Iterative Model Reconstruction Research Articles

Evaluation of an in-use chest CT protocol in lung cancer screening - A single institutional study.

Lung cancer is the most common cause of cancer-related death worldwide and therefore there has been a growing demand for low-dose computed tomography (LDCT) protocols. To investigate and evaluate the dose and image quality of patients undergoing lung cancer screening (LCS) using LDCT in Norway. Retrospective dosimetry data, volumetric CT dose index (CTDIvol) and dose-length product (DLP), from 70 average-size and 70 large-size patients who underwent LDCT scan for LCS were included in the survey. Effective dose and size-specific dose were calculated for each examination and were compared with the American Association of Physicists in Medicine (AAPM) requirement. For a quantitative image quality analysis, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were determined for different regions in the chest with two iterative reconstruction techniques, iDose and Iterative Model Reconstruction. Differences in dose and image quality between average-size and large-size patients were evaluated by Independent sample t test, and Wilcoxon signed rank test within the same patient group. The independent sample t test revealed significant differences (p < .05) in dose values between average-size and large-size patients. Mean CTDIvol and DLP for average-size patients were 2.8mGy and 115mGy.cm, respectively, with appropriate increment for the large-size patients. Image quality (image noise, SNR, and CNR) did not significantly differ between patient groups when images were reconstructed with a model based iterative reconstruction algorithm. The screening protocol assessed in this study resulted in CTDIvol values that were compliant with AAPM recommendation. No significant differences in objective image quality were found between patient groups.

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.

Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether the DLIR algorithm reduces the CT effective dose (ED) and improves CT image quality in comparison with filtered back projection (FBP) and iterative reconstruction (IR) algorithms in intensive care unit (ICU) patients. We identified all consecutive patients referred to the ICU of a single hospital who underwent at least two consecutive chest and/or abdominal contrast-enhanced CT scans within a time period of 30 days using DLIR and subsequently the FBP or IR algorithm (Advanced Modeled Iterative Reconstruction [ADMIRE] model-based algorithm or Adaptive Iterative Dose Reduction 3D [AIDR 3D] hybrid algorithm) for CT image reconstruction. The radiation ED, noise level, and signal-to-noise ratio (SNR) were compared between the different CT scanners. The non-parametric Wilcoxon test was used for statistical comparison. Statistical significance was set at p < 0.05. A total of 83 patients (mean age, 59 ± 15 years [standard deviation]; 56 men) were included. DLIR vs. FBP reduced the ED (18.45 ± 13.16 mSv vs. 22.06 ± 9.55 mSv, p < 0.05), while DLIR vs. FBP and vs. ADMIRE and AIDR 3D IR algorithms reduced image noise (8.45 ± 3.24 vs. 14.85 ± 2.73 vs. 14.77 ± 32.77 and 11.17 ± 32.77, p < 0.05) and increased the SNR (11.53 ± 9.28 vs. 3.99 ± 1.23 vs. 5.84 ± 2.74 and 3.58 ± 2.74, p < 0.05). CT scanners employing DLIR improved the SNR compared to CT scanners using FBP or IR algorithms in ICU patients despite maintaining a reduced ED.

Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction

BackgroundNeck computed tomography (NCT) is essential for diagnosing suspected neck tumors and abscesses, but radiation exposure can be an issue. In conventional reconstruction techniques, limiting radiation dose comes at the cost of diminished diagnostic accuracy. Therefore, this study aimed to evaluate the effects of an AI-based denoising post-processing software solution in low-dose neck computer tomography. Materials and MethodsFrom 01 September 2023 to 01 December 2023, we retrospectively included patients with clinically suspected neck tumors from the same single-source scanner. The scans were reconstructed using Advanced Modeled Iterative Reconstruction (Original) at 100% and simulated 50% and 25% radiation doses. Each dataset was post-processed using a novel denoising software solution (Denoising). Three radiologists with varying experience levels subjectively rated image quality, diagnostic confidence, sharpness, and contrast for all pairwise combinations of radiation dose and reconstruction mode in a randomized, blinded forced-choice setup. Objective image quality was assessed using ROI measurements of mean CT numbers, noise, and a contrast-to-noise ratio (CNR). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. ResultsAt each radiation dose level, pairwise comparisons showed significantly lower image noise and higher CNR for Denoising than for Original (p < 0.001). In subjective analysis, image quality, diagnostic confidence, sharpness, and contrast were significantly higher for Denoising than for Original at 100 and 50 % (p < 0.001). However, there were no significant differences in the subjective ratings between Original 100 % and Denoising 25 % (p = 0.906). ConclusionsThe investigated denoising algorithm enables diagnostic-quality neck CT images with radiation doses reduced to 25% of conventional levels, significantly minimizing patient exposure.

Iterative Low-Poly Building Model Reconstruction from Mesh Soups Based on Contour

Existing contour-based building-reconstruction methods face the challenge of producing low-poly results. In this study, we introduce a novel iterative contour-based method to reconstruct low-poly meshes with only essential details from mesh soups. Our method focuses on two primary targets that determine the quality of the results: reduce the total number of contours, and generate compact surfaces between contours. Specifically, we implemented an iterative pipeline to gradually extract vital contours by loss and topological variance, and potential redundant contours will be removed in a post-processing procedure. Based on these vital contours, we extracted the planar primitives of buildings as references for contour refinement to obtain compact contours. The connection relationships between these contours are recovered for surface generation by a contour graph, which is constructed using multiple bipartite graphs. Then, a low-poly mesh can be generated from the contour graph using our contour-interpolation algorithm based on polyline splitting. The experiments demonstrated that our method produced satisfactory results and outperformed the previous methods.

Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.

Background and Objectives: We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Materials and Methods: Three 5 × 5 × 5 mm3 uric acid stones were placed in a physical human phantom in various locations. Three tube voltages (120, 100, and 80 kV) and four current-time products (100, 70, 30, and 15 mAs) were implemented in 12 scans. Each scan was reconstructed with filtered back projection (FBP), statistical iterative reconstruction (IR, iDose), and knowledge-based iterative model reconstruction (IMR). By applying deep learning to each image, we took 12 more scans. Objective image assessments were calculated using the standard deviation of the Hounsfield unit (HU). Subjective image assessments were performed by one radiologist and one urologist. Two radiologists assessed the subjective assessment and found the stone under the absence of information. We used this data to calculate the diagnostic accuracy. Results: Objective image noise was decreased after applying a deep learning tool in all images of FBP, iDose, and IMR. There was no statistical difference between iDose and deep learning-applied FBP images (10.1 ± 11.9, 9.5 ± 18.5 HU, p = 0.583, respectively). At a 100 kV-30 mAs setting, deep learning-applied FBP obtained a similar objective noise in approximately one third of the radiation doses compared to FBP. In radiation doses with settings lower than 100 kV-30 mAs, the subject image assessment (image quality, confidence level, and noise) showed deteriorated scores. Diagnostic accuracy was increased when the deep learning setting was lower than 100 kV-30 mAs, except for at 80 kV-15 mAs. Conclusions: At the setting of 100 kV-30 mAs or higher, deep learning-applied FBP did not differ in image quality compared to IR. At the setting of 100 kV-30 mAs, the radiation dose can decrease by about one third while maintaining objective noise.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.

To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol. A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared. With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs. Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

ANALISIS KUALITAS CITRA ANATOMI MSCT UROGRAPHY DENGAN VARIASI INTERATIVE MODEL RECONTRUCTION (IMR)

Background: The urinary system consists of two kidneys and ureters, urinary bladder, and urethra. Various modalities in radiology can be used to radiologically detect abnormalities in the urinary system, one of which is MSCT. MSCT Urography with tracking technique can identify urolithiasis in the ureters. Iterative reconstruction is used to reduce the dose received by the patient without reducing image quality. Iterative Model Reconstruction (IMR) is the second-generation IR algorithm from the previous generation, namely iDose (Philips Healthcare). This study aims to determine the optimal anatomy image quality with IMR variations on non-contra Urography MSCT images using tracking techniques.&#x0D; Methods: This type of research is an experiment with data obtained from a comparison of the quality of anatomical images from tracking images with IMR variations. Place of data collection at the Radiology Installation of RSUD RAA Soewondo Pati. Time for data collection From 2022. An assessment of the quality of the anatomical images was carried out by 2 radiologists. Data analysis was carried out using the Friedman statistical test method. The level of significance (level of significance) is 95% or α&gt; 0.05 and is done by assessing the p-value. For a significant level of assessment p &lt;0.05 then Ho is rejected and p&gt; 0.05 then Ho is accepted.&#x0D; Results: There are differences in information on each anatomy of the renal parenchyma, pelvic calices, ureters, and perirenal space in the use of IMR variations. The results showed that the optimal IMR variation value was IMR 3 with a mean rank value of 2.7. Increasing the use of IMR levels reduces noise and increases spatial resolution, so that IMR level 3 has the best assessment of anatomical information.&#x0D; Conclusions: We recommend that the non-contrast MSCT Urography examination with the Tracking Technique use IMR 3 which can produce optimal anatomical image information on the kidney parenchyma, pelvic calices, ureters, and perirenal space.

Influence of Pitch on Surface Dose Distribution and Image Noise of Computed Tomography Scans.

This study evaluated the effect of pitch on 256-slice helical computed tomography (CT) scans. Cylindrical water phantoms (CWP) were measured using axial and helical scans with various pitch values. The surface dose distributions of CWP were measured, and reconstructed images were obtained using filtered back-projection (FBP) and iterative model reconstruction (IMR). The image noise in each reconstructed image was decomposed into a baseline component and another component that varied along the z-axis. The baseline component of the image noise was highest at the center of the reconstructed image and decreased toward the edges. The normalized 2D power spectra for each pitch were almost identically distributed. Furthermore, the ratios of the 2D power spectra for IMR and FBP at different pitch values were obtained. The magnitudes of the components varying along the z-axis were smallest at the center of the reconstructed image and increased toward the edge. The ratios of the 3D power spectra on the fx axis for IMR and FBP at different pitch values were obtained. The results showed that the effect of the pitch was related to the component that varied along the z-axis. Furthermore, the pitch had a smaller effect on IMR than on FBP.

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.

APPLICATION OF CT PULMONARY ANGIOGRAPHY WITH "ULTRA-DOUBLE-LOW" AND ITERATIVE MODEL RECONSTRUCTION FOR ACUTE PULMONARY EMBOLISM.

The study is to investigate the feasibility of computed tomography pulmonary angiography (CTPA) with iterative model reconstruction (IMR) and "Ultra-double-low" (Ultra-low dose, Ultra-low contrast agent volume). Thirty-six patients who tested positive for pulmonary embolism in CTPA were enrolled in the study. Another CTPA was performed 1 week after thrombolytic therapy. The first examination was routine CTPA (Routine Group) with the parameters as follows: automatic mA scanning, 120kV and image reconstruction by using iDose4 iterative reconstruction (Lever 4), iodine concentration and dose of contrast agent: 300mgI/ml and 0.5gI/kg, respectively. The latter one was ultra-low dose CTPA examination (Ultra-low Group): 40 mAs, 80kV and IMR (Lever 3), contrast agent: 300mgI/ml and 15mL, respectively. Effective dose (ED), CT dose index volume (CTDIvol), dose length product (DLP), attenuation of pulmonary artery, contrast noise ratio (CNR) and signal noise ratio (SNR) were recorded and calculated. The imaging qualities were subjectively assessed. The Eds/CTDIvols/DLPs of Ultra-low Group are lower than the Routine Group (P<0.05). The differences in attenuation between the two groups are not significant (P>0.05). The differences in CNR and SNR between the two groups are significant (P<0.05). The differences in imaging qualities between the two groups when subjectively assessed are not significant (P>0.05). The 256-slice spiral CT combined with IMR and "Ultra-double-low" is feasible for the acute pulmonary embolism examination and the radiation dose and the volume of contrast agent can be greatly reduced.

Anemia Detection with Contrast-enhanced Photon-counting CT

HomeRadiologyVol. 305, No. 2 PreviousNext CommunicationsFree AccessErratumAnemia Detection with Contrast-enhanced Photon-counting CTJonathan D. Dodd, Roisin MacDermottJonathan D. Dodd, Roisin MacDermottJonathan D. DoddRoisin MacDermottPublished Online:Oct 24 2022https://doi.org/10.1148/radiol.229023MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Originally published in:https://doi.org/10.1148/radiol.221471Erratum in:https://doi.org/10.1148/radiol.229023The quote on the first page was changed to read …became clearly visible on noncontrast CT images in canine hearts. The dose-length product unit was changed to mGy ∙ cm, and the associated value was changed from 59 to 592. Reference 4 DOI was changed to https://doi.org/10.1148/radiol.220063.Article HistoryPublished online: Oct 24 2022Published in print: Nov 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleAnemia Detection with Contrast-enhanced Photon-counting CTJul 26 2022RadiologyRecommended Articles Influence of Heart Rate on Image Quality and Radiation Dose Exposure in Coronary CT AngiographyRadiology2021Volume: 300Issue: 3pp. 701-703Slow and Steady Wins the Race: Lower Heart Rates Improve Diagnostic Quality for Coronary CT AngiographyRadiology2021Volume: 300Issue: 3pp. 704-705Radiation Dose Reduction by Using CT with Iterative Model Reconstruction in Patients with Pulmonary Invasive Fungal InfectionRadiology2018Volume: 288Issue: 1pp. 285-292Evaluation of Kidney Stones with Reduced–Radiation Dose CT: Progress from 2011−2012 to 2015−2016—Not There YetRadiology2017Volume: 286Issue: 2pp. 581-589MRI of Iron Oxide Nanoparticles and Myeloperoxidase Activity Links Inflammation to Brain Edema in Experimental Cerebral MalariaRadiology2018Volume: 290Issue: 2pp. 359-367See More RSNA Education Exhibits Usefulness of Spiral Flow Tube for Saline Chaser Capable of Improving Visualization Ability of Subclavian Artery in Cervical CT AngiographyDigital Posters2019Optimization of One-beat 256 Row Detector Coronary CT Angiography Using Low Contrast Media Protocols in Patients with Free Breathing under Automatic Tube Voltage SelectionDigital Posters2018Coronary CT Angiography with Wide Coverage Detector: How to Optimize Contrast Injection and Scan Protocol Based on Patient Size to Reduce Contrast and Radiation DosesDigital Posters2018 RSNA Case Collection Cardiac MyxomaRSNA Case Collection2020Chiari 1 malformation RSNA Case Collection2020Epiploic AppendagitisRSNA Case Collection2022 Vol. 305, No. 2 Metrics Altmetric Score PDF download

Pulmonary CT scans of white rabbits using the selective photon shield technique of the third-generation dual-source CT

This study aims to optimise the protocol for the low-dose pulmonary computed tomography (CT) scanning of infants by studying the effects of the selective photon shield (SPS) technique of the third-generation dual-source CT (DSCT) on the image quality and radiation dose of a chest CT in white rabbits under different tube currents. Twelve white rabbits of a similar weight to an infant were selected and randomly divided into an experimental group and a control group. The experimental groups (A1–A5) were scanned at low dose by the third-generation DSCT using SPS under different tube current × time (60, 50, 40, 30, and 20 mAs). The control group (B) was scanned under a conventional tube voltage (100 kV) and current × time (20 mAs). Advanced model iterative reconstruction at strength three was used for the objective and subjective evaluation of the image quality and radiation dose of the lung and mediastinal windows. With the standard deviation of the air in the trachea as image noise, the signal-to-noise ratio (SNR), contrast-to-noise ratio, and CT values of each site were evaluated. Radiation doses were compared using the volume CT dose index, dose length product, and effective dose. The differences in subjective image quality between groups A2 and B were not statistically significant (P = 0.34). The differences in the SNRs of the lung and mediastinal windows between groups A2 and B were not statistically significant (P > 0.05). The radiation dose of group A2 was 83.2% lower than that of group B. The SPS of the third-generation DSCT under 50 mAs might be applied in the pulmonary CT examination of infants.

AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging.

(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of “Radiation Dose”, “Scanner”, “Mode”, “Rater”, and “Timepoint” to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good–excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74–0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80–0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78–0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good–excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63–0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72–3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001). In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

Chest Computerized Tomography Images under Iterative Model Reconstruction Algorithm in Patients with Lung Cancer

To explore the effect of the full iterative model reconstruction algorithm (IMR) on chest CT image processing and its adoption value in the clinical diagnosis of lung cancer patients, multislice spiral CT (MSCT) scans were performed on 96 patients with pulmonary nodules. Reconstruction was performed by hybrid iterative reconstruction (iDose4) and IMR2 algorithms. Then, the image contrast, spatial resolution, density resolution, image uniformity, and noise of the CT reconstructed image were recorded. The benign and malignant pulmonary nodules of patients were collected and classified into malignant pulmonary nodule group and benign pulmonary nodule group, and the differences in chest CT imaging characteristics between the two groups were compared. The subject’s receiver operating characteristic (ROC) curve was used to analyze the diagnostic sensitivity, specificity, and area under the curve (AUC) of CT for benign and malignant pulmonary nodules. It was found that the spatial resolution, density resolution, image uniformity, and contrast of the CT image reconstructed by the IMR2 algorithm were remarkably greater than those of the iDose4 algorithm, and the noise was considerably less than that of the iDose4 algorithm ( P &lt; 0.05 ). Among 96 patients with pulmonary nodules, 65 were malignant nodules, including 15 squamous cell carcinoma, 31 adenocarcinoma, and 19 small cell carcinomas. There were 31 cases of benign nodules, including 14 cases of hamartoma, 10 cases of tuberculous granuloma, 2 cases of sclerosing hemangioma, and 5 cases of diffuse lymphocyte proliferation. The pulmonary nodule malignant group and the pulmonary nodule benign group had statistical differences in pulmonary nodule size, nodule morphology, burr sign, lobular sign, vascular sign, bronchial sign, and pleural depression sign ( P &lt; 0.05 ). The sensitivity, specificity, and area under the curve (AUC) of IMR2 algorithm processing chest CT images for liver cancer diagnosis were 85.7%, 82.3%, and 0.815, respectively, which were significantly higher than the original CT images ( P &lt; 0.05 ). In short, chest MSCT based on the IMR2 algorithm can greatly improve the diagnosis efficiency of lung cancer and had practical significance for the timely detection of early lung cancer.

Diagnostic Value of Chest CT Images Based on Full Model Iterative Reconstruction Algorithm for Lung Cancer Patients

Objective. To evaluate the value of low-dose CT scanning and full model iteration recombinant technology peripheral lung cancer in the paper using whole model iterative reconstruction algorithm and compare iterative model-wide restructuring, reorganization part of an iterative algorithm, affecting filtered back projection image quality. Method. Fifty-two patients with peripheral lung cancer, all of whom were diagnosed by pathological biopsy, were selected for the study. All patients received three scans of low-dose chest CT, next-low-dose, and low-dose, after which the raw data of three different doses were reconstructed using filtered back-projection, iterative partial algorithm reconstitution, and reconstructed full-model iteration, respectively, and the effect of each algorithm on the processing of chest CT images of peripheral lung cancer at different doses and the diagnosis of the disease were compared after the reconstitution was completed. Results. The average effective radiation dose for the low-dose group was (0.3±0.02) mSv At each dose level, image noise objective recombinant whole iterative model &lt; part of the reorganization of the iterative algorithm &lt; filtered back projection, the difference was significant. In the case of lung lesions, the full-model iterative algorithm has similar evaluation power to the LD-partial iterative algorithm. When a patient’s body mass index (BMI) &gt; 25 kg/m2, the whole model iteration reorganization image quality is reduced, but the lesions-to-noise ratio (SNR) is unaffected. Conclusion. The combination of a very low dose of recombinant iterative model as compared to full-dose low-dose chest CT dose can be reduced to 88% but does not reduce the overall image quality and can show good radiological signs of peripheral lung cancer and not affect BMI patients.

Study on Model Iterative Reconstruction Algorithm vs. Filter Back Projection Algorithm for Diagnosis of Acute Cerebral Infarction Using CT Images.

The aim was to explore the application value of computed tomography (CT) perfusion (CTP) imaging based on the iterative model reconstruction (IMR) in the diagnosis of acute cerebral infarction (ACI). 80 patients with ACI, admitted to hospital, were selected as the research objects and divided randomly into a routine treatment group (group A) and a low-dose group (group B) (each group with 40 patients). Patients in group A were scanned at 80 kV–150 mAs, and the traditional filtered back projection (FBP) algorithm was employed to reconstruct the images; besides, 80 kV–30 mAs was adopted to scan the patients in group B, and the images were reconstructed by IMR1, IMR2, IMR3, iDose4 (a kind of hybrid iterative reconstruction technology), and FBP, respectively. The application values of different algorithms were evaluated by CTP based on the collected CTP images of patients and detecting indicators. The results showed that the gray and white matter CT value, SD value, SNR, CNR, and subjective image scores of patients in group B were basically consistent with those of group A (p > 0.05) after the IMR1 reconstruction, and the CT and SD of gray and white matter in patients from group B reduced steeply (p < 0.05), while SNR and CNR increased dramatically after IMR2 and IMR3 reconstruction in contrast to group A (p < 0.05). Furthermore, the cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT) of contrast agent, and time to peak (TTP) of contrast agent in patients from group B after iDose4 and IMR reconstruction were basically the same as those of group A (p > 0.05). Therefore, IMR combined with low-dose CTP could obtain high-quality CTP images of the brain with stable perfusion indicators and low radiation dose, which could be clinically applied in the diagnosis of ACI.

Diagnostic Performance of a Contrast-Enhanced Ultra-Low-Dose High-Pitch CT Protocol with Reduced Scan Range for Detection of Pulmonary Embolisms

(1) Background: To evaluate the diagnostic performance of a simulated ultra-low-dose (ULD), high-pitch computed tomography pulmonary angiography (CTPA) protocol with low tube current (mAs) and reduced scan range for detection of pulmonary embolisms (PE). (2) Methods: We retrospectively included 130 consecutive patients (64 ± 16 years, 69 female) who underwent clinically indicated high-pitch CTPA examination for suspected acute PE on a 3rd generation dual-source CT scanner (SOMATOM FORCE, Siemens Healthineers, Forchheim, Germany). ULD datasets with a realistic simulation of 25% mAs, reduced scan range (aortic arch—basal pericardium), and Advanced Modeled Iterative Reconstruction (ADMIRE®, Siemens Healthineers, Forchheim, Germany) strength 5 were created. The effective radiation dose (ED) of both datasets (standard and ULD) was estimated using a dedicated dosimetry software solution. Subjective image quality and diagnostic confidence were evaluated independently by three reviewers using a 5-point Likert scale. Objective image quality was compared using noise measurements. For assessment of diagnostic accuracy, patients and pulmonary vessels were reviewed binarily for affection by PE, using standard CTPA protocol datasets as the reference standard. Percentual affection of pulmonary vessels by PE was computed for disease severity (modified Qanadli score). (3) Results: Mean ED in ULD protocol was 0.7 ± 0.3 mSv (16% of standard protocol: 4.3 ± 1.7 mSv, p < 0.001, r > 0.5). Comparing ULD to standard protocol, subjective image quality and diagnostic confidence were comparably good (p = 0.486, r > 0.5) and image noise was significantly lower in ULD (p < 0.001, r > 0.5). A total of 42 patients (32.2%) were affected by PE. ULD protocol had a segment-based false-negative rate of only 0.1%. Sensitivity for detection of any PE was 98.9% (95% CI, 97.2–99.7%), specificity was 100% (95% CI, 99.8–100%), and overall accuracy was 99.9% (95% CI, 98.6–100%). Diagnoses correlated strongly between ULD and standard protocol (Chi-square (1) = 42, p < 0.001) with a decrease in disease severity of only 0.48% (T = 1.667, p = 0.103). (4) Conclusions: Compared to a standard CTPA protocol, the proposed ULD protocol proved reliable in detecting and ruling out acute PE with good levels of image quality and diagnostic confidence, as well as significantly lower image noise, at 0.7 ± 0.3 mSv (84% dose reduction).

Effect of different reconstruction algorithms on coronary artery calcium scores using the reduced radiation dose protocol: a clinical and phantom study.

This study aimed to evaluate the effects of different iterative reconstruction (IR) algorithms on coronary artery calcium (CAC) score quantification using the reduced radiation dose (RRD) protocol in an anthropomorphic phantom and in patients. A thorax phantom, containing 9 calcification inserts with varying hydroxyapatite (HA) densities, was scanned with the reference protocol [120 kv, 80 mAs, filtered back projection (FBP)] and RRD protocol (120 kV, 20-80 mAs, 5 mAs interval) using a 256-slice computed tomography (CT) scanner. Raw data were reconstructed with different reconstruction algorithms [iDose4 levels 1-7 and iterative model reconstruction (IMR) levels 1-3]. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and Agatston score (AS) were calculated for each image series. The correction factor was derived from linear regression analysis between the reference image series and other image series with different parameters. Additionally, 40 patients were scanned with the RRD protocol (50 mAs) and reconstructed with FBP, iDose4 level 4, and IMR level 2. AS was calculated for the 3-group image series, and was corrected by applying a correction factor for the IMR group. The agreement of risk stratification with different reconstruction algorithms was also analyzed. For the phantom study, the iDose4 and IMR groups had significantly higher SNR and CNR than the FBP group (all P<0.05). There were no significant differences in the total AS after comparing image series reconstructed with iDose4 (level 1-7) and FBP (all P>0.05), while AS from the IMR (level 1-3) image series were lower than the FBP group (all P<0.05). The tube current of 50 mAs was determined for the clinical study, and the correction factor was 1.14. For the clinical study, the median AS from the iDose4 and IMR groups were both significantly lower compared to the FBP image series [(112.89 (63.01, 314.09), 113.22 (64.78, 364.95) vs. 118.59 (65.05, 374.48), both P<0.05]. After applying the correction factor, the adjusted AS from the IMR group was not significantly different from that of the FBP group [126.48 (69.62, 355.85) vs. 118.59 (65.05, 374.48), P=0.145]. Moreover, the agreement in risk stratification between FBP and IMR improved from 0.81 to 0.85. The RRD CAC scoring scan using the IMR reconstruction algorithm is clinically feasible, and a correction factor can help reduce the AS underestimation effect.

Iterative Model Reconstruction in Lumbar Spine Image Retrieval from Computed Tomography of the Abdomen and Pelvis

Iterative Model Reconstruction in Lumbar Spine Image Retrieval from Computed Tomography of the Abdomen and Pelvis