The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography

The purpose of this study was to investigate the utility of deep learning image reconstruction at medium and high intensity levels (DLIR-M and DLIR-H, respectively) for better delineation of liver metastases in pre-contrast and post-contrast CT, compared to conventional hybrid iterative reconstruction (IR) methods. Forty-one patients with liver metastases who underwent abdominal CT were studied. The raw data were reconstructed with three different algorithms: hybrid IR (ASiR-V 50%), DLIR-M (TrueFildelity-M), and DLIR-H (TrueFildelity-H). Three experienced radiologists independently rated the lesion conspicuity of liver metastases on a qualitative 5-point scale (score 1 = very poor; score 5 = excellent). The observers also selected each image series for pre- and post-contrast CT per patient that was considered most preferable for liver metastases assessment. For pre-contrast CT, lesion conspicuity scores for DLIR-H and DLIR-M were significantly higher than those for hybrid IR for two of the three observers, while there was no significant difference for one observer. For post-contrast CT, the lesion conspicuity scores for DLIR-H images were significantly higher than those for DLIR-M images for two of the three observers on post-contrast CT (Observer 1: DLIR-H, 4.3 ± 0.8 vs. DLIR-M, 3.9 ± 0.9, p = 0.0006; Observer 3: DLIR-H, 4.6 ± 0.6 vs. DLIR-M, 4.3 ± 0.6, p = 0.0013). For post-contrast CT, all observers most often selected DLIR-H as the best reconstruction method for the diagnosis of liver metastases. However, in the pre-contrast CT, there was variation among the three observers in determining the most preferred image reconstruction method, and DLIR was not necessarily preferred over hybrid IR for the diagnosis of liver metastases.

Preoperative diagnostic value of multimodal spectral CT for patients with atrial fibrillation undergoing radiofrequency ablation

Objective

Delayed enhancement cardiac computed tomography (CT) empowers the diagnosis of left atrial appendage thrombus while limited to scanning heterogeneity. We optimized the spectral CT scan and post-process protocols, incorporating delayed enhancement and spectral iodine analysis to discriminate left atrial appendage (LAA) thrombus with better morphological relationships between the left atrium, pulmonary vein, and esophagus.

Methods

A total of 278 consecutive patients were retrieved from January 2019 to June 2023. All patients underwent transesophageal echocardiography (TEE) and spectral CT scan of the left atrial and pulmonary vein, with a complete period including the pulmonary venous phase and three delay phases. TEE diagnosis was used as the standard reference. For patients exhibiting LAA filling defects during the pulmonary venous phase, a delayed scan of 30 s (phase I) was performed. If the filling defects persisted, a further delayed scan of 1 min (phase II) was conducted. In cases where the filling defects persisted, an additional delayed scan of 2 min (phase III) was carried out. Iodine concentration in the filled defect area of LAA and the left atrium was measured in phase III. Moreover, 30 patients were randomly selected for water-swallowing and the other 30 for calm breathing. The image quality and esophageal dilation of the two groups were assessed by two experienced surgeons specializing in radiofrequency ablation.

Results

In total, 14 patients were diagnosed with thrombi by TEE. The sensitivity, specificity, positive predictive values, negative predictive values, and AUC of phase III delayed combined with iodine quantification for thrombi diagnosis were all 100%. The water-swallowing group exhibited significantly greater esophageal filling and expansion than the calm-breathing group, contributing to a better morphology assessment with no significant difference in image quality.

Conclusion

Combined with iodine quantification, delayed enhancement of spectral CT imaging presents a promising diagnostic potency for LAA thrombus. Incorporating water swallowing into the CT scan process further enables anatomical visualization of the esophagus, left atrium, and pulmonary vein, thereby providing more objective and authentic imaging evidence to assess the esophageal morphology and positional relationships.

Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging

PURPOSE

To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT).

METHODS

142 participants required lung examination who underwent simultaneously ULD-CT (UL-A, 0.57 ± 0.04 mSv or UL-B, 0.33 ± 0.03 mSv), and standard CT (SDCT, 4.32 ± 0.33 mSv) plain scans were included in this prospective study. SDCT was the reference standard using ASIR-V at 50% strength (50%ASIR-V). ULD-CT was reconstructed with 50%ASIR-V, DLIR at medium and high strength (DLIR-M, DLIR-H). The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and subjective scores were measured. The presence and accuracy of nodules were analyzed using a combination of a deep learning-based nodule evaluation system and a radiologist.

RESULTS

A total of 710 nodules were detected by SDCT, including 358 nodules in UL-A and 352 nodules in UL-B. DLIR-H exhibited superior noise, SNR, and CNR performance, and achieved comparable or even higher subjective scores compared to 50%ASIR-V in ULD-CT. Nodules sensitivity detection of 50%ASIR-V, DLIR-M, and DLIR-H in ULD-CT were identical (96.90%). In multivariate analysis, body mass index (BMI), nodule diameter, and type were independent predictors for the sensitivity of nodule detection (p<.001). DLIR-H provided a lower absolute percent error (APE) in volume (3.10% ± 95.11% vs 8.29% ± 99.14%) compared to 50%ASIR-V of ULD-CT (P<.001).

CONCLUSIONS

ULD-CT scanning has a high sensitivity for detecting pulmonary nodules. Compared with ASIR-V, DLIR can significantly reduce image noise, and improve image quality, and accuracy of the nodule measurement in ULD-CT.

Utility of block-matching and 3D filter for reproducibility of lung density and denoising in low-dose chest CT: A pilot study

PURPOSE

This study aimed to acquire an image quality consistent with that of full-dose chest computed tomography (CT) when obtaining low-dose chest CT images and to analyze the effects of block-matching and 3D (BM3D) filters on lung density measurements and noise reduction in lung parenchyma.

METHODS

Using full-dose chest CT images, we evaluated lung density measurements and noise reduction in lung parenchyma images for low-dose chest CT. Three filters (median, Wiener, and the proposed BM3D) were applied to low-dose chest CT images for comparison and analysis with images from full-dose chest CT. To evaluate lung density measurements, we measured CT attenuation at the 15th percentile of the lung CT histogram. The coefficient of variation (COV) and contrast-to-noise ratio (CNR) were used to evaluate the noise level.

RESULTS

The 15th percentile of the lung CT histogram showed the smallest difference between full- and low-dose CT when applying the BM3D filter, and the highest difference between full- and low-dose CT without filters (full-dose =  - 926.28 ± 0.32, BM3D =  - 926.65 ± 0.32, and low-dose =  - 959.43 ± 0.95) (p < 0.05). The COV was smallest when applying the BM3D filter, whereas the CNR was the highest (p < 0.05).

CONCLUSIONS

The results of the study prove that the BM3D filter can reduce image noise while increasing the reproducibility of the lung density, even for low-dose chest CT.

Computed tomography dose index determination in dose modulation prospectively involving the third-generation iterative reconstruction and noise index

PURPOSE

Optimizing CT protocols is challenging in the presence of automatic dose modulation because the CT dose index (CTDIvol) at different patient sizes is unknown to the operator. The task is more difficult when both the image quality index and iterative reconstruction prospectively affect the dose determination. It is of interest in practice to be informed of the CTDIvol during the protocol initialization and evaluation. It was our objective to obtain a predictive relationship between CTDIvol, the image quality index, and iterative reconstruction strength at various patient sizes.

METHODS

Dose modulation data were collected on a GE Revolution 256-slice scanner utilizing a Mercury phantom and selections of the noise index (NI) from 8 to 17, the third generation iterative reconstruction (ASIR-V) from 0% to 80%, and phantom diameters from 16 to 36 cm. The fixed parameters were 120 kVp, a pitch of .984, and a collimation of 40 mm with a primary slice width of 2.5 mm. The CTDIvol per diameter was based on the average tube current over three adjacent slices (same or similar diameter) multiplied by a conversion factor between the average mA of the series and the reported CTDIvol. The relationship between CTDIvol, NI, and ASIR-V for each diameter was fitted with a 2nd order polynomial of ASIR-V multiplied by a power law of NI.

RESULTS

The ASIR-V fit parameters versus diameter followed a Lorentz function while the NI exponent versus diameter followed an exponential growth function. The CTDIvol predictions were accurate within 15% compared to phantom results on a separate GE Revolution. For clinical relevance, the phantom diameter was converted to an abdomen or chest equivalent diameter and was well matched to patient data.

CONCLUSION

The fitted relationship for CTDIvol. for given values of NI and ASIR-V blending for a range of phantom sizes was a good match to phantom and patient data. The results can be of direct help for selecting adequate parameters in CT protocol development.

Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (dNPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.

Improvement of diagnostic performance of hyperacute ischemic stroke in head CT using an image-based noise reduction technique with non-black-boxed process

PURPOSE

This study aims to investigate whether an image-based noise reduction (INR) technique with a conventional rule-based algorithm involving no black-boxed processes can outperform an existing hybrid-type iterative reconstruction (HIR) technique, when applied to brain CT images for diagnosis of early CT signs, which generally exhibit low-contrast lesions that are difficult to detect.

METHODS

The subjects comprised 27 patients having infarctions within 4.5 h of onset and 27 patients with no change in brain parenchyma. Images with thicknesses of 5 mm and 0.625 mm were reconstructed by HIR. Images with a thickness of 0.625 mm reconstructed by filter back projection (FBP) were processed by INR. The contrast-to-noise ratios (CNRs) were calculated between gray and white matters; lentiform nucleus and internal capsule; infarcted and non-infarcted areas. Two radiologists subjectively evaluated the presence of hyperdense artery signs (HASs) and infarctions and visually scored three properties regarding image quality (0.625-mm HIR images were excluded because of their notably worse noise appearances).

RESULTS

The CNRs of INR were significantly better than those of HIR with P < 0.001 for all the indicators. INR yielded significantly higher areas under the curve for both infarction and HAS detections than HIR (P < 0.001). Also, INR significantly improved the visual scores of all the three indicators.

CONCLUSION

The INR incorporating a simple and reproducible algorithm was more effective than HIR in detecting early CT signs and can be potentially applied to CT images from a large variety of CT systems.

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen

BACKGROUND

Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual-energy CT.

PURPOSE

To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual-energy CT of the abdomen and compare it to single-energy (SE) CT.

METHODS

An oval 25 cm x 35 cm custom-made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5 mGy) and two iodine concentrations (8 and 2 mg/mL), using three typical abdominal scan protocols: dual-energy (DE), SE 80 kV (SE-80 kV) and SE 120 kV (SE-120 kV). Reconstructions were performed with three strengths of IR (ASiR-V: AR0%, AR50%, AR100%) and three strengths of DLR (TrueFidelity: low, medium, high). The DE acquisitions were reconstructed as mono-energetic images between 40 and 80 keV. The noise power spectrum (NPS), task transfer function (TTF), and detectability index (d') were determined for the reconstructions following the recommendations of AAPM Task Group 233.

RESULTS

Noise magnitude reductions (relative to AR0%) for the SE protocols were on average (-29%, -21%) for (AR50%, TF-M), while for DE-70 keV were (-28%, -43%). There was less reduction in mean frequency (f_av ) for DLR than for IR, with similar results for SE and DE imaging. There was, however, a substantial change in the NPS shape when using DE with DLR, quantifiable by a marked reduction in the peak frequency (f_peak ) that was absent in SE mode. All protocols and reconstructions (including AR0%) exhibited slight to moderate shifts towards lower spatial frequencies at the lower dose (<12% in f_av ). Spatial resolution was consistently superior for DLR compared to IR for SE but not for DE. All protocols and reconstructions (including AR0%) showed decreased resolution with reduced dose and iodine concentration, with less decrease for DLR compared to IR. DLR displayed a higher d' than IR. The effect of energy was large: d' increased with lower keV, and SE-80 kV had higher d' than SE-120 kV. Using DE with DLR could provide higher d' than SE-80 kV at the higher dose but not at lower dose.

CONCLUSIONS

DE imaging with DLR maintained spatial resolution and reduced noise magnitude while displaying less change in noise texture than IR. The d' was also higher with DLR than IR, suggesting superiority in detectability of iodinated contrast. Despite these trends being consistent with those previously established for SE imaging, there were some noteworthy differences. For DE imaging there was no improvement in resolution compared to IR and a change in noise texture. DE imaging with low keV and DLR had superior detectability to SE DLR at the high dose but was not better than SE-80 kV at low dose.

A feasibility study of different GSI noise indexes and concentrations of contrast medium in hepatic CT angiography of overweight patients: image quality, radiation dose, and iodine intake

PURPOSE

To conduct a comparative study of image quality, radiation dose, and iodine intake in hepatic computed tomographic angiography (CTA) of overweight patients with different Gemstone Spectral Imaging (GSI) noise indexes combined with different concentrations of contrast medium.

MATERIALS AND METHODS

Ninety patients with a body mass index of ≥ 25 kg/m² were divided into three groups (A, B and C), each with 30 patients. The three groups underwent hepatic CTA with different NI of 7, 11 and 15, respectively, and were injected with different iodine concentrations of 370, 350 and 320 mgI/mL, respectively. Five sets of images at 40-60 keV (interval, 5 keV) were reconstructed in each group. The CT value, image noise, contrast-to-noise ratio (CNR) and subjective score of the hepatic artery and vein, and portal vein in different monochromatic image sets were analyzed to select the optimal energy level in each group. The differences in CT value, image noise, CNR and a subjective score of hepatic artery and vein, portal vein in the optimal monochromatic images among the three groups were compared, the volume CT dose index (CTDIvol) and dose-length product (DLP) were recorded, and the effective dose and iodine intake were calculated.

RESULTS

The 40 keV was determined to be the optimal energy level for the monochromatic image sets in each group. No significant group differences were noted in the CT value, image noise, CNR, and subjective image scores of the hepatic artery and vein, and portal vein for the optimal monochromatic images (P > 0.05). Compared with group A, the effective dose and iodine intake in group B were reduced by 50.18% and 9.3%, and by 58.12% and 14.23% in group C, respectively.

CONCLUSION

A low-concentration contrast medium combined with a high-noise GSI index in hepatic CTA of overweight patients can reduce the radiation dose and iodine intake while ensuring image quality.

Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V)

Purpose

To compare the effects of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASiR-V) on image quality in low-dose computed tomography (CT) of paranasal sinuses in children.

Methods

Low-dose CT scans of the paranasal sinuses in 25 pediatric patients were retrospectively evaluated. The raw data were reconstructed with three levels of DLIR (high, H; medium, M; and low, L), filtered back projection (FBP), and ASiR-V (30% and 50%). Image noise was measured in both soft tissue and bone windows, and the signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the images were calculated. Subjective image quality at the ethmoid sinus and nasal cavity levels of the six groups of reconstructed images was assessed by two doctors using a five-point Likert scale in a double-blind manner.

Results

The patients’ mean dose-length product and effective dose were 36.65 ± 2.44 mGy·cm and 0.17 ± 0.03 mSv, respectively. (1) Objective evaluation: 1. Soft tissue window: The difference among groups in each parameter was significant (P < 0.05). Pairwise comparisons showed that the H group’ s parameters were significantly better (P < 0.05) than those of the 50% post-ASiR-V group. 2. Bone window: No significant between-group differences were found in the noise of the petrous portion of the temporal bone or its SNR or in the noise of the pterygoid processes of the sphenoids or their SNRs (P > 0.05). Significant differences were observed in the background noise and CNR (P < 0.05). As the DLIR intensity increased, image noise decreased and the CNR improved. The H group exhibited the best image quality. (2) Subjective evaluation: Scores for images of the ethmoid sinuses were not significantly different among groups (P > 0.05). Scores for images of the nasal cavity were significantly different among groups (P < 0.05) and were ranked in descending order as follows: H, M, L, 50% post-ASiR-V, 30% post-ASiR-V, and FBP.

Conclusion

DLIR was superior to FBP and post-ASiR-V in low-dose CT scans of pediatric paranasal sinuses. At high intensity (H), DLIR provided the best reconstruction effects.

Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A Phantom Study

PURPOSE

Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) in combination with various CT scan parameters on the semi-automated quantification using various lung nodules.

METHODS

A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality.

RESULTS

The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β = 9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β = -3.499, p<0.001) and tube current (mA; β = -0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7-13.1%).

CONCLUSION

Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction. This article is protected by copyright. All rights reserved.

Effects of Adaptive Statistical Iterative Reconstruction-V Technology on the Image Quality and Radiation Dose of Unenhanced and Enhanced CT Scans of the Piglet Abdomen

To investigate the optimal pre- and post-adaptive statistical iterative reconstruction-V (ASiR-V) levels in pediatric abdominal computed tomography (CT) to minimize radiation exposure and maintain image quality using an animal model. A total of 10 standard piglets were selected and scanned to obtain unenhanced and enhanced images under different pre-ASiR-V conditions. The corresponding images were obtained using ASiR-V algorithm at different post-ASiR-V levels. CT value, signal-to-noise ratio (SNR), contrast noise ratio (CNR) of abdominal tissues, subjective image score, and radiation dose of unenhanced and enhanced scans were analyzed. With the increase of pre-ASiR-V level, the radiation dose in piglets gradually decreased (P < 0.05). Within the same group of pre-ASiR-V, the image noise was decreased (P < 0.05) by increasing post-ASiR-V level. There was no statistical difference between SNR and CNR values. In unenhanced CT, the subjective score of the images with the combination of 40% pre- and 60% post-ASiR-V levels had no statistical difference compared to the combination of 0% pre- and 60% post-ASiR-V levels, while the radiation dose decreased by 31.6%. In the enhanced CT, the subjective image score with the 60% pre- and 60% post-ASiR-V combination had no statistical difference compared to the 0% pre- and 60% post-ASiR-V combination, while the radiation dose was reduced by 48.9%. The combined use of pre- and post-ASiR-V maintains image quality at the reduced radiation dose. The optimal level for unenhanced CT is 40% pre-combined with 60% post-ASiR-V, while that for enhanced CT is 60% pre-combined with 60% post-ASiR-V in pediatric abdominal CT.

Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta

To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction (ASIR-V) and DLIR. Intravascular image noise, the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were quantified for the ascending aorta, the descending thoracic aorta, the abdominal aorta and the iliac arteries. Two readers scored subjective image quality on a five-point scale. Compared to ASIR-V, DLIR reduced the median image noise by 51–54% for the ascending aorta and the descending thoracic aorta. Correspondingly, median CNR roughly doubled for the ascending aorta and descending thoracic aorta. There was a 38% reduction in image noise for the abdominal aorta and the iliac arteries, with a corresponding improvement in CNR. Median subjective image quality improved from good to excellent at all anatomical levels. In CT angiography of the aorta, DLIR substantially improved objective and subjective image quality beyond what can be achieved by state-of-the-art iterative reconstruction. This can pave the way for further radiation or contrast dose reductions.

Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients

Iterative reconstructions (IR) might alter radiomic features extraction. We aim to evaluate the influence of Adaptive Statistical Iterative Reconstruction-V (ASIR-V) on CT radiomic features. Patients who underwent unenhanced abdominal CT (Revolution Evo, GE Healthcare, USA) were retrospectively enrolled. Raw data of filtered-back projection (FBP) were reconstructed with 10 levels of ASIR-V (10–100%). CT texture analysis (CTTA) of liver, kidney, spleen and paravertebral muscle for all datasets was performed. Six radiomic features (mean intensity, standard deviation (SD), entropy, mean of positive pixel (MPP), skewness, kurtosis) were extracted and compared between FBP and all ASIR-V levels, with and without altering the spatial scale filter (SSF). CTTA of all organs revealed significant differences between FBP and all ASIR-V reconstructions for mean intensity, SD, entropy and MPP (all p < 0.0001), while no significant differences were observed for skewness and kurtosis between FBP and all ASIR-V reconstructions (all p > 0.05). A per-filter analysis was also performed comparing FBP with all ASIR-V reconstructions for all six SSF separately (SSF0-SSF6). Results showed significant differences between FBP and all ASIR-V reconstruction levels for mean intensity, SD, and MPP (all filters p < 0.0315). Skewness and kurtosis showed no differences for all comparisons performed (all p > 0.05). The application of incremental ASIR-V levels affects CTTA across various filters. Skewness and kurtosis are not affected by IR and may be reliable quantitative parameters for radiomic analysis.

The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom

The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube voltages and tube currents, reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms.A total of 15 CT scans of whole body phantoms were taken with 3 different tube voltages and 5 different tube currents. The images were reconstructed with FBP, 30% and 50% hybrid IR adaptive statistical iterative reconstruction (ASIR-V), and low, medium and high strength DLIR algorithms. The image scanned with tube voltage/tube current of 120 kV/ 200 mA and reconstructed with FBP algorithm was chosen as the reference image. Five radiologists independently analyzed the images individually and also compared it with the reference image in overall, using the visual grading analysis. The mean score of each image was calculated and compared.Using DLIR algorithms, the radiation dose was reduced by 65.5% to 68.1% compared with the dose used in the reference image, while maintaining comparable image quality. Using the DLIR algorithm of medium strength, the image quality was even better than the reference image with a reduced radiation dose up to 36.2% to 50.0%. The DLIR algorithms generated better quality images than ASIR-V algorithms in all the data sets. In addition, among the data sets reconstructed with DLIR algorithms, image quality was the best at the medium strength level, followed by low and high.This phantom study suggests that DLIR algorithms may be considered as a new reconstruction technique by reducing radiation dose while maintaining the image quality of abdominal CTs.

A Third-Generation Adaptive Statistical Iterative Reconstruction for Contrast-Enhanced 4-Dimensional Dual-Energy Computed Tomography for Pancreatic Cancer

OBJECTIVES

The objective of this study was to assess the objective and subjective qualities of the contrast-enhanced 4-dimensional dual-energy computed tomography using adaptive statistical iterative reconstruction (ASiR) and ASiR-V.

METHODS

The virtual monochromatic images at 60 keV were reconstructed using filtered back projection, ASiR, and ASiR-V (10%-100%) for 14 patients with pancreatic cancer. The contrast-to-noise ratio (CNR) was calculated, and the subjective measurements were compared based on a 5-point score scale.

RESULTS

The ASiR-V yielded a significantly higher CNR than ASiR (P < 0.05). The subjective image quality (peak) was significantly improved (P < 0.01) with ASiR (50%) (3.8, 3.5, and 4.0; overall image quality, tumor delineation, and noise, respectively) and with ASiR-V (50%) (3.9, 3.5, and 4.2, respectively) compared with the filtered back projection (3.2, 3.2, and 3.0, respectively).

CONCLUSIONS

The ASiR-V yielded higher CNR than ASiR and provided the highest subjective scores regarding the overall image quality.

Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction

PURPOSE

To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.

METHOD

Pre and post-contrast abdominal CT images in 50 patients were reconstructed with 2 different algorithms: hybrid iterative reconstruction (hybrid IR: ASiR-V 50%) and DLIR (TrueFidelity). Standard deviation of attenuation in normal liver parenchyma was measured as the image noise on pre and post-contrast CT. The contrast-to-noise ratio (CNR) for the aorta, and the signal-to-noise ratio (SNR) of the liver were calculated on post-contrast CT. The overall image quality was graded on a 5-point scale ranging from 1 (poor) to 5 (excellent).

RESULTS

The image noise was significantly decreased by DLIR compared to hybrid-IR [hybrid IR, median 8.3 Hounsfield unit (HU) (interquartile range (IQR) 7.6-9.2 HU); DLIR, median 5.2 HU (IQR 4.6-5.8), P < 0.0001 for post-contrast CT]. The CNR and SNR were significantly improved by DLIR [CNR, median 4.5 (IQR 3.8-5.6) vs 7.3 (IQR 6.2-8.8), P < 0.0001; SNR, median 9.4 (IQR 8.3-10.1) vs 15.0 (IQR 13.2-16.4), P < 0.0001]. The overall image quality score was also higher for DLIR compared to hybrid-IR (hybrid IR 3.1 ± 0.6 vs DLIR 4.6 ± 0.5, P < 0.0001 for post-contrast CT).

CONCLUSIONS

Image noise, overall image quality, CNR and SNR for abdominal CT images are improved with DLIR compared to hybrid IR.

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 (Noise_B > Noise_A, P_muscleA&B=0.233, P_aortaA&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 (CTDI_vol) 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%.

A preliminary evaluation study of applying a deep learning image reconstruction algorithm in low-kilovolt scanning of upper abdomen

OBJECTIVE

To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen.

METHODS

A total of 64 patients (BMI<28) are selected for the enhanced upper abdomen scan and divided evenly into two groups. The tube voltages in Group A are 100kV in arterial phase and 80kV in venous phase, while tube voltages are 120kV during two phases in Group B. Image reconstruction algorithms used in Group A include the filtered back projection (FBP) algorithm, the adaptive statistical iterative reconstruction-Veo (ASIR-V 40% and 80%) algorithm, and the DLIR algorithm (DL-L, DL-M, DL-H). Image reconstruction algorithm used in Group B is ASIR-V40%. The different reconstruction algorithm images are used to measure the common hepatic artery, liver, renal cortex, erector spinae, and subcutaneous adipose in the arterial phase and the average CT value and standard deviation of the portal vein, liver, spleen, erector spinae, and subcutaneous adipose in the portal phase. The signal-to-noise ratio (SNR) is calculated, and the images are also scored subjectively.

RESULTS

In Group A, noise in the aorta, liver, portal vein (the portal phase), spleen (the portal phase), renal cortex, retroperitoneal adipose, and muscle is significantly lower in both the DL-H and ASIR-V80% images, and the SNR is significantly higher than those in the remaining groups (P<0.05). The SNR of each tissue and organ in Group B is not significantly different from that in DL-M, DL-L, and ASIR-V40% in Group A (P>0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P<0.05).

CONCLUSION

For upper abdominal low-kilovolt enhanced scan data, the DLIR-H gear yields a more satisfactory image quality than the FBP and ASIR-V.

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.

The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting

AIM

To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).

MATERIALS AND METHODS

Thirty-six patients were evaluated retrospectively. All patients underwent contrast-enhanced chest CT and thin-section images were reconstructed using filtered back projection (FBP); ASiR-V (60% and 100% blending setting); and DLIR (low, medium, and high settings). Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated objectively. Two independent radiologists evaluated ASiR-V 60% and DLIR subjectively, in comparison with FBP, on a five-point scale in terms of noise, streak artefact, lymph nodes, small vessels, and overall image quality on a mediastinal window setting (width 400 HU, level 60 HU). In addition, image texture of ASiR-Vs (60% and 100%) and DLIR-high was analysed subjectively.

RESULTS

Compared with ASiR-V 60%, DLIR-med and DLIR-high showed significantly less noise, higher SNR, and higher CNR (p<0.0001). DLIR-high and ASiR-V 100% were not significantly different regarding noise (p=0.2918) and CNR (p=0.0642). At a higher DLIR setting, noise was lower and SNR and CNR were higher (p<0.0001). DLIR-high showed the best subjective scores for noise, streak artefact, and overall image quality (p<0.0001). Compared with ASiR-V 60%, DLIR-med and DLIR-high scored worse in the assessment of small vessels (p<0.0001). The image texture of DLIR-high was significantly finer than that of ASIR-Vs (p<0.0001).

CONCLUSIONS

DLIR-high improved the objective parameters and subjective image quality by reducing noise and streak artefacts and providing finer image texture.

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.

Impact of 80 kVp with iterative reconstruction algorithm and low-dose contrast medium on the image quality of craniocervical CT angiography

PURPOSE

To assess the image quality of 80-kVp craniocervical CT angiography (CCCTA) protocol combined with adaptive statistical iterative reconstruction-V (ASIR-V) and low-dose contrast medium (CM).

METHODS

A total of 119 patients were randomly divided into three groups. For group A, 120-kVp protocol was followed with 60 ml CM and filtered back projection; for group B, 80-kVp protocol with 60 ml CM and ASIR-V; and for group C, 80-kVp protocol with 45 ml CM and ASIR-V. Both subjective and objective image quality and radiation doses were evaluated.

RESULTS

Arterial attenuation, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the head, neck, and shoulder regions were significantly higher in groups B and C compared with group A. Group C yielded significantly better subjective image quality than that observed in groups A and B (both p < .05). As compared with group A, effective radiation dose and the iodine load of group C were reduced by 51.4% and 25%, respectively.

CONCLUSIONS

The CCCTA protocol with 80 kVp, ASIR-V, and 45 ml of CM injected at 3 ml/s significantly reduced the radiation dose, iodine load, and iodine delivery rate while providing better subjective and objective image quality, including higher arterial enhancement and a higher SNR and CNR compared with the 120-kVp protocol.

Measurement accuracy of lung nodule volumetry in a phantom study: Effect of axial-volume scan and iterative reconstruction algorithm

An axial-volume scan with adaptive statistical iterative reconstruction-V (ASIR-V) is newly developed. Our goal was to identify the influence of axial-volume scan and ASIR-V on accuracy of automated nodule volumetry.An "adult' chest phantom containing various nodules was scanned using both helical and axial-volume modes at different dose settings using 256-slice CT. All CT scans were reconstructed using 30% and 50% blending of ASIR-V and filtered back projection. Automated nodule volumetry was performed using commercial software. The image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) were measured.The axial-volume scan reduced radiation dose by 19.7% compared with helical scan at all radiation dose settings without affecting the accuracy of nodule volumetric measurement (P = .375). Image noise, CNR, and SNR were not significantly different between two scan modes (all, P > .05).The use of axial-volume scan with ASIR-V achieved effective radiation dose reduction while preserving the accuracy of nodule volumetry.

Effects of different levels of CT iterative reconstruction on low-contrast detectability and radiation dose in patients of different sizes: an anthropomorphic phantom study

PURPOSE

The purpose of this study was to verify the maintenance of low-contrast detectability at different CT dose reduction levels, in patients of different sizes, as a consequence of the application of iterative reconstruction at different strengths combined with tube current modulation.

METHODS

Anthropomorphic abdominal phantoms of two sizes (small and large) were imaged at a fixed noise with iterative algorithm ASIR-V percentages in the range between 0 and 70% and corresponding dose reductions in the range of 0-83%. A total of 1400 images with and without liver low-contrast simulated lesions were evaluated by five radiologists, using the receiver operating characteristics (ROC) paradigm and evaluating the area under the ROC curve (AUC). The human observer results were then compared with AUC obtained with a channelized Hotelling observer (CHO). CNR values were also calculated.

RESULTS

For the small phantom, the AUC values lie between 0.90 and 0.93 for human evaluations of images acquired without iterative reconstruction, with 30% ASIR-V and with 50% ASIR-V. The AUC decreased significantly to 0.81 (p = 0.0001) at 70% ASIR-V. The CHO results were in coherence with human observer scores. Also, similar results were observed for the large size phantom. CNR values were stable for the different ASIR-V percentages.

CONCLUSIONS

The iterative algorithm maintained the low-contrast detectability up to a dose reduction of about 70%, following application of a 50% ASIR-V combined with automatic tube current modulation, regardless of the phantom size. At further dose reductions using greater iterative percentages, a significant decrease in detectability was observed.

Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience

OBJECTIVE. The purpose of this study was to perform quantitative and qualitative evaluation of a deep learning image reconstruction (DLIR) algorithm in contrast-enhanced oncologic CT of the abdomen. MATERIALS AND METHODS. Retrospective review (April-May 2019) of the cases of adults undergoing oncologic staging with portal venous phase abdominal CT was conducted for evaluation of standard 30% adaptive statistical iterative reconstruction V (30% ASIR-V) reconstruction compared with DLIR at low, medium, and high strengths. Attenuation and noise measurements were performed. Two radiologists, blinded to examination details, scored six categories while comparing reconstructions for overall image quality, lesion diagnostic confidence, artifacts, image noise and texture, lesion conspicuity, and resolution. RESULTS. DLIR had a better contrast-to-noise ratio than 30% ASIR-V did; high-strength DLIR performed the best. High-strength DLIR was associated with 47% reduction in noise, resulting in a 92-94% increase in contrast-to-noise ratio compared with that of 30% ASIR-V. For overall image quality and image noise and texture, DLIR scored significantly higher than 30% ASIR-V with significantly higher scores as DLIR strength increased. A total of 193 lesions were identified. The lesion diagnostic confidence, conspicuity, and artifact scores were significantly higher for all DLIR levels than for 30% ASIR-V. There was no significant difference in perceived resolution between the reconstruction methods. CONCLUSION. Compared with 30% ASIR-V, DLIR improved CT evaluation of the abdomen in the portal venous phase. DLIR strength should be chosen to balance the degree of desired denoising for a clinical task relative to mild blurring, which increases with progressively higher DLIR strengths.

Clinical application of multi-material artifact reduction (MMAR) technique in Revolution CT to reduce metallic dental artifacts

Background

This study aimed to explore the performance of Revolution CT virtual monoenergetic images (VMI) combined with the multi-material artifact reduction (MMAR) technique in reducing metal artifacts in oral and maxillofacial imaging.

Results

There were significant differences in image quality scores between VMI + MMAR images and VMI+MARS (multiple artifact reduction system) images at each monochromatic energy level (p = 0.000). Compared with the MARS technology, the MMAR technology further reduced metal artifacts and improved the image quality. At VMI_{90 keV} and VMI_{110 keV}, the SD, CNR, and AI in the Revolution CT group were significantly lower than in the Discovery CT, but no significant differences in these parameters were found between two groups at VMI_{50 keV}, VMI_{70 keV}, and VMI_{130 keV} (p > 0.05). The attenuation was comparable between two groups at any energy level (p > 0.05).

Conclusions

Compared with the MARS reconstruction technique of Discovery CT, the MMAR technique of Revolution CT is better to reduce the artifacts of dental implants in oral and maxillofacial imaging, which improves the image quality and the diagnostic value of surrounding soft tissues.

Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

OBJECTIVES

To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm.

METHODS

Data acquisitions were performed at seven dose levels (CTDI_vol : 15/10/7.5/5/2.5/1/0.5 mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity™ low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d') was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast.

RESULTS

NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF_50% obtained with DLIR was higher than that with IR. d' was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d' values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 ± 4%) and in the same range for the other simulated lesions.

CONCLUSIONS

New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR.

KEY POINTS

• This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm. • The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR. • As compared with IR, DLIR seems to open further possibility of dose optimization.

Reducing Radiation Dose and Improving Image Quality in CT Portal Venography Using 80 kV and Adaptive Statistical Iterative Reconstruction-V in Slender Patients

OBJECTIVE

To explore the feasibility of reducing radiation dose and improving image quality in CT portal venography (CTPV) using 80 kV and adaptive statistical iterative reconstruction-V(ASIR-V) in slender patients in comparison with conventional protocol using 120 kV and ASIR.

METHODS

Sixty slender patients for enhanced abdominal CT scanning were randomly divided into group A and group B. Group A used the conventional 120 kV tube voltage, 600 mgI/kg contrast dose and reconstructed with the recommended 40% ASIR. Group B used 80 kV tube voltage, 350 mgI/kg contrast dose and reconstructed with ASIR-V from 40% to 100% with 10% interval. The CT values and standard deviation (SD) values of the main portal vein, left branch, and right branch of portal vein, liver, and erector spinae at the same level were measured to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The image quality was subjectively scored by two experienced radiologists blindly using a 5-point criterion. The contrast dose, volumetric CT dose index, and dose length product were recorded in both groups and the effective dose was calculated.

RESULTS

There was no significant difference in general data between the two groups (p > 0.05), the effective dose and contrast dose in group B were reduced by 63.3% (p < 0.001) and 39.7% (p < 0.001), respectively compared with group A. With the percentage of ASIR-V increased in group B, the CT values showed no significant difference, while the SD values gradually decreased and SNR values and CNR values increased accordingly. Compared with group A, group B demonstrated similar CT values (p > 0.05), while the SD values with 80% ASIR-V to 100% ASIR-V were significantly lower than those of 40% ASIR (p < 0.001), and the SNR values and CNR values with 70% ASIR-V to 100% ASIR-V were significantly higher than those of 40% ASIR (p < 0.001). The subjective image quality scores by the two radiologists had excellent consistency (kappa value>0.75, p < 0.001), and the final subjective image quality scores and the subjective scores in each of the 5 scoring categories with 60% ASIR-V to 100% ASIR-V were all significantly higher than those of 40% ASIR, and 80% ASIR-V obtained the highest subjective score among different reconstructions.

CONCLUSION

In CTPV, the application of 80 kV and ASIR-V reconstruction in slender patients can significantly reduce radiation dose (by 63.3%) and contrast agent dose (by 39.7%). Compared with the recommended 40% ASIR using 120 kV, ASIR-V with 80% to 100% percentages can further improve image quality and with 80% ASIR-V being the best reconstruction algorithm.

ADVANCES IN KNOWLEDGE

CTPV with 80 kV and ASIR-V algorithm in slender patients can significantly reduce radiation dose and contrast agent dose as well as improve image quality, compared with the conventional 120 kV protocol using 40% ASIR.

First experiences of a low-dose protocol for CT-guided musculoskeletal biopsies combining different radiation dose reduction techniques

Background

The use of computed tomography (CT) for image guidance during biopsies is a powerful approach. The method is, however, often associated with a significant level of radiation exposure to the patient and operator.

Purpose

To investigate if a low-dose protocol for CT-guided musculoskeletal (MSK) biopsies, including a combination of different radiation dose (RD) techniques, is feasible in a clinical setting.

Material and Methods

Fifty-seven patients underwent CT-guided fine-needle aspiration cytology (FNAC) utilizing the low-dose protocol (group A). A similar number of patients underwent CT-guided FNAC using the reference protocol (group B). Between-group comparisons comprised radiation dose, success rate, image quality parameters, and workflow.

Results

In group A, the mean total dose-length product (DLP) was 41.2 ± 2.9 mGy*cm, which was statistically significantly lower than of group B (257.4 ± 22.0 mGy*cm), corresponding to a mean dose reduction of 84% ( P<0.001). The mean CTDIvol for the control scans were 1.88 ± 0.09 mGy and 13.16 ± 0.40 mGy for groups A and B, respectively ( P < 0.001). The success rate in group A was 91.2% and 87.9% in group B ( P = 0.56). No negative effect on image-quality parameters, time of FNAC, and number of control scans were found.

Conclusion

We successfully developed a low-dose protocol for CT-guided MSK biopsies that maintains diagnostic accuracy and image quality at a fraction of the RD compared to the reference biopsy protocol at our clinic.

Ultra-low-dose CT for extremities in an acute setting: initial experience with 203 subjects

OBJECTIVE

The purpose of this study was to assess if ultra-low-dose CT is a useful clinical alternative to digital radiographs in the evaluation of acute wrist and ankle fractures.

MATERIALS AND METHODS

An ultra-low-dose protocol was designed on a 256-slice multi-detector CT. Patients from the emergency department were evaluated prospectively. After initial digital radiographs, an ultra-low-dose CT was performed. Two readers independently analyzed the images. Also, the radiation dose, examination time, and time to preliminary report was compared between digital radiographs and CT.

RESULTS

In 207 extremities, digital radiography and ultra-low-dose CT detected 73 and 109 fractures, respectively (p < 0.001). The odds ratio for fracture detection with ultra-low-dose CT vs. digital radiography was 2.0 (95% CI, 1.4-3.0). CT detected additional fracture-related findings in 33 cases (15.9%) and confirmed or ruled out suspected fractures in 19 cases (9.2%). The mean effective dose was comparable between ultra-low-dose CT and digital radiography (0.59 ± 0.33 μSv, 95% CI 0.47-0.59 vs. 0.53 ± 0.43 μSv, 95% CI 0.54-0.64). The mean combined examination time plus time to preliminary report was shorter for ultra-low-dose CT compared to digital radiography (7.6 ± 2.5 min, 95% CI 7.1-8.1 vs. 9.8 ± 4.7 min, 95% CI 8.8-10.7) (p = 0.002). The recommended treatment changed in 34 (16.4%) extremities.

CONCLUSIONS

Ultra-low-dose CT is a useful alternative to digital radiography for imaging the peripheral skeleton in the acute setting as it detects significantly more fractures and provides additional clinically important information, at a comparable radiation dose. It also provides faster combined examination and reporting times.

[Sparse-view CT image restoration via multiscale wavelet residual network]

OBJECTIVE

Sparse-view CT has the advantages of accelerated data collection and reduced radiation dose, but data missing arising from the data collection process causes serious streaking artifact and noise in the images reconstructed using the traditional filtering back projection algorithm (FBP). To solve this problem, we propose a multi-scale wavelet residual network (MWResNet) to restore sparse-view CT images.

METHODS

The MWResNet was based on the combination of deep learning and traditional model in MWCNN, and the wavelet network was combined with the residual block to enhance the network's ability to embed image features and speed up network training. The network proposed herein was trained using the real spiral geometry CT image data, namely the Low-dose CT Grand Challenge dataset. The results of the proposed networks were visually and quantitatively compared to that by other existing networks, including the image restoration iterative residual convolution network (IRLNet), residual coding-decoding convolutional neural network (REDCNN) and the FBP convolutional neural network (FBPConvNet).

RESULTS

The results demonstrated that the proposed method was superior to other competing methods in terms of visual inspection and quantitative comparison.

CONCLUSIONS

The MWResNet network is an effective method for suppressing noise and artifacts and maintaining edges details in the sparse-view CT images.

Whole-Body Computed Tomography Using Low-Dose Biphasic Injection Protocol With Adaptive Statistical Iterative Reconstruction V: Assessment of Dose Reduction and Image Quality in Trauma Patients

AIM

This study aimed to evaluate potential dose savings on a revised protocol for whole-body computed tomography and image quality after implementing Adaptive Statistical Iterative Reconstruction V (ASiR-V) algorism for trauma patients and compare it with routine protocol.

MATERIALS AND METHODS

One hundred trauma patients were classified into 2 groups using 2 different scanning protocols. Group A (n = 50; age, 32.48 ± 8.09 years) underwent routine 3-phase protocol. Group B (n = 50; age, 35.94 ± 13.57 years) underwent biphasic injection protocol including unenhanced scan for the brain and cervical spines, followed by a 1-step acquisition of the thorax, abdomen, and pelvis. The ASiR-V level was kept at 50% for all examinations, and then studies were reconstructed at 0% ASiR-V level. Radiation dose, total acquisition time, and image count were compared between groups (A and B). Two radiologists independently graded image quality and artifacts between both groups and 2 ASiR-V levels (0 and 50%).

RESULTS

The mean (±SD) dose-length product value for postcontrast scans in group A was 1602.3 ± 271.8 mGy · cm and higher when compared with group B (P < 0.001), which was 951.1 ± 359.6 mGy · cm. Biphasic injection protocol gave a dose reduction of 40.4% and reduced the total acquisition time by 11.4% and image count by 37.6%. There was no statistically significant difference between the image quality scores for both groups; however, group A scored higher grades (4.62 ± 0.56 and 4.56 ± 0.67). Similarly, the image quality scores for both ASiR-V levels in both groups were not significantly different.

CONCLUSIONS

Biphasic computed tomography protocol reduced radiation dose with maintenance of diagnostic accuracy and image quality after implementing ASiR-V algorism.

Application of Adaptive Statistical Iterative Reconstruction-V With Combination of 80 kV for Reducing Radiation Dose and Improving Image Quality in Renal Computed Tomography Angiography for Slim Patients

OBJECTIVES

To explore the application of adaptive statistical iterative reconstruction-V (ASIR-V) with combination of 80 kV for reducing radiation dose and improving image quality in renal computed tomography angiography (CTA) for slim patients compared with traditional filtered back projection (FBP) reconstruction using 120 kV.

METHODS

Eighty patients for renal CTA were prospectively enrolled and randomly divided into group A and group B. Group A used 120 kV and 600 mgI/kg contrast agent and FBP reconstruction, while group B used 80 kV and 350 mgI/kg contrast agent and both FBP and ASIR-V reconstruction from 10%ASIR-V to 100%ASIR-V with 10%ASIR-V interval. The CT values and SD values of the right renal artery and left renal artery were measured to calculate the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The image quality was subjectively scored by two experienced radiologists blindly using a five-point criterion. The contrast agent, volumetric CT dose index (CTDI_vol), and dose length product in both groups were recorded and the effective radiation dose was calculated.

RESULTS

There were no significant difference in patient characteristics between two groups (p > 0.05). The CTDI_vol, dose length product and effective radiation dose in group B were 59.0%, 65.0%, and 65.1% lower than those in group A, respectively (all p < 0.05), and the contrast agent in group B was 42.2% lower than that in group A (p < 0.05). In group B, with the increase of ASIR-V percentage, CT values showed no significant difference, SD values decreased gradually, SNR values and CNR values increased gradually. The CT values showed no statistically significant difference (p > 0.05) between two groups with different reconstructions. The SD values with 40%ASIR-V to 100%ASIR-V reconstruction in group B was significantly lower(p < 0.5), while the SNR values with 50% ASIR-V to 100% ASIR-V reconstruction and CNR values with 70%ASIR-V to 100%ASIR-V were significantly higher than those of group A with FBP reconstruction (p < 0.5). Two radiologists had excellent consistency in subjective scores of image quality for renal CTA (kappa >0.75, p < 0.05). The subjective scores with 60% ASIR-V to 90% ASIR-V in group B were significantly higher than those of FBP in group A (p < 0.5), of which 70%ASIR-V reconstruction obtained the highest subjective score for renal CTA.

CONCLUSION

ASIR-V with combination of 80 kV can significantly reduce effective radiation dose (about 65.1%) and contrast agent (about 42.2%) and improve image quality in renal CTA for slim patients compared with traditional FBP reconstruction using 120 kV, and the 70% ASIR-V was the best reconstruction algorithm in 80 kV renal CTA.

ADVANCES IN KNOWLEDGE

Using 80 kV with combination of ASIR-V can significantly reduce radiation dose and contrast agent dose as well as improve image quality in renal CTA for thin patients when compared with FBP using 120 kV.

Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT

OBJECTIVE

To evaluate the clinical value of low-dose chest CT combined with the new generation adaptive statistical iterative reconstruction (ASIR-V) algorithm in the diagnosis of pulmonary nodule.

METHODS

30 patients with pulmonary nodules underwent chest CT using Revolution CT. The patients were first scanned with standard-dose at a noise index (NI) of 14, and the images were reconstructed with filtered back projection (FBP) algorithm. If pulmonary nodules were found, a low-dose targeted scan, with NI of 24, was performed localized on the nodules, and the images were reconstructed with 60% ASIR-V. The detection rate of pulmonary nodules in the two scanning modes was recorded. The size of nodules, CT value and standard deviation of nodules were measured. The signal-to-noise ratio and contrast-to-noise ratio were also calculated. Two experienced radiologists used a 5-point method to score the image quality. The volumetric CT dose index, and dose-length product were recorded and the effective dose (ED) was calculated of the two scanning modes.

RESULTS

Volumetric CT dose index (ED) of the standard-dose scan covering the entire lungs was 7.29 ± 2.38 mGy (3.52 ± 1.09 mSv), and that of low-dose targeted scan was 2.56 ± 1.87 mGy (0.51 ± 0.32 mSv). However, the ED of the virtual low-dose scan for the entire lungs was 1.44 ± 0.15 mSv, which would mean a dose reduction of 59.1% compared with the standard-dose scan. 85 of the 87 pulmonary nodules were detected in the low-dose targeted scan, with 2 of the ground-glass density nodules with size less than 1 cm missed, resulting in 97.7% overall detection rate. There was no difference between the low-dose ASIR-V images and standard-dose FBP images for the size (1.49 ± 0.74 cm vs 1.48 ± 0.75 cm), CT value [33.02 ± 1.95 Hounsfield unit (HU) vs 34.6 ± 3.07 HU], standard deviation (27.64 ± 14.42 HU vs 30.38 ± 20.04 HU), signal-to-noise ratio (1.44 ± 0.88 vs 1.43 ± 1.31) and contrast-to-noise ratio (38.95 ± 18.43 vs 38.23 ± 14.99) of nodules (all p > 0.05). There was no difference in the subjective scores between the two scanning modes.

CONCLUSION

The low-dose CT scan combined with ASIR-V algorithm is of comparable value in the detection and the display of pulmonary nodules when compared with the FBP images obtained by standard-dose scan.

ADVANCES IN KNOWLEDGE

This is a clinical study to evaluate the clinical value of pulmonary nodules using ASIR-V algorithm in the same patients in the low-dose chest CT scans. It suggests that ASIR-V provides similar image quality and detection rate for pulmonary nodules at much reduced radiation dose.

Combination of Adaptive Statistical Iterative Reconstruction-V and Lower Tube Voltage During Craniocervical Computed Tomographic Angiography Yields Better Image Quality with a Reduced Radiation Dose

RATIONALE AND OBJECTIVES

To evaluate image quality and radiation exposure when using the adaptive statistical iterative reconstruction-V (ASIR-V) algorithm for reconstructing craniocervical computed tomographic angiography images acquired at 100 kVp.

MATERIALS AND METHODS

We randomly divided 121 patients into three groups: group A (conventional protocol), 120 kVp with filtered back projection; group B, 120 kVp with 50% ASIR-V; and group C, 100 kVp with 50% ASIR-V. All patients underwent scans in a 256-slice computed tomography (CT) scanner. Radiation dose (volume CT dose index), dose-length product, and effective dose, objective parameters such as arterial attenuation value, signal-to-noise ratio, contrast-to-noise ratio, and noise obtained at head, neck, and shoulder levels were compared among the groups. Subjective image quality was independently assessed by two radiologists, and interobserver reliability was assessed using kappa analysis.

RESULTS

The radiation dose in group C was the lowest (p < 0.01) with a 40% reduction in volume CT dose index, dose-length product, and effective dose values compared to group A, and group C showed higher arterial attenuation than either group A or B (p < 0.01). Additionally, signal-to-noise ratio and contrast-to-noise ratio were higher and noise was lower in groups B and C than group A. Group C had better subjective image quality than groups A and B (p < 0.05), and the interobserver reliability between the two radiologists was high (k = 0.783).

CONCLUSION

Compared to the conventional protocol, using 50% ASIR-V and the 100 kVp protocol during craniocervical computed tomographic angiography yields better objective and subjective image quality at lower radiation doses.

Low-Dose CT With the Adaptive Statistical Iterative Reconstruction V Technique in Abdominal Organ Injury: Comparison With Routine-Dose CT With Filtered Back Projection

OBJECTIVE. The purpose of this study was to evaluate and compare the diagnostic performance and image quality of low-dose CT performed with adaptive statistical iterative reconstruction (ASIR)-V with those of routine-dose CT with filtered back projection (FBP) in the evaluation of abdominal organ injury. MATERIALS AND METHODS. The study enrolled 197 patients with trauma who underwent multiphase abdominal CT, including routine-dose portal venous phase imaging with FBP and low-dose delayed phase imaging with 50% ASIR-V. The presence of abdominal organ injuries (liver, spleen, pancreas, kidney) was reviewed, and injuries were graded according to American Association for the Surgery of Trauma (AAST) scales. CT detection rates of organ injury and AAST grading with the two protocols were compared by McNemar test. Subjective analysis of image noise and artifacts and objective analysis of CT noise were performed by unpaired t test. RESULTS. Compared with the routine-dose protocol, the low-dose protocol enabled an mean dose reduction of 59.8%. The detection rates and diagnostic performance of AAST grading did not differ significantly between the two protocols (detection rate, p = 0.289; diagnostic performance, p > 0.999). Objective image noise was significantly less with the low-dose protocol than with the routine-dose protocol (p < 0.001). Subjective imaging artifacts were similar between the low-dose and routine-dose protocols (p = 0.539). CONCLUSION. Compared with routine-dose protocol with FBP, low-dose CT with ASIR-V was useful for assessing multiorgan abdominal injury without impairing image quality.

A method to measure slice sensitivity profiles of CT images under low-contrast and high-noise conditions

Noise reduction features of iterative reconstruction (IR) methods in computed tomography might accompany the sacrifice of the longitudinal resolution, or slice sensitivity profile (SSP), at low contrast-to-noise ratio (CNR) conditions. To assess the benefit of IR methods correctly, the difference of SSP between IR methods and filtered-backprojection (FBP) must be taken into account. Therefore, SSP measurement under low-CNR conditions is necessary. Although edge methods are predominantly used, their performance under low-CNR conditions appears to be not fully established. We developed a method that is compatible with extremely low-CNR conditions. Thin plastic disk-shaped sheets embedded in acrylic resin were used as low-contrast test objects. The lowest peak contrast used was approximately 17 [HU]. We assessed the performance of our method by using FBP images. We identified a source of measurement instability aside from noise: the measured thin-slice SSP is dependent on the orbital phase of helical scan, presumably because of cone-beam artifacts. This impediment to high accuracy is manageable using phase-controlled scans. We confirmed that table position repeatability is much better than the value of the specifications, and therefore the ensemble-averaged images of multiple scans can be used for SSP measurement. Accurate measurement of SSP under extremely low-CNR conditions is possible, even when the test object is visually indiscernible from the noisy background. Low-contrast SSP behavior is elucidated for IR methods (AIDR-3D, FIRST, and AiSR-V) by using this measurement method.

Airway quantification using adaptive statistical iterative reconstruction-V on wide-detector low-dose CT: a validation study on lung specimen

PURPOSE

To evaluate the accuracy of airway quantification of adaptive statistical iterative reconstruction (ASIR)-V on low-dose CT using a human lung specimen.

METHOD

A lung specimen was scanned on Revolution CT with low-dose settings (20 mAs, 40 mAs and 60 mAs/100 kV) and standard-dose setting (100 mAs/120 kV). CT images were reconstructed using lung kernel with eleven ASIR-V levels from 0 to 100% with 10% interval. ASIR-V level from 0 to 100% with 10% interval was reconstructed on lung kernel. Wall area percentage (%WA) and wall thickness (WT) were measured.

RESULTS

Radiation dose of 20 mAs, 40 mAs and 60 mAs low-dose settings reduced by 87.6%, 75.2% and 62.8% compared to that on standard dose, respectively. Low-dose settings significantly decreased image SNR (p < 0.05) and increased noise (p < 0.001). ASIR-V level exponentially improved image SNR and linearly decreased image noise (all p < 0.001). The mean airway measurement ratios of low-dose to standard-dose were within 2% variation for %WA and within 3% variation for WT. Most %WA and WT values showed no obvious correlation with ASIR-V levels.

CONCLUSION

ASIR-V showed to improve image quality in low radiation dose. However, low-dose settings and ASIR-V strength did not significantly influence airway quantification values, although variation in measurements slightly increased with dose reduction.

Evaluation of Adaptive Statistical Iterative Reconstruction-V Reconstruction Algorithm vs Filtered Back Projection in the Detection of Hypodense Liver Lesions: Reader Performance and Preferences

OBJECTIVE

The aim of the study was to evaluate diagnostic accuracy and readers' experience in the detection of focal liver lesions on computed tomography with Adaptive Statistical Iterative Reconstruction-V (ASIR-V) reconstruction compared with filtered back projection (FBP) scans.

METHODS

Fifty-five patients with liver lesions had FBP and ASIR-V scans. Two radiologists independently reviewed both sets of computed tomography scans, identifying and characterizing liver lesions.

RESULTS

Adaptive Statistical Iterative Reconstruction-V scans had a reduction in dose length product (P < 0.0001) with no difference in image contrast (P = 0.1805); image noise was less for the ASIR-V scans (P < 0.0001) and contrast-to-noise ratio was better for ASIR-V (P = 0.0002). Both readers found more hypodense liver lesions on the FBP (P = 0.01) scans. Multiple subjective imaging scores were significantly less for the ASIR-V scans for both readers.

CONCLUSIONS

Although ASIR-V scans were objectively better, our readers performed worse in lesion detection on them, suggesting a need for better education/experience with this technology during implementation.

Comparison of Tin Filter-Based Spectral Shaping CT and Low-Dose Protocol for Detection of Urinary Calculi

OBJECTIVE

The purpose of this study was to assess the performance of tin filter-based spectral shaping CT compared with routine low-dose CT for detection of urolithiasis.

MATERIALS AND METHODS

Unenhanced third-generation dual-source CT scans of 129 consecutively registered patients were retrospectively reviewed: 43 patients underwent CT for detection of renal stones with tin filtration (Sn150 kV); 43 patients underwent a routine low-dose CT protocol at 100 kV; and 43 patients underwent a routine CT protocol with automated tube potential selection (110-120 kV). Image quality was evaluated subjectively and objectively. Volume CT dose index (CTDI_vol) and size-specific dose estimate (SSDE) were recorded. To prospectively compare the performances of the spectral shaping protocol (Sn150 kV) with the standard (120 kV) and routine low-dose (100 kV) protocols, a phantom (sheep kidneys) containing stones were also scanned with each protocol and evaluated by two radiologists.

RESULTS

CT with tin filtration resulted in 28% and 66% reduction in CTDI_vol compared with CT performed with routine low-dose and standard-dose protocols (p < 0.05). Accordingly, it also led to 24% and 55% reduction in SSDE compared with the low-dose and standard protocols (p < 0.05). Subjective image quality and signal-to-noise ratio were similar between the tin filtration and the routine low-dose groups (p > 0.05). The objective image noise was similar in the three groups (p > 0.05). The phantom study showed no difference in detection of renal stones between the three tube potential settings.

CONCLUSION

Using spectral shaping with tin filtration can substantially reduce radiation dose compared with routine standard- and low-dose abdominal CT for urinary stone disease.

Assessment of radiation dose and iodine load reduction in head-neck CT angiography using two scan protocols with wide-detector

OBJECTIVE

To compare image quality, radiation dose, and iodine intake of head-neck CT angiography (CTA) acquired by wide-detector with the gemstone spectral imaging (GSI) combination with low iodine intake or routine scan protocol.

METHODS

Three hundred patients who had head-neck CTA were enrolled and divided into three groups according to their BMI values: group A (18.5 kg/m2 ≦ BMI <24.9 kg/m2), group B (24.9 kg/m2 ≦ BMI <29.9 kg/m2) and group C (29.9 kg/m2 ≦ BMI ≦ 34.9 kg/m2) with 100 patients in each group. Patients in each group were randomly divided into two subgroups (n = 50) namely, A1, A2, B1, B2, C1 and C2. The patients in subgroups A1, B1 and C1 underwent GSI with low iodine intake (270 mgI/ml, 50 ml) and combined with the ASiR-V algorithm. Other patients underwent three dimensional (3D) smart mA modulation with routine iodine intake (350 mgI/ml, 60 ml). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of all images were calculated after angiography. Images were then subjectively assessed using a 5-point scale. CT dose index of volume and dose-length product (DLP) was converted to the effective dose (ED) and then compared.

RESULTS

The mean CT values, SNR, CNR and subjective image quality in subgroups A2, B2 and C2 are significantly lower than in subgroups A1, B1, and C1 (P < 0.01), respectively. The ED values in subgroup A1, B1, and C1 are 55.18%, 61.89%, and 69.64% lower than those in A2, B2, and C2, respectively (P < 0.01). The total iodine intakes in subgroups A1, B1, and C1 are 35.72% lower than those in subgroups A2, B2, and C2.

CONCLUSIONS

The gemstone spectral imaging with monochromatic images at 53-57 keV combined with ASiR-V algorithm allows significant reduction in iodine load and radiation dose in head-neck CT angiography than those yielded in routine scan protocol. It also enhances signal intensity of head-neck CTA and maintains image quality.