CT Image Quality Research Articles

거동 불편한 환자의 전신 CT 검사 시 DLIR 기법의 유용성 평가

When whole-body computed tomography (WBCT) is performed after a single injection of contrast media, the arm's position is altered during each examination to minimize beam hardening artifacts caused by the arms. In this study, the image quality of neck CT images using the deep learning iterative reconstruction (DLIR) algorithm for patients whose arms were elevated during WBCT scans was compared with images reconstructed by FBP and IR algorithms in patients who were examined with their arms lowered. The Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) of the reconstructed images were quantitatively compared, while the image quality and artifacts were qualitatively evaluated. As a result, TF-H was evaluated higher in both SNR and CNR in all organs compared to FBP. Compared to the ASIR-V at 40%, the CNR was high, and the SNR was similar. In the qualitative evaluation, it was confirmed that the quality of the image was higher when compared to the image examined with the arm lowered, surpassing that of the existing reconstruction method. Therefore, it is believed that enhancing the quality of the neck CT image through reconstruction using deep learning after the examination with the arm raised, without altering the posture, during the WBCT examination of patients with mobility impairments.

A Hybrid Approach for CT Image Noise Reduction Combining Method Noise-CNN and Shearlet Transform

The presence of gaussian noise commonly weakens the diagnostic precision of low-dose CT imaging. A novel CT image denoising technique that integrates the non-subsampled shearlet transform (NSST) with Bayesian thresholding, and incorporates a modern method noise Deep Convolutional neural network (DCNN) based post-processing operation on denoised images to strengthen low-dose CT imaging quality. The hybrid method commences with NSST and Bayesian thresholding to mitigate the initial noise while preserving crucial image features, such as corners and edges. The novel aspect of the proposed approach is its successive application of a DnCNN on initial denoised image, which learns and removes residual noise patterns from denoised images, thereby enhancing fine detail preservation. This dual-phase strategy addresses both noise suppression and image-detail preservation. The proposed technique is evaluated through the use of metrics, such as PSNR, SNR, SSIM, ED, and UIQI. The results demonstrate that the hybrid approach outperforms standard denoising techniques in preserving image quality and fine details.

Improvement in Image Quality of Low-Dose CT of Canines with Generative Adversarial Network of Anti-Aliasing Generator and Multi-Scale Discriminator.

Computed tomography (CT) imaging is vital for diagnosing and monitoring diseases in both humans and animals, yet radiation exposure remains a significant concern, especially in animal imaging. Low-dose CT (LDCT) minimizes radiation exposure but often compromises image quality due to a reduced signal-to-noise ratio (SNR). Recent advancements in deep learning, particularly with CycleGAN, offer promising solutions for denoising LDCT images, though challenges in preserving anatomical detail and image sharpness persist. This study introduces a novel framework tailored for animal LDCT imaging, integrating deep learning techniques within the CycleGAN architecture. Key components include BlurPool for mitigating high-resolution image distortion, PixelShuffle for enhancing expressiveness, hierarchical feature synthesis (HFS) networks for feature retention, and spatial channel squeeze excitation (scSE) blocks for contrast reproduction. Additionally, a multi-scale discriminator enhances detail assessment, supporting effective adversarial learning. Rigorous experimentation on veterinary CT images demonstrates our framework's superiority over traditional denoising methods, achieving significant improvements in noise reduction, contrast enhancement, and anatomical structure preservation. Extensive evaluations show that our method achieves a precision of 0.93 and a recall of 0.94. This validates our approach's efficacy, highlighting its potential to enhance diagnostic accuracy in veterinary imaging. We confirm the scSE method's critical role in optimizing performance, and robustness to input variations underscores its practical utility.

Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography

Abstract Objective To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients. Materials and methods From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups. Results The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3–4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001). Conclusion DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients. Clinical relevance statement Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality. Key Points Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. “Double-low” coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients.

Radiation dose reduction and image quality in pediatric paranasal sinus CT: with automatic tube current modulation and iterative reconstruction technique.

Our objective is to evaluate radiation dose and image quality in pediatric paranasal sinus computed tomography (CT) with automatic tube current modulation (ATCM) and sinogram-affirmed iterative reconstruction algorithm (SAFIRE). CT scans from 80 patients were divided into two groups: Group A [80 kVp, pitch 1.5, 40 mAs, the filtered back projection (FBP) algorithm] and Group B (70 kVp, pitch 3, ATCM with reference at 40 mAs, SAFIRE strengths 1-5). We have evaluated image quality and radiation dose. Group B demonstrated significantly lower volume computed tomography dose index, dose-length product, and effective dose than Group A (0.13±0.03 vs. 1.57±0.01mGy, 2.27±0.82 vs. 19.88±2.01mGy·cm, and 0.0081±0.0017 vs. 0.079±0.013mSv, respectively; P<.001). Increasing SAFIRE strengths correlated with noise reduction and SNR enhancement. Group B's noise and SNRsoft at SAFIRE strength 5 were comparable with Group A. Images reconstructed with SAFIRE strength 5 in Group B exhibit comparable image quality with FBP in Group A.

Synthetic CT generation from CBCT based on structural constraint cycle-EEM-GAN

Objective. Cone beam CT (CBCT) typically has severe image artifacts and inaccurate HU values, which limits its application in radiation medicines. Scholars have proposed the use of cycle consistent generative adversarial network (Cycle-GAN) to address these issues. However, the generation quality of Cycle-GAN needs to be improved. This issue is exacerbated by the inherent size discrepancies between pelvic CT scans from different patients, as well as varying slice positions within the same patient, which introduce a scaling problem during training. Approach. We introduced the Enhanced Edge and Mask (EEM) approach in our structural constraint Cycle-EEM-GAN. This approach is designed to not only solve the scaling problem but also significantly improve the generation quality of the synthetic CT images. Then data from sixty pelvic patients were used to investigate the generation of synthetic CT (sCT) from CBCT. Main results. The mean absolute error (MAE), the root mean square error (RMSE), the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) are used to assess the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 53.09 to 37.74, RMSE from 185.22 to 146.63, SNU from 0.38 to 0.35, PSNR from 24.68 to 32.33, SSIM from 0.624 to 0.981. Also, the Cycle-EEM-GAN outperformed Cycle-GAN in terms of visual evaluation and loss. Significance. Cycle-EEM-GAN has improved the quality of CBCT images, making the structural details clear while prevents image scaling during the generation process, so that further promotes the application of CBCT in radiotherapy.

Enhancing Cone-Beam CT Image Quality in TIPSS Procedures Using AI Denoising.

Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts.

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

PurposeTo compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU). MethodsA retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility. ResultsDH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05). ConclusionThe DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

Quantitative and qualitative performance evaluation of commercial metal artifact reduction methods: Dosimetric effects on the treatment planning

The presence of metal implants within CT imaging causes severe attenuation of the X-ray beam. Due to the incomplete information recorded by CT detectors, artifacts in the form of streaks and dark bands would appear in the resulting CT images. The metal-induced artifacts would firstly affect the quantitative accuracy of CT imaging, and consequently, the radiation treatment planning and dose estimation in radiation therapy. To address this issue, CT scanner vendors have implemented metal artifact reduction (MAR) algorithms to avoid such artifacts and enhance the overall quality of CT images. The orthopedic-MAR (OMAR) and normalized MAR (NMAR) algorithms are the most well-known metal artifact reduction (MAR) algorithms, used worldwide. These algorithms have been implemented on Philips and Siemens scanners, respectively. In this study, we set out to quantitatively and qualitatively evaluate the effectiveness of these two MAR algorithms and their impact on accurate radiation treatment planning and CT-based dosimetry. The quantitative metrics measured on the simulated metal artifact dataset demonstrated superior performance of the OMAR technique over the NMAR one in metal artifact reduction. The analysis of radiation treatment planning using the OMAR and NMAR techniques in the corrected CT images showed that the OMAR technique reduced the toxicity of healthy tissues by 10% compared to the uncorrected CT images.

"Ultralow-dose" CT Without Sedation in Pediatric Patients With Neuromuscular Scoliosis.

Children with neuromuscular scoliosis undergoing scoliosis surgery face substantial rates of complications. To mitigate surgical risks such as blood loss in pediatric patients with neuromuscular scoliosis, this study focuses on enabling instrumentation planning for their abnormal vertebral and pelvic anatomy and osteopenia. This study assessed the feasibility of an "ultralow-dose" CT (ULD CT) protocol without sedation in pediatric patients with neuromuscular scoliosis who often have comorbid movement disorders. Our prospective quality improvement study aims: (1) to determine if ULD CT without sedation is feasible in this patient group; (2) to quantify the radiation dose from ULD CT and compare it with preoperative spine radiographs (XR); and (3) to assess if ULD CT allows accurate anatomical assessment and intraoperative navigation given the prevalence of movement disorders. Children with neuromuscular scoliosis underwent spine XR and ULD CT scans. Chart reviews assessed disease etiology and comorbidities. Radiation dose was quantified through Monte-Carlo simulations giving dose indices and effective dose, with statistical analysis done using a paired student's t-test (α=0.05). CT image quality was assessed for its use in preoperative planning and intraoperative navigation. Fourteen patients (5 males, 9 females, average age 14±3y) participated. One patient needed sedation due to autism spectrum disorder and global developmental delay. The radiation dose for spine XR was 0.5±0.2mSv, and ULD CT was 0.6±0.1mSv. There was no statistically significant difference in radiation doses between methods. All ULD CT scans had adequate quality for preoperative assessment of pedicle diameter and orientation, obstacles impeding pedicle entry, S2 Alar-Iliac screw orientation, and intraoperative navigation. ULD CT without sedation is feasible for children with neuromuscular scoliosis. Radiation doses were comparable to standard radiographs. ULD CT provided accurate anatomical assessments and supported intraoperative navigation, proving beneficial despite movement disorders in these patients. Level 2-Development of diagnostic criteria on basis of consecutive patients (with universally applied reference widely accepted standard).

Adaptive edge prior-based deep attention residual network for low-dose CT image denoising

Improving the diagnostic quality of low-dose CT (LDCT) images relies on effective noise removal. Recent advancements have highlighted the widespread use of deep residual networks for LDCT image denoising. These networks possess properties that aid in preserving image integrity and optimizing model performance. However, the denoising process faces challenges due to the complex patterns and intensity similarities between edge details and lesion regions. To address this issue, this paper introduces a novel approach called the cross-scale attentional residual network (RCANet), which utilizes an adaptive edge prior for LDCT image denoising. The adaptive edge prior enhances the denoising network’s ability to retain image boundary features and uniqueness. To distinguish subtle differences between LDCT image edge details and lesion areas, a cross-scale mapping dual-element module (CMDM) is designed to preserve rich edge texture information during model training. To prevent over-smoothing of denoised results, a compound loss function is proposed, combining MSE loss and multi-scale attention residual perception loss. To validate the effectiveness of the method, experiments were conducted on the AAPM-Mayo Clinic LDCT Grand Challenge dataset. The results demonstrate that RCANet surpasses state-of-the-art residual structure-based network models and performs comparably to leading denoising algorithms.

Learning Scatter Artifact Correction in Cone-Beam X-Ray CT Using Incomplete Projections with Beam Hole Array

X-ray cone-beam computed tomography (CBCT) is a powerful tool for nondestructive testing and evaluation, yet the CT image quality can be compromised by artifact due to X-ray scattering within dense materials such as metals. This problem leads to the need for hardware- and software-based scatter artifact correction to enhance the image quality. Recently, deep learning techniques have merged as a promising approach to obtain scatter-free images efficiently. However, these deep learning techniques rely heavily on training data, often gathered through simulation. Simulated CT images, unfortunately, do not accurately reproduce the real properties of objects, and physically accurate X-ray simulation still requires significant computation time, hindering the collection of a large number of CT images. To address these problems, we propose a deep learning framework for scatter artifact correction using projections obtained solely by real CT scanning. To this end, we utilize a beam-hole array (BHA) to block the X-rays deviating from the primary beam path, thereby capturing scatter-free X-ray intensity at certain detector pixels. As the BHA shadows a large portion of detector pixels, we incorporate several regularization losses to enhance the training process. Furthermore, we introduce radiographic data augmentation to mitigate the need for long scanning time, which is a concern as CT devices equipped with BHA require two series of CT scans. Experimental validation showed that the proposed framework outperforms a baseline method that learns simulated projections where the entire image is visible and does not contain scattering artifacts.

Deep learning-based pseudo-CT synthesis from zero echo time MR sequences of the pelvis

ObjectivesTo generate pseudo-CT (pCT) images of the pelvis from zero echo time (ZTE) MR sequences and compare them to conventional CT.MethodsNinety-one patients were prospectively scanned with CT and MRI including ZTE sequences of the pelvis. Eleven ZTE image volumes were excluded due to implants and severe B1 field inhomogeneity. Out of the 80 data sets, 60 were used to train and update a deep learning (DL) model for pCT image synthesis from ZTE sequences while the remaining 20 cases were selected as an evaluation cohort. CT and pCT images were assessed qualitatively and quantitatively by two readers.ResultsMean pCT ratings of qualitative parameters were good to perfect (2–3 on a 4-point scale). Overall intermodality agreement between CT and pCT was good (ICC = 0.88 (95% CI: 0.85–0.90); p < 0.001) with excellent interreader agreements for pCT (ICC = 0.91 (95% CI: 0.88–0.93); p < 0.001). Most geometrical measurements did not show any significant difference between CT and pCT measurements (p > 0.05) with the exception of transverse pelvic diameter measurements and lateral center-edge angle measurements (p = 0.001 and p = 0.002, respectively). Image quality and tissue differentiation in CT and pCT were similar without significant differences between CT and pCT CNRs (all p > 0.05).ConclusionsUsing a DL-based algorithm, it is possible to synthesize pCT images of the pelvis from ZTE sequences. The pCT images showed high bone depiction quality and accurate geometrical measurements compared to conventional CT.Critical relevance statementpCT images generated from MR sequences allow for high accuracy in evaluating bone without the need for radiation exposure. Radiological applications are broad and include assessment of inflammatory and degenerative bone disease or preoperative planning studies.Key PointspCT, based on DL-reconstructed ZTE MR images, may be comparable with true CT images.Overall, the intermodality agreement between CT and pCT was good with excellent interreader agreements for pCT.Geometrical measurements and tissue differentiation were similar in CT and pCT images.Graphical

Feasibility of a single-phase portal venous CT protocol using bolus tracking technique and lean body weight-based contrast media dose.

To evaluate the impact of the use of lean body weight (LBW)-based contrast material (CM) dose and bolus tracking technique on portal venous phase abdominal CT image quality. IRB-approved prospective study; informed consent was acquired. In the period July-November 2023, we randomly selected 105 oncologic patients scheduled for a portal venous phase abdominal CT to undergo our experimental protocol (i.e., 0.7 gI/Kg of LBW CM administration and bolus tracking on the liver). Included patients had performed a "standard" portal venous phase abdominal CT (i.e., 0.6 gI/Kg of total body weight (TBW) contrast material administration and 70 s fixed delay) on the same scanner within the previous 12 months. One reader evaluated CT images measuring liver, portal vein, kidney cortex, and spleen attenuation; values were normalized to paraspinal muscles. Median administered contrast dose (350 mgI/mL CM) was 99 mL (IQR: 81-115 mL) using the experimental protocol and 110 mL (IQR: 100-120 mL) using the standard one (p < 0.0001). Median acquisition delay using the experimental protocol was 65" (IQR 59-73"). Median normalized hepatic enhancement was significantly higher using the experimental protocol (1.97, IQR: 1.83-2.47 vs. 1.86, IQR: 1.58-2.11; p < 0.0001). Median normalized portal vein enhancement was significantly higher using the experimental protocol (3.43, IQR: 2.73-4.04 vs. 2.91, IQR: 2.58-3.41; p < 0.0001). No statistically significant differences were found in the kidneys' cortex and aorta normalized enhancement (p > 0.05). The combination of LBW-based CM dose administration and bolus tracking allows a significant CM dose reduction and a significant liver and portal vein enhancement increase. Lean body weight-based contrast material (CM) dose administration and bolus tracking technique in portal venous phase CT scans overcome differences in body composition and hemodynamics, improving reproducibility. It allows a significant CM dose reduction with increased liver and portal vein enhancement. Lean body weight (LBW)-based contrast material (CM) dosing could be superior to total body weight dosing. Portal venous phase CT with a liver bolus tracking technique improved liver and spleen enhancement with a reduced contrast dose. The combination of LBW-based CM dosing and liver bolus tracking technique enables more "customized" CT examinations.

IQAGPT: computed tomography image quality assessment with vision-language and ChatGPT models

Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various tasks and attracted increasing interest as a natural language interface across many domains. Recently, large vision-language models (VLMs) that learn rich vision–language correlation from image–text pairs, like BLIP-2 and GPT-4, have been intensively investigated. However, despite these developments, the application of LLMs and VLMs in image quality assessment (IQA), particularly in medical imaging, remains unexplored. This is valuable for objective performance evaluation and potential supplement or even replacement of radiologists’ opinions. To this end, this study introduces IQAGPT, an innovative computed tomography (CT) IQA system that integrates image-quality captioning VLM with ChatGPT to generate quality scores and textual reports. First, a CT-IQA dataset comprising 1,000 CT slices with diverse quality levels is professionally annotated and compiled for training and evaluation. To better leverage the capabilities of LLMs, the annotated quality scores are converted into semantically rich text descriptions using a prompt template. Second, the image-quality captioning VLM is fine-tuned on the CT-IQA dataset to generate quality descriptions. The captioning model fuses image and text features through cross-modal attention. Third, based on the quality descriptions, users verbally request ChatGPT to rate image-quality scores or produce radiological quality reports. Results demonstrate the feasibility of assessing image quality using LLMs. The proposed IQAGPT outperformed GPT-4 and CLIP-IQA, as well as multitask classification and regression models that solely rely on images.

Impact on Image Quality and Diagnostic Performance of Dual-Layer Detector Spectral CT for Pulmonary Subsolid Nodules: Comparison With Hybrid and Model-Based Iterative Reconstruction.

To evaluate the image quality and diagnostic performance of pulmonary subsolid nodules on conventional iterative algorithms, virtual monoenergetic images (VMIs), and electron density mapping (EDM) using a dual-layer detector spectral CT (DLSCT). This retrospective study recruited 270 patients who underwent DLSCT scan for lung nodule screening or follow-up. All CT examinations with subsolid nodules (pure ground-glass nodules [GGNs] or part-solid nodules) were reconstructed with hybrid and model-based iterative reconstruction, VMI at 40, 70, 100, and 130 keV levels, and EDM. The CT number, objective image noise, signal-to-noise ratio, contrast-to-noise ratio, diameter, and volume of subsolid nodules were measured for quantitative analysis. The overall image quality, image noise, visualization of nodules, artifact, and sharpness were subjectively rated by 2 thoracic radiologists on a 5-point scale (1 = unacceptable, 5 = excellent) in consensus. The objective image quality measurements, diameter, and volume were compared among the 7 groups with a repeated 1-way analysis of variance. The subjective scores were compared with Kruskal-Wallis test. A total of 198 subsolid nodules, including 179 pure GGNs, and 19 part-solid nodules were identified. Based on the objective analysis, EDM had the highest signal-to-noise ratio (164.71 ± 133.60; P < 0.001) and contrast-to-noise ratio (227.97 ± 161.96; P < 0.001) among all image sets. Furthermore, EDM had a superior mean subjective rating score (4.80 ± 0.42) for visualization of GGNs compared to other reconstructed images (all P < 0.001), although the model-based iterative reconstruction had superior subjective scores of overall image quality. For pure GGNs, the measured diameter and volume did not significantly differ among different reconstructions (both P > 0.05). EDM derived from DLSCT enabled improved image quality and lesion conspicuity for the evaluation of lung subsolid nodules compared to conventional iterative reconstruction algorithms and VMIs.

Stroke Evaluation in the Interventional Suite Using Dual-Layer Detector Cone-Beam CT: aFirst-in-human Prospective Cohort Study (the Next GenerationX-ray Imaging System Trial).

Cone-beam CT in the interventional suite could be an alternative to CT to shorten door-to-thrombectomy time. However, image quality in cone-beam CT is limited by artifacts and poor differentiation between gray and white matter. This study compared non-contrast brain dual-layer cone-beam CT in the interventional suite to reference standard CT in stroke patients. Aprospective single-center study enrolled consecutive participants with ischemic or hemorrhagic stroke. The hemorrhage detection accuracy, per-region ASPECTS accuracy and subjective image quality (Likert scales for gray-white matter differentiation, structure perception and artifacts) were assessed by three neuroradiologists blinded to clinical data on dual-layer cone-beam CT 75 keV monoenergetic images compared to CT. Objective image quality was assessed by region-of-interest metrics. Non-inferiority for hemorrhage detection and ASPECTS accuracy was determined by the exact binomial test with aone-sided lower performance boundary prospectively set to 80% (98.75% CI). 27participants were included (74years ± 9; 19female) in the hyperacute or acute stroke phase. One reader missed asmall bleeding, but all hemorrhages were detected in the majority analysis (100% accuracy, CI lower boundary 86%, p = 0.002). ASPECTS majority analysis showed 90% accuracy (CI lower boundary 85%, p < 0.001). Sensitivity was 66% (individual readers 67%, 69%, and 76%), specificity was 97% (97%, 96%, 89%). Subjective and objective image quality were inferior to CT. In asmall single-center cohort, dual-layer cone-beam CT showed non-inferior hemorrhage detection and ASPECTS accuracy to CT. Despite inferior image quality, the technique may be useful for stroke evaluation in the interventional suite. NCT04571099 (clinicaltrials.gov). Prospectively registered 2020-09-04.

Image quality and radiation doses in abdominal CT: A multicenter study

PurposeTo benchmark image quality and corresponding radiation doses for acute abdominal CT examination across different laboratories and CT manufacturers. MethodAn anthropomorphic phantom was scanned once with local abdominal CT protocols at 40 CT scanners, from four vendors, in thirty-three sites. Quantitative image quality was evaluated by CNR and SNR in the liver and kidney parenchyma. Qualitative image quality was assessed by visual grading analysis performed by three experienced radiologists using a five-point Likert scale to score thirteen image quality criteria. The CTDIvol was recorded for each scan. Pearson’s correlation coefficient was calculated for the continuous variables, and the intraclass correlation coefficient was used to investigate interrater reliability between the radiologists. ResultsCTDIvol ranged from 3.5 to 12 mGy (median 5.3 mGy, third quartile 6.7 mGy). SNR in liver parenchyma ranged from 4.4 to 14.4 (median 8.5), and CNR ranged from 2.7 to 11.2 (median 6.1). A weak correlation was found between CTDIvol and CNR (r = 0.270, p = 0.092). Variations in CNR across scanners at the same dose level CTDIvol were observed. No significant difference in CTDIvol or CNR was found based on scanner installation year. The oldest scanners had a 15 % higher median CTDIvol and a 12 % lower median CNR. The ICC showed acceptable agreement for all dose groups: low (ICC=0.889), medium (ICC=0.767), high (ICC=0.847), and in low (ICC=0.803) and medium (ICC=0.811) CNR groups. ConclusionThere was large variation in radiation dose and image quality across the different CT scanners. Interestingly, the weak correlation between CTDIvol and CNR indicates that higher doses do not consistently improve CNR, indicating a need for systematic assessment and optimization of image quality and radiation doses for the abdominal CT examination.

Application of low-dose CT in image-guided radiotherapy based on CT-linac

BackgroundImage-guided radiation therapy (IGRT) involves online medical imaging systems. CT-guided imaging systems not only have high image quality but also allow for direct dose calculation for radiation treatment planning, making it an ideal image guidance method. However, the imaging dose from CT images may pose additional risks to patients. Therefore, the aim of this study was to evaluate the substitutability of using low-dose CT images for IGRT. Materials and methodsThe CIRS-062 phantom, Catphan 604 phantom, CT dose index phantom were used to obtain the conversion curves of the CT values to the relative electron density (RED), image quality, and imaging dose of the low-dose CT, respectively. Catphan 604 phantom and clinical cases were used to analyze the feasibility of low-dose CT for dose calculation. ResultsNo significant differences were observed in the conversion curves of CT values to RED between low-dose and standard-dose CT scans. In terms of image quality, the geometric distortion of low-dose CT was 0.1 mm, which was the same as that of standard-dose CT. The spatial resolution of the images was 0.42–0.45 lp/mm, which was slightly better than that of standard-dose CT (0.40–0.44 lp/mm). The image uniformity was 98.4%–99.0%, slightly worse than that of standard-dose CT (99.4%–99.5%), but still met the clinical requirements. The imaging dose was 2.96–4.62 mGy, significantly lower than that of standard-dose CT (9.71–26.68 mGy), especially for head CT protocol. In terms of dose calculation, the γ passing rates were not lower than 98% for either the Catphan 604 phantom or the clinical cases. ConclusionLow-dose CT can significantly reduce the imaging dose without a significant loss of image quality and can be directly used for dose calculation in radiation treatment planning, providing a safer and more effective image guidance method for IGRT.

Optimization of Fast Non-Local Means Noise Reduction Algorithm Parameter in Computed Tomographic Phantom Images Using 3D Printing Technology.

Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10-2 mm2 on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.