CT Image Quality Research Articles

Sparse-view image reconstruction with total-variation minimization applied to sparsely sampled projection data from SiPM-based photon-counting CT

We constructed a sparse-view computed tomography (CT) system that combines a compressed sensing (CS)-based image-reconstruction algorithm and SiPM-based photon-counting (PC) CT. CS-based image-reconstruction algorithms have been extensively studied for X-ray CT image reconstruction using fewer projections because they are expected to reduce CT imaging time and radiation exposure while maintaining CT image quality. In most previous studies, CS-based image-reconstruction algorithms have been applied to data obtained through numerical simulations or conventional dual-energy CT. However, studies on PC-CT have been scarce. Therefore, we applied a CS-based image-reconstruction algorithm to the projection data obtained using our previously established SiPM-based PC-CT system and evaluated its image quality. We prepared static phantoms equivalent to iodine-containing contrast agents and a mouse model injected with iodine-containing contrast agents as subjects. Thereafter, CT scanning was performed. The obtained projection data were downsampled to simulate a sparse-view situation, and a CS-based image-reconstruction algorithm with total-variation minimization was applied. Consequently, sparse-view CT images were successfully reconstructed, and the image quality was maintained even after downsampling the projection data (downsampling ratios of 1/10 and 1/2 for the rod phantom and mouse model, respectively). Thus, the imaging time and exposure dose could be remarkably reduced (by a factor of 10 or 2), indicating that the CS-based image-reconstruction algorithm is effective for PC-CT.

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.

PAE planning: Radiation exposure and image quality of CT and CBCT

PurposeTo determine accurate organ doses, effective doses, and image quality of computed tomography (CT) compared with cone beam CT (CBCT) for correct identification of prostatic arteries. MethodA dual-energy CT scanner and a flat-panel angiography system were used. Dose measurements (gallbladder (g), intestine (i), bladder (b), prostate (p), testes (t), active bone marrow of pelvis (bmp) and femura (bmf)) were performed using an anthropomorphic phantom with 65 thermoluminescent dosimeters in the pelvis and abdomen region. For the calculation of the contrast-to-noise ratio (CNR) of the pelvic arteries, a patient whose weight and height were almost identical to those of the phantom was selected for each examination type. ResultsThe effective dose of CT was 2.7 mSv and that of CBCT was 21.8 mSv. Phantom organ doses were lower for CT than for CBCT in all organs except the testes (g: 1.2 mGy vs. 3.3 mGy, i: 5.8 mGy vs. 23.9 mGy, b: 6.9 mGy vs. 19.4 mGy, p: 6.4 mGy vs. 13.2 mGy, t: 4.7 mGy vs. 2.4 mGy, bmp: 5.1 mGy vs. 18.2 mGy, bmf: 3.3 mGy vs. 6.6 mGy). For human pelvic arteries, the CNR of CT was better than that of CBCT, with the exception of one prostate artery that showed stenosis on CT. Evaluation by experienced radiologists also confirmed the better detectability of prostate arteries on CT examination. ConclusionsIn our study preprocedural CT had lower organ doses and better image quality comparedd with CBCT and should be considered for the correct identification of prostatic arteries.

Evaluating AI-generated CBCT-based synthetic CT images for target delineation in palliative treatments of pelvic bone metastasis at conventional C-arm linacs

PurposeOne-table treatments with treatment imaging, preparation and delivery occurring at one treatment couch, could increase patients’ comfort and throughput for palliative treatments. On regular C-arm linacs, however, cone-beam CT (CBCT) imaging quality is currently insufficient. Therefore, our goal was to assess the suitability of AI-generated CBCT based synthetic CT (sCT) images for target delineation and treatment planning for palliative radiotherapy. Materials and methodsCBCTs and planning CT-scans of 22 female patients with pelvic bone metastasis were included. For each CBCT, a corresponding sCT image was generated by a deep learning model in ADMIRE 3.38.0. Radiation oncologists delineated 23 target volumes (TV) on the sCTs (TVsCT) and scored their delineation confidence. The delineations were transferred to planning CTs and manually adjusted if needed to yield gold standard target volumes (TVclin). TVsCT were geometrically compared to TVclin using Dice coefficient (DC) and Hausdorff Distance (HD). The dosimetric impact of TVsCT inaccuracies was evaluated for VMAT plans with different PTV margins. ResultsRadiation oncologists scored the sCT quality as sufficient for 13/23 TVsCT (median: DC = 0.9, HD = 11 mm) and insufficient for 10/23 TVsCT (median: DC = 0.7, HD = 34 mm). For the sufficient category, remaining inaccuracies could be compensated by +1 to +4 mm additional margin to achieve coverage of V95% > 95% and V95% > 98%, respectively in 12/13 TVsCT. ConclusionThe evaluated sCT quality allowed for accurate delineation for most targets. sCTs with insufficient quality could be identified accurately upfront. A moderate PTV margin expansion could address remaining delineation inaccuracies. Therefore, these findings support further exploration of CBCT based one-table treatments on C-arm linacs.

Effect of X-ray tube on image quality and pancreatic ductal adenocarcinoma conspicuity in pancreatic protocol dual-energy CT

To compare the radiation dose, image quality, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic protocol dual-energy computed tomography (CT) between two X-ray tubes mounted in the same CT machine. This retrospective study comprised 80 patients (median age, 73 years; 45 men) who underwent pancreatic protocol dual-energy CT from January 2019 to March 2022 using either old (Group A, n=41) or new (Group B, n=39) X-ray tubes mounted in the same CT machine. The imaging parameters were completely matched between the two groups, and CT data were reconstructed at 70 and 40 keV. The CT dose-index volume (CTDIvol); CT attenuation of the abdominal aorta, pancreas, and PDAC; background noise; and qualitative scores for the image noise, overall image quality, and PDAC conspicuity were compared between the two groups. The CTDIvol was lower in Group B than Group A (7.9 versus 9.2 mGy; p<0.001). The CT attenuation of all anatomical structures at 70 and 40 keV was comparable between the two groups (p=0.06-0.78). The background noise was lower in Group B than Group A (12 versus 14 HU at 70 keV, p=0.046; and 26 versus 30 HU at 40 keV, p<0.001). Qualitative scores for image noise and overall image quality at 70 and 40 keV and PDAC conspicuity at 40 keV were higher in Group B than Group A (p<0.001-0.045). The latest X-ray tube could reduce the radiation dose and improve image quality in pancreatic protocol dual-energy CT.

An Evaluation Analysis for Computed Tomography Image Quality of Primary Liver Cancer Lesions Based on Deep Learning Image Reconstruction.

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically. The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V). We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated. High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant. The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

Development of a Mind Map-based Predictive Nursing Protocol and its Impact on the Clarity of Images in Patients Undergoing High-concentration Contrast Three-dimensional Computed Tomography Imaging of Liver Blood Vessels.

To explore the development of a mind map-based predictive nursing protocol and assess its impact on the quality of images in patients undergoing high-concentration contrast three-dimensional computed tomography (CT) imaging of liver blood vessels. A total of 600 patients who were admitted to Beijing You an Hospital were chosen for this prospective study and underwent high-concentration contrast three-dimensional CT imaging of liver blood vessels between April 2021 and December 2021. The patients were divided into two groups using the digital table method, with 300 cases. The control group received conventional nursing intervention, while the research group was provided with a mind map-based predictive nursing protocol. We recorded the image quality of three-dimensional CT imaging of liver blood vessels, satisfaction scores regarding nurse examination guidance, and the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) in both groups. The research group achieved a perfect rate of 100.00% for the high-quality three-dimensional CT imaging of liver blood vessels, which was noticeably higher compared to the rate of the control group of 98.67%. Patients in the research group expressed higher satisfaction levels regarding the guidance provided by nurses, including their attitude, timeliness, accuracy, and overall satisfaction, compared to the control group. Initially, the two groups had no notable differences in the SAS and SDS scores. However, after the intervention, both groups experienced a significant decrease in SAS and SDS scores, with the research group showing an even more substantial decline. Through the creation of a mind map-based predictive nursing protocol and its implementation on patients undergoing high-concentration contrast three-dimensional CT imaging of liver blood vessels, it is possible to significantly enhance the quality of CT scans, alleviate feelings of anxiety and depression, increase patient satisfaction with examination guidance by nurses, and effectively decrease the occurrences of contrast agent leakage and allergic reactions to iodine.

How subjective CT image quality assessment becomes surprisingly reliable: pairwise comparisons instead of Likert scale

ObjectivesThe aim of this study is to improve the reliability of subjective IQ assessment using a pairwise comparison (PC) method instead of a Likert scale method in abdominal CT scans.MethodsAbdominal CT scans (single-center) were retrospectively selected between September 2019 and February 2020 in a prior study. Sample variance in IQ was obtained by adding artificial noise using dedicated reconstruction software, including reconstructions with filtered backprojection and varying iterative reconstruction strengths. Two datasets (each n = 50) were composed with either higher or lower IQ variation with the 25 original scans being part of both datasets. Using in-house developed software, six observers (five radiologists, one resident) rated both datasets via both the PC method (forcing observers to choose preferred scans out of pairs of scans resulting in a ranking) and a 5-point Likert scale. The PC method was optimized using a sorting algorithm to minimize necessary comparisons. The inter- and intraobserver agreements were assessed for both methods with the intraclass correlation coefficient (ICC).ResultsTwenty-five patients (mean age 61 years ± 15.5; 56% men) were evaluated. The ICC for interobserver agreement for the high-variation dataset increased from 0.665 (95%CI 0.396–0.814) to 0.785 (95%CI 0.676–0.867) when the PC method was used instead of a Likert scale. For the low-variation dataset, the ICC increased from 0.276 (95%CI 0.034–0.500) to 0.562 (95%CI 0.337–0.729). Intraobserver agreement increased for four out of six observers.ConclusionThe PC method is more reliable for subjective IQ assessment indicated by improved inter- and intraobserver agreement.Clinical relevance statementThis study shows that the pairwise comparison method is a more reliable method for subjective image quality assessment. Improved reliability is of key importance for optimization studies, validation of automatic image quality assessment algorithms, and training of AI algorithms.Key Points• Subjective assessment of diagnostic image quality via Likert scale has limited reliability.• A pairwise comparison method improves the inter- and intraobserver agreement.• The pairwise comparison method is more reliable for CT optimization studies.Graphical

Quantitative parameters of dual-layer spectral detector computed tomography for evaluating Ki-67 and human epidermal growth factor receptor 2 expression in colorectal adenocarcinoma.

Ki-67 and human epidermal growth factor receptor 2 (HER2) are key biomarkers in evaluating the prognosis of colorectal adenocarcinoma (CRAC). The purpose of this study was to investigate the value of quantitative parameters in dual-layer spectral detector computed tomography (SDCT) for evaluating the expression of Ki-67 and HER2 in CRAC. In this retrospective, cross-sectional study, 88 eligible patients with pathologically confirmed CRAC were selected from Taicang Hospital of Traditional Chinese Medicine between May 2021 and April 2023. The study participants underwent enhanced SDCT of the whole abdomen within 2 weeks before to surgery, did not receive antitumor therapy, and had complete immunohistochemical (IHC) indexes. Patients with nonadenocarcinoma pathologic types, poor quality of spectral CT images, or no complete immunohistochemistry results were excluded. Spectral parameters including CT values at 40 and 100 keV, effective atomic number, iodine concentration (IC), the slope of the spectral Hounsfield unit (HU) curve (λHU), and normalized iodine concentration (NIC) in the arterial phase (AP) and venous phase (VP) were analyzed for their value in distinguishing between the high and low expression of Ki-67 and HER2-positive and -negative status in CRAC. The statistical significance of the SDCT parameters between the different groups of Ki-67 expression and those of HER2 status was assessed with the Mann-Whitney test. Spearman correlation analysis was used to analyze the correlation between the SDCT parameters and the extent of Ki-67 expression and HER2 expression status. The receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated. The SDCT parameters of CT values at 40 keV, effective atomic number, IC, and the λHU in the VP showed significant differences between the Ki-67 high- and low-expression groups in CRAC (P=0.035, P=0.041, P=0.036, and P=0.044, respectively), with AUCs of 0.639 [95% confidence interval (CI): 0.512-0.766], 0.634 (95% CI: 0.508-0.761), 0.638 (95% CI: 0.510-0.766), and 0.633 (95% CI: 0.504-0.762), respectively. The expression of CRAC Ki-67 was positively correlated with CT values at 40 keV (r=0.227; P=0.034), effective atomic number (r=0.219; P=0.040), IC (r=0.225; P=0.035), and the λHU in VP (r=0.216; P=0.043). SDCT parameter values showed no statistical difference between negative and positive expression in HER2 (all P values >0.05). There was no significant correlation between SDCT parameters and the expression of HER2 in CRAC (all P values >0.05). The quantitative parameters of SDCT in the VP provide valuable information for distinguishing between the low expression and high expression of Ki-67 in CRAC.

Hypernetwork-Based Physics-Driven Personalized Federated Learning for CT Imaging.

In clinical practice, computed tomography (CT) is an important noninvasive inspection technology to provide patients' anatomical information. However, its potential radiation risk is an unavoidable problem that raises people's concerns. Recently, deep learning (DL)-based methods have achieved promising results in CT reconstruction, but these methods usually require the centralized collection of large amounts of data for training from specific scanning protocols, which leads to serious domain shift and privacy concerns. To relieve these problems, in this article, we propose a hypernetwork-based physics-driven personalized federated learning method (HyperFed) for CT imaging. The basic assumption of the proposed HyperFed is that the optimization problem for each domain can be divided into two subproblems: local data adaption and global CT imaging problems, which are implemented by an institution-specific physics-driven hypernetwork and a global-sharing imaging network, respectively. Learning stable and effective invariant features from different data distributions is the main purpose of global-sharing imaging network. Inspired by the physical process of CT imaging, we carefully design physics-driven hypernetwork for each domain to obtain hyperparameters from specific physical scanning protocol to condition the global-sharing imaging network, so that we can achieve personalized local CT reconstruction. Experiments show that HyperFed achieves competitive performance in comparison with several other state-of-the-art methods. It is believed as a promising direction to improve CT imaging quality and personalize the needs of different institutions or scanners without data sharing. Related codes have been released at https://github.com/Zi-YuanYang/HyperFed.

A Physics-informed Deep Neural Network for Harmonization of CT Images.

An adversarial generative network was trained on virtual CT images acquired under various imaging conditions using a virtual imaging platform with 40 computational patient models. These models featured anthropomorphic lungs with different levels of pulmonary diseases, including nodules and emphysema. Imaging was conducted using a validated CT simulator at two dose levels and varying reconstruction kernels. The trained model was tested on an independent virtual test dataset and two clinical datasets. On the virtual test set, the harmonizer improved the structural similarity index from 79.3 ±16.4% to 95.8 ±1.7%, normalized mean squared error from 16.7 ±9.7% to 9.2 ±1.7%, and peak signal-to-noise ratio from 27.7 ±3.7dB to 32.2 ±1.6dB. Moreover, the harmonized images yielded more precise quantification of emphysema-based imaging biomarkers for lung attenuation, LAA-950 from 5.6 ±8.7% to 0.23 ±0.16%, Perc15 from 43.4 ±45.4 HU to 20.0 ±7.5 HU, and Lung Mass from 0.3 ±0.3 g to 0.1 ±0.2 g. In clinical data, the harmonizer reduced biomarker variability by an average of 70%. For lung nodules, harmonized images improved the detectability index by 6.5-fold and DNN-based precision by 6%. The proposed harmonizer significantly enhances image quality and quantification accuracy in CT imaging. The study demonstrated the potential utility of image harmonization for consistent CT image quality and reliable quantification, which is crucial for clinical applications and patient management.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Pancreatic SABR using peritumoral fiducials, triggered imaging and breath-hold.

Background: We aim to present our linear accelerator-based workflow for pancreatic stereotactic ablative radiotherapy (SABR) in order to address the following issues: intrafractional organ motion management, Cone Beam CT (CBCT) image quality, residual errors with dosimetric consequences, treatment time, and clinical results. Methods: Between 2016 and 2021, 14 patients with locally advanced pancreatic cancer were treated with induction chemotherapy and SABR using volumetric modulated arc therapy (VMAT). Internal target volume (ITV) concept (5), phase-gated (4), or breath hold (5) techniques were used. Treatment was verified by CBCT before and after irradiation, while tumor motion was monitored and controlled by kV triggered imaging and beam hold using peritumoral surgical clips. Beam interruptions and treatment time were recorded. The CBCT image quality was scored and supplemented by an agreement analysis (Krippendorff's-α) of breath-hold CBCT images to determine the position of OARs relative to the planning risk volumes (PRV). Residual errors and their dosimetry impact were also calculated. Progression free (PFS) and overall survival (OS) were assessed by the Kaplan-Meier analysis with acute and late toxicity reporting (CTCAEv4). Results: On average, beams were interrupted once (range: 0-3) per treatment session on triggered imaging. The total median treatment time was 16.7 ± 10.8min, significantly less for breath-hold vs. phase-gated sessions (18.8 ± 6.2 vs. 26.5 ± 13.4, p < 0.001). The best image quality was achieved by breath hold CBCT. The Krippendorff's-α test showed a strong agreement among five radiation therapists (mean K-α value: 0.8 (97.5%). The mean residual errors were <0.2cm in each direction resulting in an average difference of <2% in dosimetry for OAR and target volume. Two patients received offline adaptation. The median OS/PFS after induction chemotherapy and SABR was 20/12months and 15/8months. No Gr. ≥2 acute/late RT-related toxicity was noted. Conclusion: Linear accelerator based pancreatic SABR with the combination of CBCT and triggered imaging + beam hold is feasible. Peritumoral fiducials improve utility while breath-hold CBCT provides the best image quality at a reasonable treatment time with offline adaptation possibilities. In well-selected cases, it can be an effective alternative in clinics where CBCT/MRI-guided online adaptive workflow is not available.

Medical image segmentation of gastric adenocarcinoma based on dense connection of residuals.

Accurate segmentation of gastric cancer based on CT images of gastric adenocarcinoma is crucial for physicians to screen gastric diseases, clinical diagnosis, preoperative prediction, and postoperative evaluation plans. To address the issue of the inability of the segmentation algorithm to depict the correct boundaries due to unclear gastric contours in the lesion area and the visible irregular band-like dense shadow extending to the perigastric region, a 3D medical image segmentation model 3D UNet based on residual dense jumping method is proposed. In the method we proposed, Residual Dense Block, which is applied to the image super-resolution module to remove CT artifacts, and Residual Block in ResNet are further fused. The quality of CT images is improved by Residual Dense Skip Block, which removes banded dense shadows, preserves image details and edge information, captures features, and improves the segmentation performance of gastric adenocarcinoma. The Instance Normalization layer position is modified to select the best result. Different loss functions are also combined in order to obtain the best gastric adenocarcinoma segmentation performance. We tested the model on a hospital-provided gastric adenocarcinoma dataset. The experimental results show that our model outperforms the existing methods in CT gastric adenocarcinoma segmentation, in which the method combining the hybrid loss function of Dice and CE obtains an average dice score of 82.3%, which is improved by 5.3% and 3.8% compared to TransUNet and Hiformer, respectively, and improves the cross-merge rate to 70.8%, compared to nnFormer, nnUNet by 1% and 0.9%, respectively. The residual jump connection structure indeed improves segmentation performance. The proposed method has the potential to be used as a screen for gastric diseases and to assist physicians in diagnosis.

Effect of a second-generation motion correction algorithm on image quality and measurement reproducibility of coronary CT angiography in patients with a myocardial bridge and mural coronary artery

To determine the effect of second-generation motion correction (MC2) on image quality and measurement reproducibility of cardiac CT images in patients with a myocardial bridge and mural coronary artery (MB-MCA) compared to standard (STD) images without motion correction and with first-generation motion correction (MC1). A total of 66 patients with MB-MCA in the left anterior descending branch who underwent 256-detector CT with single-heartbeat acquisition were included. Images were reconstructed at 45% and 75% R-R intervals using STD, MC1, and MC2 algorithms. Image quality for MB-MCA was assessed by two observers on a four-point scale (1=poor and 4=excellent) and compared among STD, MC1, and MC2. Depth and length of MB, lumen area, and minimal diameter of MCA were measured and compared. At 45% R-R interval, image quality scores were 1.59±0.78, 2.21±0.97, and 3.21±0.62 for MCA, and 2.48±0.79, 2.76±0.75, and 3.58±0.58 for MB with STD, MC1 and MC2, respectively. At 75% R-R interval, these values were 2.26±0.60, 3.03±0.89, and 3.59±0.55 for MCA and 3.00±0.93, 3.17±0.83, and 3.80±0.44 for MB. Although MC1 was superior to STD in displaying MCA, there was no statistical difference between the two algorithms for MB (p>0.05). Compared with STD and MC1, MC2 statistically improved image quality and interpretability for both MCA and MB and had narrower limits in interobserver agreement for measurements at both 45% and 75% R-R intervals. MC2 improves CT image quality and measurement reproducibility in patients with MB-MCA compared to STD and MC1.

Cardiac computed tomography angiography with and without bolus tracking methods in infants with congenital heart disease.

The study investigated radiation dose, vascular computed tomography (CT) enhancement and image quality of cardiac computed tomography angiography (CCTA) with and without bolus tracking (BT) methods in infants with congenital heart disease (CHD). The volume CT dose index (CTDIvol) and dose length product (DLP) were recorded for all CT scans, and the effective dose was obtained using a conversion factors. The CT number for the ascending aorta (AO) and pulmonary artery (PA), image noise of muscle tissue and contrast-to-noise ratio (CNR) were measured and calculated. The median values in the groups with and without BT were 2.20mGy versus 0.44mGy for CTDIvol, 8.10mGy·cm versus 6.20mGy·cm for DLP, and 0.66mSv versus 0.51mSv for effective dose (p<0.001). There were no statistical differences in vascular CT enhancement, image noise, and CNR. CCTA without BT methods can reduce the radiation dose while maintaining vascular CT enhancement and image quality compared to CCTA with BT methods.

Low-dose versus conventional CT urography using dual-source CT with different time-current product values and the same tube voltage: image quality and diagnostic performance in various diagnoses.

To compare the image quality and diagnostic performance of low-dose CT urography to that of concurrently acquired conventional CT using dual-source CT. This retrospective study included 357 consecutive CT urograms performed by third-generation dual-source CT in a single institution between April 2020 and August 2021. Two-phase CT images (unenhanced phase, excretory phase with split bolus) were obtained with two different tube current-time products (280 mAs for the conventional-dose protocol and 70 mAs for the low-dose protocol) and the same tube voltage (90 kVp) for the two X-ray tubes. Iterative reconstruction was applied for both protocols. Two radiologists independently performed quantitative and qualitative image quality analysis and made diagnoses. The correlation between the noise level or the effective radiation dose and the patients' body weight was evaluated. Significantly higher noise levels resulting in a significantly lower liver signal-to-noise ratio and contrast-to-noise ratio were noted in low-dose images compared to conventional images (P < .001). Qualitative analysis by both radiologists showed significantly lower image quality in low-dose CT than in conventional CT images (P < .001). Patient's body weight was positively correlated with noise and effective radiation dose (P < .001). Diagnostic performance for various diseases, including urolithiasis, inflammation, and mass, was not different between the two protocols. Despite inferior image quality, low-dose CT urography with 70 mAs and 90 kVp and iterative reconstruction demonstrated diagnostic performance equivalent to that of conventional CT for identifying various diseases of the urinary tract. Low-dose CT (25% radiation dose) with low tube current demonstrated diagnostic performance comparable to that of conventional CT for a variety of urinary tract diseases.

Deep learning-driven multi-view multi-task image quality assessment method for chest CT image

BackgroundChest computed tomography (CT) image quality impacts radiologists’ diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}IQA) method for assessing the quality of chest CT images in patients, to determine if they are suitable for assessing the patient’s physical condition.MethodsThis retrospective study utilizes and analyzes chest CT images from 327 patients. Among them, 1613 images from 286 patients are used for model training and validation, while the remaining 41 patients are reserved as an additional test set for conducting ablation studies, comparative studies, and observer studies. The M2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}IQA method is driven by DL technology and employs a multi-view fusion strategy, which incorporates three scanning planes (coronal, axial, and sagittal). It assesses image quality for multiple tasks, including inspiration evaluation, position evaluation, radiation protection evaluation, and artifact evaluation. Four algorithms (pixel threshold, neural statistics, region measurement, and distance measurement) have been proposed, each tailored for specific evaluation tasks, with the aim of optimizing the evaluation performance of the M2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}IQA method.ResultsIn the additional test set, the M2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}IQA method achieved 87% precision, 93% sensitivity, 69% specificity, and a 0.90 F1-score. Extensive ablation and comparative studies have demonstrated the effectiveness of the proposed algorithms and the generalization performance of the proposed method across various assessment tasks.ConclusionThis study develops and validates a DL-driven M2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^2$$\\end{document}IQA method, complemented by four proposed algorithms. It holds great promise in automating the assessment of chest CT image quality. The performance of this method, as well as the effectiveness of the four algorithms, is demonstrated on an additional test set.

Optimization of the Reconstruction Settings for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lungs

Photon-counting detector computed tomography (PCD-CT) yields improved spatial resolution. The combined use of PCD-CT and a modern iterative reconstruction method, known as quantum iterative reconstruction (QIR), has the potential to significantly improve the quality of lung CT images. In this study, we aimed to analyze the impacts of different slice thicknesses and QIR levels on low-dose ultra-high-resolution (UHR) PCD-CT imaging of the lungs. Our study included 51 patients with different lung diseases who underwent unenhanced UHR-PCD-CT scans. Images were reconstructed using three different slice thicknesses (0.2, 0.4, and 1.0 mm) and three QIR levels (2–4). Noise levels were determined in all reconstructions. Three raters evaluated the delineation of anatomical structures and conspicuity of various pulmonary pathologies in the images compared to the clinical reference reconstruction (1.0 mm, QIR-3). The highest QIR level (QIR-4) yielded the best image quality. Reducing the slice thickness to 0.4 mm improved the delineation and conspicuity of pathologies. The 0.2 mm reconstructions exhibited lower image quality due to high image noise. In conclusion, the optimal reconstruction protocol for low-dose UHR-PCD-CT of the lungs includes a slice thickness of 0.4 mm, with the highest QIR level. This optimized protocol might improve the diagnostic accuracy and confidence of lung imaging.

Deep learning reconstruction improves the image quality of low-dose temporal bone CT with otitis media and mastoiditis patients

ObjectiveTo evaluate the image quality of low-dose temporal bone computed tomography (CT) in otitis media and mastoiditis patients by using deep learning reconstruction (DLR). Materials and methodsA total of ninety-seven temporal bones from 53 consecutive adult patients who had suspected otitis media and mastoiditis and underwent temporal bone CT were prospectively enrolled. All patients underwent high resolution CT protocol (group A) and an additional low-dose protocol (group B). In group A, high resolution data were reconstructed by filter back projection (FBP). In group B, low-dose data were reconstructed by DLR mild (B1), DLR standard (B2) and DLR strong (B3). The objective image quality was analyzed by measuring the CT value and image noise on the transverse image and calculating the signal-to-noise ratio (SNR) on incudomallear joint, retroauricular muscle, vestibule and subcutaneous fat. Subjective image quality was analyzed by using a five-point scale to evaluate nine anatomical structures of middle and inner ear. The number of temporal bone lesions which involved in five structures of middle ear were assessed in group A, B1, B2 and B3 images. ResultsThere were no significant differences in the CT values of the four reconstruction methods at four structures (all p > 0.05). The DLR group B1, B2 and B3 had significantly less image noise and a significantly higher SNR than group A at four structures (all p < 0.001). The group B1 had comparable subjective image quality as group A in nine structures (all p > 0.05), however, the group B3 had lower subjective image quality than group A in modiolus, spiral osseous lamina and stapes (all p < 0.001), the group B2 had lower subjective image quality than group A in modiolus and spiral osseous lamina (both p < 0.05). The number of temporal bone lesions which involved in five structures for group A, B1 and B2 images were no significant difference (all p > 0.05), however, the number of temporal bone lesions which involved in mastoid for group B3 images were significantly more than group A (p < 0.05). The radiation dose of high resolution CT protocol and low-dose protocol were 0.55 mSv and 0.11 mSv, respectively. ConclusionCompared with high resolution CT protocol, in the low-dose protocol of temporal bone CT, DLR mild and standard could improve the objective image quality, maintain good subjective image quality and satisfy clinical diagnosis of otitis media and mastoiditis patients.