CT Image Quality Research Articles

Deep learning-based image reconstruction for few-view computed tomography

Few-view computed tomography (CT) is able to provide the internal information of an object for diagnosis, while reducing radiation dose and decreasing side effects. In spite of these benefits, few-view CT images with conventional reconstruction algorithms are suffer from structural distortion and artifacts due to insufficient projections. In this study, a deep learning-based network was proposed for reconstructing few-view CT images and improving image quality. The proposed network consisted of fully connected, convolution and deconvolution layers, and the network training was implemented to directly transform few-view projections into reconstructed images without expert reconstruction knowledge. The quality of the output images obtained from the trained network was compared with those reconstructed by using the filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) algorithms. The results showed that the proposed network is able to improve the quantitative accuracy and noise property of reconstructed images, and the proposed network is more suitable for few-view CT reconstruction than the other algorithms. In conclusion, the proposed network can be potentially used to improve few-view CT image quality and considered as an alternative for few-view CT reconstruction. The source code is available on a GitHub repository at https://github.com/ YIMDOBIN/CT_reconstruction_via_DL.

Is routine preoperative computed tomography imaging justified in otosclerosis? A retrospective single-centre analysis

Objectives The improvement in the quality of CT imaging in recent years has led to increasing use in the preoperative workup before primary stapedotomy. The aim of this study is to determine whether routine CT scanning is justified by analysing whether the detectability of otoclerotic foci in the CT imaging is associated with a different clinical picture or surgical outcome. Methods Retrospective analysis of patient charts, CT data sets, preoperative and postoperative audiograms of 41 patients who underwent 47 primary stapedotomies. The preoperative and short-term postoperative audiometric thresholds were analysed at 0.5, 1, 2 and 4 kHz. Results In 36 of 47 ears (76.5%), fenestral otosclerosis was observed in the CT scans. Among those 36 ‘CT-positive’ ears, five ears (13.8%) showed additional signs of retrofenestral (cochlear) otosclerosis. The preoperative audiogram results showed no significant differences in the air conduction threshold, bone conduction threshold or air-bone gap between the CT-positive and CT-negative groups. The overall average preoperative air-bone gap (26.4 dB ± 8.3 dB) improved to 12 dB (±7.6 dB) postoperatively, without a significant difference between the CT-positive and CT-negative groups. Conclusion The detectability of otosclerotic foci on CT scans did not correlate with the preoperative audiogram or the ability to close the air-bone gap postoperatively. The CT scans did not reveal new information that influenced the decision to operate or which side to operate. The findings do not support routine preoperative CT scanning in suspected otosclerosis cases. CT imaging may complement audiological tests in atypical cases. Even in straightforward cases, preoperative CT scanning may be offered to patients in order to guide the preoperative counselling. However, the patients should be made aware of the associated radiation risks.

Discussion of grading method of small opacity profusion of pneumoconiosis on CT scans and the corresponding reference images

Objective: To explore the CT grading method of small opacity profusion of pneumoconiosis, and draw up the corresponding CT reference film. Methods: In December 2019, Three hundred thirty-seven cases of pneumoconosis and suspected pneumoconiosis were examined by chest radiography and Computed Tomography (CT) in the same period. According to Diagnosis of Occupational Pneumoconiosis (GBZ 70-2015) , small opacity profusion of pneumoconiosis in each zone of lung was divided. On CT scans, it was divided into 5 grades of 0, 0+, 1, 2 and 3. Grade 0 corresponded to Sub-grade 0/- and Sub-grade 0/0 of Grade 0 in chest radiograph. Grade 0+ was equivalent to Sub-grade 0/1 of Grade 0. Grade 1, 2, 3 were equivalent to Grade 1, 2 and 3, respectively (including each sub-grade) . The CT image quality of each zone of lung was divided into 1 to 4 levels. Results of level 4 were not included in statistical analyses.Based on the results of small opacity profusion in each zone of lung, consistency analysis was performed between chest radiograph and CT. The selection method of reference films was developed. Based on the types and grades of small opacity, the final reference films were determined. Results: There were 1877 zones of lung with CT image quality from level 1 to 3, including 335 in upper right, 319 in middle right, 284 in lower right, 334 in upper left, 320 in middle left and 286 in lower left. The Kappa values of small opacity profusion in upper right zone, upper left zone, left middle zone, and lower left zone were all between 0.4-0.75. In middle right zone and lower right zone, they were all above 0.75.Among all 6 zones of lung, the diagnostic concordance rates between CT and chest radiograph were all above 80%.The corresponding CT reference films were proposed, including type p and q in Grade 2 and 3, type r in Grade 2, type s and t in Grade 0+ to 3. Conclusion: The CT grading method for small opacity profusion of pneumoconiosis is feasible, and the application value of its reference films needs to be further verified.

Comparison of CT image quality between the AIDR 3D and FIRST iterative reconstruction algorithms: an assessment based on phantom measurements and clinical images

Modern CT iterative reconstruction algorithms are transitioning from a statistical-based to model-based approach. However, increasing complexity does not ensure improved image quality for all indications, and thorough characterization of new algorithms is important to understand their potential clinical impacts. This study performs both quantitative and qualitative analyses of image quality to compare Canon’s statistical-based Adaptive Iterative Dose Reduction 3D (AIDR 3D) algorithm to its model-based algorithm, Forward-projected model-based Iterative Reconstruction SoluTion(FIRST). A phantom was used to measure the task-specific modulation transfer function (MTFTask), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNRLO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTFTask was measured at four contrast levels, and CNRLO was measured for two object sizes. Lastly, three radiologists participated in a preference study to compare clinical image quality for three study types: non-contrast abdomen, pulmonary embolism (PE), and lung screening. Nine questions related to the appearance of anatomical features or image quality characteristics were scored for twenty exams of each type. The behavior of both algorithms depended strongly on the kernel selected. Phantom measurements suggest that FIRST should be beneficial over AIDR 3D for abdomen imaging, but do not suggest a clear overall benefit to FIRST for lung or brain imaging; metrics suggest performance may be equivalent to or slightly favor AIDR 3D, depending on the size of the object being imaged and whether spatial resolution or low-contrast resolution is more important for the task at hand. Overall, radiologists strongly preferred AIDR 3D for lung screening, slightly preferred AIDR 3D for non-contrast abdomen, and had no preference for PE. FIRST was superior for the reduction of metal artifacts. Radiologist preference may be influenced by changes to noise texture.

Thoracic CTA in infants and young children: Image quality of dual-source CT (DSCT) with high-pitch spiral scan mode (turbo flash spiral mode) with or without general anesthesia with free-breathing technique.

To determine whether diagnostic quality thoracic computed tomography angiography (CTA) studies can be obtained without general anesthesia (GA) in infants and young children using dual-source computed tomography (DSCT) with turbo flash spiral mode (TFSM) and free-breathing technique. All consecutive infants and young children (≤ 6 years old) who underwent thoracic CTA studies from January 2018 to October 2020 for suspected congenital thoracic disorders were categorized into two groups: with GA (Group 1) and without GA (Group 2). All thoracic CTA studies were performed on a DSCT scanner using TFSM and free-breathing technique. Two pediatric thoracic radiologists independently evaluated motion artifact in three lung zones (upper, mid, and lower). Degree of motion artifact was graded 0-3 (0, none; 1, mild; 2, moderate; and 3, severe). Logistic models adjusted for age and gender were used to compare the degree of motion artifact between lung zones. Interobserver agreement between reviewers was evaluated with kappa statistics. There were a total of 73 pediatric patients (43 males (59%) and 30 females (41%); mean age, 1.4 years; range, 0-5.9 years). Among these 73 patients, 42 patients (58%) underwent thoracic CTA studies with GA (Group 1) and the remaining 31 patients (42%) underwent thoracic CTA studies without GA (Group 2). Overall, the degree of motion artifact was higher for Group 2 (without GA). However, only a very small minority (1/31, 3%) of Group 2 (without GA) thoracic CTA studies had severe motion artifact. There was no significant difference between the two groups with respect to the presence of severe motion artifact (odds ratio [OR] = 6, p = .222). When two groups were compared with respect to the presence of motion artifact for individual lung zones, motion artifact was significantly higher in the upper lung zone for Group 2 (without GA) (OR = 20, p = .043). Interobserver agreement for motion artifact was high, the average Kappa being 0.81 for Group 1 and 0.95 for Group 2. Although the degree of motion artifact was higher in the group without GA, only a small minority (3%) of thoracic CTA studies performed without GA had severe motion artifact, rendering the study nondiagnostic. Therefore, the results of this study support the use of thoracic CTA without GA using DSCT with TFSM and free-breathing in infants and young children. In addition, given that motion artifact was significantly higher in the upper lung zone without GA, increased stabilization in the upper chest and extremities should be considered.

Phantom Study on the Effect of Automatic Tube Current Modulation on Image Quality and Radiation Dose of Abdominal CT

Phantom Study on the Effect of Automatic Tube Current Modulation on Image Quality and Radiation Dose of Abdominal CT

Visualization of small visceral arteries on abdominal CT angiography using ultra-high-resolution CT scanner

PurposeTo evaluate the image quality and ability to delineate the small visceral arteries of high-resolution (HR) abdominal CT angiography (CTA) using an ultra-high-resolution computed tomography (UHR CT) scanner.Materials and methodsThirty-seven patients were enrolled who underwent abdominal CTA using a UHR CT scanner. The images were reconstructed with a matrix of 1024 × 1024 and 0.25 mm thickness for HR CTA and with a matrix of 512 × 512 and 0.5 mm thickness for normal resolution (NR) CTA. Maximum CT value, image quality, and delineation of the small arteries were compared between HR CTA and NR CTA.ResultsHR CTA showed significantly higher maximum CT value, higher image quality, and better delineation of the small arteries than did NR CTA (P < .005).ConclusionHR CTA using a UHR CT scanner showed higher image quality than NR CTA and enhanced the delineation of visceral arteries.

Realistic Lung Nodule Synthesis With Multi-Target Co-Guided Adversarial Mechanism.

The important cues for a realistic lung nodule synthesis include the diversity in shape and background, controllability of semantic feature levels, and overall CT image quality. To incorporate these cues as the multiple learning targets, we introduce the Multi-Target Co-Guided Adversarial Mechanism, which utilizes the foreground and background mask to guide nodule shape and lung tissues, takes advantage of the CT lung and mediastinal window as the guidance of spiculation and texture control, respectively. Further, we propose a Multi-Target Co-Guided Synthesizing Network with a joint loss function to realize the co-guidance of image generation and semantic feature learning. The proposed network contains a Mask-Guided Generative Adversarial Sub-Network (MGGAN) and a Window-Guided Semantic Learning Sub-Network (WGSLN). The MGGAN generates the initial synthesis using the mask combined with the foreground and background masks, guiding the generation of nodule shape and background tissues. Meanwhile, the WGSLN controls the semantic features and refines the synthesis quality by transforming the initial synthesis into the CT lung and mediastinal window, and performing the spiculation and texture learning simultaneously. We validated our method using the quantitative analysis of authenticity under the Fréchet Inception Score, and the results show its state-of-the-art performance. We also evaluated our method as a data augmentation method to predict malignancy level on the LIDC-IDRI database, and the results show that the accuracy of VGG-16 is improved by 5.6%. The experimental results confirm the effectiveness of the proposed method.

Application of machine-learning models to improve the image quality of photon-counting CT images

X-ray computed tomography (CT) has been widely used in medical diagnostic imaging. However, conventional, energy-integrated CT requires a high radiation dose and can only provide monochromatic images that cannot eliminate various artifacts. In contrast, photon-counting CT (PC-CT) provides low-dose multicolor CT imaging, which enables the identification of multiple contrast agents. However, in the PC-CT system, the lack of photon statistics, which is also caused by image reconstruction in the limited energy band, severely affects the image quality. In this study, we applied three types of machine-learning (ML) techniques to improved the image quality of PC-CT, that is, dictionary learning, U-Net, and Noise2Noise. These ML models were trained using low- and high-dose image pairs created in simple steps. The trained ML models were applied to simulated data, and experimental PC-CT images of contrast agents used in clinical practice. Consequently, in the simulated data, the peak signal-to-noise ratio (PSNR) value improved from 21.3 for the input to 26.6, 33.3, and 30.1 for dictionary learning, U-Net, and Noise2Noise, respectively. Furthermore, in the actual PC-CT images, we successfully reproduced PC-CT images with high PSNR, which enabled simultaneous imaging of multiple contrast agents with improved accuracy of concentration estimation. As a future perspective, we will develop a processing technique that can be applied to in vivo CT images.

Abdominal CT radiation dose reduction at Siriraj Hospital (Phase III)

OBJECTIVE: To compare the image quality and the radiation dose between fixed tube current (FTC) low dose abdominal CT currently performed at our hospital and new automatic tube current modulation (ATCM) low dose abdominal CT. MATERIALS AND METHODS: We prospectively performed ATCM low dose abdominal CT in 88 participants who had prior FTC low dose CT for comparison. Four experienced abdominal radiologists independently and blindly assessed the quality of FTC and ATCM low dose CT images by using a 5-point-scale satisfaction score (1 = unacceptable, 2 = poor, 3 = average, 4 = good, and 5 = excellent image quality). Each reader selected the preferred image set between FTC and ATCM low dose techniques for each participant. The image noise of the liver and the aorta in both techniques was measured. The volume CT dose index (CTDIvol) of both techniques was compared. RESULTS: The mean satisfaction scores (SD) for FTC and ATCM low dose CT were 4.38 (0.66) and 4.38 (0.64), respectively with the ranges of 3 to 5 in both techniques, which were all acceptable for CT interpretation. The preferred image set between FTC and ATCM low dose techniques of each participant randomly selected by each reader were varied, depending on the readers’ opinions. The mean image noise of the aorta on FTC and ATCM low dose CT accounted for 34.75 and 36.46, respectively, while the mean image noise of the liver was 28.86 and 29.81, respectively. The mean CTDIvol (SD) of FTC and ATCM low dose CT were 8.42 (0.32) and 8.12 (0.43) mGy, respectively. CONCLUSION: FTC and ATCM low dose abdominal CT provided comparable acceptable image quality and showed no clinical significance in radiation dose optimization.

Head and neck single- and dual-energy CT: differences in radiation dose and image quality of 2nd and 3rd generation dual-source CT.

Objectives:To compare radiation dose and image quality of single-energy (SECT) and dual-energy (DECT) head and neck CT examinations performed with second- and third-generation dual-source CT (DSCT) in matched patient cohorts.Methods:200 patients (mean age 55.1 ± 16.9 years) who underwent venous phase head and neck CT with a vendor-preset protocol were retrospectively divided into four equal groups (n = 50) matched by gender and BMI: second (Group A, SECT, 100-kV; Group B, DECT, 80/Sn140-kV), and third-generation DSCT (Group C, SECT, 100-kV; Group D, DECT, 90/Sn150-kV). Assessment of radiation dose was performed for an average scan length of 27 cm. Contrast-to-noise ratio measurements and dose-independent figure-of-merit calculations of the submandibular gland, thyroid, internal jugular vein, and common carotid artery were analyzed quantitatively. Qualitative image parameters were evaluated regarding overall image quality, artifacts and reader confidence using 5-point Likert scales.Results:Effective radiation dose (ED) was not significantly different between SECT and DECT acquisition for each scanner generation (p = 0.10). Significantly lower effective radiation dose (p < 0.01) values were observed for third-generation DSCT groups C (1.1 ± 0.2 mSv) and D (1.0 ± 0.3 mSv) compared to second-generation DSCT groups A (1.8 ± 0.1 mSv) and B (1.6 ± 0.2 mSv). Figure-of-merit/contrast-to-noise ratio analysis revealed superior results for third-generation DECT Group D compared to all other groups. Qualitative image parameters showed non-significant differences between all groups (p > 0.06).Conclusion:Contrast-enhanced head and neck DECT can be performed with second- and third-generation DSCT systems without radiation penalty or impaired image quality compared with SECT, while third-generation DSCT is the most dose efficient acquisition method.Advances in knowledge:Differences in radiation dose between SECT and DECT of the dose-vulnerable head and neck region using DSCT systems have not been evaluated so far. Therefore, this study directly compares radiation dose and image quality of standard SECT and DECT protocols of second- and third-generation DSCT platforms.

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study

PurposeTo compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR). MethodTotally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging. ResultsGroups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001). ConclusionsReduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.

Downsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning.

X-ray computed tomography (CT) is widely used clinically to diagnose a variety of diseases by reconstructing the tomographic images of a living subject using penetrating X-rays. For accurate CT image reconstruction, a precise imaging geometric model for the radiation attenuation process is usually required to solve the inversion problem of CT scanning, which encodes the subject into a set of intermediate representations in different angular positions. Here, we show that accurate CT image reconstruction can be subsequently achieved by downsampled imaging geometric modeling via deep-learning techniques. Specifically, we first propose a downsampled imaging geometric modeling approach for the data acquisition process and then incorporate it into a hierarchical neural network, which simultaneously combines both geometric modeling knowledge of the CT imaging system and prior knowledge gained from a data-driven training process for accurate CT image reconstruction. The proposed neural network is denoted as DSigNet, i.e., downsampled-imaging-geometry-based network for CT image reconstruction. We demonstrate the feasibility of the proposed DSigNet for accurate CT image reconstruction with clinical patient data. In addition to improving the CT image quality, the proposed DSigNet might help reduce the computational complexity and accelerate the reconstruction speed for modern CT imaging systems.

Radiation dose reduction with the wide-volume scan mode for interstitial lung diseases.

The wide-volume mode, available on wide-area detector row CTs, has the advantage of reducing exposure time and radiation dose. It is infrequently used for lung diseases. The purpose of this study is to compare image quality and radiation dose of wide-volume chest CT to those of standard helical CT in the setting of interstitial lung diseases. Retrospective monocentric study including 50 consecutive patients referred for follow-up or screening of interstitial lung diseases, requiring prone scan, acquired with the wide-volume mode, in addition to the routine supine scan, acquired with the helical mode. The optimal collimation in wide-volume mode (320 × 0.5mm or 240 × 0.5mm) was chosen according to the length of the thorax. Wide-volume acquisitions were compared to helical acquisitions for radiation dose (CTDIvol, DLP) and image quality, including analysis of normal structures, lesions, overall image quality, and artifacts (Wilcoxon signed-rank test). Median CTDIvol and DLP with wide volumes (3.1 mGy and 94.6 mGy·cm) were significantly reduced (p < 0.0001) as compared to helical mode (3.7mGy and 122.1 mGy·cm), leading to a median 21% and 32% relative reduction of CTDIvol and DLP, respectively. Image noise and quality were not significantly different between the two modes. Misalignment artifact at the junction of two volumes was occasionally seen in the wide-volume scans and, when present, did not impair the diagnostic quality in the majority of cases. Wide-volume mode allows 32% radiation dose reduction compared to the standard helical mode and could be used routinely for diagnosis and follow-up of interstitial lung diseases. • Retrospective monocentric study showed that wide-volume scan mode reduces radiation dose by 32% in comparison to helical mode for chest CT in the setting of interstitial lung diseases. • Mild misalignment may be observed at the junction between volumes with the wide-volume mode, without decrease of image quality in the majority of cases and without impairing diagnostic quality. • Wide-volume mode could be used routinely for the diagnosis and follow-up of interstitial lung diseases.

Proposals for the Russian quality assurance program in computed tomography

To ensure the quality assurance of CT-examinations, it is necessary to obtain the high-quality diagnostic information and maintain the optimal exposure levels of patients and medical staff. This paper is focused on the requirements and main aspects of quality assurance of CT-examinations, which include quality control of the equipment, methods of CT-image quality control, optimization of radiation protection, as well as management of the unintended and accidental medical exposure. The paper contains recommendations on quality control of diagnostic equipment, methods for monitoring the quality control of CT-images, values of diagnostic reference levels for the detection of abnormally high patient doses and optimization of the radiation protection of patients, as well as the recommendations for management of radiation and non-radiation accidents. All main sections of the paper represent an unified quality assurance system in computed tomography.

MONTE CARLO DOSE ASSESSMENT IN DENTAL CONE-BEAM COMPUTED TOMOGRAPHY.

Most dental cone-beam computed tomography (CBCT) uses an x-ray beam field covering the maxillomandibular region and the width-truncated detector geometry. The spatial dose distribution in dental CBCT is analyzed in terms of local primary and remote secondary doses by using a list-mode analysis of x-ray interactions obtained from the Monte Carlo simulations. The patient-dose benefit due to the width-truncated detector geometry is also investigated for a wide range of detector offsets. The developed dose estimation agrees with the measurement in a relative error of 7.7%. The secondary dose outside of the irradiation field becomes larger with increasing tube voltage. The dose benefit with the width-truncated geometry linearly increases as the detector-offset width is decreased. Leaving the CT image quality out of the account, the MC results reveal that the operation of dental CBCT with a lower tube voltage and a smaller detector-offset width is beneficial to the patient dose.

Polyurethane compositions of Bismuth used for breast shields during chest CT

Background: Bismuth Polyurethane composite is a novel shield for breast protection while saving image quality in chest CT. Bismuth different concentrations used for constructing polymer composites breast shields in this study. Materials and Methods: Bismuth with particle size <150 micrometer used in bismuth polyurethane composite (BPC) with a ratio of 10%, 15% and 1.1, 2.2 mm thicknesses. The BPC was examined on the female chest phantom (skin and fourth layers), 120 kV and 80 mA selected as an exposure factors in chest CT. The dose reduction was recorded by TLD and the image quality was evaluated quantitatively and quantitatively. Results: Using BPC shields 10% and 15% (2.2 mm) induced to breast dose reduction to 69.1% and 77.5% for skin layer and 44.7% and 52.8% for fourth layer, respectively. The image noise Increasing in BPC for breast and mediastinum areas were 9.43% (P=0.347) and 15.44% (P=0.386), respectively. Conclusion: Application of Bismuth with different weight ratios in polyurethane composite was useful shield for radiation dose reduction of the breast in chest CT.

Imaging pediatric acute head trauma using 100-kVp low dose CT with adaptive statistical iterative reconstruction (ASIR-V) in single rotation on a 16 cm wide-detector CT.

To demonstrate the ability of achieving low dose and high-quality head CT images for children with acute head trauma using 100 kVp and adaptive statistical iterative reconstruction (ASIR-V) algorithm in single rotation on a 16 cm wide-detector system. We retrospectively analyzed the CT dose index (CTDI) and image quality of 104 children aged 0-6 years with acute head trauma (1 hour -3 days) in two groups: Group 1(n = 50) on a 256-row CT with single rotation at a reduced-dose of 100 kVp/240 mA and reconstructed using ASIR-V at 70%level; Group 2(n = 54) on a 64-row CT with multiple rotations at a standard dose of 120 kVp/ 180mA and reconstructed using a conventional filtered back-projection (FBP). Both groups used the 0.5 s/r axial scan mode. CT dose index (CTDI) and quantitative image quality measurements were compared using the Student t test; qualitative image quality comparison was carried out using Mann-Whitney rank test and the inter-reviewer agreement was evaluated using Kappa test. The exposure time was 0.5 s for Group 1 and 3.27±0.29 s for Group 2. The CTDI in Group 1 was 9.74±0.86mGy, 36.38%lower than the 15.31mGy in Group 2 (p < 0.001). Group 1 and Group 2 had similar artifact index (2.06±1.06 vs. 2.37±1.18) in the cerebellar hemispheres, and similar contrast-to-noise ratio (2.32±0.83 vs. 1.69±0.68), (1.47±0.72 vs. 1.10±0.43) respectively for cerebellum and thalamus (p > 0.05). Image quality was acceptable for diagnosis, and motion artifacts were reduced in Group 1 (p < 0.001). Single rotation CT with 100 kVp and 70%ASIR-V on 16 cm wide-detector CT reduces radiation dose and motion artifacts for children with acute head trauma without compromising diagnostic quality as compared with standard dose protocol. Thus, it provides a novel imaging method in management of pediatric acute head trauma.

Improving stroke patient care through parameter optimization of non-contrast brain CT

Neuroradiologists at MMUH have been concerned with inconsistencies in brain CT image quality received from the various scanners and hospitals. Our study aimed to develop an objective method of measuring low contrast detectability (LCD) in CT, identify which combination of CT acquisition parameters maximises LCD and to investigate the variation in imaging protocols for suspected acute stroke at different institutions in the regional hospital network.

Comparison of Quantitative Image Quality of Brain CT between Head-first and Feet-first Position with an Emphasis on Image Sharpness

Comparison of Quantitative Image Quality of Brain CT between Head-first and Feet-first Position with an Emphasis on Image Sharpness 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.