Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy

Application value of individualized tube voltage, contrast injection, and adaptive statistical iterative reconstruction V algorithm based on body mass index in renal computed tomography angiography for radiation and iodinated contrast dose reduction

OBJECTIVES

To explore the application value of body mass index (BMI)-based kilovoltage peak (kVp) selection and contrast injection protocol combined with different adaptive statistical iterative reconstruction V (ASIR-V) strengths in renal computed tomography angiography (CTA) in reducing radiation and contrast medium (CM) doses.

METHODS

One-hundred renal CTA patients were prospectively enrolled and were divided into individualized kVp group (group A, n = 50) and conventional 100 kVp group (group B, n = 50), both with automatic tube current modulation and CM of Iohexol at 350 mgI/mL concentration. Group A: 70 kVp, noise index (NI) of 18 and CM dose rate of 17 mgI/kg/s for 10 s for BMI <25 kg/m2 patients; 80 kVp, NI = 17, and CM dose rate of 19 mgI/kg/s for 10 s for 25 kg/m2≤BMI≤30 kg/m2 patients. Group B: 100 kVp, 50 mL of CM at the flow rate of 4.5 mL/s. The objective image quality, effective radiation dose, CM dose, injection rate, and image quality were compared between the 2 groups.

RESULTS

There was no significant difference in patient characteristics between the 2 groups (P > .05). Compared to group B, group A significantly reduced effective radiation dose by 28.4%, CM dose by 27.2%, and injection rate by 22.7% (all P < .001). The 2 groups had similar SD values in erector spine (P > .05). Group A had significantly higher CT values, SNR, and CNR values of the renal arteries than group B (all P < .001). The 2 radiologists had excellent agreement (Kappa value > 0.8) in the subjective scores of renal CTA images and showed no statistically significant difference between the 2 groups (4.57 ± 0.42 vs 4.41 ± 0.49) (P > .05).

CONCLUSIONS

BMI-based scan and reconstruction protocol in renal CTA significantly reduces radiation and contrast doses while maintaining diagnostic image quality.

ADVANCES IN KNOWLEDGE

(i) BMI-based individualized tube voltage selection and contrast injection protocol in renal CTA reduces both radiation and contrast doses over conventional protocol. (ii) The combination of lower kVp and higher weight ASIR-V maybe used to improve image quality in terms of contrast enhancement and image noise under lower radiation and contrast dose conditions. (iii) Renal CTA of normal size (BMI ≤ 30 kg/m2) patients acquired at low radiation dosage and low iodine contrast dose through the combination of low tube voltage and ASIR-V algorithm achieves excellent diagnostic image quality with a good inter-rater agreement.

Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT

OBJECTIVE

To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT).

MATERIALS AND METHODS

An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared.

RESULTS

Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP.

CONCLUSION

Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review

Background

The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.

Methods

We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines.

Results

Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations.

Conclusions

DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Computed tomography-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer

PURPOSE

To investigate computed tomography (CT)-based prediction model for identifying patients with high probability of non-muscle-invasive bladder cancer (NMIBC).

METHODS

This retrospective study evaluated 147 consecutive patients who underwent contrast-enhanced CT and surgery for bladder cancer. Using corticomedullary-to-portal venous phase images, two independent readers analyzed bladder muscle invasion, tumor stalk, and tumor size, respectively. Three-point scale (i.e., from 0 to 2) was applied for assessing the suspicion degree of muscle invasion or tumor stalk. A multivariate prediction model using the CT parameters for achieving high positive predictive value (PPV) for NMIBC was investigated. The PPVs from raw data or 1000 bootstrap resampling and inter-reader agreement using Gwet's AC1 were analyzed, respectively.

RESULTS

Proportion of patients with NMIBC was 81.0% (119/147). The CT criteria of the prediction model were as follows: (a) muscle invasion score < 2; (b) tumor stalk score > 0; and (c) tumor size < 3 cm. From the raw data, PPV of the model for NMIBC was 92.7% (51/55; 95% confidence interval [CI] 82.4-98.0) in reader 1 and 93.3% (42/45; 95% CI 81.7-98.6) in reader 2. From the bootstrap data, PPV was 92.8% (95% CI 85.2-98.3) in reader 1 and 93.4% (95% CI 84.9-99.9) in reader 2. The model's AC1 was 0.753 (95% CI 0.647-0.859).

CONCLUSION

The current CT-derived prediction model demonstrated high PPV for identifying patients with NMIBC. Depending on CT findings, approximately 30% of patients with bladder cancer may have a low need for additional MRI for interpreting vesical imaging-reporting and data system.

Detection, measurement, and diagnosis of lung nodules by ultra-low-dose CT in lung cancer screening: a systematic review

Objective

There is a lack of recent meta-analyses and systematic reviews on the use of ultra-low-dose CT (ULDCT) for the detection, measurement, and diagnosis of lung nodules. This review aims to summarize the latest advances of ULDCT in these areas.

Methods

A systematic review of studies in PubMed and Web of Science was conducted, using search terms specific to ULDCT and lung nodules. The included studies were published in the last 5 years (January 2019-August 2024). Two reviewers independently selected articles, extracted data, and assessed the risk of bias and concerns using the Quality Assessment of Diagnostic Accuracy Studies-II (QUADAS-II) tool. The standard-dose, low-dose, or contrast-enhanced CT served as the reference-standard CT to evaluate ULDCT.

Results

The literature search yielded 15 high-quality articles on a total of 1889 patients, of which 10, 3, and 2 dealt with the detection, measurement, and diagnosis of lung nodules. QUADAS-II showed a generally low risk of bias. The mean radiation dose for ULDCT was 0.22 ± 0.10 mSv (7.7%) against 2.84 ± 1.80 mSv for reference-standard CT. Nodule detection rates ranged from 86.1% to 100%. The variability of diameter measurements ranged from 2.1% to 14.4% against contrast-enhanced CT and from 3.1% to 8.29% against standard CT. The diagnosis rate of malignant nodules ranged from 75% to 91%.

Conclusions

ULDCT proves effective in detecting lung nodules while substantially reducing radiation exposure. However, the use of ULDCT for the measurement and diagnosis of lung nodules remains challenging and requires further research.

Advances in knowledge

When ULDCT reduces radiation exposure to 7.7%, it detects lung nodules at a rate of 86.1%-100%, with a measurement variance of 2.1%-14.4% and a diagnostic accuracy for malignancy of 75%-91%, suggesting the potential for safe and effective lung cancer screening.

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.

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.

Effect of Low Tube Voltage (100 kV) Combined with ASIR-V on the Visualization and Image Quality of the Adamkiewicz Artery: A Comparison with 120 kV Protocol

OBJECTIVE

To evaluate the effect of low tube voltage (100 kV) combined with adaptive statistical iterative reconstruction-V (ASIR-V) on the visualization and image quality of the Adamkiewicz artery (AKA).

METHODS

One hundred patients were prospectively enrolled and randomly assigned into two groups (both n = 50). Group A (100 kV) was reconstructed with filtered back projection (FBP) and ASIR-V from 10% to 100% with 10% intervals. Group B (120 kV) was only reconstructed with FBP. The objective image quality was evaluated by using CT values of the aorta (CTAorta), background noise, signal-to-noise ratio of the descending aorta (SNRAorta), and contrast-to-noise ratio of the spinal cord (CNRSpinal cord). The subjective image quality and visualization scores of the AKA were assessed on a 5-point scale.

RESULTS

CTAorta was significantly higher in Group A than in Group B (p < 0.001). When ASIR-V weights were ≥60%, significant differences were found in the background noise, SNRAorta, and CNRSpinal cord between the two groups (all p < 0.05). In Group A, compared with FBP, the subjective score gradually increased as ASIR-V increased to 80%, which decreased when ASIR-V exceeded 80%. The visualization scores of the AKA (≥60%) and the ability to detect vessel continuity (≥80%) gradually increased as the ASIR-V weights increased (p < 0.05). The effective radiation dose was reduced by about 40.36% in Group A compared to Group B.

CONCLUSIONS

compared with conventional scanning protocol, using a combination of low tube voltage (100 kV) and 80% ASIR-V protocol could not only increase the visualization of the AKA, but also improve image quality and reduce the radiation doses.

Quality Assessment of Computed Tomography Images using a Channelized Hoteling Observer: Optimization of Protocols in Clinical Practice

Background

This study investigated the feasibility of channelized hoteling observer (CHO) model in computed tomography (CT) protocol optimization regarding the image quality and patient exposure. While the utility of using model observers such as to optimize the clinical protocol is evident, the pitfalls associated with the use of this method in practice require investigation.

Materials and Methods

This study was performed using variable tube current and adaptive statistical iterative reconstruction (ASIR) level (ASIR 10% to ASIR 100%). Various criteria including noise, high-contrast spatial resolution, CHOs model were used to compare image quality at different captured levels. For the implementation of CHO, we first tuned the model in a restricted dataset and then it to the evaluation of a large dataset of images obtained with different reconstruction ASIR and filtered back projection (FBP) levels.

Results

The results were promising in terms of CHO use for the stated purposes. Comparisons of the noise of reconstructed images with 30% ASIR and higher levels of noise in rebuilding images using the FBP approach showed a significant difference (P < 0.05). The spatial resolution obtained using various ASIR levels and tube currents were 0.8 pairs of lines per millimeter, which did not differ significantly from the FBP method (P > 0.05).

Conclusions

Based on the results, using 80% ASIR can reduce the radiation dose on lungs, abdomen, and pelvis CT scans while maintaining image quality. Furthermore using ASIR 60% only for the reconstruction of lungs, abdomen, and pelvis images at standard radiation dose leads to optimal image quality.

Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose

BACKGROUND

Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population.

METHODS

A pediatric phantom was used for the pilot study, and 20 children were recruited for clinical verification. The preset scan parameter noise index (NI) was 5, 8, 11, 13, 15, and 18 for the phantom study, and 8 and 13 for the clinical pediatric study. We reconstructed CT images with ASiR-V 30%, ASiR-V 70%, DLIR-M (medium) and DLIR-H (high). The regions of interest (ROI) were marked on the organs of the abdomen (liver, kidney, and subcutaneous fat) and the chest (lung, mediastinum, and spine). The CT dose index volume (CTDIvol), CT value, image noise (N), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated. The subjective image quality was assessed by 3 radiologists blindly using a 5-point scale. The dose reduction efficiency of DLIR was estimated.

RESULTS

In the phantom study, the interobserver assessment of the data measurement demonstrated good agreement [intraclass correlation coefficient (ICC) =0.814 for abdomen, 0.801 for chest]. Within the same dose level, the N, SNR, and CNR were statistically different among reconstructions, while the CT value remained the same. The N increased and SNR decreased as the radiation dose decreased. The DLIR-H performed better than ASiR-V when the radiation dose was reduced, without sacrificing image quality. In the patient study, the interobserver assessment of the data measurement demonstrated good agreement (ICC =0.774 for abdomen, 0.751 for chest). DLIR-H had the highest subjective and objective scores in the abdomen.

CONCLUSIONS

Application of DLIR could help to reduce radiation dose without sacrificing the image quality of pediatric CT scans. Further clinical validation is required.

Dual-energy CT of acute bowel ischemia

Acute bowel ischemia is a condition with high mortality and requires rapid intervention to avoid catastrophic outcomes. Swift and accurate imaging diagnosis is essential because clinical findings are commonly nonspecific. Conventional contrast enhanced CT of the abdomen has been the imaging modality of choice to evaluate suspected acute bowel ischemia. However, subtlety of image findings and lack of non-contrast or arterial phase images can make correct diagnosis challenging. Dual-energy CT provides valuable information toward assessing bowel ischemia. Dual-energy CT exploits the differential X-ray attenuation at two different photon energy levels to characterize the composition of tissues and reveal the presence or absence of faint intravenous iodinated contrast to improve reader confidence in detecting subtle bowel wall enhancement. With the same underlying technique, virtual non-contrast images can help to show non-enhancing hyperdense hemorrhage of the bowel wall in intravenous contrast-enhanced scans without the need to acquire actual non-contrast scans. Dual-energy CT derived low photon energy (keV) virtual monoenergetic images emphasize iodine contrast and provide CT angiography-like images from portal venous phase scans to better evaluate abdominal arterial patency. In Summary, dual-energy CT aids diagnosing acute bowel ischemia in multiple ways, including improving visualization of the bowel wall and mesenteric vasculature, revealing intramural hemorrhage in contrast enhanced scans, or possibly reducing intravenous contrast dose.

Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study

BACKGROUND AND PURPOSE

To investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study.

MATERIALS AND METHODS

High-contrast spatial resolution, low-contrast detectability, modulation function test (MTF), noise power spectrum (NPS), and image noise were evaluated for physical image quality on Caphan 500 phantom. Three calcium hydroxyapatite (HA) inserts were used for accurate BMD measurement on European Spine Phantom (ESP). CT images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction-veo 50% (ASiR-V50%), and three levels of DLIR(L/M/H). Subjective evaluation of the image high-contrast spatial resolution and low-contrast detectability were compared visually by qualified radiologists, whilst the statistical difference in the objective evaluation of the image high-contrast spatial resolution and low-contrast detectability, image noise, and relative measurement error were compared using one-way analysis of variance (ANOVA). Cohen's kappa coefficient (k) was performed to determine the interobserver agreement in qualitative evaluation between two radiologists.

RESULTS

Overall, for three levels of DLIR, 50% MTF was about 4.50 (lp/cm), better than FBP (4.12 lp/cm) and ASiR-V50% (4.00 lp/cm); the 2 mm low-contrast object was clearly resolved at a 0.5% contrast level, while 3mm at FBP and ASiR-V50%. As the strength level decreased and radiation dose increased, DLIR at three levels showed a higher NPS peak frequency and lower noise level, leading to leftward and rightward shifts, respectively. Measured L1, L2, and L3 were slightly lower than that of nominal HA inserts (44.8, 95.9, 194.9 versus 50.2, 100.6, 199.2mg/cm³) with a relative measurement error of 9.84%, 4.08%, and 2.60%. Coefficients of variance for the L1, L2, and L3 HA inserts were 1.51%, 1.41%, and 1.18%. DLIR-M and DLIR-H scored significantly better than ASiR-V50% in image noise (4.83 ± 0.34, 4.50 ± 0.50 versus 4.17 ± 0.37), image contrast (4.67 ± 0.73, 4.50 ± 0.70 versus 3.80 ± 0.99), small structure visibility (4.83 ± 0.70, 4.17 ± 0.73 versus 3.83 ± 1.05), image sharpness (3.83 ± 1.12, 3.53 ± 0.90 versus 3.27 ± 1.16), and artifacts (3.83 ± 0.90, 3.42 ± 0.37 versus 3.10 ± 0.83). The CT value, image noise, contrast noise ratio, and image artifacts in DLIR-M and DLIR-H outperformed ASiR-V50% and FBP (P<0.001), whilst it showed no statistically significant between DLIR-L and ASiR-V50% (P>0.05). The prevalence of osteoporosis was 74 (24.67%) in women and 49 (11.79%) in men, whilst the osteoporotic vertebral fracture rate was 26 (8.67%) in women and (5.29%) in men.

CONCLUSION

Image quality with DLIR was high-qualified without affecting the accuracy of BMD measurement. It has a potential clinical utility in osteoporosis screening.

Comparing feasibility of different tube voltages and different concentrations of contrast medium in coronary CT angiography of overweight patients

OBJECTIVES

To compare image quality, radiation dose, and iodine intake of coronary computed tomography angiography (CCTA) acquired by wide-detector using different tube voltages and different concentrations of contrast medium (CM) for overweight patients.

MATERIALS AND METHODS

A total of 150 overweight patients (body mass index≥25 kg/m2) who underwent CCTA are enrolled and divided into three groups according to scan protocols namely, group A (120 kVp, 370 mgI/ml CM); group B (100 kVp, 350 mgI/ml CM); and group C (80 kVp, 320 mgI/ml CM). The CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and figure-of-merit (FOM) of all images are calculated. Images are subjectively assessed using a 5-point scale. In addition, the CT dose index volume (CTDIvol) and dose length product (DLP) of each patient are recorded. The effective radiation dose (ED) is also calculated. Above data are then statistically analyzed.

RESULTS

The mean CT values, SNR, CNR, and subjective image quality of group A are significantly lower than those of groups B and C (P < 0.001), but there is no significant difference between groups B and C (P > 0.05). FOMs show a significantly increase trend from group A to C (P < 0.001). The ED values and total iodine intake in groups B and C are 30.34% and 68.53% and 10.22% and 16.85% lower than those in group A, respectively (P < 0.001).

CONCLUSION

The lower tube voltage and lower concentration of CM based on wide-detector allows for significant reduction in iodine load and radiation dose in CCTA for overweight patients comparing to routine scan protocols. It also enhances signal intensity of CCTA and maintains image quality.

Expanding the Utilization of Low-Dose Computed Tomography in Plastic and Reconstructive Surgery Based on Validated Practices Among Surgical Specialties

INTRODUCTION

As computed tomography (CT) usage increases, so have concerns over radiation-induced malignancy. To mitigate these risks, low-dose CT (LDCT) has emerged as a versatile alternative by other specialties, although its use in plastic surgery remains sparse. This study aimed to investigate validated uses of LDCT across surgical specialties and extrapolate these insights to expand its application for plastic surgeons.

METHODS

A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using search terms "low dose CT" OR "low dose computed tomography" AND "surgery," where the name of each surgical specialty was substituted for word "surgery" and each specialty term was searched separately in combination with the 2 CT terms. Data on radiation dose, outcomes, and level of evidence were collected. Validated surgical applications were correlated with similar procedures and diagnostic tests performed routinely by plastic surgeons to extrapolate potential applications for plastic surgeons.

RESULTS

A total of 3505 articles were identified across surgical specialties, with 27 ultimately included. Depending on the application, use of LDCT led to a 25% to 97% reduction in radiation dose and all studies reported noninferior image quality and diagnostic capability compared with standard-dose CT. Potential identified uses included the following: evaluation of soft tissue infections, preoperative and postoperative management of facial and hand fractures, flap design, 3D modeling, and surgical planning.

DISCUSSION

Low-dose CT is a valid imaging alternative to standard-dose CT. Expanded utilization in plastic surgery should be considered to minimize the iatrogenic effects of radiation and to promote patient safety without compromising outcomes.

Low keV portal venous phase as a surrogate for pancreatic phase in a pancreatic protocol dual-energy CT: feasibility, image quality, and lesion conspicuity

OBJECTIVE

To assess the feasibility of a proposed pancreatic protocol CT generated from portal-venous phase (PVP) dual-energy CT (DECT) acquisition and its impact on image quality, lesion conspicuity, and arterial visualization/involvement.

METHODS

We included 111 patients (mean age, 66.8 years) who underwent pancreatic protocol DECT (pancreatic phase, PP, and PVP). The original DECT acquisition was used to create two data sets-standard protocol (50 keV PP/65 keV PVP) and proposed protocol (40 keV/65 keV PVP). Three reviewers evaluated the two data sets for image quality, lesion conspicuity, and arterial visualization/involvement using a 5-point scale. The signal-to-noise ratio (SNR) of pancreas and lesion-to-pancreas contrast-to-noise ratio (CNR) was calculated. Qualitative scores, quantitative parameters, and dose-length product (DLP) were compared between standard and proposed protocols.

RESULTS

The image quality, SNR of pancreas, and lesion-to-pancreas CNR of the standard and proposed protocol were comparable (p = 0.11-1.00). Lesion conspicuity was comparable between the standard and proposed protocols for pancreatic ductal adenocarcinoma (p = 0.55) and pancreatic cysts (p = 0.28). The visualization of larger arteries and arterial involvement were comparable between the two protocols (p = 0.056-1.00) while the scores were higher for smaller vessels in the standard protocol (p < 0.0001-0.0015). DLP of the proposed protocol (670.4 mGy·cm) showed a projected 42% reduction than the standard protocol (1145.9 mGy·cm) (p < 0.0001).

CONCLUSION

Pancreatic protocol CT generated from a single PVP DECT acquisition is feasible and could potentially be an alternative to the standard pancreatic protocol with PP and PVP.

KEY POINTS

• The lesion conspicuity for focal pancreatic lesions was comparable between the proposed protocol and standard dual-phase pancreatic protocol CT. • Qualitative and quantitative image assessments were almost comparable between two protocols. • The radiation dose of a proposed protocol showed a projected 42% reduction from the conventional protocol.

Deep learning image reconstruction for pancreatic low-dose computed tomography: comparison with hybrid iterative reconstruction

PURPOSE

To evaluate image quality, image noise, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic low-dose computed tomography (LDCT) reconstructed using deep learning image reconstruction (DLIR) and compare with those of images reconstructed using hybrid iterative reconstruction (IR).

METHODS

Our institutional review board approved this prospective study. Written informed consent was obtained from all patients. Twenty-eight consecutive patients with PDAC undergoing chemotherapy (14 men and 14 women; mean age, 68.4 years) underwent pancreatic LDCT for therapy evaluation. The LDCT images were reconstructed using 40% adaptive statistical iterative reconstruction-Veo (hybrid-IR) and DLIR at medium and high levels (DLIR-M and DLIR-H). The image noise, diagnostic acceptability, and conspicuity of PDAC were qualitatively assessed using a 5-point scale. CT numbers of the abdominal aorta, portal vein, pancreas, PDAC, background noise, signal-to-noise ratio (SNR) of the anatomical structures, and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative and quantitative parameters were compared between the hybrid-IR, DLIR-M, and DLIR-H images.

RESULTS

CT dose-index volumes and dose-length product in pancreatic LDCT were 2.3 ± 1.0 mGy and 74.9 ± 37.0 mGy•cm, respectively. The image noise, diagnostic acceptability, and conspicuity of PDAC were significantly better in DLIR-H than those in hybrid-IR and DLIR-M (all P < 0.001). The background noise was significantly lower in the DLIR-H images (P < 0.001) and resulted in improved SNRs (P < 0.001) and CNR (P < 0.001) compared with those in the hybrid-IR and DLIR-M images.

CONCLUSION

DLIR significantly reduced image noise and improved image quality in pancreatic LDCT images compared with hybrid-IR.

Simulated twin-phase pancreatic CT generated using single portal venous phase dual-energy CT acquisition in pancreatic ductal adenocarcinoma

PURPOSE

To evaluate the diagnostic performance of a simulated twin-phase pancreatic protocol CT generated from a single portal venous phase (PVP) dual-energy CT (DECT) acquisition in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

In this retrospective study, we included 63 patients with PDAC who underwent pancreatic protocol (pancreatic phase [PP] and PVP) DECT. Two data sets were created from this original acquisition-(1) Standard protocol (50 keV PP/65 keV PVP) and (2) Simulated protocol (40 keV/65 keV PVP). Using a 5-point scale, three readers scored image quality, tumor conspicuity, and arterial involvement by the PDAC. Signal-to-noise ratio (SNR) of the pancreas and tumor-to-pancreas contrast-to-noise ratio (CNR) were calculated. Qualitative scores, quantitative parameters, and radiation dose were compared between standard and simulated protocols.

RESULTS

No significant difference in detection rate of PDAC was seen between the standard (58/63, 92.1%) and simulated protocols (56/63, 88.9%) (P = 0.76). Subjective scoring for arterial involvement for celiac (P = 0.86), superior mesenteric (P = 0.88), splenic (P = 0.86), common hepatic (P = 0.52), gastroduodenal (P = 0.95), first jejunal (P = 0.48) arteries, and aorta (P = 1.00) were comparable between two protocols. The image quality (P = 0.14), the SNR of the pancreas (P = 0.15), and CNR (P = 0.54) were comparable between two protocols. The projected mean dose-length product (DLP) (629.6 ± 148.3 mGy cm) in the simulated protocol showed a 44% reduction in radiation dose compared to the standard protocol (mean DLP, 1123.3 ± 268.9 mGy cm) (P < 0.0001).

CONCLUSIONS

Low keV images generated from a PVP DECT acquisition allows creation of a twin-phase pancreatic protocol CT with comparable diagnostic accuracy for detecting PDAC with significant reduction in radiation dose. Reduced radiation dose is desirable in surveillance and screening for pancreatic diseases.

Reducing contrast medium dose with low photon energy images in renal dual-energy spectral CT angiography and adaptive statistical iterative reconstruction (ASIR)

OBJECTIVE

To evaluate the value of using low energy (keV) images in renal dual-energy spectral CT angiography (CTA) and adaptive statistical iterative reconstruction (ASIR) to reduce contrast medium dose.

METHODS

40 patients with renal CTA on a Discovery CT750HD were randomly divided into two groups: 20 cases (Group A) with 600 mgI kg^-1 and 20 cases (Group B) with 300 mgI kg^-1. The scan protocol for both groups was: dual-energy mode with mA selection for noise index of 10 HU, pitch 1.375:1, rotating speed 0.6 s/r. Images were reconstructed at 0.625 mm thickness with 40%ASIR, Group A used the conventional 70keV monochromatic images, and Group B used monochromatic images from 40 to 70 keV at 5 keV interval for analysis. The CT values and standard deviation (SD) values of the renal artery and erector spine in the plain and arterial phases were measured with the erector spine SD value representing image noise. The enhancement degree of the renal artery (ΔCT = CT(arterial) -CT(plain)), signal-to-noise ratio (SNR=CT_renal-artery/SD_renal-artery) and contrast-to-noise ratio (CNR=(CT_renal-artery-CT_{erector spine})/SD_{erector-spine}) were calculated. The single factor analysis of variance was used to analyze the difference of ΔCT, SNR and CNR among image groups with p < 0.05 being statistically significant. The subjective image scores of the groups were assessed blindly by two experienced physicians using a 5-point system and the score consistency was compared by the κ test.

RESULTS

Contrast medium dose in the 300 mgI kg^-1 group was reduced by 50% compared with the 600 mgI kg^-1 group, while radiation dose was similar between the two groups. The subjective scores were 4.00 ± 0.65, 4.50 ± 0.60 and 3.70 ± 0.80 for images at 70 keV (600 mgI kg^-1 group), 40 keV (300 mgI kg^-1 group) and 45 keV (300 mgI kg^-1 group), respectively with good consistency between the two reviewers (p > 0.05). The 40 keV images in the 300 mgI kg^-1 group had similar ΔCT (469.77 ± 86.95 HU vs 398.54 ± 73.68 HU) and CNR (15.52 ± 3.32 vs 18.78 ± 6.71) values as the 70 keV images in the 600 mgI kg^-1) group but higher SNR values (30.19 ± 4.41 vs 16.91 ± 11.12, p < 0,05).

CONCLUSION

Contrast dose may be reduced by 50% while maintaining image quality by using lower energy images combined with ASIR in renal dual-energy CTA.

ADVANCES IN KNOWLEDGE

Combined with ASIR and energy spectrum, can reduce the amount of contrast dose in renal CTA.

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

OBJECTIVES

To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR).

METHODS

The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDI_vol) and dose-length product (DLP) were compared between SD and LD scans.

RESULTS

The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28-0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDI_vol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001).

CONCLUSION

LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR.

ADVANCES IN KNOWLEDGE

Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

Hyperattenuating adrenal lesions in lung cancer: biphasic CT with unenhanced and 1-min enhanced images reliably predicts benign lesions

OBJECTIVES

To investigate usefulness of biphasic computed tomography (CT) in characterizing hyperattenuating adrenal lesions in lung cancer.

METHODS

This retrospective study included 239 patients with lung cancer who underwent adrenal CT for hyperattenuating (> 10 Hounsfield unit) adrenal lesions. Adrenal CT comprised unenhanced and 1-min and 15-min enhanced images. We dichotomized adrenal lesions depending on benign or metastatic lesions. Reference standard for benignity was histologic confirmation or ≥ 6-month stability on follow-up CT. Two independent readers analyzed absolute (APW) or relative percentage wash-out (RPW) using triphasic CT, and enhancement ratio (ER) or percentage wash-in (PWI) using biphasic CT (i.e., unenhanced and 1-min enhanced CT). Criteria for benignity were as follows: criteria 1, (a) APW ≥ 60% or (b) RPW ≥ 40%, and criteria 2, (a) ER > 3 and (b) PWI > 200%. We analyzed area under the curve (AUC) and accuracy for benignity, and inter-reader agreement.

RESULTS

Proportion of benign adrenal lesion was 71.1% (170/239). For criteria 1 and 2, AUCs were 0.872 (95% confidence interval [CI], 0.822-0.911) and 0.886 (95% CI, 0.838-0.923), respectively, for reader 1 (p = 0.566) and 0.816 (95% CI, 0.761-0.863) and 0.814 (95% CI, 0.759-0.862), respectively, for reader 2 (p = 0.955), and accuracies were 87.9% (210/239) and 86.2% (206/239), respectively, for reader 1 (p = 0.479) and 81.2% (194/239) and 80.3% (192/239), respectively, for reader 2 (p = 0.763). Weighted kappa was 0.725 (95% CI, 0.634-0.816) for criteria 1 and 0.736 (95% CI, 0.649-0.824) for criteria 2.

CONCLUSION

Biphasic CT can reliably characterize hyperattenuating adrenal lesions in patients with lung cancer.

KEY POINTS

• Criteria from biphasic computed tomography (CT) for diagnosing benign adrenal lesions were enhancement ratio of > 3 and percentage wash-in of > 200%. • In the analysis by two independent readers, area under the curve between criteria 1 and 2 was not significantly different (0.872 and 0.886 for reader 1; 0.816 and 0.814, for reader 2; p > 0.05 for each comparison). • Wash-in characteristics from biphasic CT are helpful to predict benign adrenal lesions in lung cancer.

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.

Ultra-low-dose CT reconstructed with ASiR-V using SmartmA for pulmonary nodule detection and Lung-RADS classifications compared with low-dose CT

AIM

To evaluate the accuracy of ultra-low-dose computed tomography (ULDCT) with ASiR-V using a noise index (SmartmA) for pulmonary nodule detection and Lung CT Screening Reporting And Data System (Lung-RADS) classifications compared with low-dose CT (LDCT).

MATERIALS AND METHODS

Two-hundred and ten patients referred for lung cancer screening underwent conventional chest LDCT (0.80 ± 0.28 mSv) followed immediately by ULDCT (0.16 ± 0.03 mSv). ULDCT was scanned using 120 kV/SmartmA with a noise index of 28 HU and reconstructed with ASiR-V70%. The types and diameters of all nodules were recorded. The attenuation of pure ground-glass nodules (pGGNs) was measured on LDCT. All nodules were further classified using Lung-RADS. Sensitivities of nodule detection on ULDCT were analysed using LDCT as the reference standard. Logistic regression was used to establish a prediction model for the sensitivity of nodules.

RESULTS

LDCT revealed 362 nodules and the overall sensitivity on ULDCT was 90.1%. The sensitivity for solid nodules (SNs) of ≥1 mm diameter was 96.6% (228/236) and 100% (26/26) for SNs of ≥6 mm diameter. For pGGNs of ≥6 mm, the overall sensitivity was 93% (40/43) and 100% (29/29) for nodules with a attenuation value -700 HU or more. The agreement of Lung-RADS classification between two scans was good. On logistic regression, diameter was the only independent predictor for sensitivity of SNs (p<0.05). Diameter and attenuation value were predictors for pGGNs (p<0.05).

CONCLUSION

ULDCT with ASiR-V using SmartmA is suitable for lung-cancer screening in people with a BMI ≤35 kg/m² as it has a low radiation dose of 0.16 mSv, high sensitivity for nodule detection and good performance of Lung-RADS classifications.

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.