Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography

Objectives:To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V).Methods:This was a prospective, single-institutional study using the excretory phase CTU images for analysis. Patients were assigned to the LD-DLIR group (100kV and automatic mA modulation for noise index (NI) of 23) and C-ASIR-V group (100kV and NI of 10) according to the scan protocols in the excretory phase. Two radiologists independently assessed the overall image quality, artifacts, noise and sharpness of urinary tracts. Additionally, the mean CT attenuation, signal-to-noise ratio (SNR) and contrast-to-noise (CNR) in the urinary tracts were evaluated.Results:26 patients each were included in the LD-DLIR group (10 males and 16 females; mean age: 57.23 years, range: 33–76 years) and C-ASIR-V group (14 males and 12 females; mean age: 60 years, range: 33–77 years). LD-DLIR group used a significantly lower effective radiation dose compared with the C-ASIR-V group (2.01 ± 0.44 mSv vs 6.9 ± 1.46 mSv, p < 0.001). LD-DLIR group showed good overall image quality with average score >4 and was similar to that of the C-ASIR-V group. Both groups had adequate and similar attenuation value, SNR and CNR in most segments of urinary tracts.Conclusion:It is feasibility to provide comparable image quality while reducing 71% radiation dose in low-dose CTU with a deep learning image reconstruction algorithm compared to the conventional CTU with ASIR-V.Advances in knowledge:(1) CT urography with deep learning reconstruction algorithm can reduce the radiation dose by 71% while still maintaining image quality.     

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.     

深度学习重建算法改善颅脑低剂量CT图像质量的可行性研究

目的探讨深度学习重建算法(DLIR)较自适应统计迭代重建(ASIR-V)算法在改善颅脑低剂量CT图像质量方面的效果。方法回顾性纳入2021年11月至2022年8月在解放军总医院第二医学中心接受颅脑CT检查的患者, 对所有患者的低剂量CT采用4种不同算法重建:获得30%强度ASIR-V(ASIR-V-30%)图像、低强度DLIR(DLIR-L)图像、中等强度DLIR(DLIR-M)图像和高强度DLIR(DLIR-H)图像。在4组图像的表浅白质、表浅灰质、深部白质和深部灰质内选取感兴趣区并测量其CT值, 计算信噪比(SNR)和对比噪声比(CNR)。由3名神经影像医师按照Likert 5分量表对图像质量进行主观评分。对4组图像的客观、主观评分进行分析, 若总体存在差异, 则进行组内两两比较。结果共纳入109例患者, 男104例、女5例, 年龄65～110岁, 平均(89.16±9.53)岁。颅脑CT低剂量扫描的辐射剂量为(0.93±0.01)mSv, 显著低于常规扫描(2.92±0.01)mSv(t = 56.15, P < 0.05 )。颅脑低剂量CT的4组图像的SNR深部灰质、SN...     

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

ObjectiveTo investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis.Materials and MethodsBetween June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively.ResultsWith the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT.ConclusionThe optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.     

Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT

ObjectiveThe purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT.Materials and MethodsThis retrospective study included 52 patients (26 male and 26 female; median age, 60.5 years) who had undergone CT-guided lumbar bone biopsy between October 2015 and April 2020. Initial 100-mAs survey images and 50-mAs intraprocedural images were reconstructed by filtered back projection. Denoising was performed using a vendor-agnostic DL model (ClariCT.AI™, ClariPI) for the 50-mAS images, and the 50-mAs, denoised 50-mAs, and 100-mAs CT images were compared. Noise, signal-to-noise ratio (SNR), and edge rise distance (ERD) for image sharpness were measured. The data were summarized as the mean ± standard deviation for these parameters. Two musculoskeletal radiologists assessed the visibility of the normal anatomical structures.ResultsNoise was lower in the denoised 50-mAs images (36.38 ± 7.03 Hounsfield unit [HU]) than the 50-mAs (93.33 ± 25.36 HU) and 100-mAs (63.33 ± 16.09 HU) images (p < 0.001). The SNRs for the images in descending order were as follows: denoised 50-mAs (1.46 ± 0.54), 100-mAs (0.99 ± 0.34), and 50-mAs (0.58 ± 0.18) images (p < 0.001). The denoised 50-mAs images had better edge sharpness than the 100-mAs images at the vertebral body (ERD; 0.94 ± 0.2 mm vs. 1.05 ± 0.24 mm, p = 0.036) and the psoas (ERD; 0.42 ± 0.09 mm vs. 0.50 ± 0.12 mm, p = 0.002). The denoised 50-mAs images significantly improved the visualization of the normal anatomical structures (p < 0.001).ConclusionDL-based reconstruction may enable simultaneous noise reduction and improvement in image quality with the preservation of edge sharpness on low-dose lumbar spine CT. Investigations on further radiation dose reduction and the clinical applicability of this technique are warranted.     

Improvement in Image Quality and Visibility of Coronary Arteries,Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction

ObjectiveThis study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods.Materials and MethodsCCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%–90% edge rise slope (ERS) and 10%–90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods.ResultsDLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR.ConclusionDLR reconstruction provided better images than FBP and hybrid IR reconstruction.     

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise

ObjectiveIterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%).Materials and MethodsThis retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated.ResultsBased on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001).ConclusionDLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.     

Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

Objectives:Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)].Methods:In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences.Results:On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images.Conclusion:The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging.Advances in knowledge:The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information.The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.     

Improvement of Image Quality Using Hybrid Iterative Reconstruction with Noise Power Spectrum Model in Computed Tomography During Hepatic Arteriography

Objectives:In CT during hepatic arteriography (CTHA), the addition of a noise power spectrum (NPS) model to conventional hybrid iterative reconstruction (HIR) may improve spatial resolution and reduce image noise. This study aims at assessing the image quality provided by HIR with a NPS model at CTHA.Methods:This institutional review board-approved retrospective analysis included 26 patients with hepatocellular carcinomas (HCCs) who underwent CTHA. In all acquisitions, images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR), and AIDR enhanced (eAIDR) with the NPS model. Four radiologists analyzed the signal-to-noise ratio (SNR) of HCC nodules and its associated feeding arteries. The radiologists used a semiquantitative scale (–3 to +3) to rate the subjective image quality comparing both the FBP and eAIDR images with the AIDR images.Results:The feeding arteries’ attenuation was significantly higher in eAIDR compared to AIDR [514.3 ± 121.4 and 448.3 ± 107.3 Hounsfield units (HU), p < 0.05]. The image noise of eAIDR was significantly lower than that of FBP (15.2 ± 2.2 and 28.5 ± 4.8 HU, p < 0.05) and comparable to that of AIDR. The SNR of feeding arteries on eAIDR was significantly higher than on AIDR (34.1 ± 7.9 and 27.4 ± 6.3, p < 0.05). Subjective assessment scores showed that eAIDR provided better visibility of feeding arteries and overall image quality compared to AIDR (p < 0.05). The HCC nodule visibility was not significantly different among the three reconstructions.Conclusion:In CTHA, eAIDR improved the visibility of feeding arteries associated with HCC nodules without compromising nodule detection.     

Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction

ObjectiveTo evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images.Materials and MethodsThis retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM).ResultsLDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581).ConclusionOverall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.     

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

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

Application of low-dose CT combined with model-based iterative reconstruction algorithm in oncologic patients during follow-up: dose reduction and image quality

Objectives:To compare image quality and radiation dose of CT images reconstructed with model-based iterative reconstruction (MBIR) and hybrid-iterative (HIR) algorithm in oncologic patients.Methods:125 oncologic patients underwent both contrast-enhanced low- (100 kV), and standard (120 kV) dose CT, were enrolled. Image quality was assessed by using a 4-point Likert scale. CT attenuation values, expressed in Hounsfield unit (HU), were recorded within a regions of interest (ROI) of liver, spleen, paraspinal muscle, aortic lumen, and subcutaneous fat tissue. Image noise, expressed as standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated. Radiation dose were analyzed. Paired Student’s t-test was used to compare all continuous variables.Results:The overall median score assessed as image quality for CT images with the MBIR algorithm was significantly higher in comparison with HIR [4 (range 3–4) vs 3 (3-4), p = 0.017].CT attenuation values and SD were significantly higher and lower, respectively, in all anatomic districts in images reconstructed with MBIR in comparison with HIR ones (all p < 0.001). SNR and CNR values were higher in CT images reconstructed with MBIR, reaching a significant difference in all districts (all p < 0.001). Radiation dose were significantly lower in the MBIR group compared with the HIR group (p < 0.001).Conclusions:MBIR combined with low-kV setting allows an important dose reduction in whole-body CT imaging, reaching a better image quality both qualitatively and quantitatively.Advances in knowledge:MBIR with low-dose approach allows a reduction of dose exposure, maintaining high image quality, especially in patients which deserve a longlasting follow-up.     

Gender is a determinant of carotid artery stiffness independent of age and blood pressure

Objective:To identify the gender-specific differences in carotid artery structural and stiffening parameters by radiofrequency ultrasound (RFU) with an automatic arterial stiffness analyzing system.Methods:Seventy-two consecutive individuals (32 males and 40 females, age range from 36 to 62 years) with no history of significant cardiovascular diseases or carotid artery plaques were enrolled between September and December 2017. Quality intima-media thickness (QIMT) and quality arterial stiffness (QAS) parameters were automatically computed, including pulse wave velocity (PWV), vascular distension, compliance coefficient (CC), distensibility coefficient (DC), stiffness index α and β, augmentation pressure (AP), and augmentation index (AIx). Those parameters were compared between males and females. Multiple linear regression analysis was performed to assess the independent association between gender and RFU parameters.Results:The mean age had no difference between males and females (47.8 ± 3.3 vs 50.0 ± 8.5 years, p = 0.19). Females had higher systolic blood pressure (134.53 ± 9.65 vs 127.78 ± 6.12 mm Hg) and diastolic blood pressure (85.83 ± 3.94 vs 78.03 ± 5.22 mm Hg), greater carotid QIMT (598.73 ± 72.16 vs 550.84 ± 29.37 µm), advanced PWV (8.08 ± 1.60 vs 6.24 ± 0.70 m/s), higher stiffness index α (6.21 ± 1.94 vs 3.95 ± 0.78) and β (9.43 ± 3.17 vs 6.38 ± 0.78), higher AP (6.68 ± 2.24 vs 3.64 ± 1.22 mm Hg) and AIx (7.42 ± 2.08 vs 4.69 ± 1.26%), all p < 0.001. Multiple linear regression analysis demonstrated gender was independently associated with carotid structural and elastic parameters.Conclusion:Gender independently impacts carotid structure and function, with females more vulnerable to the progression of arterial aging. Awareness of the gender differences on the risk stratification of carotid artery disease will benefit reliable assessments and specific management recommendations in clinical practice.Advances in knowledge:(1) RFU provides an μm-unit quality IMT measurement and multiple quality arterial stiffness parameters. (2) Gender is an independent determinant in both the arterial structural and elastic aspects, with females of stiffer arteries in low CVD risk individuals.     

Impact of total variation regularized expectation maximization reconstruction on the image quality of 68Ga-PSMA PET: a phantom and patient study

Objectives:To investigate the impact of total variation regularized expectation maximization (TVREM) reconstruction on the image quality of ⁶⁸Ga-PSMA-11 PET/CT using phantom and patient data.Methods:Images of a phantom with small hot sphere inserts and 20 prostate cancer patients were acquired with a digital PET/CT using list-mode and reconstructed with ordered subset expectation maximization (OSEM) and TVREM with seven penalisation factors between 0.01 and 0.42 for 2 and 3 minutes-per-bed (m/b) acquisition. The contrast recovery (CR) and background variability (BV) of the phantom, image noise of the liver, and SUV_max of the lesions were measured. Qualitative image quality was scored by two radiologists using a 5-point scale (1-poor, 5-excellent).Results:The performance of CR, BV, and image noise, and the gain of SUV_max was higher for TVREM 2 m/b groups with the penalization of 0.07 to 0.28 compared to OSEM 3 m/b group (all p < 0.05). The image noise of OSEM 3 m/b group was equivalent to TVREM 2 and 3 m/b groups with a penalization of 0.14 and 0.07, while lesions’ SUV_max increased 15 and 20%. The highest qualitative score was attained at the penalization of 0.21 (3.30 ± 0.66) for TVREM 2 m/b groups and the penalization 0.14 (3.80 ± 0.41) for 3 m/b group that equal to or greater than OSEM 3 m/b group (2.90 ± 0.45, p = 0.2 and p < 0.001).Conclusions:TVREM improves lesion contrast and reduces image noise, which allows shorter acquisition with preserved image quality for PSMA PET/CT.Advances in knowledge:TVREM reconstruction with optimized penalization factors can generate higher quality PSMA-PET images for prostate cancer diagnosis.     

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.     

The effect of adaptive iterative dose reduction on image quality in 320-detector row CT coronary angiography

Objective

To evaluate the effect of adaptive iterative dose reduction (AIDR) on image noise and image quality as compared with standard filtered back projection (FBP) in 320-detector row CT coronary angiography (CTCA).

Methods

50 patients (14 females, mean age 68±9 years) who underwent CTCA (100 kV or 120 kV, 400–580 mA) within a single heartbeat were enrolled. Studies were reconstructed with FBP and subsequently AIDR. Image noise, vessel contrast and contrast-to-noise ratio (CNR) in the coronary arteries were evaluated. Overall image quality for coronary arteries was assessed using a five-point scale (1, non-diagnostic; 5, excellent).

Results

All the examinations were performed in a single heartbeat. Image noise in the aorta was significantly lower in data sets reconstructed with AIDR than in those reconstructed with FBP (21.4±3.1 HU vs 36.9±4.5 HU; p<0.001). No significant differences were observed between FBP and AIDR for the mean vessel contrast (HU) in the proximal coronary arteries. Consequently, CNRs in the proximal coronary arteries were higher in the AIDR group than in the FBP group (p<0.001). The mean image quality score was improved by AIDR (3.75±0.38 vs 4.24±0.38; p<0.001).

Conclusion

The use of AIDR reduces image noise and improves image quality in 320-detector row CTCA.CT coronary angiography (CTCA) is a robust non-invasive imaging modality with high spatial and temporal resolution that enables accurate diagnosis or exclusion of coronary artery disease [1-4]. However, CTCA usually exposes the patient to a substantial amount of radiation (9.4–21.4 mSv) [5-7]. Therefore, several scanning techniques, such as ECG-based tube current modulation, prospective ECG triggering and reduced tube voltage scanning, have been developed to reduce the patient''s radiation exposure [6-8]. Reductions of the tube current also lead to lower radiation exposure, as the tube current correlates to dose in a linear fashion. However, lower radiation leads to an increase in CT image noise because the current reconstruction method, filtered back projection (FBP), is unable to consistently generate diagnostic-quality images with reduced tube currents [9].Recently, the adaptive iterative dose reduction technique has been developed as a new reconstruction algorithm to improve image noise [10-12], and has already been shown to reduce the radiation dose in clinical practice [13-16]. Adaptive iterative dose reduction (AIDR) developed for CT by Toshiba Medical Systems Corporation is a modified iterative reconstruction technique in which the original high-noise image undergoes a number of reconstructions that reduce image noise until the resultant image displays the desired noise level. This technique is expected to reduce the radiation dose for a similar noise level to FBP.To our knowledge, no study has evaluated the quality of CT images using AIDR. The purpose of this study was to evaluate the effect of AIDR regarding image noise and image quality in comparison with FBP, using the same raw data set for both FBP and AIDR, in 320-detector row CTCA.     

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.     

Reliability of respiratory-triggered two-dimensional cine k-adaptive-t-autocalibrating reconstruction for Cartesian sampling for the assessment of biventricular volume and function in patients with repaired tetralogy of Fallot

Objective:To compare left ventricular (LV) and right ventricular (RV) volume, function, and image quality of a respiratory-triggered two-dimensional (2D)-cine k-adaptive-t-autocalibrating reconstruction for Cartesian sampling (2D kat-ARC) with those of the standard reference, namely, breath-hold 2D balanced steady-state free precession (2D SSFP), in patients with repaired tetralogy of Fallot (TOF).Methods:30 patients (14 males, mean age 32.2 ± 13.9 years) underwent cardiac magnetic resonance, and 2D kat-ARC and 2D SSFP images were acquired on short-axis view. Biventricular end-diastolic volume (EDV) and end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), and LV mass (LVM) were analysed.Results:The 2D kat-ARC had significantly shorter scan time (35.2 ± 9.1 s vs 80.4 ± 16.7 s; p < 0.0001). Despite an analysis of image quality showed significant impairment using 2D kat-ARC compared to 2D SSFP cine (p < 0.0001), the two sequences demonstrated no significant difference in terms of biventricular EDV, LVESV, LVSV, LVEF, and LVM. However, the RVESV was overestimated for 2D kat-ARC compared with that for 2D SSFP (73.8 ± 43.2 ml vs 70.3 ± 44.5 ml, p = 0.0002) and the RVSV and RVEF were underestimated (RVSV = 46.2±20.5 ml vs 49.4 ± 20.4 ml, p = 0.0024; RVEF = 40.2±12.7% vs. 43.5±14.0%, p = 0.0002).Conclusion:Respiratory-triggered 2D kat-ARC cine is a reliable technique that could be used in the evaluation of LV volumes and function.Advances in knowledge:2D cine kat-ARC is a reliable technique for the assessment LV volume and function in patients with repaired TOF.     

Stent evaluation by coronary computed tomography angiography: a comparison between Iopamidol-370 and Ioversol-320 hypo-osmolar iodine concentration contrasts

Objective:Qualitative and quantitative image analysis between Iopamidol-370 and Ioversol-320 in stents´ evaluation by coronary computed tomography angiography (CTA).Methods:Sixty-five patients with low-risk stable angina undergoing stent follow-up with coronary CTA were assigned to Iopamidol I-370 (n = 33) or Ioversol I-320 (n = 32) in this prospective, double-blind, non-inferiority, randomized trial. Stent lumen image quality was graded by 5-point Likert Scale. Lumen mean attenuation was measured at native coronary segments: pre-stent, post-stent, distal segments and at coronary plaques. Lumen attenuation increase (LAI) ratio was calculated for all stents. Heart rate (HR) variation, premature heart beats (PHB), heat sensation (HS), blooming and beam hardening were also assessed.Results:Image quality was similar between groups, with no significant difference (Likert score 4.48 ± 0.75 vs 4.54 ± 0.65, p = 0.5). There were similarities in LAI ratio between I-370 and I-320 (0.39 ± 0.42 vs 0.48 ± 0.44 HU, p = 0.08). Regarding lumen mean attenuation at native coronary segments, a significant difference was observed, with I-320 presenting lower values, including contrast mean attenuation in distal segments. After statistical multivariate analysis, three variables correlated with stent image quality: 1) stent diameter, 2) HR variation and 3) stent lumen LAI ratio.Conclusions:There was no significant difference between Iopamidol-370 mgI ml⁻¹ and Ioversol-320 mgI ml⁻¹ contrasts regarding overall stent lumen image quality, which was mainly influenced by stent diameter, HR and LAI ratio. Advances in knowledge: Coronary CTA allows adequate stents'' visualization and image quality is influenced by stent diameter, HR variation and LAI ratio.Stents'' image quality showed no difference between different concentration contrasts (I-370 vs. I-320); however, higher concentration contrasts may provide an improved overall visualization, especially regarding coronary distal segments.     

Comparative assessment of three image reconstruction techniques for image quality and radiation dose in patients undergoing abdominopelvic multidetector CT examinations

Objective

To compare image quality and radiation dose of abdominal CT examinations reconstructed with three image reconstruction techniques.

Methods

In this Institutional Review Board-approved study, contrast-enhanced (CE) abdominopelvic CT scans from 23 patients were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR) and iterative reconstruction in image space (IRIS) and were reviewed by two blinded readers. Subjective (acceptability, sharpness, noise and artefacts) and objective (noise) measures of image quality were recorded for each image data set. Radiation doses in CT dose index (CTDI) dose–length product were also calculated for each examination type and compared. Imaging parameters were compared using the Wilcoxon signed rank test and a paired t-test.

Results

All 69 CECT examinations were of diagnostic quality and similar for overall acceptability (mean grade for ASiR, 3.9±0.3; p=0.2 for Readers 1 and 2; IRIS, 3.9±0.4, p=0.2; FBP, 3.8±0.9). Objective noise was considerably lower with both iterative techniques (p<0.0001 and 0.0016 for ASiR and IRIS). Recorded mean radiation dose, i.e. CTDI_vol, was 24% and 10% less with ASiR (11.4±3.4 mGy; p<0.001) and IRIS (13.5±3.7 mGy; p=0.06), respectively, than with FBP: 15.0±3.5 mGy.

Conclusion

At the system parameters used in this study, abdominal CT scans reconstructed with ASiR and IRIS provide diagnostic images with reduced image noise and 10–24% lower radiation dose than FBP.

Advances in knowledge

CT images reconstructed with FBP are frequently noisy on lowering the radiation dose. Newer iterative reconstruction techniques have different approaches to produce images with less noise; ASiR and IRIS provide diagnostic abdominal CT images with reduced image noise and radiation dose compared with FBP. This has been documented in this study.CT continues to expand its role as an essential imaging modality [1]. However, with its increasing use, concerns of radiation overexposure have prompted efforts to reduce the cumulative dose to a patient [2]. Recent studies have highlighted increased utilisation of radiological examination and 10-fold increase in medical radiation exposure at the population level [3,4]. Therefore, lowering the CT radiation dose without compromising the image quality is desirable. Several technical approaches have been proposed to accomplish these goals including commonly used tube current modulation and adopting lower peak kilovoltage [5-8]. However, excessive dose reduction has remained difficult in the abdomen and pelvis CT due to increased levels of image noise and artefacts that lower the quality of the CT examination. Moreover, abdominopelvic CT demands higher image quality for confident detection of low-contrast lesions in various viscera [9]. The conventional technique of image reconstruction, filtered back projection (FBP), is an efficient method for image production, but makes several assumptions and therefore requires higher dose for delivering diagnostic quality images [10,11]. To overcome these limitations, iterative reconstruction (IR) techniques have been introduced, which have been shown to render optimal image quality at lower radiation dose [12-20]. Unlike advanced iterative techniques, partial IR approaches such as adaptive statistical iterative reconstruction (ASiR) and iterative reconstruction in image space (IRIS) are computationally less demanding and therefore faster to process images. In essence, both rely on mathematic modelling of the CT raw data to selectively identify image noise and reduce it. The ASiR technique models statistical variations in the distribution of noise from acquired image data and improves the signal-to-noise ratio while preserving image contrast [5,8,12]. Since its introduction, several investigators have confirmed its capabilities to deliver diagnostic quality images at 30–50% lower radiation dose. [8,12]. IRIS, on the other hand, reduces image noise by forming multiple iterations within the image space itself [10-14]. Phantom studies have demonstrated its ability in maintaining transverse and z-axis spatial resolution, as well as CT number accuracy and linearity while reducing image noise [18]. Its capability in preserving diagnostic accuracy and improving image quality at lower tube potential settings has been documented by Schindera et al [18] as has its ability to reduce noise and radiation dose in clinical studies by approximately 35–50% [11-16]. Owing to differences in image reconstruction approach by ASiR and IRIS, we investigated the performance of these two IR methods on image quality and radiation dose in patients undergoing contrast-enhanced (CE) abdominal CT examinations compared with the FBP technique.