Standard-dose Chest Computed Tomography Research Articles

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant etal. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n=3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n=80) and validation (n=40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n=236) was evaluated. The vertebral segmentation module achieved a Dice score of.984 as compared to manual outlining results as reference (n=100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being.980 and.989, respectively. The vertebral labelling module achieved 100% accuracy (n=100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of.995 and.999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n=236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Retrospective Analysis of Subsolid Nodules' Frequency Using Chest Computed Tomography Detection in an Outpatient Population.

The study was designed to evaluate the frequency of detection and the characteristics of subsolid nodules (SSNs) in outpatients' chest computed tomography (CT) scans from a private hospital in Southern Brazil. A retrospective analysis of all chest CT scans was performed in adult patients from ambulatory care (non-lung cancer screening population) over a thirty-day period. Inclusion criteria were age > 18 years and lung-scanning protocols, including standard-dose high-resolution chest CT (HRCT), enhanced CT, CT angiography, and low-dose chest CT (LDCT). SSNs main features collected were mean diameter, number, density (pure or heterogenous ground glass nodules and part-solid), and localization. TheLungRADS system and the updated Fleischner Society's pulmonary nodules recommendations were used for categorization only for study purposes, although not specifically fitting the population. The presence of emphysema, as well as calcified and solid nodules were also addressed. Statistical analysis was performed using R software, categorial variables are shown as absolute or relative frequencies, and continuous variables as mean and interquartile ranges. Chest computed tomography were performed in 756 patients during the study period (September 2019), and 650 met the inclusion criteria. The IQR for age was 53/73 years; most participants were female (58.3%) and 10.6% had subsolid nodules detected. The frequency of SSNs detection in patients in daily clinical practice, not related to screening populations, is not negligible. Regardless of the final etiology, follow-up is often indicated, given the likelihood of malignancy for persistent lesions.

Ultra-Low-Dose Computed Tomography Chest for Screening of COVID-19 Patients: Assessment of Radiation Dose Reduction

Abstract Introduction: Chest computed tomography (CT) scans have a significant role in the identification and treatment of COVID-19 disease. In earlier studies, the introduction of chest CT has been a highly sensitive method in testing COVID-19 pneumonia. With a radiation dose comparable to a chest X-ray, ultra-low-dose CT has been promised to be a successful method for lowering radiation dose and motion artifact. Materials and Methods: The research was carried out at a tertiary care facility’s radiology department between August 1 and August 31, 2020. A sum of 109 individuals aged over 18 years old and been called for a noncontrast CT scan chest and received were included. Results: The minimum age was 18 and the maximum was 87, where 14.67% of them are between 21 and 40 years, 40.37% of them are between 41 and 60 years and 44.96% of them are between 61 and 87 years. Male’s mean age is 55.89 years and female’s mean age is 60.14 years. The overall mean age is 57.02 years and standard deviation is 13.49 years. The average body mass index was 26.51 ± 3.5 kg/m2. In the standard dose and ultra-low-dose groups, the mean effective dose values were 6.70 ± 0.85 and 0.33 ± 0.09 mSv, respectively (P < 0.001). Conclusion: When identifying lung infiltrations among the patients admitted for elective or moderately urgent surgical or medical operations at the time of the COVID-19 pandemic, ultra-low-dose chest CT is an effective method that exposes patients to less radiation than standard dose CT.

Radiation Exposure and Lifetime Attributable Risk of Cancer Incidence and Mortality from Low- and Standard-Dose CT Chest: Implications for COVID-19 Pneumonia Subjects.

Since the novel coronavirus disease 2019 (COVID-19) outbreak, there has been an unprecedented increase in the acquisition of chest computed tomography (CT) scans. Nearly 616 million people have been infected by COVID-19 worldwide to date, of whom many were subjected to CT scanning. CT exposes the patients to hazardous ionizing radiation, which can damage the genetic material in the cells, leading to stochastic health effects in the form of heritable genetic mutations and increased cancer risk. These probabilistic, long-term carcinogenic effects of radiation can be seen over a lifetime and may sometimes take several decades to manifest. This review briefly describes what is known about the health effects of radiation, the lowest dose for which there exists compelling evidence about increased radiation-induced cancer risk and the evidence regarding this risk at typical CT doses. The lifetime attributable risk (LAR) of cancer from low- and standard-dose chest CT scans performed in COVID-19 subjects is also discussed along with the projected number of future cancers that could be related to chest CT scans performed during the COVID-19 pandemic. The LAR of cancer Incidence from chest CT has also been compared with those from other radiation sources, daily life risks and lifetime baseline risk.

Detection of Incidental Nonosseous Thoracic Pathology on State-of-the-Art Ultralow-Dose Protocol Computed Tomography in Pediatric Patients With Pectus Excavatum.

The aim of the study was to compare a pediatric ultralow-dose pectus excavatum computed tomography (CT) protocol versus standard-dose pediatric thoracic CT in terms of radiation dose, subjective and objective image quality, and its ability to detect incidental nonosseous thoracic pathology compared with imaging and clinical reference. A single institution radiology database identified a total of 104 ultralow-dose pediatric thoracic CT cases with an equal number of age-matched standard-dose chest CT cases also selected for retrospective analysis. Objective image quality (contrast-to-noise and signal-to-noise ratios) and radiation dose were assessed. Qualitative Likert scorings of the bone, lung, and soft tissues were performed by 2 expert radiologists. Electronic health records of the ultralow-dose cohort were reviewed for at least 1 year to evaluate for potentially missed thoracic pathology and symptoms. Variables were compared using parametric and nonparametric tests in R software 4.0.5. The ultralow-dose protocol group had statistically significant reductions (P < 0.001) in the volume CT dose index (0.31 ± 0.19 vs 2.20 ± 1.64 mGy), effective radiation dose (0.14 ± 0.08 vs 1.07 ± 0.86 mSv), and size-specific dose estimates (0.50 ± 0.30 vs 3.43 ± 2.56 mGy) compared with the standard protocol, yielding an 86.51% and 85.32% reduction, respectively. The signal-to-noise ratio (20.49 ± 6.19 vs 36.48 ± 10.20), contrast-to-noise (21.65 ± 6.57 vs 38.47 ± 10.59), and subjective measures of image quality (lung parenchyma [3.07 ± 0.92 vs 4.42 ± 0.47], bony structures [3.30 ± 0.86 vs 4.52 ± 0.51], and surrounding soft tissues [2.57 ± 0.63 vs 3.89 ± 0.65]) were also significantly lower in the ultralow-dose protocol (P < 0.001). No differences were seen in the number and size of pulmonary nodules between groups. Clinical and imaging follow of all 104 patients undergoing ultralow-dose CT demonstrated no evidence of missed thoracic pathology causing symptoms. Ultralow-dose thoracic CT is an acceptable modality for imaging pediatric patients with pectus excavatum and other conditions primarily causing osseous pathology, with effective radiation dose comparable to plain radiographs and a moderate increase in image noise that did not significantly reduce its ability to detect incidental nonosseous thoracic pathology.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of 89.5%pm 0.11, CAP sensitivity of 95%pm 0.11, normal cases sensitivity (specificity) of 85.7%pm 0.16, and accuracy of 90%pm 0.06. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of 94.3%pm 0.05, CAP sensitivity of 96.7%pm 0.07, normal cases sensitivity (specificity) of 91%pm 0.09 , and accuracy of 94.1%pm 0.03. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

Performance of Low-Dose Chest CT Scan for Initial Triage of COVID-19

Background: Chest computed tomography (CT) scan is frequently used for diagnosis of coronavirus disease 2019 (COVID-19), especially in regions with limited availability of reverse-transcription polymerase chain reaction test (RT-PCR) test. Low-dose CT of chest offers acceptable image quality with lower radiation dose, particularly important in younger patients. Objectives: We have designed the current study to evaluate the diagnostic efficacy of low-dose chest CT versus early RT-PCR results, for triage of COVID-19 patients. Patients and Methods: From February 20 to April 15, 2020, 163 patients including 100 males (61.3%) with the median age of 65 years (21 to 97), who underwent both RT-PCR and chest CT were registered in the study. Low-dose chest CT protocol was applied with parameters modified from the lung cancer screening protocol. The accuracy of low-dose chest CT for COVID-19 diagnosis was evaluated, considering first RT-PCR results as reference. Results: Of 163 patients, 89 cases (54.6%) were presented with positive initial RT-PCR result. Lymphocyte percentage and lymphocyte count were significantly lower in the positive RT-PCR group (15% versus 19%, and 0.98 vs. 1.3, respectively); while, erythrocyte sedimentation rate (ESR) was significantly higher (53 vs. 22). Positive chest CT findings were present in 133/163 cases (81.6%). The sensitivity, specificity, positive and negative predictive values (PPV and NPV) and accuracy of low-dose chest CT scan were 96.6% (95% confidence interval [CI], 90% - 99%), 36.5% (95% CI, 26% - 49%), 64.7% (95% CI, 56% - 73%), 90% (95% CI, 72% - 97%) and 69.3% (95% CI, 61% - 76%), respectively based on positive RT-PCR results. Conclusion: Low-dose chest CT scan provides both high sensitivity and negative predictive value in diagnosing COVID-19 compared to initial RT-PCR as the gold standard. It can be used as an alternate to standard-dose CT scan in areas with high prevalence of COVID-19 disease and limited availability of RT-PCR for early triage.

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019.

BackgroundThe Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19.MethodsThe KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital.ResultsThe cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11–97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs.ConclusionThe KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.

Usefulness of dictionary learning-based processing for improving image quality of sub-millisievert low-dose chest CT: initial experience.

To develop a dictionary learning (DL)-based processing technique for improving the image quality of sub-millisievert chest computed tomography (CT). Standard-dose and sub-millisievert chest CT were acquired in 12 patients. Dictionaries including standard- and low-dose image patches were generated from the CT datasets. For each patient, DL-based processing was performed for low-dose CT using the dictionaries generated from the remaining 11 patients. This procedure was repeated for all 12 patients. Image quality of normal thoracic structures on the processed sub-millisievert CT images was assessed with a 5-point scale (5 = excellent, 1 = very poor). Lung lesion conspicuity was also assessed on a 5-point scale. Image noise on sub-millisievert CT was significantly decreased with DL-based image processing (48.5 ± 13.7 HU vs 20.4 ± 7.9 HU, p = 0.0005). Image quality of lung structures was significantly improved with DL-based method (middle level of lung, 2.25 ± 0.75 vs 2.92 ± 0.79, p = 0.0078). Lung lesion conspicuity was also significantly improved with DL-based technique (solid nodules, 3.4 ± 0.6 vs 2.7 ± 0.6, p = 0.0273). Image quality and lesion conspicuity on sub-millisievert chest CT images may be improved by DL-based post-processing.

Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study.

To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs. CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDIvol): 0.03-2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP). Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (-0.1%±4.3% to -0.3%±3.8%). Dose settings (P<0.001), reconstruction algorithms (P<0.001), nodule diameters (P<0.001) and nodule density (P=0.011) had statistically significant influence on APE. Post-hoc Bonferroni tests showed slightly higher APE when scanning with 1/70th of standard dose [mean difference: 3.4%, 95% confidence interval (CI): 2.5-4.3%; P<0.001], and for image reconstruction with ADMIRE5 (mean difference: 1.8%, 95% CI: 1.0-2.5%; P<0.001). No significant differences for scanning with 1/20th of standard dose (P=0.42), and image reconstruction with ADMIRE3 (P=0.19) were found. Scanning with 1/70th of standard dose and image reconstruction with FBP showed the widest range of PDs (-16.8% to 23.4%) compared to standard dose-FBP. Our phantom study showed no significant difference between nodule volume measurements on standard dose CT (CTDIvol: 2 mGy) and ULDCT with 1/20th of standard dose (CTDIvol: 0.10 mGy).

The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma: A Phantom Study

This phantom study aimed to evaluate low-dose (LD) chest computed tomography (CT) protocols using model-based iterative reconstruction (MBIR) for diagnosing lung metastases in patients with sarcoma. An adult female anthropomorphic phantom was scanned with a 64-slice CT using four LD protocols and a standard-dose protocol. Absorbed organ doses were measured with 10 metal-oxide-semiconductor field-effect transistor dosimeters. Furthermore, Monte Carlo simulations were performed to estimate organ and effective doses. Image quality in terms of image noise, contrast, and resolution was measured from the CT images reconstructed with conventional filtered back projection, adaptive statistical iterative reconstruction, and MBIR algorithms. All the results were compared to the performance of the standard-dose protocol. Mean absorbed organ and effective doses were reduced by approximately 95% with the LD protocol (100-kVp tube voltage and a fixed 10-mA tube current) compared to the standard-dose protocol (120-kVp tube voltage and tube current modulation) while yielding an acceptable image quality for diagnosing round-shaped lung metastases. The effective doses ranged from 0.16 to 2.83 mSv in the studied protocols. The image noise, contrast, and resolution were maintained or improved when comparing the image quality of LD protocols using MBIR to the performance of the standard-dose chest CT protocol using filtered back projection. The small round-shaped lung metastases were delineated at levels comparable to the used protocols. Radiation exposure in patients can be reduced significantly by using LD chest CT protocols and MBIR algorithm while maintaining image quality for detecting round-shaped lung metastases.

Low-voltage chest CT: another way to reduce the radiation dose in asbestos-exposed patients

To assess whether low voltage chest computed tomography (CT) can be used to successfully diagnose disease in patients with asbestos exposure. Fifty-six former employees of the shipbuilding industry, who were candidates to receive a standard-dose chest CT due to their occupational exposure to asbestos, underwent a routine CT. Immediately after this initial CT, they underwent a second acquisition using low-dose chest CT parameters, based on a low potential (80kV) and limited tube current. The findings of the two CT protocols were compared based on typical diseases associated with asbestos exposure. The kappa coefficient for each parameter and for an overall rating (grouping them based on mediastinal, pleural, and pulmonary findings) were calculated in order to test for correlations between the two protocols. A good correlation between routine and low-dose CT was demonstrated for most parameters with a mean radiation dose reduction of up to 83% of the effective dose based on the dose-length product between protocols. Low-dose chest CT, based on a limited tube potential, is useful for patients with an asbestos exposure background. Low-dose chest CT can be successfully used to minimise the radiation dose received by patients, as this protocol produced an estimated mean effective dose similar to that of an abdominal or pelvis plain film.

Computer-aided detection (CAD) of solid pulmonary nodules in chest x-ray equivalent ultralow dose chest CT - first in-vivo results at dose levels of 0.13 mSv

ObjectivesTo determine the value of computer-aided detection (CAD) for solid pulmonary nodules in ultralow radiation dose single-energy computed tomography (CT) of the chest using third-generation dual-source CT at 100kV and fixed tube current at 70 mAs with tin filtration. Methods202 consecutive patients undergoing clinically indicated standard dose chest CT (1.8±0.7 mSv) were prospectively included and scanned with an additional ultralow dose CT (0.13±0.01 mSv) in the same session. Standard of reference (SOR) was established by consensus reading of standard dose CT by two radiologists. CAD was performed in standard dose and ultralow dose CT with two different reconstruction kernels. CAD detection rate of nodules was evaluated including subgroups of different nodule sizes (<5, 5–7, >7mm). Sensitivity was further analysed in multivariable mixed effects logistic regression. ResultsThe SOR included 279 solid nodules (mean diameter 4.3±3.4mm, range 1–24mm). There was no significant difference in per–nodule sensitivity of CAD in standard dose with 70% compared to 68% in ultralow dose CT both overall and in different size subgroups (all p>0.05). CAD led to a significant increase of sensitivity for both radiologists reading the ultralow dose CT scans (all p<0.001). In multivariable analysis, the use of CAD (p<0.001), and nodule size (p<0.0001) were independent predictors for nodule detection, but not BMI (p=0.933) and the use of contrast agents (p=0.176). ConclusionsComputer-aided detection of solid pulmonary nodules using ultralow dose CT with chest X-ray equivalent radiation dose has similar sensitivities to those from standard dose CT. Adding CAD in ultralow dose CT significantly improves the sensitivity of radiologists.

Evaluation of pulmonary nodules and infection on chest CT with radiation dose equivalent to chest radiography: Prospective intra-individual comparison study to standard dose CT

PurposeTo compare prospectively, in patients undergoing chest computed tomography (CT) for pulmonary-nodules or infection, image-quality and accuracy of standard dose (SD) and reduced dose (RD) CT with tin-filtration. Material and methodsThis IRB-approved study included 100 consecutive patients (36 female;median age 56 years) referred for follow-up of pulmonary-nodules (n=43) or suspicion of infection (n=57) undergoing single-energy CT with SD and RD using tin-filtration at 100kVp (CTDIvol 2.47mGy and 0.07mGy, respectively). Images were reconstructed with advanced modeled iterative reconstruction (ADMIRE) at strength 3 and 5. Image-noise was measured. Two independent readers evaluated nodules and pulmonary-infection. SD CT served as reference standard. ResultsNo significant difference was found in noise between RD with ADMIRE5 and SD with ADMIRE3 (118HU±14 vs. 120HU±17; p=0.08). Sensitivity for detection of atelectasis and interstitial lung changes was higher in images reconstructed with ADMIRE5 (93% and 88%; respectively) than in those reconstructed with ADIMRE3 (77% and 78%; respectively). Sensitivity for detection of consolidations was 90% for ADMIRE3 and 89% for ADMIRE5. Sensitivity for nodule detection was 71% for ADMIRE3 and 81% for ADMIRE5. Specificity for detection of atelectasis and interstitial lung changes was 99% and 96% with ADMIRE5 and 99% and 96% with ADMIRE3. Specificity for detection of consolidations was 99% for ADMIRE3 and 5. Specificity for detection of nodules was 87% for both ADMIRE3 and 5. ConclusionChest CT with a radiation dose equivalent to conventional radiography is feasible and allows for detection of pulmonary infection with high sensitivity, whereas the accuracy for detecting nodules is only moderate.

CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction

BackgroundThe recently developed model-based iterative reconstruction (MBIR) enables significant reduction of image noise and artifacts, compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). The purpose of this study was to evaluate lesion detectability of low-dose chest computed tomography (CT) with MBIR in comparison with ASIR and FBP.MethodsChest CT was acquired with 64-slice CT (Discovery CT750HD) with standard-dose (5.7 ± 2.3 mSv) and low-dose (1.6 ± 0.8 mSv) conditions in 55 patients (aged 72 ± 7 years) who were suspected of lung disease on chest radiograms. Low-dose CT images were reconstructed with MBIR, ASIR 50% and FBP, and standard-dose CT images were reconstructed with FBP, using a reconstructed slice thickness of 0.625 mm. Two observers evaluated the image quality of abnormal lung and mediastinal structures on a 5-point scale (Score 5 = excellent and score 1 = non-diagnostic). The objective image noise was also measured as the standard deviation of CT intensity in the descending aorta.ResultsThe image quality score of enlarged mediastinal lymph nodes on low-dose MBIR CT (4.7 ± 0.5) was significantly improved in comparison with low-dose FBP and ASIR CT (3.0 ± 0.5, p = 0.004; 4.0 ± 0.5, p = 0.02, respectively), and was nearly identical to the score of standard-dose FBP image (4.8 ± 0.4, p = 0.66). Concerning decreased lung attenuation (bulla, emphysema, or cyst), the image quality score on low-dose MBIR CT (4.9 ± 0.2) was slightly better compared to low-dose FBP and ASIR CT (4.5 ± 0.6, p = 0.01; 4.6 ± 0.5, p = 0.01, respectively). There were no significant differences in image quality scores of visualization of consolidation or mass, ground-glass attenuation, or reticular opacity among low- and standard-dose CT series. Image noise with low-dose MBIR CT (11.6 ± 1.0 Hounsfield units (HU)) were significantly lower than with low-dose ASIR (21.1 ± 2.6 HU, p < 0.0005), low-dose FBP CT (30.9 ± 3.9 HU, p < 0.0005), and standard-dose FBP CT (16.6 ± 2.3 HU, p < 0.0005).ConclusionMBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT without compromising image quality. With radiation dose reduction of >70%, MBIR can provide equivalent lesion detectability of standard-dose FBP CT.

Usefulness of low-dose CT in the detection of pulmonary metastasis of gestational trophoblastic tumours

To determine whether a low-dose spiral chest computed tomography (CT) examination could replace standard-dose chest CT in detecting pulmonary metastases in patients with gestational trophoblastic tumour (GTT). In a prospective investigation, 67 chest CT examinations of 39 GTT patients were undertaken. All the patients underwent CT examinations using standard-dose (150 mAs, pitch 1, standard reconstruction algorithm) and low-dose (40 mAs, pitch 2, bone reconstruction algorithm) protocols. Two radiologists interpreted images independently. A metastasis was defined as a nodule within lung parenchyma that could not be attributed to a pulmonary vessel. The number of metastases detected with each protocol was recorded. The size of each lesion was measured and categorized as <5, 5-9.9, and > or = 10 mm. Wilcoxon's signed rank test was used to assess the difference between the numbers of lesion detected by the two protocols. The CT dose index (CTDI) for the standard-dose and low-dose CT protocols was 10.4 mGy and 1.4 mGy, respectively. One thousand, six hundred, and eighty-two metastases were detected by standard-dose CT, and 1460 lesions by the low-dose protocol. The numbers detected by low-dose CT were significantly less than those detected by standard-dose CT (Z=-3.776, p<0.001), especially for nodules smaller than 5mm (Z=-4.167, p<0.001). However, the disease staging and risk score of the patients were not affected by use of the low-dose protocol. Low-dose chest CT can be used as a staging and follow-up procedure for patients with GTT.

Reduced computed tomography radiation dose in HIV-related pneumonia: effect on diagnostic image quality

The objective of this study was to compare the diagnostic quality of low-dose computed tomography (CT) with that of standard-dose chest CT in the diagnosis of infectious lung diseases. Thirty chest CT scans [high-resolution computed tomography (HRCT), 15; spiral CT, 15] were performed in HIV-positive patients with an infectious lung disease. Two additional slices were obtained at two lower exposures (HRCT, 120 kV/70 mAs and 120 kV/50 mAs; spiral CT, 100 kV/56 mAs and 100 kV/40 mAs) after chest routine CT. Observers compared the quality of the images obtained at different parameters and image noise. Objective evaluation of image noise was also made. Diagnostic image quality was excellent in 93% of the low-dose HRCT scans and in 86% of the low-dose spiral CT scans, rates that are always acceptable in any case. Significant differences were found in noise levels between the low-dose and reference scans; however, artifacts did not compromise detection of abnormalities. In HRCT, a mean reduction of 77% from the standard technique to the low-dose scan is possible in total and in lung effective doses. In spiral CT, this reduction is lower at 71%. These values can reach a further reduction with ultra-low-dose imaging (84% in HRCT and 80% in spiral CT). Chest CT image quality appears to be adequate to evaluate pulmonary infectious diseases, even with an effective reduction in radiation dose. Standard-dose CT with a higher patient effective dose may be appropriate for selected cases.