Monoenergetic Reconstructions Using Detector-based Spectral CT for Imaging of Small Lesions in the Rabbit VX2 Liver Cancer Models

Objective To assess image quality and optimal keV levels of spectral CT virtual monochromatic series in atypical vertebral osteoblastic metastases of lung cancer. Methods Patients with pathologically proven lung cancer who had undergone spectral CT (Discovery CT750 HD, GE) of the chest or abdomen more than twice were analyzed retrospectively. Vertebral osteoblastic metastases were confirmed by follow-up CT examinations, biopsy ,emission CT, PET/CT. The bone lesions were missed due to atypicalappearance or presented with suspicious density at the first exmaination were chosen. A total of twenty two patients with 72 lesions were enrolled. Conventional polychromatic images (140 kVp) and monochromatic images were generated (40 to 140 keV at an interval of 10 keV). The signal to noise ratio(SNR) and contrast to noise ratio (CNR) of the lesions were measured and calculated at all the 12 series images by two radiologists. The two radiologists scored the different series of images independently. Paired sample t test was used to compare SNR and CNR among 40 to 140 keV and 140 kVp images. The inter-observer agreement between the two radiologists was evaluated using Kappa statistics and the difference of different images intrarobserver was compared using Wilcoxon signed rank test. Results The SNR of 140 kVp was 48.03±20.17, and those of 50, 60 70 keV images were 58.52±22.92, 64.07±24.88, 59.54±22.04. SNR of 50, 60, 70 keV images was higher than that of 140 kVp (t values were 13.366,16.278, 18.889,P<0.01), 60 keV images had the highest SNR. The CNR of 140 kVp was 7.64±4.52 and those of 50, 60, 70 and 80 keV images were 7.95±4.99, 8.12±5.08, 8.16±5.05, 7.89±4.81. CNR of 50, 60, 70 and 80 keV images were higher than that of 140 kVp image (t values were 3.519, 5.050, 6.231, 4.413, P<0.01), 60 keV and 70 keV images had the higher CNR than 50 keV and 80 keV. Good interobserver agreement was found for the evaluation of image quality (Kappa =0.864,P<0.05).Moreover, the scores of both the two observes for 60 keV images were higher than 140 kVp images and higher than other monochromatic images. Conclusion Atypical vertebral osteoblastic metastases of lung cancer are more conspicuous in virtual monochromatic images as compared wtih 140 kVp polychromatic images and the optimal energy level was 60 keV, repectively. Key words: Lung neoplasms; Bone neoplasms; Neoplasm metastasis; Tomography, X-ray computed

ObjectiveTo assess the feasibility of visualizing hand and foot tendon anatomy and disorders by Gemstone Spectral Imaging (GSI) high-definition CT (HDCT).Materials and MethodsThirty-five patients who suffered from hand or foot pain were scanned with GSI mode HDCT and MRI. Spectrum analysis was used to select the monochromatic images that provide the optimal contrast-to-noise ratio (CNR) for tendons. The image quality at the best selected monochromatic level and the conventional polychromatic images were compared. Tendon anatomy and disease were also analyzed at GSI and MRI.ResultsThe monochromatic images at about 65 keV (mean 65.09 ± 2.98) provided the optimal CNR for hand and foot tendons. The image quality at the optimal selected monochromatic level was superior to conventional polychromatic images (p = 0.005, p < 0.05). GSI was useful in visualizing hand and foot tendon anatomy and disorders. There were no statistical differences between GSI and MRI with regard to tendon thickening (χ2 = 0, p > 0.05), compression (χ2 = 0.5, p > 0.05), absence (χ2 = 0, p > 0.05) and rupture (χ2 = 0, p > 0.05). GSI was significantly less sensitive than MRI in displaying tendon adhesion (χ2 = 4.17, p < 0.05), degeneration (χ2 = 4.17, p < 0.05), and tendinous sheath disease (χ2 = 10.08, p < 0.05).ConclusionGSI with monochromatic images at 65 keV displays clearly the most hand and foot tendon anatomy and disorders with image quality improved, as compared with conventional polychromatic images. It may be used solely or combined with MRI in clinical work, depending on individual patient disease condition.

To assess the impact of a deep learning-based noise reduction (DLD) technique on image quality and diagnostic accuracy for the evaluation of coronary arteries in transcatheter aortic valve implantation (TAVI) CT imaging. Two hundred patients with severe aortic stenosis who underwent CT scans for pre-TAVI planning between October 2022 and April 2024 were retrospectively enrolled. Conventional images were reconstructed and denoised images were generated using dedicated software. Objective image quality was evaluated by measuring the mean Hounsfield unit (HU) and standard deviation (SD) in regions of interest within the aortic root, coronary arteries, and subcutaneous fat to calculate signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). For subjective assessment, two readers used a 5-point Likert scoring system to evaluate sharpness, noise, contrast and overall image quality. The diagnostic performance of both datasets was assessed using invasive coronary angiography as reference standard. Denoised reconstructions showed significantly higher SNR (37.5±12.8 vs.12.3±4.1) and CNR (45.3±15.4 vs. 14.7±4.4), and lower noise (16.9±7.9 vs. 47.9±11.6 HU) (all p<0.001). Subjective assessment demonstrated that denoised images received the highest score for sharpness, noise, contrast and overall image quality (all p<0.001). For the evaluation of diagnostic accuracy, a total of 800 vessels and 1787 segments were analyzed. The per-segment diagnostic performance of the DLD for detection of CAD revealed an AUC of 90% (95% CI: 88.5-91.3), with accuracy of 93.9% (95% CI: 92.7-95), 85.7% (95% CI: 78.7-90.4) sensitivity and 94.7% (95% CI: 93.5-95.7) specificity, in the absence of a statistically significant difference compared with the evaluation performed on standard images (p=0.056). The DLD substantially improves image quality without affecting diagnostic accuracy for the evaluation of coronary arteries in patients undergoing pre-TAVI CT scans.

ObjectivesTo investigate whether the increased soft tissue contrast of virtual monoenergetic images (VMIs) obtained from a spectral detector computed tomography (SDCT) system improves washout assessment of arterially hyper-enhancing liver lesions.MethodsFifty-nine arterially hyper-enhancing lesions in 31 patients (age 65 ± 9 years, M/W 20/11) were included in this IRB-approved study. All patients underwent multi-phase SDCT for HCC screening. MRI, CEUS or biopsy within 3 months served as standard of reference to classify lesions as LiRADS 3 or 4/5. VMIs and conventional images (CIs) were reconstructed. Visual analysis was performed on 40, 60, and 80 kiloelectronvolt (keV) and CIs by 3 radiologists. Presence and visibility of washout were assessed; image quality and confidence of washout evaluation were evaluated on 5-point Likert scales. Signal-to-noise ratio (SNR), lesion-to-liver contrast-to-noise ratio (CNR) (|HUlesion–HUliver|/SDliver) and washout (|HUlesion–HUliver|) were calculated. Statistical assessment was performed using ANOVA and Wilcoxon test.ResultsOn subjective lesion analysis, the highest level of diagnostic confidence and highest sensitivity for the detection of lesion washout were found for 40-keV VMIs (40 keV vs. CI, 81.3 vs. 71.3%). Image quality parameters were significantly better in low-kiloelectronvolt VMIs than in CIs (p < 0.05; e.g. SNRliver: 40 keV vs. CIs, 12.5 ± 4.1 vs. 5.6 ± 1.6). In LiRADS 4/5 lesions, CNR and quantitative washout values were significantly higher in 40-keV VMIs compared to CIs (p < 0.05; e.g. CNR and washout in 40 keV vs. CIs, 2.3 ± 1.6 vs. 0.8 ± 0.5 and 29.0 ± 19.1 vs. 12.9 ± 6.9 HU, respectively).ConclusionBy increasing lesion contrast, low-kiloelectronvolt VMIs obtained from SDCT improve washout assessment of hyper-enhancing liver lesions with respect to washout visibility and diagnostic confidence.Key Points• Low-kiloelectronvolt virtual monoenergetic images from spectral detector CT facilitate washout assessment in arterially hyper-enhancing liver lesions.• Image quality and quantitative washout parameters as well as subjective washout visibility and diagnostic confidence benefit from low-kiloelectronvolt virtual monoenergetic images.

This study aimed to evaluate chest computed tomography (CT) angiography image quality using the contrast enhancement (CE)-boost technique compared with conventional images. Forty patients who underwent contrast-enhanced chest CT were included. Combined CT angiography images of the iodinated image obtained from the subtraction of nonenhanced CT images and CT angiography images were used to generate CE-boost images. Computed tomography attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for the right and left pulmonary arteries as the central and subsegmental arteries as peripheral vessels were assessed. Subjective image quality was rated on a 5-point scale by 2 radiologists. Image quality was assessed using a paired t test. Computed tomography attenuation in the main pulmonary artery was significantly higher for the CE-boost images (311.05 ± 91.94) than for the conventional images (221.25 ± 61.21, P < 0.001). Similarly, the CE-boost images resulted in significantly higher CT attenuation in the subsegmental arteries (right, 305.34 ± 90.13; left, 313.05 ± 97.21) than in the conventional images (right, 218.45 ± 63.16; left, 223.89 ± 74.27). The CE-boost technique demonstrated marked improvement in the visualization of the peripheral pulmonary artery without the administration of a higher iodine delivery rate. The mean SNR and CNR were also significantly higher in the central and peripheral vessels in the CE-boost images than in the conventional images (P < 0.001). In the subjective analysis, the image contrast and vascular contrast edge were significantly higher for the CE-boost images than for conventional images (P < 0.001). The CE-boost technique increases not only the visualization of peripheral arteries by improving vascular attenuation but also the SNR and CNR.

This study aimed to evaluate the image quality and visualization of hepatocellular carcinoma (HCC) on arterial phase computed tomography (CT) using the contrast enhancement (CE)-boost technique. This retrospective study included 527 consecutive patients who underwent dynamic liver CT between June 2021 and February 2022. Quantitative and qualitative image analyses were performed on 486 patients after excluding 41 patients. HCC conspicuity was evaluated in 40 of the 486 patients with at least one HCC in the liver. Iodinated images obtained by subtracting nonenhanced images from arterial phase images were combined to generate CE-boost images. For quantitative image analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for the liver, pancreas, muscles, and aorta. For qualitative analysis, the overall image quality and noise were graded using a 3-point scale. Artifact, sharpness, and HCC lesion conspicuity were assessed using a 5-point scale. The paired-sample t test was used to compare quantitative measures, whereas the Wilcoxon signed-rank test was used to compare qualitative measures. The mean SNR and CNR of the aorta, liver, pancreas, and muscle were significantly higher, and the image noise was significantly lower in the CE-boost images than in the conventional images ( P < 0.001). The mean CNR of HCC was also significantly higher in the CE-boost images than in the conventional images ( P < 0.001). In the qualitative analysis, CE-boost images showed higher scores for HCC lesion conspicuity than conventional images ( P < 0.001). The overall image quality and visibility of HCC were improved using the CE-boost technique.

BackgroundProstate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.MethodsWe conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People’s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40–100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.ResultsVirtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).ConclusionsVirtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

We investigated the performance of the conventional and high frame rate ultrasound imaging techniques for strain and elasticity imaging. High frame rate imaging is necessary to track fast deformation in elasticity imaging, but the ultrasound image quality is known to be compromised. Both numerical and experimental studies were performed using point targets and tissue mimicking phantoms. The results of our study confirm that the signal to noise ratio (SNR), contrast to noise ratio (CNR), and axial/lateral resolution of the displacement and strain images acquired using high frame rate ultrasound imaging are comparable with those obtained using the conventional imaging. Thus, the high frame rate ultrasound imaging can be reliably used for both static and dynamic elasticity imaging if the frame rate to capture the internal tissue motion is critical

Virtual monoenergetic imaging (VMI) with dual-layer spectral detector computed tomography (CT) enables keV optimization, offering the potential for contrast dose reduction, particularly beneficial in pediatric imaging. However, its clinical utility in infants and children with complex congenital heart disease (CHD) remains underexplored. To investigate the feasibility of reducing the contrast dose and the optimal keV of virtual monoenergetic images (VMIs) on dual-layer spectral detector CT in infants and children with complex CHD. Prospectively enrolled 80 infants and children with complex CHD and underwent preoperative dual-layer spectral detector CT. Patients were randomly divided into two groups: group A (n=36, standard dose, 1.5-2ml/kg) and group B (n=44, low dose, 1ml/kg). Conventional image and VMIs in range of 40-85keV were reconstructed in group B while only conventional image was reconstructed in group A. Image quality was evaluated using qualitative scores and objective metrics including CT-value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for the pulmonary artery and ascending aorta. Group B received 24% less contrast than group A [8.0 (5.0; 11.0) ml vs. 10.5 (8.0; 21.0) ml, P<0.05]. VMI 40keV in group B yielded the highest CT-value, SNR, CNR of pulmonary artery, and ascending aorta among different VMIs, and significantly outperformed conventional image in group A (P<0.05). Subjective evaluation score on VMI 40-50keV of group B was significantly higher than conventional image of group A (P<0.05). Diagnostic accuracy for CHD at 40-50keV in group B was 97.7%, significantly higher than group A (93.4%, P<0.05), while no significant difference in sensitivity and specificity. VMI 40keV in dual-layer spectral detector CT can significantly improve image quality, allowing contrast dose reduction in infants and children with complex CHD without compromising diagnostic performance.

To determine the optimal pre-adaptive and post-adaptive level statistical iterative reconstruction V (ASiR-V) for improving image quality and reducing radiation dose in coronary computed tomography angiography (CCTA). The study was divided into two parts. In part I, 150 patients for CCTA were prospectively enrolled and randomly divided into 5 groups (A, B, C, D, and E) with progressive scanning from 40% to 80% pre-ASiR-V with 10% intervals and reconstructing with 70% post-ASiR-V. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Subjective image quality was assessed using a 5-point scale. The CT dose index volume (CTDIvol) and dose-length product (DLP) of each patient were recorded and the effective radiation dose (ED) was calculated after statistical analysis by optimizing for the best pre-ASiR-V value with the lowest radiation dose while maintaining overall image quality. In part II, the images were reconstructed with the recommended optimal pre-ASiR-V values in part I (D group) and 40%-90% of post-ASiR-V. The reconstruction group (D group) was divided into 6 subgroups (interval 10%, D0:40% post-ASiR-V, D1:50% post - ASiR-V, D2:60% post-ASiR-V, D3:70% post-ASiR-V, D4:80% post-ASiR-V, and D5:90% post-ASiR-V).The SNR and CNR of D0-D5 subgroups were calculated and analyzed using one-way analysis of variance, and the consistency of the subjective scores used the k test. There was no significant difference in the SNRs, CNRs, and image quality scores among A, B, C, and D groups (P > 0.05). The SNR, CNR, and image quality scores of the E group were lower than those of the A, B, C, and D groups (P < 0.05). The mean EDs in the B, C, and D groups were reduced by 7.01%, 13.37%, and 18.87%, respectively, when compared with that of the A group. The SNR and CNR of the D4-D5 subgroups were higher than the D0-D3 subgroups, and the image quality scores of the D4 subgroups were higher than the other subgroups (P < 0.05). The wide-detector combined with 70% pre-ASiR-V and 80% post-ASiR-V significantly reduces the radiation dose of CCTA while maintaining overall image quality as compared with the manufacture's recommendation of 40% pre-ASiR-V.

PurposeTo evaluate 18F-FDG myocardial metabolism imaging (MMI) using a total-body PET/CT scanner and explore the feasible scan duration to guide the clinical practice.MethodsA retrospective analysis was conducted on 41 patients who underwent myocardial perfusion-metabolism imaging to assess myocardial viability. The patients underwent 18F-FDG MMI with a total-body PET/CT scanner using a list-mode for 600 s. PET data were trimmed and reconstructed to simulate images of 600-s, 300-s, 120-s, 60-s, and 30-s acquisition time (G600-G30). Images among different groups were subjectively evaluated using a 5-point Likert scale. Semi-quantitative evaluation was performed using standardized uptake value (SUV), myocardial to background activity ratio (M/B), signal to noise ratio (SNR), contrast to noise ratio (CNR), contrast ratio (CR), and coefficient of variation (CV). Myocardial viability analysis included indexes of Mismatch and Scar. G600 served as the reference.ResultsSubjective visual evaluation indicated a decline in the scores of image quality with shortening scan duration. All the G600, G300, and G120 images were clinically acceptable (score ≥ 3), and their image quality scores were 4.9 ± 0.3, 4.8 ± 0.4, and 4.5 ± 0.8, respectively (P > 0.05). Moreover, as the scan duration reduced, the semi-quantitative parameters M/B, SNR, CNR, and CR decreased, while SUV and CV increased, and significant difference was observed in G300-G30 groups when comparing to G600 group (P < 0.05). For myocardial viability analysis of left ventricular and coronary segments, the Mismatch and Scar values of G300-G30 groups were almost identical to G600 group (ICC: 0.968-1.0, P < 0.001).ConclusionSufficient image quality for clinical diagnosis could be achieved at G120 for MMI using a total-body PET/CT scanner, while the image quality of G30 was acceptable for myocardial viability analysis.

BackgroundTo compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR).MethodsConventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists. Contrast-to-noise ratios (CNR) between bladder and fat and muscle and fat were measured. Wilcoxon signed-rank tests with Holm-Bonferroni correction were used.ResultsSignificantly higher image quality, higher diagnostic confidence, and less severe metal artifacts were observed on DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001 for all comparisons). Higher image quality, higher diagnostic confidence for bone and soft tissue adjacent to the prosthesis, and less severe metal artifacts were observed on DL-MAR when compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.014). CNRs were higher for DL-MAR and images with O-MAR compared to images without O-MAR (p < 0.001). Higher CNRs were observed on DL-MAR images compared to conventional images and 130-keV monoenergetic images with O-MAR (p ≤ 0.010).ConclusionsDL-MAR showed higher image quality, diagnostic confidence, and superior metal artifact reduction compared to conventional CT images and 130-keV monoenergetic images with and without O-MAR in unilateral THA patients.Relevance statementDL-MAR resulted into improved image quality, stronger reduction of metal artifacts, and improved diagnostic confidence compared to conventional and virtual monoenergetic images with and without metal artifact reduction, bringing DL-based metal artifact reduction closer to clinical application.Key points• Metal artifacts introduced by total hip arthroplasty hamper radiologic assessment on CT.• A deep-learning algorithm (DL-MAR) was compared to dual-layer CT images with O-MAR.• DL-MAR showed best image quality and diagnostic confidence.• Highest contrast-to-noise ratios were observed on the DL-MAR images.Graphical

Diffusion-weighted imaging (DWI) using single-shot echo-planar imaging (ss-EPI) is prone to artifacts, geometric distortion, and T2* blurring. Readout-segmented echo-planar imaging (rs-EPI) may improve image quality in the DWI of cervical cancer (CC). This study aimed to compare the image quality between rs-EPI and ss-EPI DWI in CC and to evaluate whether the apparent diffusion coefficient (ADC) values of ss-EPI (ssADC) and rs-EPI (rsADC) can differentiate the status of lymphovascular space invasion (LVSI) and lymph node metastasis (LNM). This prospective study included 69 patients with CC who underwent ss-EPI and rs-EPI DWI before surgery. Qualitative reader scores, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC values derived from ss-EPI and rs-EPI were compared. The differences in ADC values were analyzed in patients who were (a) LNM-positive (LNM+, n = 17) and LNM-negative (LNM-, n = 52); (b) LVSI-positive (LVSI+, n = 33) and LVSI-negative (LVSI-, n = 36). The rs-EPIs of CC had higher subjective image quality scores and a lower SNR than ss-EPI (all P < 0.001); no significant differences existed between rs-EPI and ss-EPI for either CNR or ADC (CNR, P = 0.313; ADC, P = 0.949; P > 0.05 for all). The rsADC and ssADC of the LNM+ group were substantially lower than those of the LNM- group (rsADC, P = 0.000; ssADC, P = 0.000; P < 0.001 for all); the areas under the receiver operating characteristic curve were 0.855 and 0.851, respectively. However, there were no differences in ADC values between the LVSI+ and LVSI- groups (rsADC, P = 0.271; ssADC, P = 0.200; P > 0.05 for all). Over a similar scan time, rs-EPI improves the qualitative image quality of DWI significantly more than ss-EPI and has good diagnostic accuracy for LNM status in CC. However, neither could predict the LVSI status. Readout-segmented EPI improves the qualitative image quality of DWI and has good diagnostic accuracy for LNM status in CC, compared with conventional ss-EPI. It is more inclined to qualitative analysis of CC foci and provides a better scheme when choosing the DWI sequence scanning strategy for CC.

Non-contrast compressed sensing whole-heart coronary magnetic resonance angiography at 3T: A comparison with conventional imaging

OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable.RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively.CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.