highest NPS average frequency, TTF 50% , and detectability for all task objects., Conclusion: SR-DLR considerably improved the subjective and objective image qualities and object detectability of CCTA relative to HIR, MBIR, and NR-DLR algorithms., Clinical Relevance Statement: The novel SR-DLR algorithm has the potential to facilitate accurate assessment of coronary artery disease on CCTA by providing excellent image quality in terms of spatial resolution, noise characteristics, and object detectability., Key Points: • SR-DLR designed for CCTA improved image sharpness, noise property, and delineation of cardiac structures with reduced blooming artifacts from calcified plaques relative to HIR, MBIR, and NR-DLR. • In the task-based image-quality assessments, SR-DLR yielded better spatial resolution, noise property, and detectability for objects simulating the coronary lumen, coronary calcifications, and noncalcified plaques than other reconstruction techniques. • The image reconstruction times of SR-DLR were shorter than those of MBIR, potentially serving as a novel standard-of-care reconstruction technique for CCTA performed on a 320-row CT scanner., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)