Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction v

Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

BACKGROUND

Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial.

OBJECTIVES

To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V).

AIMS

This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality.

METHOD

In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared.

RESULTS

The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30.

CONCLUSION

DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

EANM procedural recommendations for managing the paediatric patient in diagnostic nuclear medicine

PURPOSE

The manuscript aims to characterize the principles of best practice in performing nuclear medicine procedures in paediatric patients. The paper describes all necessary technical skills that should be developed by the healthcare professionals to ensure the best possible care in paediatric patients, as it is particularly challenging due to psychological and physical conditions of children.

METHODS

We performed a comprehensive literature review to establish the most relevant elements of nuclear medicine studies in paediatric patients. We focused the attention to the technical aspects of the study, such as patient preparation, imaging protocols, and immobilization techniques, that adhere to best practice principles. Furthermore, we considered the psychological elements of working with children, including comforting and distraction strategies.

RESULTS

The extensive literature review combined with practical conclusions and recommendations presented and explained by the authors summarizes the most important principles of the care for paediatric patient in the nuclear medicine field.

CONCLUSION

Nuclear medicine applied to the paediatric patient is a very special and challenging area, requiring proper education and experience in order to be performed at the highest level and with the maximum safety for the child.

Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?

BACKGROUND

CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to improve image quality.

OBJECTIVE

To evaluate and compare 1.25 mm thin-section abdominal CT images reconstructed with deep learning image reconstruction (DLIR) with 5 mm thick images reconstructed with adaptive statistical iterative reconstruction (ASIR-V).

METHODS

This retrospective study included 52 patients (31 F; 56.9±16.9 years) who underwent abdominal CT scans between August-October 2019. Image reconstruction was performed to generate 5 mm images at 40% ASIR-V and 1.25 mm DLIR images at three strengths (low [DLIR-L], medium [DLIR-M], and high [DLIR-H]). Qualitative assessment was performed to determine image noise, contrast, visibility of small structures, sharpness, and artifact based on a 5-point-scale. Image preference determination was based on a 3-point-scale. Quantitative assessment included measurement of attenuation, image noise, and contrast-to-noise ratios (CNR).

RESULTS

Thin-section images reconstructed with DLIR-M and DLIR-H yielded better image quality scores than 5 mm ASIR-V reconstructed images. Mean qualitative scores of DLIR-H for noise (1.77 ± 0.71), contrast (1.6 ± 0.68), small structure visibility (1.42 ± 0.66), sharpness (1.34 ± 0.55), and image preference (1.11 ± 0.34) were the best (p<0.05). DLIR-M yielded intermediate scores. All DLIR reconstructions showed superior ratings for artifacts compared to ASIR-V (p<0.05), whereas each DLIR group performed comparably (p>0.05, 0.405-0.763). In the quantitative assessment, there were no significant differences in attenuation values between all reconstructions (p>0.05). However, DLIR-H demonstrated the lowest noise (9.17 ± 3.11) and the highest CNR (CNRliver = 26.88 ± 6.54 and CNRportal vein = 7.92 ± 3.85) (all p<0.001).

CONCLUSION

DLIR allows generation of thin-section (1.25 mm) abdominal CT images, which provide improved image quality with higher inter-reader agreement compared to 5 mm thick images reconstructed with ASIR-V.

CLINICAL IMPACT

Improved image quality of thin-section CT images reconstructed with DLIR has several benefits in clinical practice, such as improved diagnostic performance without radiation dose penalties.

The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images

This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare’s TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians’ diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.

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.

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.

Comparison of the image quality and radiation dose of different scanning modes in head-neck CT angiography

OBJECTIVES

To analyze and compare the radiation dose and image quality of different CT scanning modes on head-neck CT angiography.

METHODS

A total of 180 patients were divided into Group A and Group B. The groups were further subdivided according to different scanning modes: subgroups A1, A2, A3, B1, B2, and B3. Subgroups A1 and B1 used conventional CT protocol, subgroups A2 and B2 used the kV-Assist scan mode, and subgroups A3 and B3 used the dual-energy gemstone spectral imaging protocol. The CT dose index and dose-length product were recorded. The objective image quality and subjective image evaluation was conducted by two independent radiologists.

RESULTS

The signal-to-noise ratios, contrast-to-noise ratios, and subjective scores of subgroups A3 and B3 were higher than the other subgroups. In subgroups B1 and B2, the subjective scores of 9 patients and 12 patients were lower than 3, respectively. The subjective scores of subgroups B1 and B2 were lower than the other subgroups. There was no statistically significant difference in signal-to-noise ratios, contrast-to-noise ratios, and subjective scores between subgroups A1 and A2. The effective dose of subgroup A2 was 41.7 and 36.4% lower than that in subgroups A1 and A3, respectively (p < 0.05). In Group B, there were no statistically significant differences in CT dose indexvol, dose-length product, and ED among the subgroups (p > 0.05).

CONCLUSION

In the head-neck CT angiography, the kV-Assist scan mode is recommended for patients with body mass index between 18.5 and 34.9 kg m^-2; gemstone spectral imaging scanning mode is recommended for patients with body mass index ≥34.9 kg m^-2.

Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations

OBJECTIVES

To investigate the image quality and perception of a sinogram-based deep learning image reconstruction (DLIR) algorithm for single-energy abdominal CT compared to standard-of-care strength of ASIR-V.

METHODS

In this retrospective study, 50 patients (62% F; 56.74 ± 17.05 years) underwent portal venous phase. Four reconstructions (ASIR-V at 40%, and DLIR at three strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H)) were generated. Qualitative and quantitative image quality analysis was performed on the 200 image datasets. Qualitative scores were obtained for image noise, contrast, small structure visibility, sharpness, and artifact by three blinded radiologists on a 5-point scale (1, excellent; 5, very poor). Radiologists also indicated image preference on a 3-point scale (1, most preferred; 3, least preferred). Quantitative assessment was performed by measuring image noise and contrast-to-noise ratio (CNR).

RESULTS

DLIR had better image quality scores compared to ASIR-V. Scores on DLIR-H for noise (1.40 ± 0.53), contrast (1.41 ± 0.55), small structure visibility (1.51 ± 0.61), and sharpness (1.60 ± 0.54) were the best (p < 0.05) followed by DLIR-M (1.85 ± 0.52, 1.66 ± 0.57, 1.69 ± 0.59, 1.68 ± 0.46), DLIR-L (2.29 ± 0.58, 1.96 ± 0.61, 1.90 ± 0.65, 1.86 ± 0.46), and ASIR-V (2.86 ± 0.67, 2.55 ± 0.58, 2.34 ± 0.66, 2.01 ± 0.36). Ratings for artifacts were similar for all reconstructions (p > 0.05). DLIRs did not influence subjective textural perceptions and were preferred over ASIR-V from the beginning. All DLIRs had a higher CNR (26.38-102.30%) and lower noise (20.64-48.77%) than ASIR-V. DLIR-H had the best objective scores.

CONCLUSION

Sinogram-based deep learning image reconstructions were preferred over iterative reconstruction subjectively and objectively due to improved image quality and lower noise, even in large patients. Use in clinical routine may allow for radiation dose reduction.

KEY POINTS

• Deep learning image reconstructions (DLIRs) have a higher contrast-to-noise ratio compared to medium-strength hybrid iterative reconstruction techniques. • DLIR may be advantageous in patients with large body habitus due to a lower image noise. • DLIR can enable further optimization of radiation doses used in abdominal CT.

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 study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions

OBJECTIVE

To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT.

METHODS

This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0.

RESULTS

Compared to AP, DP significantly reduced radiation dose by 76% (0.76 ± 0.09 mSv vs 3.18 ± 0.48 mSv), DLIR-H DP images had lower image noise (14.08 ± 2.89 HU vs 16.67 ± 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 ± 0.34 vs 4.05 ± 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 ± 2.61 HU, 14.26 ± 2.67 HU, 14.08 ± 2.89 HU and 16.25 ± 4.42 HU) from (24.95 ± 4.32 HU, 25.42 ± 4.99 HU, 23.99 ± 5.26 HU and 27.01 ± 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 ± 0.34 vs 2.87 ± 0.53; p < 0.05).

CONCLUSION

DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction.

ADVANCES IN KNOWLEDGE

(1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images.

Visibility and image quality of peripheral pulmonary arteries in pulmonary embolism patients using free-breathing combined with a high-threshold bolus-triggering technique in CT pulmonary angiography

Objective

To investigate the visibility of peripheral pulmonary arteries by computed tomography pulmonary angiography (CTPA) and image quality using a free-breathing combined with a high-threshold bolus triggering technique and to explore the feasibility of this technique in pulmonary embolism (PE) patients who cannot hold their breath.

Methods

Patients with suspected PE who underwent CTPA (n=240) were randomly assigned to two groups: free-breathing (n=120) or breath-holding (n=120).

Results

The mean scanning time or visible pulmonary artery distal branches were not different between the groups. Mean CT main pulmonary artery (MPA) values, apical segment (S1), and posterior basal segment (S10) in the free-breathing group were higher compared with the breath-holding group. The subjective image quality score in the free-breathing group was higher compared with the breath-holding group. In the free-breathing group, no respiratory artifact was observed. In the breath-holding group, obvious respiratory artifacts were caused by severe chronic obstructive pulmonary disease (COPD), dyspnea, or other diseases that preclude patients from holding their breath.

Conclusion

The free-breathing mode CTPA combined with a high-threshold bolus triggering technique can provide high quality images with a lower incidence of respiratory and cardiac motion artifacts, which is especially valuable for patients who cannot hold their breath.

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 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.

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.

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.

Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT

Purpose To evaluate colorectal cancer hepatic metastasis detection and characterization between reduced radiation dose (RD) and standard dose (SD) contrast material-enhanced CT of the abdomen and to qualitatively compare between filtered back projection (FBP) and iterative reconstruction algorithms. Materials and Methods In this prospective study (from May 2017 through November 2017), 52 adults with biopsy-proven colorectal cancer and suspected hepatic metastases at baseline CT underwent two portal venous phase CT scans: SD and RD in the same breath hold. Three radiologists, blinded to examination details, performed detection and characterization of 2-15-mm lesions on the SD FBP and RD adaptive statistical iterative reconstruction (ASIR)-V 60% series images. Readers assessed overall image quality and lesions between SD FBP and seven different iterative reconstructions. Two nonblinded consensus reviewers established the reference standard using the picture archiving and communication system lesion marks of each reader, multiple comparison examinations, and clinical data. Results RD CT resulted in a mean dose reduction of 54% compared with SD. Of the 260 lesions (233 metastatic, 27 benign), 212 (82%; 95% confidence interval [CI]: 76%, 86%) were detected with RD CT, whereas 252 (97%; 95% CI: 94%, 99%) were detected with SD (P < .001); per-lesion sensitivity was 79% (95% CI: 74%, 84%) and 94% (95% CI: 90%, 96%) (P < .001), respectively. Mean qualitative scores ranked SD images as higher quality than RD series images, and ASIR-V ranked higher than ASIR and Veo 3.0. Conclusion CT evaluation of colorectal liver metastases is compromised with modest radiation dose reduction, and the use of iterative reconstructions could not maintain observer performance. © RSNA, 2018.

Low-tube-voltage combined with adaptive statistical iterative reconstruction-V technique in CT venography of lower limb deep vein thrombosis

This study contains 2 arms: (1) the ASIR-V technique combined with low-tube-voltage in lower limb deep vein thrombosis (DVT) diagnosis was investigated; and (2) CT venography and ultrasound results in DVT diagnosis were compared. For arm 1, 90 patients suspected of DVT were randomly divided into 3 groups (30/group): groups A and B were scanned under 100-kV with pre-set ASIR-V weights of 30% and 50% respectively; group C were scanned under 70-kV with a 50% weight. For arm 2, 75 patients were divided into 3 groups (25/group), each group was CT scanned as in arm 1 and then all subjects were examined by ultrasound. Groups A, B and C had 16, 14 and 17 patients diagnosed with DVTs, respectively. There was no significant difference in subjective ratings of image quality among all groups. The 70-kV protocol remarkably increased venous attenuation value while all groups had similar DVT attenuation value. Higher noise was observed in group C, the CNR however, was actually augmented due to elevated venous attenuations. More importantly, group C had significantly lower CTDI_vol and DLP values. In conclusion, the 70-kV protocol is superior to the 100 kV protocols, which was supported by findings from the second arm study.

Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT

OBJECTIVE

To compare image quality of two adaptive statistical iterative reconstruction (ASiR and ASiR-V) algorithms using objective and subjective metrics for routine liver CT, with the conventional filtered back projection (FBP) reconstructions as reference standards.

METHODS

This institutional review board-approved study included 52 patients with clinically suspected hepatic metastases. Patients were divided equally into ASiR and ASiR-V groups with same scan parameters. Images were reconstructed with ASiR and ASiR-V from 0 (FBP) to 100% blending percentages at 10% interval in its respective group. Mean and standard deviation of CT numbers for liver parenchyma were recorded. Two experienced radiologists reviewed all images for image quality blindly and independently. Data were statistically analyzed.

RESULTS

There was no difference in CT dose index between ASiR and ASiR-V groups. As the percentage of ASiR and ASiR-V increased from 10 to 100% , image noise reduced by 8.6 -57.9% and 8.9-81.6%, respectively, compared with FBP. There was substantial interobserver agreement in image quality assessment for ASiR and ASiR-V images. Compared with FBP reconstruction, subjective image quality scores of ASiR and ASiR-V improved significantly as percentage increased from 10 to 80% for ASiR (peaked at 50% with 32.2% noise reduction) and from 10 to 90% (peaked at 60% with 51.5% noise reduction) for ASiR-V.

CONCLUSION

Both ASiR and ASiR-V improved the objective and subjective image quality for routine liver CT compared with FBP. ASiR-V provided further image quality improvement with higher acceptable percentage than ASiR, and ASiR-V60% had the highest image quality score. Advances in knowledge: (1) Both ASiR and ASiR-V significantly reduce image noise compared with conventional FBP reconstruction. (2) ASiR-V with 60 blending percentage provides the highest image quality score in routine liver CT.

Computed Tomography Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (Adaptive Statistical Iterative Reconstruction-V) a Comparison With Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection Reconstructions

OBJECTIVE

The purpose of this study was to compare abdominopelvic computed tomography images reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) with model-based iterative reconstruction (Veo 3.0), ASIR, and filtered back projection (FBP).

METHODS AND MATERIALS

Abdominopelvic computed tomography scans for 36 patients (26 males and 10 females) were reconstructed using FBP, ASIR (80%), Veo 3.0, and ASIR-V (30%, 60%, 90%). Mean ± SD patient age was 32 ± 10 years with mean ± SD body mass index of 26.9 ± 4.4 kg/m. Images were reviewed by 2 independent readers in a blinded, randomized fashion. Hounsfield unit, noise, and contrast-to-noise ratio (CNR) values were calculated for each reconstruction algorithm for further comparison. Phantom evaluation of low-contrast detectability (LCD) and high-contrast resolution was performed.

RESULTS

Adaptive statistical iterative reconstruction-V 30%, ASIR-V 60%, and ASIR 80% were generally superior qualitatively compared with ASIR-V 90%, Veo 3.0, and FBP (P < 0.05). Adaptive statistical iterative reconstruction-V 90% showed superior LCD and had the highest CNR in the liver, aorta, and, pancreas, measuring 7.32 ± 3.22, 11.60 ± 4.25, and 4.60 ± 2.31, respectively, compared with the next best series of ASIR-V 60% with respective CNR values of 5.54 ± 2.39, 8.78 ± 3.15, and 3.49 ± 1.77 (P <0.0001). Veo 3.0 and ASIR 80% had the best and worst spatial resolution, respectively.

CONCLUSIONS

Adaptive statistical iterative reconstruction-V 30% and ASIR-V 60% provided the best combination of qualitative and quantitative performance. Adaptive statistical iterative reconstruction 80% was equivalent qualitatively, but demonstrated inferior spatial resolution and LCD.

New adaptive statistical iterative reconstruction ASiR-V: Assessment of noise performance in comparison to ASiR

Purpose

To assess the noise characteristics of the new adaptive statistical iterative reconstruction (ASiR‐V) in comparison to ASiR.

Methods

A water phantom was acquired with common clinical scanning parameters, at five different levels of CTDI_vol. Images were reconstructed with different kernels (STD, SOFT, and BONE), different IR levels (40%, 60%, and 100%) and different slice thickness (ST) (0.625 and 2.5 mm), both for ASiR‐V and ASiR. Noise properties were investigated and noise power spectrum (NPS) was evaluated.

Results

ASiR‐V significantly reduced noise relative to FBP: noise reduction was in the range 23%–60% for a 0.625 mm ST and 12%–64% for the 2.5 mm ST. Above 2 mGy, noise reduction for ASiR‐V had no dependence on dose. Noise reduction for ASIR‐V has dependence on ST, being greater for STD and SOFT kernels at 2.5 mm. For the STD kernel ASiR‐V has greater noise reduction for both ST, if compared to ASiR. For the SOFT kernel, results varies according to dose and ST, while for BONE kernel ASIR‐V shows less noise reduction. NPS for CT Revolution has dose dependent behavior at lower doses. NPS for ASIR‐V and ASiR is similar, showing a shift toward lower frequencies as the IR level increases for STD and SOFT kernels. The NPS is different between ASiR‐V and ASIR with BONE kernel. NPS for ASiR‐V appears to be ST dependent, having a shift toward lower frequencies for 2.5 mm ST.

Conclusions

ASiR‐V showed greater noise reduction than ASiR for STD and SOFT kernels, while keeping the same NPS. For the BONE kernel, ASiR‐V presents a completely different behavior, with less noise reduction and modified NPS. Noise properties of the ASiR‐V are dependent on reconstruction slice thickness. The noise properties of ASiR‐V suggest the need for further measurements and efforts to establish new CT protocols to optimize clinical imaging.

Assessment of noise reduction potential and image quality improvement of a new generation adaptive statistical iterative reconstruction (ASIR-V) in chest CT

OBJECTIVE

To evaluate the image quality improvement and noise reduction in routine dose, non-enhanced chest CT imaging by using a new generation adaptive statistical iterative reconstruction (ASIR-V) in comparison with ASIR algorithm.

METHODS

30 patients who underwent routine dose, non-enhanced chest CT using GE Discovery CT750HU (GE Healthcare, Waukesha, WI) were included. The scan parameters included tube voltage of 120 kVp, automatic tube current modulation to obtain a noise index of 14HU, rotation speed of 0.6 s, pitch of 1.375:1 and slice thickness of 5 mm. After scanning, all scans were reconstructed with the recommended level of 40%ASIR for comparison purpose and different percentages of ASIR-V from 10% to 100% in a 10% increment. The CT attenuation values and SD of the subcutaneous fat, back muscle and descending aorta were measured at the level of tracheal carina of all reconstructed images. The signal-to-noise ratio (SNR) was calculated with SD representing image noise. The subjective image quality was independently evaluated by two experienced radiologists.

RESULTS

For all ASIR-V images, the objective image noise (SD) of fat, muscle and aorta decreased and SNR increased along with increasing ASIR-V percentage. The SD of 30% ASIR-V to 100% ASIR-V was significantly lower than that of 40% ASIR (p < 0.05). In terms of subjective image evaluation, all ASIR-V reconstructions had good diagnostic acceptability. However, the 50% ASIR-V to 70% ASIR-V series showed significantly superior visibility of small structures when compared with the 40% ASIR and ASIR-V of other percentages (p < 0.05), and 60% ASIR-V was the best series of all ASIR-V images, with a highest subjective image quality. The image sharpness was significantly decreased in images reconstructed by 80% ASIR-V and higher.

CONCLUSION

In routine dose, non-enhanced chest CT, ASIR-V shows greater potential in reducing image noise and artefacts and maintaining image sharpness when compared to the recommended level of 40%ASIR algorithm. Combining both the objective and subjective evaluation of images, non-enhanced chest CT images reconstructed with 60% ASIR-V have the highest image quality. Advances in knowledge: This is the first clinical study to evaluate the clinical value of ASIR-V in the same patients using the same CT scanner in the non-enhanced chest CT scans. It suggests that ASIR-V provides the better image quality and higher diagnostic confidence in comparison with ASIR algorithm.

Feasibility of Free-breathing CCTA using 256-MDCT

Usually, coronary computed tomography angiography (CCTA) is performed during breath-holding to reduce artifact caused by respiration. The objective of this study was to evaluate the feasibility of free-breathing CCTA compared to breath-holding using CT scanner with wide detector. To evaluate the feasibility of CCTA during free-breathing using a 256-MDCT. In 80 patients who underwent CCTA, 40 were performed during breath-holding (group A), and the remaining 40 during free-breathing (group B). The quality scores for coronary arteries were analyzed and defined as: 3 (excellent), 2 (good), and 1 (poor). The image noise, signal-to-noise ratio and effective radiation dose as well as the heart rate variation were compared. The noise, signal-to-noise ratio, and effective radiation dose were not significantly different between the 2 groups. The mean heart rate variation between planning and scanning for group A was 7 ± 7.6 bpm, and larger than 3 ± 2.6 bpm for group B (P = 0.012). Quality scores of the free-breathing group were better than those of the breath-holding group (group A: 2.55 ± 0.64, group B: 2.85 ± 0.36, P = 0.018). Free-breathing CCTA is feasible on wide detector CT scanner to provide acceptable image quality with reduced heart rate variation and better images for certain patients.

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

OBJECTIVE

To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR.

METHODS

27 consecutive patients (mean body mass index: 23.55 kg m(-2) underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded.

RESULTS

At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19-49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP.

CONCLUSION

Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR.

ADVANCES IN KNOWLEDGE

This study represents the first clinical research experiment to use ASIR-V, the newest version of iterative reconstruction. Use of the ASIR-V algorithm decreased image noise and increased image quality when compared with the ASIR and FBP methods. These results suggest that high-quality low-dose CT may represent a new clinical option.