Objective:The point dose and volume CT dosimetry index(CTDIvol)at different X-ray energies were measured using the Raysafe-X2 air ionization chamber(referred to as"RX2"),the Piranha CT dose profiler semiconductor ionization chamber(referred to as"CDP"),and the 30013 Farmer air ionization chamber(referred to as"PT3"),in order to explore the effect of X-ray energy on the radiation dose evaluation by different dose meters.Methods:The tube voltage settings for each layer were as follows:single energy mode included 80kV,90kV,100kV,120kV,140kV,and 150kV(Sn);dual energy mode included 70/150kV(Sn),80/150kV(Sn),90/150kV(Sn),100/150kV(Sn),and 80/140kV.The tube current was manually adjusted to ensure that the CTDIvol for each group was approximately 20 mGy,and the displayed CTDIvol and DLP were recorded.Point dose values were measured at the center,0-point,3-point,6-point,and 9-point positions of the 32cm phantom using CDP and PT3.The actual CTDIvol was measured and calculated using RX2,CDP,and PT3.Results:(1)Point Dose:In single energy mode(except for Sn150kV),CDP measured higher point doses at the center compared to PT3.At low energies,the point dose measured by CDP at non-center positions was 75%higher than that of PT3,while at high energies,88%of the point doses measured by PT3 were higher than those measured by CDP.In dual energy mode(except for 90+Sn150kV),CDP measured higher point doses at the center compared to PT3.However,except for 80+140kV,PT3 measured higher point doses than CDP at non-center positions.(2)As for CTDIvol,the measured values were as follows:RX2 measured(18.89±0.38)mGy,CDP measured 18.31(19.25,20.84)mGy,and PT3 measured(20.35±0.38)mGy.Except when CDP was used at 150kV(Sn),the CTDIvol measured by all three dose meters for other X-ray energies ranged between 16～24mGy.Conclusion:Different dosimeters exhibit varying responses to X-ray energy.Therefore,selecting an appropriate dosimeter is crucial depending on the measurement conditions and objectives.

Objective.Computed tomography (CT) is an indispensable tool in clinical diagnosis. However, it involves non-negligible risks associated with exposure to ionizing radiation. Accurate radiation dose assessment is essential for quantifying radiation-related risks. To enable the accurate evaluation of radiation doses in Chinese patients undergoing CT examinations, we aim to develop a Chinese adult mesh phantom library based on the Chinese population.Approach.Firstly, we analyze the relationships between body size and anthropometric parameters in the Chinese adult population. Secondly, anatomical contours are obtained from clinical images using both automated and manual image segmentation algorithms, and these contours are then used to construct personalized mesh phantoms. Finally, organ doses are simulated using an in-house Geant4-based CT simulation software to investigate their correlations with body size.Main results.Compared with the Chinese anatomical reference data, the organ masses of the normal-weight phantom show discrepancies of approximately 10%. At the commonly used tube voltage of 120 kVp for clinical abdominal CT examinations, the maximum dose differences among male phantoms with different body sizes were 5.04 mGy for the liver, 8.49 mGy for the kidneys, 8.21 mGy for the spleen, and 7.92 mGy for the pancreas. The corresponding maximum dose differences were 8.48 mGy for the liver, 7.33 mGy for the kidneys, 6.73 mGy for the stomach, and 7.14 mGy for the pancreas for female phantoms.Significance.We establish a cohort of mesh phantoms that accurately represent Chinese adults according to the linear regression relationship between body size and human parameters. The observed differences in organ doses among different phantoms further highlight the importance of developing individualized computational phantoms for Chinese adults.

Objective: This study investigates the effect of a 0.3125 mm ultra-high-resolution detector combined with a ClearInfinity (CI) deep-learning reconstruction algorithm on the image quality of orbital computed tomography (CT). Methods: Scans were performed using a NeuViz Epoch Elite CT scanner on a Catphan 600 phantom and three 7-year-old rhesus monkeys. The collimation widths were set to 64 mm×0.625 mm and 128 mm×0.3125 mm. Images were acquired using filtered back projection (FBP), 60% adaptive iterative reconstruction algorithm ClearView (CV), and 60% deep learning reconstruction algorithm CI. Image quality was evaluated using objective indicators such as the modulation transfer function (MTF) and contrast-to-noise ratio (CNR), as well as using double-blind subjective scoring. Additionally, statistical analyses were performed. Results: In phantom experiments, under standard and bone algorithms, images with a collimation width of 128×0.3125 mm showed significantly better performances in terms of MTF50%, MTF10%, and some CNR indicators compared with those with a collimation width of 64×0.625 mm. The CNR of the CI algorithm was significantly higher than those of the FBP and CV algorithms. In animal experiments, the CNR of the medial rectus in images with a 128×0.3125 mm collimation width was significantly higher than that in images with a 64×0.625 mm collimation width. The CI algorithm achieved the optimal CNR for the medial rectus and eyeball, as well as the highest subjective scores, with good consistency between two radiologists’ subjective scores (Kappa≥0.75). Conclusion: The 0.3125 mm ultra-high-resolution detector combined with the CI deep-learning algorithm significantly improved the resolution and contrast of orbital CT images as well as reduced noise and artifacts, thereby demonstrating promising clinical-application prospects.

Objective:This study investigates the effects of different scanning modes and noise index(NI)on inner ear computed tomography(CT)image quality and radiation dose.Methods:(1)Head anthropomorphs phantoms were scanned using a 64-slice helical CT scanner.Both Axial and Helical scanning modes were employed,with each mode using progressively increasing NI values(3-6,totaling 21 values).In the Helical mode,each NI value was used to reconstruct images in two modes:Conventional Helical reconstruction and Helical reconstruction with spiral artifact correction(IQE).Three groups of images were obtained,each comprising 63 sequential images collected and reconstructed.Axial scanning was performed with a rotational speed of 1 second,a Helical scanning pitch of 0.531︰1,and a speed of 1 second,whereas the other scanning parameters remained constant.(2)Volume CT dose index(CTDIvol)and dose-length product(DLP)were recorded under different scanning conditions to analyze radiation dose.(3)Both objective and subjective image quality assessments were performed.Objective evaluations were conducted on three image groups,calculating the signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR),and figure of merit(FOM)for the region of interest(ROI).Subjective evaluations included assessments of image edge contrast,image intensity,axial noise and artifacts,and multiplanar reconstruction(MPR)quality;a double-blinded method with a 5-point scale was used.The optimal NI value was determined based on the FOM values and subjective image quality assessments.Results:(1)In both Axial and Helical scanning modes,CTDIvol and DLP decreased as NI values increased.No significant difference was observed in CTDIvol,but a significant difference was noted in DLP(2)Statistically significant differences were observed in soft tissue SNR,as well as SNR,CNR,and FOM for bone tissue,between Axial and Helical conventional modes and IQE mode,Axial imaging with an NI of 4.7 yielded optimal FOM and subjective evaluation scores.Conclusion:For inner ear CT scanning(range≤4 mm)using a 64-row CT scanner,MPR reconstruction without fault artifacts can be achieved by obtaining thin-layer images through single-layer scanning without bed movement.Considering that the radiation dose and image quality are better than those of conventional spiral scans or even IQE scans,MPR reconstruction without fault artifacts can be performed using 64-row CT.The recommended NI setting for inner ear imaging is 4.7,as it provides satisfactory image quality while minimizing radiation exposure.

Objective: To investigate the radiation dose levels of adult head and chest CT examinations in a grassroots medical institution in a suburban area of Beijing, evaluate their rationality, and provide a basis for establishing the diagnostic reference level (DRL) for this region. Methods: Based on 15 CT devices of 11 grassroots medical institutions within the jurisdiction, the relevant scanning parameters and radiation dose data of head and chest CT examinations were collected, the dose distribution was statistically analyzed, and comparisons were made with the recommended values of DRL from both domestic and international sources. Results: The DRL of CT dose index (CTDI) and dose-length product (DLP) for head CT was 59.83 mGy and 814.08 mGy·cm, respectively, while the DRL of volume CT dose index (CTDIvol) and DLP for chest CT was 13.68 mGy and 444.51 mGy·cm, both of which were lower than the industry standards in China. The DRL of head CT was superior to most national standards, and the differences in other DRLs were not significant. The radiation dose in tertiary hospitals was significantly lower than that in primary hospitals, and there were significant differences in both scanning parameters and radiation doses. Conclusion: The overall radiation dose of adult head and chest CT in grassroots medical institutions in this region is within a reasonable range. However, the CT scanning parameters of some grassroots hospitals need to be optimized to further reduce the radiation risk for patients.

Objective: This study aimed to investigate the effect of ultra-high-resolution (UHR) detector computed tomography (CT) combined with a deep learning reconstruction algorithm (ClearInfinity (CI)) on cranial CT image quality and its potential for radiation dose reduction. Methods: A NeuViz Epoch Elite CT scanner was used to scan a Catphan 600 phantom (with volume CT dose index (CTDIvol) set to 50, 37.5, and 25 mGy) and three rhesus monkeys (CTDIvol=50 mGy). The collimation width was 128×0.3125 mm. Images were reconstructed using filtered back projection (FBP), adaptive iterative reconstruction (ClearView, CV30% and CV60%), and deep learning reconstruction (ClearInfinity, CI30% and CI60%). Image quality was evaluated using objective metrics, such as modulation transfer function (MTF), contrast-to-noise ratio (CNR), and artifact severity, as well as double-blind subjective scoring on a 5-point scale. Statistical analyses were then performed. Results: (1) Phantom experiments: At all dose levels, the CNR increased significantly with higher reconstruction levels, with the CI60% images showing a significantly higher CNR than the other algorithms. At 25 mGy, the CNR of CI60% was comparable to that of FBP at 50 mGy, and no significant decrease was observed for MTF10% or MTF50%. (2) Animal experiments: At the centrum semiovale level, the CNR of the CI60% images was significantly higher than that obtained with other algorithms, and artifacts tended to decrease with increasing iteration levels. Inter-observer agreement for image quality assessment was good (Kappa≥0.75). Overall, the subjective scores increased with higher CV/CI levels, with CI60% achieving the highest scores. Conclusion: In UHR detector CT, deep learning reconstruction can improve cranial CT image contrast and reduce noise and artifacts without compromising high-contrast spatial resolution, showing significant potential for radiation dose reduction and demonstrating good clinical application value.

Computed tomography (CT) plays a crucial role in modern clinical diagnostics due to its ability to provide rapid and detailed anatomical imaging. However, CT imaging presents substantial concerns regarding ionizing radiation exposure, which can increase the risk of radiation-induced complications, especially with repeated scans. Therefore, minimizing radiation dose without compromising diagnostic accuracy has become a critical research topic. Artificial intelligence (AI), characterized by its powerful data-processing capabilities and adaptive learning mechanisms, is revolutionizing numerous aspects of modern medicine. In particular, AI demonstrates promising potential to optimize various stages of the CT examination process. This optimization not only improves clinical efficiency but also contributes to a significant reduction in unnecessary radiation exposure. The comprehensive review aims to systematically explore the diverse applications of AI for reducing radiation dose across the entire CT examination pipeline. During the pre-scanning and data acquisition phase, AI enables automated patient positioning by employing real-time visual recognition. AI can identify anatomical landmarks and adjust patient alignment to ensure optimal image acquisition geometry, thereby reducing the need for repeat scans caused by misalignment. In the scanning phase, AI algorithms are employed to determine personalized scanning parameters based on patient-specific characteristics such as body size, anatomical region, and clinical indication. Moreover, AI enhances automatic exposure control systems by integrating historical imaging data and contextual information, ensuring that radiation output is dynamically adjusted during scanning to maintain diagnostic quality at the lowest feasible dose. Following data acquisition, AI has facilitated the advancement of deep learning-based reconstruction algorithms in the image reconstruction stage. These algorithms are particularly effective in low-dose CT denoising and sparse-view reconstruction, producing high-quality diagnostic images even when fewer projections or lower radiation levels are used. This approach enables clinicians to maintain diagnostic confidence while minimizing radiation exposure. After scanning, AI continues to perform automated radiation dose estimation and analysis. These tools provide quantitative feedback on delivered dose metrics and enable the generation of personalized scanning protocols tailored to individual risk profiles. Over time, such feedback mechanisms contribute to the development of adaptive systems that learn from cumulative imaging data to continuously optimize safety and efficacy. Despite the encouraging progress, there are several challenges in translating AI technologies into routine clinical practice. Issues such as limited interpretability of complex models, data privacy and security concerns, and uncertainty in image reliability under extremely low-dose conditions must be addressed. Transparent model design, robust validation across diverse populations, and adherence to regulatory frameworks are necessary to facilitate responsible integration. In conclusion, we highlight how AI can be systematically leveraged to intelligently manage radiation dose throughout the CT workflow. Looking forward, the integration of large language foundation models and multi-modal data fusion holds great potential to develop end-to-end intelligent CT examination systems that not only minimize radiation exposure but also elevate diagnostic precision and workflow efficiency.

Objective:To investigate the efficacy of the combined application of bismuth shielding and organ dose-modulation(ODM)techniques in reducing doses to superficial radiosensitive organs and its effect on image quality during lung computed tomography(CT)scanning.Methods:Based on a clinical lung CT scanning protocol,four scanning groups are created on a chest phantom:Group 1,without bismuth shielding and ODM;Group 2,with ODM only;Group 3,with bismuth shielding only;and Group 4,with both bismuth shielding and ODM.Skin doses in the thyroid and breast regions are measured,and the volume CT dose index(CTDIvol)is recorded.Coronal images measuring 5 mm thick are reformed,and the contrast noise ratio(CNR)and figure of merit(FOM)are calculated.The image quality is evaluated subjectively.The subjective scores and CNR are analyzed for different ODM methods and to determine whether bismuth shielding two-factor ANOVA is required.Results:Compared with the case of Group 1,the CTDIvol of Groups 2-4 decrease by 8.2%,-0.06%,and 8.8%,respectively;the thyroid doses decrease by 17.9%,44.7%,and 47.3%,respectively;and the breast doses decrease by 12.43%,31.8%,and 41.1%,respectively.Although the CNR of the images decreases slightly after bismuth shielding and ODM are performed,the differences are insignificant.Group 1 indicates the highest subjective score while Group 4 indicates the lowest,with no statistical difference between the groups.Conclusion:The combined application of bismuth shielding and ODM techniques can significantly reduce radiation doses during lung CT scanning while ensuring image quality.

Objective:To explore the feasibility of the SEMI mode of the intelligent optimal tube voltage selection technique(Care kV SEMI)in combination with an iterative algorithm in low-dose calcium score scanning for coronary artery examination.Methods:SEMI mode of Care kV and tube current modulation(CareDose 4D)were used in the phantom experiment.For the SEMI group SEMI 120 kV(ref.kV was 100 and 120 kV),ref.mAs was 40,60,and 80 mAs.The reconstruction algorithms were filtered back projection(FBP)ADMIRE 3,4,5.The volume CT dose index(CTDIvol),contrast-to-noise ratio(CNRp)and figure of merit(FOM)of each group were compared,and a set of parameters were selected for clinical patient image acquisition after a comprehensive comparison.A retrospective analysis of coronary artery calcium score scanning images was conducted,using 30 patients as a control group(ref.kV,120 kV;ref.mAs,80 mAs;reconstruction algorithm,filtered back projection,FBP)and a prospective collection of 109 patients with coronary artery calcium score CT images as an experimental group(Care kV SEMI,120 kV;ref.kV,100 kV;ref.mAs,80 mAs).The reconstruction algorithms were FBP and ADMIRE 3,5.The dose length product(DLP),effective dose(ED),contrast-to-noise ratio(CNR)at the left main coronary artery(LM)and right coronary artery(RCA)ostial level,Agaston score,and risk classification were recorded and compared between groups.The images of the patients were evaluated by two senior diagnostic doctors on a four-point scale.The radiation dose,calcification score,risk classification,and image quality were statistically analyzed using SPSS software.Results:(1)Phantom experiment:The radiation dose of the experimental group was lower than that of the control group.Under the same scanning parameters,the CNRp increased with an increasing reconstruction algorithm level.The FOM of the four reconstruction algorithms in the ref.kV 100 kV+ref.mAs 80 mAs group was higher than that in the control group.(2)Clinical study:There was a statistically significant difference in ED between the experimental group and the control group.There was no statistically significant difference in CNRc between the experimental group with FBP and the control group on the LM and RCA levels.There was no significant difference in Agaston score between the experimental and control groups.The consistency of the risk grade in the experimental group was good,with kappa values of 0.93 and 0.88,respectively.There was no statistically significant difference in FBP and CNRc between the experimental and control groups at either level.The subjective evaluation results of doctors A and B were consistent,and the kappa value was 0.952.There was a statistically significant difference in the subjective evaluation between the two groups.Conclusion:Care kV SEMI combined with an iterative algorithm has little effect on the calcification score and risk classification,and it can effectively reduce the radiation doses of patients with a BMI of 18-25.

Objective:This study aims to explore the impact of an ultra-high resolution detector combined with a deep learning reconstruction algorithm,ClearInfinity(CI),on the image quality of temporomandibular joint(TMJ)computed tomography(CT)scans.Methods:Seven fresh cadaveric head specimens were scanned using the NeuViz Epoch Elite CT scanner,with two ultra-high resolution collimation widths(76×0.156 mm and 128×0.312 5 mm).For each collimation width,filtered back projection(FBP),adaptive iterative reconstruction ClearView(CV)60%,and deep learning reconstruction algorithm CI60%were applied,resulting in six sets of images.The image quality was assessed by comparing the contrast-to-noise ratio(CNR),signal-to-noise ratio(SNR),and subjective scores using a double-blind method,followed by a statistical analysis.Results:In the objective evaluation,the CNR of the condyle and joint for the 0.156 mm collimation width images were significantly higher than those for the 0.312 5 mm collimation width images.The CNR of the condyle and SNRs of the condyle joint in the CI60%images were significantly higher than those obtained using other algorithms.In the subjective evaluation,the scores for the condylar cortical bone and joint fossa in the 0.156 mm collimation width images were significantly higher than those in the 0.312 5 mm collimation width images.The subjective scores for condylar cortical bone,trabecular bone,joint eminence,and joint fossa in the CI60%images were significantly higher than those obtained using other algorithms,and the inter-rater consistency was good(Kappa≥0.75).Conclusion:The 0.156 mm ultra-high resolution detector combined with the deep learning algorithm CI could significantly improve the resolution and contrast of TMJ CT images,reduce noise and artifacts,and show promising clinical application prospects.

Objective:To investigate the effect of using organ dose modulation(ODM)technology at different tube voltages on image quality and eye lens radiation dose in brain CT perfusion(CTP).Methods:Based on a clinical CTP scanning protocol,five tube voltages(70,80,100,120,and 140 kV)were used to scan a Catphan phantom and a fresh,isolated human head specimen with three tube current modulation modes:without dose modulation technology(Manual mode),with smart tube current modulation technology(Smart mA mode),and with organ dose modulation technology(ODM mode).A long rod ionization chamber was fixed at a consistent position in front of the right eye lens.Each parameter combination was scanned nine times,and the average eye lens dose(Dav)was recorded.The modulation transfer function(MTF)of the CTP528 high-contrast resolution module in the Catphan phantom was measured.In the images of the head specimen,the heads of the caudate nucleus,lenticular nucleus,and thalamus were selected as signal regions at the basal ganglia level,and the lateral ventricle,anterior limb of the internal capsule,and posterior limb of the internal capsule were selected as respective background regions to measure the contrast-to-noise ratio(CNR).The same level of brain tissue was segmented to measure three texture feature parameters:contrast,correlation,and difference variance.Two-factor analysis of variance was used to compare MTF and CNR,and multi-factor non-parametric analysis of variance was used to compare eye lens dose and texture parameters.Results:The differences in MTF across the different tube voltages were statistically significant,with MTF values positively correlated with tube voltage above 80 kV.However,there was no significant difference between different tube-current modulation modes.The CNR of each signal and background region showed significant differences among different tube voltages.The CNR for the caudate nucleus head to lateral ventricle was the largest,at 120 kV.The CNR for the lenticular nucleus/thalamus were negatively correlated with tube voltage.There were no significant differences in CNR among different tube-current modulation modes.No texture parameter was significantly associated with the tube current modulation mode.There were significant differences in Dav values with respect to both different tube voltages and tube current modulation modes.When the tube voltage was below 120 kV,the eye lens dose was negatively correlated with tube voltage,and the ODM technology reduced the eye lens dose by approximately 20%at each tube voltage.Conclusion:Employing 80～100 kV CTP scanning with organ dose modulation technology can balance image parameters and reduce radiation dose to the eye lens while ensuring adequate image quality,as recommended for clinical use.

Objective:Ultralow tube voltage scanning was combined with a deep-learning-based image reconstruction algorithm(ClearInfinity,CI).The impacts of this method on the image quality and organ radiation dose in low-dose chest computed tomography(CT)were investigated.Methods:A Lungman PH-1 chest phantom was scanned using two protocols:120 and 70 kV standard-and low-dose protocols,respectively.Images were reconstructed using three algorithms,filtered back-projection(FBP);adaptive iterative reconstruction(ClearView,CV)at 20%,40%,60%,and 80%strengths;and CI at 20%,40%,60%,and 80%strengths,totaling nine reconstruction schemes.Objective image quality metrics for the soft tissue thoracic regions were evaluated,including the signal-to-noise ratio(SNR)for 100 HU solid nodules,contrast-to-noise ratio(CNR)for nodules with varying densities(100 HU,-630 HU,-800 HU),figure of merit(FOM),and the standard deviation(SD)of the CT values.The nodule detection rates were analyzed using an AI-assisted diagnosis system.Four radiomic texture parameters were extracted for-630 HU ground-glass nodules:sum of squares(SumSquares),difference,contrast,and correlation.Image quality was subjectively assessed using a five-point Likert scale,focusing on noise and clarity in high-artifact regions near the cervicothoracic junction.The doses to the breast,thyroid,and thymus were estimated using Monte Carlo simulations.Image quality metrics were compared using one-or multiway ANOVA or the Scheirer-ray-hare test.Results:CI reconstruction at 60%-80%significantly reduced image noise as well as increased the nodule SNR,CNR,and FOM compared with the baseline method.The performance of CI 80%ranked first overall.The AI model detected 100%of the lung nodules at CI reconstruction strengths of 60%or higher with the 70 kV low-dose protocol.The texture of the nodules was most accurately reconstructed with 80%CI under the 70 kV low-dose protocol,with 18.04%,1.59%,and 13.54%mean deviations in the SumSquares,difference entropy,and contrast,respectively,from those of the 120 kV FBP reference.Correlation restoration was highest at CI 40%,with a mean deviation of-16.36%.The image quality did not subjectively significantly differ between the 70 kV CI and 120 kV FBP images when the CI strength exceeded 40%,and these images were deemed diagnostically acceptable.The doses to the thyroid,breast,and thymus in the 70 kV low-dose protocol were 86.32%,81.79%,and 81.90%lower,respectively,than in the 120 kV standard-dose protocol.Conclusion:CI reconstruction,particularly at 60%-80%strength,with ultralow tube voltage chest CT considerably enhances image quality,reduces noise,increases the accuracy AI-based nodule detection,and preserves radiomic texture features compared with the baseline method.The 70 kV low-dose protocol combined with CI reconstruction≥60%balances image quality and radiation dose,demonstrating the potential for clinical application with low-dose chest CT.

Background Protective shielding is a standard practice in CT to reduce radiation exposure to radiosensitive organs. However, its effectiveness on wide-detector CT remains insufficiently evaluated. This study aimed to compare the protective efficacy of two shielding strategies—wrapping and covering—on superficial radiosensitive organs across various scanning protocols using a wide-detector CT scanner. Methods A total of 402 patients undergoing wide-detector CT examinations (cranial, cervical, lung, abdominopelvic, and coronary CTA) were randomized into wrapping and covering shielding groups. All scans were performed using a 256-row wide-detector CT scanner. Radiation surface doses to the eye lens, thyroid, breast, and gonads were measured using thermoluminescent dosimeters (TLD). The study evaluated internal surface doses beneath shields, external doses, and unshielded organ doses. Results In cranial axial and helical scans, the wrapping method resulted in significantly higher thyroid surface doses compared to the covering method ( P < 0.001). In lung and abdominopelvic scans using automatic tube current modulation (ATCM), the unshielded doses to the thyroid and gonads exceeded CTDI vol by approximately 58% and 70%, respectively. A dose inversion phenomenon (internal dose > external dose) was observed in cervical and abdominopelvic scans due to internal scatter accumulation. Compared to helical scans, single-rotation axial scans significantly reduced radiation doses to adjacent organs outside the scan field. Conclusions The effectiveness of shielding strategies in wide-detector CT is highly site-dependent. The covering method is superior for thyroid protection during cranial scans. Optimization of scanning modes, particularly the use of axial scanning to eliminate z-axis over-scanning, is more critical for organ protection than shielding alone. Due to ATCM and anatomical synergy, CTDI vol alone is insufficient for assessing radiation risks to unshielded organs.

Objective:This study aimed to compare the effects of different scanning modes on image quality and radiation dose in thoracic aortic computed tomography angiography(CTA).Methods:The image quality and radiation dose of 30 cases of thoracic aortic CTA(Group 1,control group),30 cases of retrospective CTA diastolic phase(Group 2)and systolic phase(Group 3),and 30 cases of wide-exposure pulse prospective electrocardiogram(ECG)(Group 4)were retrospectively analyzed.The CT value,noise,contrast-to-noise ratio(CNR),signal-to-noise ratio(SNR),and radiation dose(volume CT dose index(CTDIvol))were recorded and measured.The image quality was evaluated subjectively using the four points method.The subjective and objective indicators were analyzed statistically.Results:No differences were observed in the objective indexes of noise,CNR and SNR among the groups.The subjective score of pairwise comparison of Groups 2-4 was significantly higher than that of Group 1,and no difference was observed among Groups 2-4.The CTDIvol values of the non-gated group(Group 1),retrospective gated groups(Groups 2 and 3),and prospective gated group(Group 4)were 13.70(11.87,16.58),12.62(10.03,15.01),and 11.54(8.92,15.56),respectively,without a statistically significant difference.

To investigate the diagnostic value of dual-energy computed tomography (DECT)–derived quantitative parameters combined with morphological features for assessing subtle orbital tissue changes in Graves’ ophthalmopathy (GO), and to evaluate their feasibility as imaging biomarkers throughout the disease course. Data from patients suspected of having GO were retrospectively collected. All these patients underwent DECT scans and had no history of thyroid function treatment or other medical history, which may have affected the measurement of orbital tissues. Three clinical features, four morphological features, and twenty-four DECT parameters were measured. The overall data were divided into training and test cohorts. Univariate and multivariate analyses were applied to select relevant parameters and construct a nomogram. Among the 206 patients suspected of having GO, 134 patients were diagnosed as positive for GO (GO+), and 72 patients were diagnosed as negative (GO-) according to relevant diagnostic criteria. (1) The average thickness, average width, weighted average thickness and weighted average width of orbital muscles significantly differed between the GO + and GO- groups (p < 0.05). (2) The minimum and average values of electron density in orbital muscles and lacrimal glands were significantly different (p < 0.05). (3) A nomogram was constructed to predict the risk of GO, and the area under the curve, sensitivity, and specificity in the training and test cohorts were 0.812, 92.1

Objective: To investigate the effects of bismuth shielding and organ dose modulation (ODM) on image quality and lens radiation dose in brain computed tomography (CT) with different scanning baselines. Methods: GE (General Electric Company) Revolution CT was used to scan isolated skull specimens, with the glabellomeatal and orbitomeatal lines established as the scanning baselines. The volume CT dose index (CTDIvol) remained constant. Four scanning methods were adopted as follows: fixed mA, fixed mA combined with bismuth shielding, ODM, and ODM combined with bismuth shielding. The lens radiation dose for each scan was measured using a dosimeter. CT values and image noise levels were measured in the left cerebellum, temporal lobe of the brain, and left adipose body of the orbit. Results:The lens dose was highest when the baseline was the orbitomeatal line with a fixed mA (43.49 mGy), while the dose was lowest when the baseline was the glabellomeatal line with ODM combined with bismuth shielding (14.81 mGy). The CT values and image noise levels in the adipose body of the orbit increased when bismuth shielding and ODM combined with bismuth shielding were used. No significant difference was observed in image quality among the other groups. Conclusion: The organ dose of the eye lens could be reduced using the glabellomeatal line, and bismuth shielding combined with ODM were used for brain CT. However, the image quality of the intraorbital soft tissue might be decreased. In clinical practice, appropriate scanning methods can be used according to different patient conditions and diagnostic requirements.

The escalating utilization of ionizing radiation across medicine and industry underscored the paramount urgency of effective radioprotective materials. Conventional materials such as lead and concrete are widely used, and lead-free materials have also emerged to solve the problems of cumbersome and toxic lead, such as metal-containing micro/nano materials and polymers. Nevertheless, there is still a significant challenge in meeting the urgent need for lightweight and biocompatible alternatives. To tackle this challenge, this work utilizes molecular engineering of melanin to develop a panel of metal-free melanin materials with enhanced conjugation, heightened physical shielding against radiation and effective antioxidant properties. Furthermore, engineered melanin materials demonstrated in vivo γ-ray protection, increasing mice survival from ~12% to 100% after 6 Gy total body irradiation.

PURPOSE:To assess the accuracy of relative electron density (RED) and effective atomic number (EAN) measurements using dual-layer spectral CT (DLSCT) in axial and helical scanning modes. METHODS:The CIRS 062M phantom was scanned with three collimation widths (16 × 0.625 mm, 32 × 0.625 mm, 64 × 0.625 mm) in axial mode and combined with two pitches (0.61 and 0.98) in helical mode. The RED and EAN were quantified using the IntelliSpace Portal, with absolute and relative errors calculated for each insert. Error metrics, including mean absolute error (MAE) and mean relative error (MRE), were subsequently derived over all the inserts. High accuracy was defined as the absolute error < 0.2. Statistical analysis was conducted using one-way ANOVA. RESULTS:In axial mode, the MAE for RED was within 0.01 and the MRE was within 2%; for EAN, the MAE was within 0.25 and the MRE was within 3%. In helical mode, the MAE for RED was within 0.02 and the MRE was within 2.5%; for EAN, the MAE was within 0.5 and the MRE was within 6%. Lung insert showed the highest deviation, while for all other inserts, the MAE was within 0.2 and the MRE was within 2% for EAN across both scanning modes. Smaller collimation widths in axial scanning showed lower relative errors for RED and EAN compared to helical scanning for bone and most of the soft inserts. CONCLUSIONS:DLSCT accurately estimates RED and EAN in both scanning modes, suitable for clinical quantitative tissue analysis, with the exception of lung tissue. Smaller collimation widths in axial scanning are recommended for enhanced measurement accuracy and stability.

PURPOSE:To quantitatively evaluate the image quality and the radiation dose reduction potential when a deep learning reconstruction (DLR) algorithm is combined with an ultra-high-resolution (UHR) detector, using a task-based assessment framework (MTF, NPS, TTF, and detectability index d'). METHODS:A Catphan 600 phantom was scanned at five CTDIvol levels (CTDIvol: 10, 7.5, 5, 2.5, 1 mGy). Data acquired with collimation width of 64 × 0.625 mm were reconstructed with Filtered Back-Projection (FBP) and adaptive statistical iterative reconstruction (ClearView 50 %, CV50); for 128 × 0.3125 mm, five algorithms were applied: FBP, CV50, and ClearInfinity (deep learning reconstruction algorithm) 10 % (CI10), 50 % (CI50), 90 % (CI90). The Modulation Transfer Function (MTF), Noise Power Spectrum (NPS), Task Transfer Function (TTF), and Detectability Index (d') for large, subtle and small features were measured. RESULTS:At all dose levels, MTF50% and MTF10% with a 0.3125 mm collimation width was higher than that with 0.615 mm, improving the d' of small features but increasing noise. CI markedly reduced NPS peaks without shifting average spatial frequency, thereby increasing d' for large and subtle features. The combination of the two achieved the lowest noise peak and the highest detectability index. CONCLUSIONS:Integrating a deep learning reconstruction algorithm with a UHR detector enhances spatial resolution, reduces noise magnitude without texture alteration, improves lesion detectability, and demonstrates substantial potential for radiation dose reduction.

Objective:Exploring the effect of metal artifact reduction(O-MAR)technology combined with iterative algorithms on the computed tomography(CT)image quality of patients after lumbar spine internal fixation surgery,to provide an accurate basis for postoperative effect evaluation.Methods:CT images were collected from 20 patients who underwent lumbar spine internal fixation surgery.Using O-MAR,filtered back projection(FBP),and iDose4 algorithms,bone(iDose4-1～7 levels)and soft tissue images(iDose4-1～6 levels)were reconstructed.Screws were displayed the best in transverse and sagittal bone reconstructed images.The screw area(center plane)of the transverse soft tissue images of the intervertebral disc was also reconstructed.Noise levels in standard deviation(SD)of bone and muscle were measured,and the artifact index(AI)was calculated.Two radiologists separately rated the metal artifact suppression and diagnostic information in the bone and soft tissue images.The subjective and objective evaluation indicators of the two groups were compared,conducting multiple comparisons between groups.Statistical analyses were further performed on subjective and objective evaluation indicators,including O-MAR comparisons and/or multiple comparisons between groups with different levels of iDose4.Results:In the bone images,the SD and AI of images using O-MAR were significantly lower than those without O-MAR,and the AI values of iDose4 images at different levels gradually decreased as the dose level increased.The subjective score of metal artifacts in O-MAR images significantly improved,and the score of iDose4-5～7 was higher than those of filtered back projection(FBP)and iDose4-1～2.With O-MAR,the diagnostic information score significantly improved.The score of iDose4-2～4 was higher than that of FBP and other iterative levels,with iDose4-3 being the best.In soft tissue images,the SD and AI of images using O-MAR were lower than those without O-MAR.The metal artifact score of images using O-MAR was higher than that of images without,but the diagnostic information score of images not using O-MAR was higher than that of images with O-MAR;For different iteration levels,regardless of whether O-MAR was used or not,no difference was observed in image artifacts and diagnostic information scores.Conclusions:We suggest combining O-MAR technology with intermediate iteration level iDose4-3 for bone image reconstruction.The use of iterative algorithms for image reconstruction using soft-tissue algorithms is not recommended.The images with and without O-MAR should be simultaneously reconstructed for comparative observations.