An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

BACKGROUND: Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. OBJECTIVE: The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). METHODS: We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. RESULTS: High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. CONCLUSION: The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Impact of deep learning-based image reconstruction on image quality and lesion visibility in renal computed tomography at different doses.

Background: Numerous computed tomography (CT) image reconstruction algorithms have been developed to improve image quality, and high-quality renal CT images are crucial to clinical diagnosis. This study evaluated the image quality and lesion visibility of deep learning-based image reconstruction (DLIR) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V) in contrast-enhanced renal CT at different reconstruction strengths and doses. Methods: From January 2020 to May 2021, we prospectively included 101 patients who underwent renal contrast-enhanced CT scanning (69 at 120 kV; 32 at 80 kV). All image data were reconstructed with ASiR-V (30% and 70%) and DLIR at low, medium, and high reconstruction strengths (DLIR-L, DLIR-M, and DLIR-H, respectively). The CT number, noise, noise reduction rate (NRR), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, and the proportion of acceptable images were compared. Lesions of DLIR groups were evaluated, and the conspicuity-to-noise ratio (C/N) was calculated. Results: Quantitative values (noise, SNR, CNR, and NRR) significantly differed between all reconstructions at 120 and 80 kV (P<0.001) and between each reconstruction, except ASiR-V 70% vs. DLIR-M. At 120 kV, the overall image quality and the proportion of acceptable images significantly differed between all reconstructions (P<0.001) and between each reconstruction, except ASiR-V 30% vs. DLIR-L and ASiR-V 70% vs. DLIR-M. At 80 kV, the overall image quality significantly differed between all reconstructions (P<0.001) and between each reconstruction, except between ASiR-V 30%, ASiR-V 70%, and DLIR-L. Quantitative and qualitative values were highest in DLIR-H, while the values were close in DLIR-H (80 kV) vs. ASiR-V 70% (120 kV) and DLIR-M (80 kV) vs. ASiR-V 30% (120 kV). The lesion conspicuity and noise significantly differed in DLIR at 120 kV and 80 kV (P<0.001). C/N significantly differed in DLIR at 120 kV (P<0.001) but not at 80 kV. DLIR-L and DLIR-M exhibited much-improved lesion display (C/N >1), and DLIR-H exhibited much-improved noise (C/N <1) at 120 kV. Conclusions: DLIR significantly improved the image quality and lesion visibility of renal CT compared with ASiR-V, even at a low dose.

Associations between paraspinal muscles fatty infiltration and lumbar vertebral bone mineral density - An investigation by fast kVp switching dual-energy CT and QCT.

Purpose: To investigate the relationship between paraspinal muscles fat content and lumbar bone mineral density (BMD). Methods: A total of 119 participants were enrolled in our study (60 males, age: 50.88 ± 17.79 years, BMI: 22.80 ± 3.80 kg·m-2; 59 females, age: 49.41 ± 17.69 years, BMI: 22.22 ± 3.12 kg·m-2). Fat content of paraspinal muscles (erector spinae (ES), multifidus (MS), and psoas (PS)) were measured at (ES L1/2-L4/5; MS L2/3-L5/S1; PS L2/3-L5/S1) levels using dual-energy computed tomography (DECT). Quantitative computed tomography (QCT) was used to assess BMD of L1 and L2. Linear regression analysis was used to assess the relationship between BMD of the lumbar spine and paraspinal muscles fat content with age, sex, and BMI. The variance inflation factor (VIF) was used to detect the degree of multicollinearity among the variables. P < .05 was considered to indicate a statistically significant difference. Results: The paraspinal muscles fat content had a fairly significant inverse association with lumbar BMD after controlling for age, sex, and BMI (adjusted R 2 = 0.584-0.630, all P < .05). Conclusion: Paraspinal muscles fat content was negatively associated with BMD.Paraspinal muscles fatty infiltration may be considered as a potential marker to identify BMD loss.

[The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images].

Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V（ ASiR-V） and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning. Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset. Then structures including three soft tissue organs (bladder, bowelbag, small intestine) and five bone organs (left and right femoral head, left and right femur, pelvic) were segmented automatically by deep learning neural network. Performance was evaluated by dice similarity coefficient（ DSC） and Hausdorff distance, using filter back projection(FBP) as the reference. Results Auto-segmentation of deep learning is greatly affected by ASIR-V, but less affected by convolution kernel, especially in soft tissues. Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm. In practical application, it is necessary to find a balance between image quality and segmentation quality, or improve segmentation network to enhance the stability of auto-segmentation.

The Effects of Different Adaptive Statistical Iterative Reconstruction-V and Convolution Kernel Parameters on Auto-Segmentation Stability in CT Images / 中国医疗器械杂志

Objective The study aims to investigate the effects of different adaptive statistical iterative reconstruction-V（ ASiR-V） and convolution kernel parameters on stability of CT auto-segmentation which is based on deep learning. Method Twenty patients who have received pelvic radiotherapy were selected and different reconstruction parameters were used to establish CT images dataset. Then structures including three soft tissue organs (bladder, bowelbag, small intestine) and five bone organs (left and right femoral head, left and right femur, pelvic) were segmented automatically by deep learning neural network. Performance was evaluated by dice similarity coefficient（ DSC） and Hausdorff distance, using filter back projection(FBP) as the reference. Results Auto-segmentation of deep learning is greatly affected by ASIR-V, but less affected by convolution kernel, especially in soft tissues. Conclusion The stability of auto-segmentation is affected by parameter selection of reconstruction algorithm. In practical application, it is necessary to find a balance between image quality and segmentation quality, or improve segmentation network to enhance the stability of auto-segmentation.

Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose. METHODS: The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared. RESULTS: For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%). CONCLUSION: Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.

Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules.

BACKGROUND: The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT. METHODS: Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared. RESULTS: Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT70% vs. 90% (78.5%) and ULDCT90% vs. LDCT50% (50.5%). In SNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.67±0.26, not different with that for ULDCT50% vs. LDCT50% (0.68±0.24) and ULDCT70% vs. LDCT50% (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.79±0.19, higher than that for ULDCT50% vs. LDCT50% (0.61±0.28) and ULDCT70% vs. LDCT50% (0.76±0.24) (P<0.05). CONCLUSIONS: ASiR-V levels significantly affected ULDCT radiomic feature quantification in pulmonary nodules, with stronger effects in pGGNs than in SNs. The reproducibility of radiomic features was highest between ULDCT90% and LDCT50%.

Impact of preset and postset adaptive statistical iterative reconstruction-V on image quality in nonenhanced abdominal-pelvic CT on wide-detector revolution CT.

BACKGROUND: Adaptive statistical iterative reconstruction-V technique (ASIR-V) is usually set at different strengths according to the different clinical requirements and scenarios encountered when setting scanning protocols, such as setting a more aggressive tube current reduction (defined as preset ASIR-V). Reconstruction with ASIR-V is useful after scanning using image algorithms to improve image quality (defined as postset ASIR-V). The aim of this study was to investigate the quality of images reconstructed with preset and postset ASIR-V, using the same noncontrast abdominal-pelvic computed tomography (CT) protocols in the same individual on a wide detector CT. METHODS: We prospectively enrolled 141 patients. The scan protocols in Groups A-E were 0%, 20%, 40%, 60%, and 80% preset ASIR-V, respectively, in the 256 wide-detector row Revolution CT (GE Healthcare, Waukesha, WI, USA). Each group was further divided into 5 subgroups with 0%, 20%, 40%, 60%, and 80% postset ASIR-V, respectively. The 64-detector Discovery 750 HDCT (GE, USA) was used for Group F as a control group, using 0%, 20%, 40%, 60%, and 80% ASIR, respectively. Image noise was measured in the spleen, aorta, and muscle. The CT attenuation and image noise were analyzed using the paired t-test; analysis of variance and post hoc multiple comparisons were made using the Student-Newman-Keuls (SNK) method. RESULTS: The CT attenuation in Groups A-F exhibited no significant difference between subgroups in three organs (P>0.05). Only with increasing preset ASIR-V% (Groups A to E), did the image noise decrease, except in Group B in the aorta and muscle (NoiseB > NoiseA, PmuscleA&B=0.233, PaortaA&B=0.796). Only with increasing postset ASIR-V or ASIR% (Groups A and F), did the image noise decrease in the three organs. After preset and postset ASIR-V were combined, with preset ASIR-V% being equal to postset ASIR-V%, the image become similar to the corresponding preset ASIR-V part with the line of postset ASIR-V 0% (baseline of each group). When preset ASIR-V% was greater than the postset ASIR-V%, the image noise was higher than the baseline of each group. When preset ASIR-V% was less than the postset ASIR-V%, the image noise was lower than the baseline of each group. The radiation dose from B to E decreased from 11.2% to 57.1%. The CT dose index volume (CTDIvol) and dose length product (DLP) in Group F were significantly higher than those in Group A. CONCLUSIONS: Using both preset and postset ASIR-V allows dose reduction, with a potential to improve image quality only when postset ASIR-V% is higher than or equal to preset ASIR-V%. The image quality depends on postset ASIR-V%, whereas the decrease of radiation dose depends on preset ASIR-V%.

Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography.

OBJECTIVE: 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). METHODS: 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. RESULTS: 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). CONCLUSION: 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.

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.

BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruction (DLIR) offers unique opportunities to overcome these limitations. The present study compared the impact of DLIR and adaptive statistical iterative reconstruction-Veo (ASiR-V) on quantitative and qualitative image parameters and the diagnostic accuracy of CCTA using invasive coronary angiography (ICA) as the standard of reference. METHODS: This retrospective study includes 43 patients who underwent clinically indicated CCTA and ICA. Datasets were reconstructed with ASiR-V 70% (using standard [SD] and high-definition [HD] kernels) and with DLIR at different levels (i.e., medium [M] and high [H]). Image noise, image quality, and coronary luminal narrowing were evaluated by three blinded readers. Diagnostic accuracy was compared against ICA. RESULTS: Noise did not significantly differ between ASiR-V SD and DLIR-M (37 vs. 37 HU, p = 1.000), but was significantly lower in DLIR-H (30 HU, p < 0.001) and higher in ASiR-V HD (53 HU, p < 0.001). Image quality was higher for DLIR-M and DLIR-H (3.4-3.8 and 4.2-4.6) compared to ASiR-V SD and HD (2.1-2.7 and 1.8-2.2; p < 0.001), with DLIR-H yielding the highest image quality. Consistently across readers, no significant differences in sensitivity (88% vs. 92%; p = 0.453), specificity (73% vs. 73%; p = 0.583) and diagnostic accuracy (80% vs. 82%; p = 0.366) were found between ASiR-V HD and DLIR-H. CONCLUSION: DLIR significantly reduces noise in CCTA compared to ASiR-V, while yielding superior image quality at equal diagnostic accuracy.

A feasibility study of pulmonary nodule detection by ultralow-dose CT with adaptive statistical iterative reconstruction-V technique.

PURPOSE: To evaluate the clinical value of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) in the detection of pulmonary nodules in a Chinese population. METHOD: One hundred eighty-eight patients (16.41 ≤ BMI ≤ 29.87 kg/m2) with pulmonary nodules detected on low-dose chest CT (LDCT) underwent local ULDCT at the center of the chosen nodule with a scan length of 3 cm. LDCT was performed using the Assist kV (120/100 kV)/Smart mA mode and at 120 kV/2.8 mAs for ULDCT. After scanning, CT images were reconstructed with ASiR-V 50%. For both scans, nodule diameters were measured and reference standards were established for the presence and types of lung nodules found on LDCT. The sensitivity of ULDCT was compared against the standard, and logistic regression analysis was used to determine the independent predictors for nodule detection. RESULTS: Compared with LDCT (0.93 ±â€¯0.32 mSv), a 89.7% dose decrease was seen with ULDCT, for which the calculated effective dose was 0.096 ±â€¯0.006 mSv (P < 0.001). LDCT showed 188 nodules, including 123 solid and 65 subsolid nodules. The overall sensitivity for nodule detection in ULDCT was 90.4% (170/188), and 98.2% (54/55) for nodules ≥ 6 mm. In multivariate analysis, nodule types and diameters were independent predictors of sensitivity (P < 0.05). However, patients' BMI had no effect on nodule detection (P > 0.05). CONCLUSIONS: ULDCT can be used in the management of pulmonary nodules for people with BMI ≤ 30 kg/m2 at 10% radiation dose of LDCT.

Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp.

OBJECTIVE: To compare the objective and subjective image quality indicators and radiation doses of computed tomography (CT) venography performed using model-based iterative reconstruction (MBIR) at 80 kVp and adaptive statistical iterative reconstruction (ASIR)-V at 70 kVp. MATERIALS AND METHODS: Eighty-three patients who had undergone CT venography of the lower extremities with MBIR at 80 kVp (Group A; 21 men and 20 women; mean age, 55.5 years) or ASIR-V at 70 kVp (Group B; 18 men and 24 women; mean age, 57.3 years) were enrolled. Two radiologists retrospectively evaluated the objective (vascular enhancement, image noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and subjective (quantum mottle, delineation of contour, venous enhancement) image quality indicators at the inferior vena cava and femoral and popliteal veins. Clinical information, radiation dose, reconstruction time, and objective and subjective image quality indicators were compared between groups A and B. RESULTS: Vascular enhancement, SNR, and CNR were significantly greater in Group B than in Group A (p ≤ 0.015). Image noise was significantly lower in Group B (p ≤ 0.021), and all subjective image quality indicators, except for delineation of vein contours, were significantly better in Group B (p ≤ 0.021). Mean reconstruction time was significantly shorter in Group B than in Group A (1 min 43 s vs. 131 min 1 s; p < 0.001). Clinical information and radiation dose were not significantly different between the two groups. CONCLUSION: CT venography using ASIR-V at 70 kVp was better than MBIR at 80 kVp in terms of image quality and reconstruction time at similar radiation doses.

Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality.

PURPOSE: To optimize image quality and radiation dose of chest CT with respect to various iterative reconstruction levels, detector collimations and body sizes. METHOD: A Kyoto Kagaku Lungman with and without extensions was scanned using fixed ultra-low doses of 0.25, 0.49 and 0.74 mGy CTDIvol, and collimations of 40 and 80 mm. Images were reconstructed with the lung kernel, filtered back projection (FBP) and different ASIR-V levels (10-100%). Contrast-to-noise ratios (CNR) were calculated for 12 mm simulated lesions of different densities in the lung. Image noise, signal-to-noise ratios (SNR), variations in Hounsfield units (HU), noise power spectrum (NPS) and noise texture deviations (NTD) were evaluated for all reconstructions. NTD was calculated as percentage of pixels outside 3 standard deviations to evaluate IR-specific artefacts. RESULTS: Compared to the FBP, image noise reduced (5-55%) with ASIR-V levels irrespective of dose or collimation. SNR correlated positively (r ≥ 0.925, p ≤ 0.001) with ASIR-V levels at all doses, collimations, and phantom sizes. ASIR-V enhanced the CNR of the lesion with the lowest contrast from 12.7-42.1 (0-100% ASIR-V) at 0.74 mGy with 40 mm collimation. As expected, higher SNR and CNR were measured in the smaller phantom than the bigger phantom. Uniform HU were observed between FBP and ASIR-V levels at all doses, collimations, and phantom sizes. NPS curves left-shifted towards lower frequencies at increasing levels of ASIR-V irrespective of collimation. A positive correlation (r ≥ 0.946, p ≥ 0.001) was observed between NTD and ASIR-V levels. NTD of the FBP was not significantly (p ≤ 0.087) different from NTD of ASIR-V ≤ 20%. The data from the NPS and NTD indicates a blotchier and coarser noise texture at higher levels of ASIR-V, especially at 100% ASIR-V. CONCLUSION: In comparison with the FBP technique, ASIR-V enhanced quantitative image quality parameters at all ultra-low doses tested. Moreover, the use of ASIR-V showed consistency with body size and collimation. Hence, ASIR-V may be useful for improving image quality of chest CT at ultra-low doses.

Image Quality and Radiation Dose in CT Venography Using Model-Based Iterative Reconstruction at 80 kVp versus Adaptive Statistical Iterative Reconstruction-V at 70 kVp

OBJECTIVE: To compare the objective and subjective image quality indicators and radiation doses of computed tomography (CT) venography performed using model-based iterative reconstruction (MBIR) at 80 kVp and adaptive statistical iterative reconstruction (ASIR)-V at 70 kVp. MATERIALS AND METHODS: Eighty-three patients who had undergone CT venography of the lower extremities with MBIR at 80 kVp (Group A; 21 men and 20 women; mean age, 55.5 years) or ASIR-V at 70 kVp (Group B; 18 men and 24 women; mean age, 57.3 years) were enrolled. Two radiologists retrospectively evaluated the objective (vascular enhancement, image noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR]) and subjective (quantum mottle, delineation of contour, venous enhancement) image quality indicators at the inferior vena cava and femoral and popliteal veins. Clinical information, radiation dose, reconstruction time, and objective and subjective image quality indicators were compared between groups A and B. RESULTS: Vascular enhancement, SNR, and CNR were significantly greater in Group B than in Group A (p ≤ 0.015). Image noise was significantly lower in Group B (p ≤ 0.021), and all subjective image quality indicators, except for delineation of vein contours, were significantly better in Group B (p ≤ 0.021). Mean reconstruction time was significantly shorter in Group B than in Group A (1 min 43 s vs. 131 min 1 s; p < 0.001). Clinical information and radiation dose were not significantly different between the two groups. CONCLUSION: CT venography using ASIR-V at 70 kVp was better than MBIR at 80 kVp in terms of image quality and reconstruction time at similar radiation doses.

A new low-dose multi-phase trauma CT protocol and its impact on diagnostic assessment and radiation dose in multi-trauma patients.

PURPOSE: Computed tomography (CT) examinations, often using high-radiation dosages, are increasingly used in the acute management of polytrauma patients. This study compares a low-dose polytrauma multi-phase whole-body CT (WBCT) protocol on a latest generation of 16-cm detector 258-slice multi-detector CT (MDCT) scanner with advanced dose reduction techniques to a single-phase polytrauma WBCT protocol on a 64-slice MDCT scanner. METHODS: Between March and September 2015, 109 polytrauma patients (group A) underwent acute WBCT with a low-dose multi-phase WBCT protocol on a 258-slice MDCT whereas 110 polytrauma patients (group B) underwent single-phase trauma CT on a 64-slice MDCT. The diagnostic accuracy to trauma-related injuries, radiation dose, quantitative and semiquantitative image quality parameters, subjective image quality scorings, and workflow time parameters were compared. RESULTS: In group A, statistically significantly more arterial injuries (p = 0.04) and arterial dissections (p = 0.002) were detected. In group A, the mean (±SD) dose length product value was 1681 ± 183 mGy*cm and markedly lower when compared to group B (p < 0.001). The SDs of the mean Houndsfield unit values of the brain, liver, and abdominal aorta were lower in group A (p < 0.001). Mean signal-to-noise ratios (SNRs) for the brain, liver, and abdominal aorta were significantly higher in group A (p < 0.001). Group A had significantly higher image quality scores for all analyzed anatomical locations (p < 0.02). However, the mean time from patient registration until completion of examination was significantly longer for group A (p < 0.001). CONCLUSIONS: The low-dose multi-phase CT protocol improves diagnostic accuracy and image quality at markedly reduced radiation. However, due to technical complexities and surplus electronic data provided by the newer low-dose technique, examination time increases, which reduces workflow in acute emergency situations.