Subjective Image Quality Evaluation Research Articles

Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of Radiation Dose, Image Noise, and Image Quality.

The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to incentivize dose reduction without compromising the diagnostic utility of the tests. However, no existing quantitative method for assessing CT image quality has been validated against radiologists' image quality assessments on a large number of CT examinations. Thus to develop an automated measure of image quality, we tested the relationship between radiologists' subjective ratings of image quality with measurements of radiation dose and image noise. Board-certified, posttraining, clinically active radiologists rated the image quality of 200 diagnostic CT examinations from a set of 734, representing 14 CT categories. Examinations with significant distractions, motion, or artifact were excluded. Radiologists rated diagnostic image quality as excellent, adequate, marginally acceptable, or poor; the latter 2 were considered unacceptable for rendering diagnoses. We quantified the relationship between ratings and image noise and radiation dose, by category, by analyzing the odds of an acceptable rating per standard deviation (SD) increase in noise or geometric SD (gSD) in dose. One hundred twenty-five radiologists contributed 24,800 ratings. Most (89%) were acceptable. The odds of an examination being rated acceptable statistically significantly increased per gSD increase in dose and decreased per SD increase in noise for most categories, including routine dose head, chest, and abdomen-pelvis, which together comprise 60% of examinations performed in routine practice. For routine dose abdomen-pelvis, the most common category, each gSD increase in dose raised the odds of an acceptable rating (2.33; 95% confidence interval, 1.98-3.24), whereas each SD increase in noise decreased the odds (0.90; 0.79-0.99). For only 2 CT categories, high-dose head and neck/cervical spine, neither dose nor noise was associated with ratings. Radiation dose and image noise correlate with radiologists' image quality assessments for most CT categories, making them suitable as automated metrics in quality programs incentivizing reduction of excessive radiation doses.

Optimization of the "Perth CT" Protocol for Preoperative Planning and Postoperative Evaluation in Total Knee Arthroplasty.

Background and Objectives: Total knee arthroplasty (TKA) has become the treatment of choice for advanced osteoarthritis. The aim of this paper was to show the possibilities of optimizing the Perth CT protocol, which is highly effective for preoperative planning and postoperative assessment of alignment. Materials and Methods: The cross-sectional study comprised 16 patients for preoperative planning or postoperative evaluation of TKA. All patients were examined with the standard and optimized Perth CT protocol using advance techniques, including automatic exposure control (AEC), iterative image reconstruction (IR), as well as a single-energy projection-based metal artifact reduction algorithm for eliminating prosthesis artifacts. The effective radiation dose (E) was determined based on the dose report. Imaging quality is determined according to subjective and objective (values of signal to noise ratio (SdNR) and figure of merit (FOM)) criteria. Results: The effective radiation dose with the optimized protocol was significantly lower compared to the standard protocol (p < 0.001), while in patients with the knee prosthesis, E increased significantly less with the optimized protocol compared to the standard protocol. No significant difference was observed in the subjective evaluation of image quality between protocols (p > 0.05). Analyzing the objective criteria for image quality optimized protocols resulted in lower SdNR values and higher FOM values. No significant difference of image quality was determined using the SdNR and FOM as per the specified protocols and parts of extremities, and for the presence of prothesis. Conclusions: Retrospecting the ALARA ('As Low As Reasonably Achievable') principles, it is possible to optimize the Perth CT protocol by reducing the kV and mAs values and by changing the collimation and increasing the pitch factor. Advanced IR techniques were used in both protocols, and AEC was used in the optimized protocol. The effective dose of radiation can be reduced five times, and the image quality will be satisfactory.

Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model

PurposeComputed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative reconstruction (IR) methods. However, most comparative studies employ low-dose simulations due to ethical constraints. We used real intraindividual animal scans to investigate the dose-reduction capabilities of a DLD algorithm in comparison to IR. Materials and methodsFourteen veterinarian-sedated alive pigs underwent 2 CT scans on the same 3rd generation dual-source scanner with two months between each scan. Four additional scans ensued each time, with mAs reduced to 50 %, 25 %, 10 %, and 5 %. All scans were reconstructed ADMIRE levels 2 (IR2) and a novel DLD algorithm, resulting in 280 datasets. Objective image quality (CT numbers stability, noise, and contrast-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (−1 = inferior, 0 = equal, 1 = superior). The points were averaged for a semiquantitative score, and inter-rater agreement was measured using Spearman's correlation coefficient and adequately corrected mixed-effects modeling analyzed objective and subjective image quality. ResultsNeither dose-reduction nor reconstruction method negatively impacted CT number stability (p > 0.999). In objective image quality assessment, the lowest radiation dose achievable by DLD when comparing noise (p = 0.544) and CNR (p = 0.115) to 100 % IR2 was 25 %. Overall, inter-rater agreement of the subjective image quality ratings was strong (r ≥ 0.69, mean 0.93 ± 0.05, 95 % CI 0.92–0.94; each p < 0.001), and subjective assessments corroborated that DLD at 25 % radiation dose was comparable to 100 % IR2 in image quality, sharpness, and contrast (p ≥ 0.281). ConclusionsThe DLD algorithm can achieve image quality comparable to the standard IR method but with a significant dose reduction of up to 75%. This suggests a promising avenue for lowering patient radiation exposure without sacrificing diagnostic quality.

Application of dual-energy CT with prospective ECG-gating in cardiac CT angiography for children: Radiation and contrast agent dose

ObjectiveThis research aimed to investigate the feasibility of utilizing dual-energy CT virtual monoenergetic images (VMI11VMI:virtual monoenergetic images.) with prospective electrocardiogram (ECG22ECG:electrocardiogram.) gating for reducing radiation and contrast agent doses in pediatric patients with congenital heart disease (CHD33CHD:congenital heart disease.). MethodsThere were 100 pediatric patients with CHD included in this study. Group A (n = 50) underwent dual-energy scanning with prospective ECG-gating, and group B (n = 50) underwent conventional scanning with retrospective ECG-gating. Comparative analysis of CT values of lumen, objective image quality assessment, subjective image quality evaluations, and diagnostic efficacy were performed. ResultsCT values, image noise, signal-to-noise ratio (SNR44SNR:signal-to-noise ratio.), and contrast-to-noise ratio (CNR55CNR:contrast-to-noise ratio.) were significantly affected by the VMI energy level, and they all increased with decreasing energy levels (P > 0.05). Combining subjective evaluation, the 45 keV VMI was considered the optimum image in group A. The 45 keV VMI exhibited higher CT values of lumen compared to conventional scanning images (P < 0.003 ∼ 0.836), but meanwhile, the image noise was also higher in the 45 keV VMI (P = 0.004). Differences between the two groups in SNR, CNR, and diagnostic accuracy were not statistically significant. Compared to group B, the 45 keV VMI showed fewer contrast-induced artifacts (P < 0.001) and higher image quality score (P = 0.037). Group A had a 64 % reduction in radiation dose and a 40 % decrease in iodine dose compared to group B. ConclusionThe combination of dual-energy CT with prospective ECG-gating reduces radiation and iodine doses in pediatric patients with CHD. The 45 keV VMI can provide clinically acceptable image quality while declining contrast agent artifacts.

New Whole-Heart motion correction algorithm enables diagnostic CT of aortic valve and coronary arteries in systolic phase for transcatheter aortic valve implantation candidates

ObjectivesTo investigate the ability of new generation snapshot freeze (NGSSF) algorithm in improving diagnostic image quality of both aortic valve and coronary arteries for transcatheter aortic valve implantation (TAVI) candidates in TAVI planning CT. MethodsSixty-four TAVI candidates underwent TAVI planning CT were enrolled. Scans from coronary CT angiography were reconstructed at 20%, 30%, 40%, and 75% R-R cardiac phases with NGSSF and standard (STD) algorithm. In each phase, following parameters were compared: aortic valve measurements and their reproducibility; image quality of aortic valve and coronary arteries. The diagnostic accuracies of TAVI planning CT for coronary artery stenosis in 30% R-R phase with NGSSF and STD algorithms were calculated in 47out of 64 patients with invasive coronary angiography as reference standard. ResultsFor subjective image quality evaluation, the excellent rate for aortic valve improved from 25.0% to 93.8% and the interpretable rate for coronary arteries increased from 20.3% to 95.3% in the 30% phase images with NGSSF compared with images with STD. For the detection of > 50% coronary artery stenosis, the 30% phase images with NGSSF provided a sensitivity of 90%, specificity of 81.48%, negative predictive value of 91.7%, and positive predictive value of 78.3% on a per-patient basis; While images with STD, had a corresponding results of 95.0%, 33.33%, 90.0%, and 51.4%, respectively. ConclusionsNGSSF significantly improves image quality for both aortic valve and coronary arteries compared with STD for TAVI patients of all heart rates. NGSSF enables the accurate measurement for aortic valve and satisfactory diagnostic performance for coronary arteries stenosis in the same systolic phase for TAVI planning.

Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination.

In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain's gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head.

79‐2: Comparison of HDR MicroLED Display and High‐Resolution(8K) Impact on Image Quality

The clear impression of 4K MicroLED with higher HDR and 8K QLED and OLED were compared through a subjective image quality evaluation. Four variables; edge, contrast, lightness, and chroma was rendering for the HDR display to compare with high resolution(8K) displays. Display with HDR had the most impact and contrast and chroma were attributes that effected the clear impression. The measurement values were used to prove the phenomenon in which MicroLED, an HDR display, gives a clearer impression compared to other displays. HDR having high contrast can be seen more clearly than high resolution. Clear impression does not unconditionally represent the entire integrated image quality, but HDR plays an important role in enhancing image quality. This paper explains why we are seeing clearer images on a higher contrast display than on a higher resolution display.

P‐141: Late‐News Poster: Chroma Remapping Power Saving Technique for OLEDs

This paper proposes a novel image processing algorithm that maintains perceivable luminance as much as possible while it significantly reduces EL power consumption of OLED based upon a well‐known Helmholtz‐Kohl effect which is one of the representative theories based on human visual system's (HVS) characteristics of recognizing higher saturation images to be brighter. Proposed method effectively resolves various image artifacts incurred by the modification of ‘saturation’ value in each pixel, by carefully analyzing various image artifacts in each of problematic cases. Experimental results show that we have achieved a score of 4.18 based upon a subjective image quality evaluation methodology (Mean Opinion Score) and an average of 15.3% of EL power consumption savings were achieved in 20 evaluation images at the same time.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20mGy AV-40 images, the DECT 10mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

The impact of subjective image quality evaluation in mammography

IntroductionAccording to the guidelines, the mammograms obtained in a screening program must be evaluated to ensure that the quality of the images obtained is above 75% of score 1 (perfect and good) and less than 3% of score 3 (inadequate). This is performed by a person (usually a radiographer), so subjectivity may influence the final evaluation of the images. The aim of this study was to evaluate the impact of subjectivity on breast positioning assessments on resultant screening mammograms. Materials and methodsFive radiographers evaluated a total of 1000 mammograms. One radiographer was an expert in assessing mammography images, while the other four evaluators had varying levels of experience. All images were anonymized, and the ViewDEX software was used for visual grading analysis. The evaluators were divided into two groups, each with two evaluators. Each group evaluated 600 images, with 200 images identical between the two groups. All images had already been evaluated by the expert radiographer. All scores were compared using the Fleiss' and Cohen's kappa coefficient and accuracy score. ResultsThe results from Fleiss' kappa showed fair agreement in the mediolateral oblique (MLO) projection in the first group of evaluators whereas the other results showed poor agreement. When comparing the results from Cohen's kappa a maximum value of agreement between the evaluators was moderate 0.433 [95% CI 0.264–0.587] for the craniocaudal (CC) projection and 0.374 [95% CI 0.212–0.538] for the MLO projection. ConclusionsBased on our results, we can conclude that the agreement between all five raters was poor for both CC (κ = 0.165) and MLO (κ = 0.135) projections, based on the results of Fleiss' kappa statistic. The results show that the influence of subjectivity has a great impact on the evaluation of the quality of mammography images. Implications for practiceThus, the images are evaluated by a person, which has a high impact on subjectivity in the assessment of positioning in mammography. To achieve a more objective assessment of the images and the resulting agreement between the evaluators, we would propose to change the method of assessment. The images could be evaluated by two persons, and in the event of a discrepancy, the images would be evaluated by a third person. A computer programme could also be developed that would allow a more objective evaluation based on the geometric characteristics of the image (angle and length of the pectoral muscle, symmetry, etc.)

Pediatric low-dose head CT: Image quality improvement using iterative model reconstruction.

To evaluate the differences in pediatric non-contrast low-dose head computed tomography (CT) between filtered-back projection and iterative model reconstruction using objective and subjective image quality evaluation. A retrospective study evaluated children undergoing low-dose non-contrast head CT. All CT scans were reconstructed using both filtered-back projection and iterative model reconstruction. Objective image quality analysis was performed using contrast and signal-to-noise ratios for the supra- and infratentorial brain regions of identical regions of interest on the two reconstruction methods. Two experienced pediatric neuroradiologists evaluated subjective image quality, visibility of structures, and artifacts. We evaluated 233 low-dose brain CT scans of 148 pediatric patients. There was a ∼2-fold improvement in the contrast-to-noise ratio between gray and white matter in the infra- and supratentorial regions (p < 0.001) using iterative model reconstruction compared to filtered-back projection. The white and gray matter signal-to-noise ratio improved more than 2-fold using iterative model reconstruction (p < 0.001). Furthermore, radiologists graded anatomical details, gray-white matter differentiation, beam hardening artifacts, and image quality using iterative model reconstructions as superior to filtered-back projection reconstructions. Iterative model reconstructions had better contrast-to-noise and signal-to-noise ratios with fewer artifacts in pediatric CT brain scans using low-dose radiation protocols. This image quality improvement was demonstrated in the supra- and infratentorial regions. This method thus comprises an important tool for reducing children's exposure while maintaining diagnostic capability.

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography

PurposeTo perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V).Material and methodsFifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.ResultsDLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021).DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4–4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).ConclusionDLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.

Potential of Unenhanced Ultra-Low-Dose Abdominal Photon-Counting CT with Tin Filtration: A Cadaveric Study.

This study investigated the feasibility and image quality of ultra-low-dose unenhanced abdominal CT using photon-counting detector technology and tin prefiltration. Employing a first-generation photon-counting CT scanner, eight cadaveric specimens were examined both with tin prefiltration (Sn 100 kVp) and polychromatic (120 kVp) scan protocols matched for radiation dose at three different levels: standard-dose (3 mGy), low-dose (1 mGy) and ultra-low-dose (0.5 mGy). Image quality was evaluated quantitatively by means of contrast-to-noise-ratios (CNR) with regions of interest placed in the renal cortex and subcutaneous fat. Additionally, three independent radiologists performed subjective evaluation of image quality. The intraclass correlation coefficient was calculated as a measure of interrater reliability. Irrespective of scan mode, CNR in the renal cortex decreased with lower radiation dose. Despite similar mean energy of the applied x-ray spectrum, CNR was superior for Sn 100 kVp over 120 kVp at standard-dose (17.75 ± 3.51 vs. 14.13 ± 4.02), low-dose (13.99 ± 2.6 vs. 10.68 ± 2.17) and ultra-low-dose levels (8.88 ± 2.01 vs. 11.06 ± 1.74) (all p ≤ 0.05). Subjective image quality was highest for both standard-dose protocols (score 5; interquartile range 5-5). While no difference was ascertained between Sn 100 kVp and 120 kVp examinations at standard and low-dose levels, the subjective image quality of tin-filtered scans was superior to 120 kVp with ultra-low radiation dose (p < 0.05). An intraclass correlation coefficient of 0.844 (95% confidence interval 0.763-0.906; p < 0.001) indicated good interrater reliability. Photon-counting detector CT permits excellent image quality in unenhanced abdominal CT with very low radiation dose. Employment of tin prefiltration at 100 kVp instead of polychromatic imaging at 120 kVp increases the image quality even further in the ultra-low-dose range of 0.5 mGy.

A New Algorithm for Automatically Calculating Noise, Spatial Resolution, and Contrast Image Quality Metrics: Proof-of-Concept and Agreement With Subjective Scores in Phantom and Clinical Abdominal CT.

The aims of this study were to develop a proof-of-concept computer algorithm to automatically determine noise, spatial resolution, and contrast-related image quality (IQ) metrics in abdominal portal venous phase computed tomography (CT) imaging and to assess agreement between resulting objective IQ metrics and subjective radiologist IQ ratings. An algorithm was developed to calculate noise, spatial resolution, and contrast IQ parameters. The algorithm was subsequently used on 2 datasets of anthropomorphic phantom CT scans, acquired on 2 different scanners (n = 57 each), and on 1 dataset of patient abdominal CT scans (n = 510). These datasets include a range of high to low IQ: in the phantom dataset, this was achieved through varying scanner settings (tube voltage, tube current, reconstruction algorithm); in the patient dataset, lower IQ images were obtained by reconstructing 30 consecutive portal venous phase scans as if they had been acquired at lower mAs. Five noise, 1 spatial, and 13 contrast parameters were computed for the phantom datasets; for the patient dataset, 5 noise, 1 spatial, and 18 contrast parameters were computed. Subjective IQ rating was done using a 5-point Likert scale: 2 radiologists rated a single phantom dataset each, and another 2 radiologists rated the patient dataset in consensus. General agreement between IQ metrics and subjective IQ scores was assessed using Pearson correlation analysis. Likert scores were grouped into 2 categories, "insufficient" (scores 1-2) and "sufficient" (scores 3-5), and differences in computed IQ metrics between these categories were assessed using the Mann-Whitney U test. The algorithm was able to automatically calculate all IQ metrics for 100% of the included scans. Significant correlations with subjective radiologist ratings were found for 4 of 5 noise ( R2 range = 0.55-0.70), 1 of 1 spatial resolution ( R2 = 0.21 and 0.26), and 10 of 13 contrast ( R2 range = 0.11-0.73) parameters in the phantom datasets and for 4 of 5 noise ( R2 range = 0.019-0.096), 1 of 1 spatial resolution ( R2 = 0.11), and 16 of 18 contrast ( R2 range = 0.008-0.116) parameters in the patient dataset. Computed metrics that significantly differed between "insufficient" and "sufficient" categories were 4 of 5 noise, 1 of 1 spatial resolution, 9 and 10 of 13 contrast parameters for phantom the datasets and 3 of 5 noise, 1 of 1 spatial resolution, and 10 of 18 contrast parameters for the patient dataset. The developed algorithm was able to successfully calculate objective noise, spatial resolution, and contrast IQ metrics of both phantom and clinical abdominal CT scans. Furthermore, multiple calculated IQ metrics of all 3 categories were in agreement with subjective radiologist IQ ratings and significantly differed between "insufficient" and "sufficient" IQ scans. These results demonstrate the feasibility and potential of algorithm-determined objective IQ. Such an algorithm should be applicable to any scan and may help in optimization and quality control through automatic IQ assessment in daily clinical practice.

Optimisation of the CT pulmonary angiogram (CTPA) protocol using a low kV technique combined with high iterative reconstruction (IR): A phantom study

IntroductionThis study aims to optimise the current CTPA protocol at a public general hospital in Malta using lower kV combined with high Iterative Reconstruction (IR) (>50%). MethodsThe research consisted of a 2-phase anthropomorphic phantom study. Phase 1: radiation dose evaluation of 6 experimental protocols consisting of the low kV technique and high IR values and comparison with the current protocol. Phase 2: image evaluation. Objective image quality was evaluated in terms of contrast to noise ratio (CNR) and signal to noise ratio (SNR). Subjective image quality evaluation was performed by 3 radiologists undertaking Absolute Visual Grading Analysis (VGA). Resultant image quality scores were analysed using Visual Grading Characteristics (VGC). ResultsAll experimental protocols achieved significant (p < 0.05) dose reductions. SNR and CNR improved in almost all protocols, however, differences were not significant (p > 0.05). In subjective image quality analysis, the current protocol provided significant superior image quality (AUC > 0.5; p < 0.05) when compared to the experimental protocols consisting of 80 kV with 70%, 80%, 90% and 100% IR. The only two experimental protocols yielding comparable image quality to the current protocol were 80 kV with 50% IR (AUC: 0.195; p: 0.137) and 80 kV with 60% IR (AUC: 0.554; p: 0.624). The protocol yielding the greatest decrease in radiation dose being 80 kV with 60% IR. ConclusionsThe optimal IR value was 60%. When applying the optimal experimental protocol (80 kV combined with 60% IR), a significant dose reduction was achieved while maintaining diagnostic image quality. Implications for practiceThe low kV technique combined with high IR parameter is easily implemented and involves no additional cost and equipment.

Contrast-Enhanced Chest Computed Tomography (CT) Scan with Low Radiation and Total Iodine Dose for Lung Cancer Detection Using Adaptive Statistical Iterative Reconstruction

Background: Contrast-enhanced chest computed tomography (CT) is useful for the detection and follow-up of patients with lung cancer. However, reaching balance between diagnostic image quality, radiation dose, and iodixanol dose is a cause of concern. Objectives: To investigate the clinical value of adaptive statistical iterative reconstruction (ASIR) in reducing the iodixanol content and radiation dose during contrast-enhanced chest CT scan for patients diagnosed with lung masses/nodules based on the analysis of image quality. Patients and Methods: This prospective study was conducted on 80 patients diagnosed with nodules or masses, who required contrast-enhanced chest CT scans. The experimental group (n = 40) was subjected to iohexol at a high concentration (350 mgI/L) with a tube voltage of 120 kVp and a filter back projection (FBP) reconstruction algorithm. The comparison group (n = 40) was subject to iodixanol at a lower concentration (270 mgI/L) with a tube voltage of 100 kVp and ASIR (blending ratio, 40%). The radiation dose and total iodixanol content, as well as subjective and objective evaluations of image quality, were analyzed and compared. Results: The two groups obtained non-significantly different subjective scores for five structures detected in the lung window and five structures detected in the mediastinal window, as well as the overall image (P &gt; 0.05 for all). Both the two-group images obtained diagnosis-acceptable scores (≥ 3 points) on displays of 10 structures and overall image quality. The mean CT value of vessels (100 kVp vs. 120 kVp: 314.90 ± 23.42 vs. 308.93 ± 21.40; P &gt; 0.05), standard deviation (13.03 ± 0.88 vs. 12.83 ± 0.90; P &gt; 0.05), and contrast-to-noise ratio (20.77 ± 2.20 vs. 20.36 ± 1.94; P &gt; 0.05) were not significantly different between two groups. However, the CT dose index, dose-length product, effective dose, and total iodine dose were reduced by 27.58%, 36.65%, 36.59%, and 22.86% in the 100-kVp group compared to the 120-kVp group. Conclusion: The ASIR showed great potential in reducing the radiation dose and iodine contrast dose, while maintaining good image quality and providing strong confidence for the diagnosis of lung cancer.

Application of Different Levels of Advanced Modeling Iterative Reconstruction in Brain CT Scanning.

Advanced Modeling Iterative Reconstruction (ADMIRE) algorithm has five intensity levels; it is important to study which algorithm is better for brain CT scanning. The aim of the study is to compare the influence of different strength levels of ADMIRE and traditional Filtered Back Projection (FBP) on image quality in brain CT scanning. 60 patients were retrospectively selected, and the data from each of these patients' brains were reconstructed by four different reconstruction methods (FBP, ADMIRE1, ADMIRE3, and ADMIRE5). A five-point Likert Scale was implemented to evaluate the subjective image quality. Image noise, CT value of brain tissue , signal-to-noise ratio (SNR) of gray white matter, contrast-to-noise ratio (CNR), and beam hardening artifact index (AI) of the posterior fossa, were measured for evaluating the objective image quality. Finally, the differences between the subjective and objective evaluations were compared. There were no statistical differences observed in CT values of gray matter and white matter between the four groups (all P >0.05). The image noise gradually decreased with the increase of ADMIRE algorithm level. The AI exhibited no statistical difference between the four groups (F =0.793, P =0.499), but it tended to decrease slightly with the increase of ADMIRE algorithm level. Compared to other groups (all p <0.001), the ADMIRE5 group demonstrated the best objective image quality. Nevertheless, the highest subjective score was observed in the ADMIRE3 group, which exhibited significant differences with other images (all P <0.001). ADMIRE algorithm can clearly improve image quality, but it cannot significantly improve the linear sclerosis artifacts in the posterior cranial fossa. Based on the subjective evaluation of image quality, ADMIRE3 algorithm is recommended in brain CT scanning.

Self-calibrated dilated convolutional neural networks for SAR image despeckling

ABSTRACT Synthetic aperture radar (SAR) imaging process is essentially disturbed by speckle noise due to its unique mechanism. Speckle noise causes severe degradation of SAR image quality, which significantly limits its practical application. Recently, convolutional neural networks (CNNs) have indicated good potential for various image processing tasks. In this article, we propose a self-calibrated dilated convolutional neural network for SAR image despeckling called SAR-SCDCNN. The main body of SAR-SCDCNN is formed by seven self-calibrated blocks (SeCaBlock). Firstly, in each SeCaBlock, the input features are split into two branches: one represents the contextual features in the original space, and another represents those in the long-range space. Then, the down-up sampling operation and the convolutions with hybrid dilated rates are employed to enlarge the receptive field. The weights for calculating features themselves are adaptively extracted in the second branch. And then, two branch features are concatenated and fused through a convolution. Finally, a skip connection is used between the input and output of each SeCaBlock to give full play to the expressive power of the deep neural network and enhance training stability. Experiments on synthetic speckled and real SAR images are conducted to perform objective quantitative and subjective visual evaluations of image quality. Results show that our proposed method can effectively suppress speckle noise and adequately preserve detailed features.

QAPP: A quality-aware and privacy-preserving medical image release scheme

The direct release of medical image may face the dilemma: the privacy protection of medical images inevitably affects the visual quality of images. To balance medical image quality and privacy, this paper proposes a quality-aware and privacy-preserving medical image release scheme, QAPP, which effectively integrates the discrete cosine transform (DCT) with differential privacy (DP). Specifically, QAPP is composed of three phases. First, DCT is applied to each medical image to obtain its cosine coefficients matrix. Second, the original cosine coefficients matrix is compressed into k*k cosine coefficients matrix, which can retain the main features of each image. Third, the appropriate Laplace noise is injected into the formed k*k matrix to achieve differential privacy, and these noise-added coefficients are used to reconstruct the noise-added medical images through inverse DCT. Especially, considering there two error sources affecting the image quality in our work: the compression error caused by DCT, and the injected noise error caused by DP, Therefore, a selection function is proposed to determine the optimal compression dimension k, which can minimize the influence of these two errors to improve the visualization quality of the medical image. Subjective and objective image quality evaluation, and extensive experiments of image classification and segmentation using the real medical image dataset demonstrate that the proposed method QAPP can better balance medical image quality and privacy than other similar DP-based methods.

[Clinical Application of "Three-Low" Technique Combined with Artificial Intelligence Iterative Reconstruction Algorithm in Aortic CT Angiography].

To explore the application value of the "three-low" technique (low radiation dose, low contrast agent dosage and low contrast agent flow rate) combined with artificial intelligence iterative reconstruction (AIIR) in aortic CT angiography (CTA). A total of 33 patients who underwent aortic CTA were prospectively enrolled. Based on the time of their follow-up examinations, the imaging data were divided into Group A and Group B, with Group A being the control group (100 kV, 0.8 mL/kg, 5 mL/s) and Group B being the "three-low" technique group (70 kV, 0.5 mL/kg, 3 mL/s). In group A, the images were reconstructed by Karl iterative algorithm. Group B was divided into B1 and B2 subgroups, with their images being reconstructed by Karl iterative algorithm and AIIR, respectively. The CT and SD values of the ascending aorta, descending aorta, abdominal aorta, left common iliac artery and right common iliac artery were measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective scoring of image quality was performed. The radiation dose parameters were documented. Differences in the CT value, SD value, SNR and CNR of the three groups were statistically significant ( P<0.001). The CT value, SNR and CNR of group B2 were significantly higher than those of group B1, while the SD value of group B2 was significantly lower than that of group B1 ( P<0.017). There was no significant difference between the CT values of group A and those of group B2 ( P>0.017). The SD values, SNR and CNR in group B2 were better than those in group A ( P>0.017). There was significant difference in the subjective evaluation of image quality among the three groups ( P<0.05), but there was no significant difference between group A and group B2 ( P>0.017). The radiation dose and contrast medium dosage in group B decreased 84.14% and 37.08%, respectively, compared with those of group A. With the "three-low" technique combined with AIIR algorithm, the image quality of aortic CTA obtained is comparable to that of conventional dose scanning, while the radiation dose, contrast agent dosage and contrast agent flow rate of patients are significantly reduced.