Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis

Towards ultra-low-dose CT for detecting pulmonary nodules using DenseNet

Low-radiation techniques should be used to detect and follow lung nodules on CT images, but reducing radiation dose to ultra-low-dose CT with submilliSievert dose level would drastically impede image quality and sensitivity for nodule detection. This study investigated the feasibility of using DenseNet to suppress image noise in ultra-low-dose CT for lung cancer screening. DenseNet was trained using input-label pairs from 1, 2, 4, and 6 patients. After training, the model was tested with chest CT from 14 patients that were not used in training process. Seven patients have solid nodules and 7 patients have subsolid nodules. Root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) were calculated to quantify the difference between reference and test images. The contrast-to-noise ratio (CNR) between lung nodule and lung parenchyma was calculated to evaluate the target detectability of chest CT. Subjective image quality assessment was performed using 4-point ranking scale to evaluate the visual quality of CT images perceived by end user. Substantial improvements in RMSE and PSNR were observed after denoising. The lung nodules in denoised images could be distinguished more easily in comparison with those in the original ultra-low-dose CT, which is supported by the CNRs and subjective image quality scores. The comparison of intensity profiles for lung nodules demonstrated that the image noise in ultra-low-dose CT could be suppressed effectively after denoising without causing edge blurring or variation in Hounsfield unit (HU) values. A two-sample t-test revealed no statistically significant differences between full-dose CT and denoised ultra-low-dose CT in the evaluation of lung nodules, lung parenchyma, paraspinal muscle, or vertebral body. Since the linear no-threshold model suggests that no amount of ionizing radiation is entirely risk-free, the quest for further dose reduction remains a consistently important focus in radiology. Overall, our findings suggest that DenseNet could be a viable approach for reducing image noise in ultra-low-dose CT scans used for lung cancer screening.

Improvement of image quality of dentomaxillofacial region in ultra-high-resolution CT: a phantom study

OBJECTIVES

The purpose of this study was to compare the image quality of ultra-high-resolution CT (U-HRCT) with that of conventional multidetector row CT (convCT) and demonstrate its usefulness in the dentomaxillofacial region.

METHODS

Phantoms were helically scanned with U-HRCT and convCT scanners using clinical protocols. In U-HRCT, phantoms were scanned in super-high-resolution (SHR) mode, and hybrid iterative reconstruction (HIR) and filtered-back projection (FBP) techniques were performed using a bone kernel (FC81). The FBP technique was performed using the same kernel as in convCT (reference). Two observers independently evaluated the 54 resulting images using a 5-point scale (5 = excellent diagnostic image quality; 4 = above average; 3 = average; 2 = subdiagnostic; and 1 = unacceptable). The system performance function (SPF) was calculated for a comprehensive evaluation of the image quality using the task transfer function and noise power spectrum. Statistical analysis using the Kruskal-Wallis test was performed to compare the image quality among the 3 protocols.

RESULTS

The observers assigned higher scores to images acquired with the SHRHIR and SHRFBP protocols than to those acquired with the reference (P < 0.0001 and P < 0.0001, respectively). The relative SPF value at 1.0 cycles/mm in SHRHIR and SHRFBP compared to the reference protocol were 151.5% and 45.6%, respectively.

CONCLUSIONS

Through phantom experiments, this study demonstrated that U-HRCT can provide superior-quality images compared to conventional CT in the dentomaxillofacial region. The development of a better image reconstruction method is required to improve image quality and optimize the radiation dose.

Right main pulmonary artery distensibility on dynamic ventilation CT and its association with respiratory function

BACKGROUND

Heartbeat-based cross-sectional area (CSA) changes in the right main pulmonary artery (MPA), which reflects its distensibility associated with pulmonary hypertension, can be measured using dynamic ventilation computed tomography (DVCT) in patients with and without chronic obstructive pulmonary disease (COPD) during respiratory dynamics. We investigated the relationship between MPA distensibility (MPAD) and respiratory function and how heartbeat-based CSA is related to spirometry, mean lung density (MLD), and patient characteristics.

METHODS

We retrospectively analyzed DVCT performed preoperatively in 37 patients (20 female and 17 males) with lung cancer aged 70.6 ± 7.9 years (mean ± standard deviation), 18 with COPD and 19 without. MPA-CSA was separated into respiratory and heartbeat waves by discrete Fourier transformation. For the cardiac pulse-derived waves, CSA change (CSAC) and CSA change ratio (CSACR) were calculated separately during inhalation and exhalation. Spearman rank correlation was computed.

RESULT

In the group without COPD as well as all cases, CSACR exhalation was inversely correlated with percent residual lung volume (%RV) and RV/total lung capacity (r = -0.68, p = 0.003 and r = -0.58, p = 0.014). In contrast, in the group with COPD, CSAC inhalation was correlated with MLDmax and MLD change rate (MLDmax/MLDmin) (r = 0.54, p = 0.020 and r = 0.64, p = 0.004) as well as CSAC exhalation and CSACR exhalation.

CONCLUSION

In patients with insufficient exhalation, right MPAD during exhalation was decreased. Also, in COPD patients with insufficient exhalation, right MPAD was reduced during inhalation as well as exhalation, which implied that exhalation impairment is a contributing factor to pulmonary hypertension complicated with COPD.

RELEVANCE STATEMENT

Assessment of MPAD in different respiratory phases on DVCT has the potential to be utilized as a non-invasive assessment for pulmonary hypertension due to lung disease and/or hypoxia and elucidation of its pathogenesis.

KEY POINTS

• There are no previous studies analyzing all respiratory phases of right main pulmonary artery distensibility (MPAD). • Patients with exhalation impairment decreased their right MPAD. • Analysis of MPAD on dynamic ventilation computed tomography contributes to understanding the pathogenesis of pulmonary hypertension due to lung disease and/or hypoxia in patients with expiratory impairment.

Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window

OBJECTIVES

To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma.

METHODS

Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction.

RESULTS

The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05).

CONCLUSION

LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR.

CLINICAL RELEVANCE STATEMENT

The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications.

KEY POINTS

• DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Detection, measurement, and diagnosis of lung nodules by ultra-low-dose CT in lung cancer screening: a systematic review

Objective

There is a lack of recent meta-analyses and systematic reviews on the use of ultra-low-dose CT (ULDCT) for the detection, measurement, and diagnosis of lung nodules. This review aims to summarize the latest advances of ULDCT in these areas.

Methods

A systematic review of studies in PubMed and Web of Science was conducted, using search terms specific to ULDCT and lung nodules. The included studies were published in the last 5 years (January 2019-August 2024). Two reviewers independently selected articles, extracted data, and assessed the risk of bias and concerns using the Quality Assessment of Diagnostic Accuracy Studies-II (QUADAS-II) tool. The standard-dose, low-dose, or contrast-enhanced CT served as the reference-standard CT to evaluate ULDCT.

Results

The literature search yielded 15 high-quality articles on a total of 1889 patients, of which 10, 3, and 2 dealt with the detection, measurement, and diagnosis of lung nodules. QUADAS-II showed a generally low risk of bias. The mean radiation dose for ULDCT was 0.22 ± 0.10 mSv (7.7%) against 2.84 ± 1.80 mSv for reference-standard CT. Nodule detection rates ranged from 86.1% to 100%. The variability of diameter measurements ranged from 2.1% to 14.4% against contrast-enhanced CT and from 3.1% to 8.29% against standard CT. The diagnosis rate of malignant nodules ranged from 75% to 91%.

Conclusions

ULDCT proves effective in detecting lung nodules while substantially reducing radiation exposure. However, the use of ULDCT for the measurement and diagnosis of lung nodules remains challenging and requires further research.

Advances in knowledge

When ULDCT reduces radiation exposure to 7.7%, it detects lung nodules at a rate of 86.1%-100%, with a measurement variance of 2.1%-14.4% and a diagnostic accuracy for malignancy of 75%-91%, suggesting the potential for safe and effective lung cancer screening.

Ultra-low-dose CT vs. chest X-ray in non-traumatic emergency department patients - a prospective randomised crossover cohort trial

Background

Ultra-low-dose CT (ULDCT) examinations of the chest at only twice the radiation dose of a chest X-ray (CXR) now offer a valuable imaging alternative to CXR. This trial prospectively compares ULDCT and CXR for the detection rate of diagnoses and their clinical relevance in a low-prevalence cohort of non-traumatic emergency department patients.

Methods

In this prospective crossover cohort trial, 294 non-traumatic emergency department patients with a clinically indicated CXR were included between May 2nd and November 26th of 2019 (www.clinicaltrials.gov: NCT03922516). All participants received both CXR and ULDCT, and were randomized into two arms with inverse reporting order. The detection rate of CXR was calculated from 'arm CXR' (n = 147; CXR first), and of ULDCT from 'arm ULDCT' (n = 147; ULDCT first). Additional information reported by the second exam in each arm was documented. From all available clinical and imaging data, expert radiologists and emergency physicians built a compound reference standard, including radiologically undetectable diagnoses, and assigned each finding to one of five clinical relevance categories for the respective patient.

Findings

Detection rates for main diagnoses by CXR and ULDCT (mean effective dose: 0.22 mSv) were 9.1% (CI [5.2, 15.5]; 11/121) and 20.1% (CI [14.2, 27.7]; 27/134; P = 0.016), respectively. As an additional imaging modality, ULDCT added 9.1% (CI [5.2, 15.5]; 11/121) of main diagnoses to prior CXRs, whereas CXRs did not add a single main diagnosis (0/134; P < 0.001). Notably, ULDCT also offered higher detection rates than CXR for all other clinical relevance categories, including findings clinically irrelevant for the respective emergency department visit with 78.5% (CI [74.0, 82.5]; 278/354) vs. 16.2% (CI [12.7, 20.3]; 58/359) as a primary modality and 68.2% (CI [63.3, 72.8]; 245/359) vs. 2.5% (CI [1.3, 4.7]; 9/354) as an additional imaging modality.

Interpretation

In non-traumatic emergency department patients, ULDCT of the chest offered more than twice the detection rate for main diagnoses compared to CXR.

Funding

The Department of Biomedical Imaging and Image-guided Therapy of Medical University of Vienna received funding from Siemens Healthineers (Erlangen, Germany) to employ two research assistants for one year.

Area-Detector Computed Tomography for Pulmonary Functional Imaging

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed

The ITALUNG trial started in 2004 and compared lung cancer (LC) and other-causes mortality in 55-69 years-aged smokers and ex-smokers who were randomized to four annual chest low-dose CT (LDCT) or usual care. ITALUNG showed a lower LC and cardiovascular mortality in the screened subjects after 13 years of follow-up, especially in women, and produced many ancillary studies. They included recruitment results of a population-based mimicking approach, development of software for computer-aided diagnosis (CAD) and lung nodules volumetry, LDCT assessment of pulmonary emphysema and coronary artery calcifications (CAC) and their relevance to long-term mortality, results of a smoking-cessation intervention, assessment of the radiations dose associated with screening LDCT, and the results of biomarkers assays. Moreover, ITALUNG data indicated that screen-detected LCs are mostly already present at baseline LDCT, can present as lung cancer associated with cystic airspaces, and can be multiple. However, several issues of LC screening are still unaddressed. They include the annual vs. biennial pace of LDCT, choice between opportunistic or population-based recruitment. and between uni or multi-centre screening, implementation of CAD-assisted reading, containment of false positive and negative LDCT results, incorporation of emphysema. and CAC quantification in models of personalized LC and mortality risk, validation of ultra-LDCT acquisitions, optimization of the smoking-cessation intervention. and prospective validation of the biomarkers.

Lung Cancer Screening Using Clinical Photon-Counting Detector Computed Tomography and Energy-Integrating-Detector Computed Tomography: A Prospective Patient Study

OBJECTIVE

To evaluate the diagnostic quality of photon-counting detector (PCD) computed tomography (CT) in patients undergoing lung cancer screening compared with conventional energy-integrating detector (EID) CT in a prospective multireader study.

MATERIALS

Patients undergoing lung cancer screening with conventional EID-CT were prospectively enrolled and scanned on a PCD-CT system using similar automatic exposure control settings and reconstruction kernels. Three thoracic radiologists blinded to CT system compared PCD-CT and EID-CT images and scored examinations using a 5-point Likert comparison score (-2 [left image is worse] to +2 [left image is better]) for artifacts, sharpness, image noise, diagnostic image quality, emphysema visualization, and lung nodule evaluation focusing on the border. Post hoc correction of Likert scores was performed such that they reflected PCD-CT performance in comparison to EID-CT. A nonreader radiologist measured objective image noise.

RESULTS

Thirty-three patients (mean, 66.9 ± 5.6 years; 11 female; body mass index; 30.1 ± 5.1 kg/m 2 ) were enrolled. Mean volume CT dose index for PCD-CT was lower (0.61 ± 0.21 vs 0.73 ± 0.22; P < 0.001). Pooled reader results showed significant differences between imaging modalities for all comparative rankings ( P < 0.001), with PCD-CT favored for sharpness, image noise, image quality, and emphysema visualization and lung nodule border, but not artifacts. Photon-counting detector CT had significantly lower image noise (74.4 ± 10.5 HU vs 80.1 ± 8.6 HU; P = 0.048).

CONCLUSIONS

Photon-counting detector CT with similar acquisition and reconstruction settings demonstrated improved image quality and less noise despite lower radiation dose, with improved ability to depict pulmonary emphysema and lung nodule borders compared with EID-CT at low-dose lung cancer CT screening.

Association of Respiratory Functional Indices and Smoking with Pleural Movement and Mean Lung Density Assessed Using Four-Dimensional Dynamic-Ventilation Computed Tomography in Smokers and Patients with COPD

Purpose

To correlate the ratio of the non-dependent to dependent aspects of the maximal pleural movement vector (MPMV_ND/D) and gravity-oriented collapse ratio (GCR_ND/D), and the mean lung field density (MLD) obtained using four-dimensional (4D) dynamic-ventilation computed tomography (DVCT) with airflow limitation parameters and the Brinkman index.

Materials and Methods

Forty-seven patients, including 22 patients with COPD, 13 non-COPD smokers, and 12 non-smokers, with no/slight pleural adhesion confirmed using a thoracoscope, underwent 4D-DVCT with 16 cm coverage. Coordinates for the lung field center, as well as ventral and dorsal pleural points, set on the central trans-axial levels in the median and para-median sagittal planes at end-inspiration, were automatically measured (13-17 frame images, 0.35 seconds/frame). MPMV_ND/D and GCR_ND/D were calculated based on MPMV and GCR values for all the included points and the lung field center. MLD was automatically measured in each of the time frames, and the maximal change ratio of MLD (MLD_CR) was calculated. These measured values were compared among COPD patients, non-COPD smokers, and non-smokers, and were correlated with the Brinkman index, FEV₁/FVC, FEV₁ predicted, RV/TLC, and FEF_25-75% using Spearman's rank coefficients.

Results

MPMV_ND/D was highest in non-smokers (0.819±0.464), followed by non-COPD smokers (0.405±0.131) and patients with COPD (-0.219±0.900). GCR_ND/D in non-smokers (1.003±1.384) was higher than that in patients with COPD (-0.164±1.199). MLD_CR in non-COPD smokers (0.105±0.028) was higher than that in patients with COPD (0.078±0.027). MPMV_ND/D showed positive correlations with FEV₁ predicted (r=0.397, p=0.006), FEV₁/FVC (r=0.501, p<0.001), and FEF_25-75% (r=0.368, p=0.012). GCR_ND/D also demonstrated positive correlations with FEV₁ (r=0.397, p=0.006), FEV₁/FVC (r=0.445, p=0.002), and FEF_25-75% (r=0.371, p=0.011). MPMV_ND/D showed a negative correlation with the Brinkman index (r=-0.398, p=0.006).

Conclusion

We demonstrated that reduced MPMV_ND/D and GCR_ND/D were associated with respiratory functional indices, in addition to a negative association of MPMV_ND/D with the Brinkman index, which should be recognized when assessing local pleural adhesion on DVCT, especially for ventral pleural aspects.

Low-radiation dose scan protocol for preoperative imaging for dental implant surgery using deep learning-based reconstruction in multidetector CT

OBJECTIVES

This study aimed to investigate the impact of a deep learning-based reconstruction (DLR) technique on image quality and reduction of radiation exposure, and to propose a low-dose multidetector-row computed tomography (MDCT) scan protocol for preoperative imaging for dental implant surgery.

METHODS

The PB-1 phantom and a Catphan phantom 600 were scanned using volumetric scanning with a 320-row MDCT scanner. All scans were performed with a tube voltage of 120 kV, and the tube current varied from 120 to 60 to 40 to 30 mA. Images of the mandible were reconstructed using DLR. Additionally, images acquired with the 120-mA protocol were reconstructed using filtered back projection as a reference. Two observers independently graded the image quality of the mandible images using a 4-point scale (4, superior to reference; 1, unacceptable). The system performance function (SPF) was calculated to comprehensively evaluate image quality. The Wilcoxon signed-rank test was employed for statistical analysis, with statistical significance set at p value < 0.05.

RESULTS

There was no significant difference between the image quality acquired with the 40-mA tube current and reconstructed with the DLR technique (40DLR), and that acquired with the reference protocol (3.00, 3.00, p = 1.00). The SPF at 1.0 cycles/mm acquired with 40DLR was improved by 156.7% compared to that acquired with the reference protocol.

CONCLUSIONS

Our proposed protocol, which achieves a two-thirds reduction in radiation dose, can provide a minimally invasive MDCT scan of acceptable image quality for dental implant surgery.

Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?

Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.

Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography

PURPOSE

Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict human observer performance in clinical CT tasks that involve realistic anatomical background. Deep-learning-based model observers (DL-MO) have recently been developed, but have not been validated for challenging low contrast diagnostic tasks in abdominal CT. We consequently sought to validate a DL-MO for a low-contrast hepatic metastases localization task.

METHODS

We adapted our recently developed DL-MO framework for the liver metastases localization task. Our previously-validated projection-domain lesion-/noise-insertion techniques were used to synthesize realistic positive and low-dose abdominal CT exams, using the archived patient projection data. Ten experimental conditions were generated, which involved different lesion sizes/contrasts, radiation dose levels, and image reconstruction types. Each condition included 100 trials generated from a patient cohort of 7 cases. Each trial was presented as liver image patches (160×160×5 voxels). The DL-MO performance was calculated for each condition and was compared with human observer performance, which was obtained by three sub-specialized radiologists in an observer study. The performance of DL-MO and radiologists was gauged by the area under localization receiver-operating-characteristic curves. The generalization performance of the DL-MO was estimated with the repeated twofold cross-validation method over the same set of trials used in the human observer study. A multi-slice Channelized Hoteling Observers (CHO) was compared with the DL-MO across the same experimental conditions.

RESULTS

The performance of DL-MO was highly correlated to that of radiologists (Pearson's correlation coefficient: 0.987; 95% CI: [0.942, 0.997]). The performance level of DL-MO was comparable to that of the grouped radiologists, that is, the mean performance difference was -3.3%. The CHO performance was poorer than the grouped radiologist performance, before internal noise could be added. The correlation between CHO and radiologists was weaker (Pearson's correlation coefficient: 0.812, and 95% CI: [0.378, 0.955]), and the corresponding performance bias (-29.5%) was statistically significant.

CONCLUSION

The presented study demonstrated the potential of using the DL-MO for image quality assessment in patient abdominal CT tasks.

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 (MTF_Task), the noise power spectrum (NPS), and the low-contrast object-specific CNR (CNR_LO) for each algorithm using three dose levels and the convolution algorithm (kernel) appropriate for abdomen, lung, and brain imaging. Additionally, MTF_Taskwas measured at four contrast levels, and CNR_LOwas 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.

Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification

PURPOSE

Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes.

METHODS

The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three-dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment.

RESULTS

The proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane.

CONCLUSION

Our approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.

Diagnostic accuracy of low-dose and ultra-low-dose CT in detection of chest pathology: a systematic review

PURPOSE

Studies have evaluated imaging modalities with a lower radiation dose than standard-dose CT (SD-CT) for chest examination. This systematic review aimed to summarize evidence on diagnostic accuracy of these modalities - low-dose and ultra-low-dose CT (LD- and ULD-CT) - for chest pathology.

METHOD

Ovid-MEDLINE, Ovid-EMBASE and the Cochrane Library were systematically searched April 29th-30th, 2019 and screened by two reviewers. Studies on diagnostic accuracy were included if they defined their index tests as 'LD-CT', 'Reduced-dose CT' or 'ULD-CT' and had SD-CT as reference standard. Risk of bias was evaluated on study level using the Quality Assessment of Diagnostic Accuracy Studies-2. A narrative synthesis was conducted to compare the diagnostic accuracy measurements.

RESULTS

Of the 4257 studies identified, 18 were eligible for inclusion. SD-CT (3.17 ± 1.47 mSv) was used as reference standard in all studies to evaluate diagnostic accuracy of LD- (1.22 ± 0.34 mSv) and ULD-CT (0.22 ± 0.05 mSv), respectively. LD-CT had high sensitivities for detection of bronchiectasis (82-96%), honeycomb (75-100%), and varying sensitivities for nodules (63-99%) and ground glass opacities (GGO) (77-91%). ULD-CT had high sensitivities for GGO (93-100%), pneumothorax (100%), consolidations (90-100%), and varying sensitivities for nodules (60-100%) and emphysema (65-90%).

CONCLUSION

The included studies found LD-CT to have high diagnostic accuracy in detection of honeycombing and bronchiectasis and ULD-CT to have high diagnostic accuracy for pneumothorax, consolidations and GGO. Summarizing evidence on diagnostic accuracy of LD- and ULD-CT for other chest pathology was not possible due to varying outcome measures, lack of precision estimates and heterogeneous study design and methodology.

Preoperative assessment of localized pleural adhesion: Utility of software-assisted analysis on dynamic-ventilation computed tomography

PURPOSE

To assess the usefulness of software analysis using dynamic-ventilation CT for localized pleural adhesion (LPA).

MATERIALS AND METHODS

Fifty-one patients scheduled to undergo surgery underwent both dynamic-ventilation CT and static chest CT as preoperative assessments. Five observers independently evaluated the presence and severity of LPA on a three-point scale (non, mild, and severe LPA) for 9 pleural regions (upper, middle, and lower pleural aspects on ventral, lateral, and dorsal areas) on the chest CT by three different methods by observing images from: static high-resolution CT (static image); dynamic-ventilation CT (movie image), and dynamic-ventilation CT while referring to the adhesion map (movie image with color map), which was created using research software to visualize movement differences between the lung surface and chest wall. The presence and severity of LPA was confirmed by intraoperative thoracoscopic findings. Parameters of diagnostic accuracy for LPA presence and severity were assessed among the three methods using Wilcoxon signed rank test in total and for each of the three pleural aspects.

RESULTS

Mild and severe LPA were confirmed in 14 and 8 patients. Movie image with color map had higher sensitivity (56.9 ± 10.7 %) and negative predictive value (NPV) (91.4 ± 1.7 %) in LPA detection than both movie image and static image. Additionally, for severe LPA, detection sensitivity was the highest with movie image with color map (82.5 ± 6.1 %), followed by movie image (58.8 ± 17.0 %) and static image (38.8 ± 13.9 %). For LPA severity, movie image with color map was similar to movie image and superior to static image in accuracy as well as underestimation and overestimation, with a mean value of 80.2 %.

CONCLUSION

Software-assisted dynamic-ventilation CT may be a useful novel imaging approach to improve the detection performance of LPA.

Deep-learning-based model observer for a lung nodule detection task in computed tomography

Purpose: Task-based image quality assessment using model observers (MOs) is an effective approach to radiation dose and scanning protocol optimization in computed tomography (CT) imaging, once the correlation between MOs and radiologists can be established in well-defined clinically relevant tasks. Conventional MO studies were typically simplified to detection, classification, or localization tasks using tissue-mimicking phantoms, as traditional MOs cannot be readily used in complex anatomical background. However, anatomical variability can affect human diagnostic performance. Approach: To address this challenge, we developed a deep-learning-based MO (DL-MO) for localization tasks and validated in a lung nodule detection task, using previously validated projection-based lesion-/noise-insertion techniques. The DL-MO performance was compared with 4 radiologist readers over 12 experimental conditions, involving varying radiation dose levels, nodule sizes, nodule types, and reconstruction types. Each condition consisted of 100 trials (i.e., 30 images per trial) generated from a patient cohort of 50 cases. DL-MO was trained using small image volume-of-interests extracted across the entire volume of training cases. For each testing trial, the nodule searching of DL-MO was confined to a 3-mm thick volume to improve computational efficiency, and radiologist readers were tasked to review the entire volume. Results: A strong correlation between DL-MO and human readers was observed (Pearson's correlation coefficient: 0.980 with a 95% confidence interval of [0.924, 0.994]). The averaged performance bias between DL-MO and human readers was 0.57%. Conclusion: The experimental results indicated the potential of using the proposed DL-MO for diagnostic image quality assessment in realistic chest CT tasks.

Low-dose chest CT and the impact on nodule visibility

INTRODUCTION

The need to continually optimise CT protocols is essential to ensure the lowest possible radiation dose for the clinical task and individual patient. The aim of this study was to explore the effect of reducing effective mAs on nodule detection and radiation dose across six scanners.

METHODS

An anthropomorphic chest phantom was scanned using a low-dose chest CT protocol, with the effective mAs lowered to the lowest permissible level. All other acquisition parameters remained consistent. Images were evaluated by five radiologists to determine their sensitivity in detecting six simulated nodules within the phantom. Image noise was calculated together with DLP.

RESULTS

The lowest possible mAs achievable ranged from 7 to 19 mAs. The two highest mAs setting (17 mAs + 19 mAs) had kV modulation enabled (100 kV instead of 120 kV) which consequently resulted in a higher nodule detection rate. Overall nodule detection averaged at 91% (range 80-97%). Out of a possible 180 nodules, 16 were missed, with 12 of those 16 being the same nodule. Noise was double for the Somatom Sensation scanner when compared to the others; however, this scanner did not have iterative reconstruction and it was installed over 10 years ago. There was a strong correlation between image noise and scanner age.

CONCLUSION

This study highlighted that nodules can be detected at very low effective mAs (<20 mAs) but only when other acquisition parameters are optimised i.e. iterative reconstruction and kV modulation. Nodule detection rates were affected by nodule location and image noise.

IMPLICATIONS FOR PRACTICE

This study consolidates previous findings on how to successfully optimise low-dose chest CT. It also highlights the difficulty with standardisation owing to factors such as scanner age and different vendor attributes.

Improved scan method for dental imaging using multidetector computed tomography: a phantom study

OBJECTIVES

This study aimed to propose an improved scan method to shorten irradiation time and reduce radiation exposure.

METHODS

The maxilla of a human head CT phantom and a Catphan phantom were used for qualitative and quantitative assessment, respectively. The phantoms were scanned by a 160-row multidetector CT scanner using volumetric and helical scanning. In volumetric scanning, the tube current varied from 120 to 60 to 30 to 20 mA with a tube voltage of 120 kV. Images were reconstructed with a bone kernel using iterative reconstruction (IR) and filtered back projection. As a reference protocol, helical scanning was performed using our clinical setting with 120 kV. Two dental radiologists independently graded the quality of dental images using a 4-point scale (4, superior to reference; 1, unacceptable). For the quantitative assessment, we assessed the system performance from each scan.

RESULTS

There was no significant difference between the image quality of volumetric scanning using the 60 mA protocol reconstructed with IR and that of the reference (3.08 and 3.00, p = 0.3388). The system performance values at 1.0 cycles/mm of volumetric scanning and 60 mA protocol reconstructed with IR and reference were 0.0038 and 0.0041, respectively. The effective dose of volumetric scanning using the 60 mA protocol was 51.8 µSv, which is a 64.2% reduction to that of the reference.

CONCLUSIONS

We proposed an improved scan method resulting in a 64.2% reduction of radiation dose with one-fourth of irradiation time by combining volumetric scanning and IR technique in multidetector CT.

Technical Note: Increased photon starvation artifacts at low helical pitch in ultra-low-dose CT

PURPOSE

The aim of this study was to demonstrate that a low helical pitch causes increased photon starvation artifacts at ultra-low-dose CT.

METHODS

A cylindrical water phantom with a diameter of 30 cm was scanned on two different generation CT scanners: a 64-slice scanner (Sensation 64, Siemens Healthcare) and a 192-slice scanner (Somatom Force, Siemens Healthcare) at multiple effective mAs levels (mAs/pitch = 200, 100, 50, 25, and 12). The corresponding CTDI_vol values were 4.1, 2.0, 1.0, 0.5 mGy, on the 64-slice scanner and 3.8, 1.9, 1.0, 0.5 mGy on the 192-slice scanner, for the selected effective mAs values. For each dose setting, the scan was repeated at four helical pitches: 1.2, 0.9, 0.6, and the lowest achievable pitch on each scanner. The tube current was automatically adjusted by the scanner so that the effective mAs, and thus CTDI_vol , were kept the same for different pitches. All CT data sets were reconstructed with a slice thickness of 3mm and a medium smooth kernel. Images acquired at the same dose level but different helical pitches were visually inspected to assess photon starvation artifacts and noise levels.

RESULTS

At the same radiation dose, image noise increased with the decreasing helical pitch. The increase was more severe on the old-generation 64-slice scanner. Photon starvation artifacts were evident at 200 effective mAs on the 64-slice scanner at 80 kV. On the 192-slice scanner there was no visible photon starvation artifacts at both 200 and 50 effective mAs (CTDI_vol  = 4.1 mGy and 1.0 mGy, respectively); nor was there a visible impact from the lower helical pitch. Only when the dose was lowered to be extremely low (~0.26 mGy, achievable at 70 kV), did photon starvation artifacts become evident.

CONCLUSIONS

A low helical pitch may increase image noise and photon starvation artifacts compared to a higher pitch for the same dose level, particularly at ultra-low dose CT.

Detection of pulmonary nodules: a clinical study protocol to compare ultra-low dose chest CT and standard low-dose CT using ASIR-V

INTRODUCTION

Lung cancer screening in individuals at risk has been recommended by various scientific institutions. One of the main concerns for CT screening is repeated radiation exposure, with the risk of inducing malignancies in healthy individuals. Therefore, lowering the radiation dose is one of the main objectives for radiologists. The aim of this study is to demonstrate that an ultra-low dose (ULD) chest CT protocol, using recently introduced hybrid iterative reconstruction (ASiR-V, GE medical Healthcare, Milwaukee, Wisconsin, USA), is as performant as a standard 'low dose' (LD) CT to detect non-calcified lung nodules ≥4 mm.

METHODS AND ANALYSIS

The total number of patients to include is 150. Those are referred for non-enhanced chest CT for detection or follow-up of lung nodule and will undergo an additional unenhanced ULD CT acquisition, the dose of which is on average 10 times lower than the conventional LD acquisition. Total dose of the entire exam (LD+ULD) is lower than the French diagnostic reference level for a chest CT (6.65 millisievert). ULD CT images will be reconstructed with 50% and 100% ASiR-V and LD CT with 50%. The three sets of images will be read in random order by two pair of radiologists, in a blind test, where patient identification and study outcomes are concealed. Detection rate (sensitivity) is the primary outcome. Secondary outcomes will include concordance of nodule characteristics; interobserver reproducibility; influence of subjects' characteristics, nodule location and nodule size; and concordance of emphysema, coronary calcifications evaluated by visual scoring and bronchial alterations between LD and ULD CT. In case of discordance, a third radiologist will arbitrate.

ETHICS AND DISSEMINATION

The study was approved by the relevant ethical committee. Each study participant will sign an informed consent form.

TRIAL REGISTRATION NUMBER

NCT03305978; Pre-results.

Reproducibility of an airway tapering measurement in computed tomography with application to bronchiectasis

We propose a pipeline to acquire a scalar tapering measurement from the carina to the most distal point of an individual airway visible on computed tomography (CT). We show the applicability of using tapering measurements on clinically acquired data by quantifying the reproducibility of the tapering measure. We generate a spline from the centerline of an airway to measure the area and arclength at contiguous intervals. The tapering measurement is the gradient of the linear regression between area in log space and arclength. The reproducibility of the measure was assessed by analyzing different radiation doses, voxel sizes, and reconstruction kernel on single timepoint and longitudinal CT scans and by evaluating the effect of airway bifurcations. Using 74 airways from 10 CT scans, we show a statistical difference, p = 3.4 × 10 - 4 , in tapering between healthy airways ( n = 35 ) and those affected by bronchiectasis ( n = 39 ). The difference between the mean of the two populations is 0.011    mm - 1 , and the difference between the medians of the two populations was 0.006    mm - 1 . The tapering measurement retained a 95% confidence interval of ± 0.005    mm - 1 in a simulated 25 mAs scan and retained a 95% confidence of ± 0.005    mm - 1 on simulated CTs up to 1.5 times the original voxel size. We have established an estimate of the precision of the tapering measurement and estimated the effect on precision of the simulated voxel size and CT scan dose. We recommend that the scanner calibration be undertaken with the phantoms as described, on the specific CT scanner, radiation dose, and reconstruction algorithm that are to be used in any quantitative studies.

Wide-volume versus helical acquisition in unenhanced chest CT: prospective intra-patient comparison of diagnostic accuracy and radiation dose in an ultra-low-dose setting

OBJECTIVES

Diagnostic performance and potential radiation dose reduction of wide-area detector CT sequential acquisition ("wide-volume" acquisition (WV)) in unenhanced chest examination are unknown. This study aims to assess the image quality, the diagnostic performance, and the radiation dose reduction of WV mode compared with the classical helical acquisition for lung parenchyma analysis in an ultra-low-dose (ULD) protocol.

METHODS

After Institutional Review Board Approval and written informed consent, 64 patients (72% men; 67.6 ± 9.7 years old; BMI 26.1 ± 5.3 kg/m²) referred for a clinically indicated unenhanced chest CT were prospectively included. All patients underwent, in addition to a standard helical acquisition (120 kV, automatic tube current modulation), two ULD acquisitions (135 kV, fixed tube current at 10 mA): one in helical mode and one in WV mode. Image noise, subjective image quality (5-level Likert scale), and diagnostic performance for the detection of 9 predetermined parenchymal abnormalities were assessed by two radiologists and compared using the chi-square or Fisher non-parametric tests.

RESULTS

Subjective image quality (4.2 ± 0.7 versus 4.2 ± 0.8, p = 0.56), image noise (41.7 ± 8 versus 40.9 ± 8.7, p = 0.3), and diagnostic performance were equivalent between ULD WV and ULD helical. Radiation dose was significantly lower for the ULD WV acquisition (mean dose-length product 14.1 ± 1.3 mGy cm versus 15.8 ± 1.3, p < 0.0001).

CONCLUSION

An additional 11% dose reduction is achieved with the WV mode in ULD chest CT with fixed tube current, with equivalent image quality and diagnostic performance when compared with the helical acquisition.

KEY POINTS

• Image quality and diagnostic performance of ultra-low-dose unenhanced chest CT are identical between wide-volume mode and the reference helical acquisition. • Wide-volume mode allows an additional radiation dose reduction of 11% (mean dose-length product 14.1 ± 1.3 mGy cm versus 15.8 ± 1.3, p < 0.0001).

Continuous quantitative measurement of the main bronchial dimensions and lung density in the lateral position by four-dimensional dynamic-ventilation CT in smokers and COPD patients

Purpose

The purpose of this study was to measure changes in lung density and airway dimension in smokers in the lateral position using four-dimensional dynamic-ventilation computed tomography (CT) during free breathing and to evaluate their correlations with spirometric values.

Materials and methods

Preoperative pleural adhesion assessments included dynamic-ventilation CT of 42 smokers (including 22 patients with COPD) in the lateral position, with the unoperated lung beneath (dependent lung). The scanned lungs' mean lung density (MLD) and the bilateral main bronchi's luminal areas (Ai) were measured automatically (13-18 continuous image frames, 0.35 seconds/frame). Calculations included cross-correlation coefficients (CCCs) between the MLD and Ai time curves, and correlations between the quantitative measurements and spirometric values were evaluated by using Spearman's rank coefficient.

Results

The ΔMLD_1.05 (from the peak inspiration frame to the third expiratory frame, 1.05 seconds later) in the nondependent lung negatively correlated with FEV₁/FVC (r=-0.417, P<0.01), suggesting that large expiratory movement of the nondependent lung would compensate limited expiratory movement of the dependent lung due to COPD. The ΔAi_1.05 negatively correlated with the FEV₁/FVC predicted in both the lungs (r=-0.465 and -0.311, P<0.05), suggesting that early expiratory collapses of the main bronchi indicate severe airflow limitation. The CCC correlated with FEV₁/FVC in the dependent lung (r=-0.474, P<0.01), suggesting that reduced synchrony between the proximal airway and lung occurs in patients with severe airflow limitation.

Conclusion

In COPD patients, in the lateral position, the following abnormal dynamic-ventilation CT findings are associated with airflow limitation: enhanced complementary ventilation in the nondependent lung, early expiratory airway collapses, and reduced synchrony between airway and lung movements in the dependent lung.

[Improvement of Image Quality in the Axial Section Using High-resolution Scan Mode and Hybrid Iterative Reconstruction in Ultra-high-resolution Computed Tomography]

The purpose of this study is to compare the physical characteristics and visibility of high-resolution and conventional images acquired with the same X-ray dose, and to investigate the superiority of super high-resolution imaging. A Catphan phantom was scanned in the normal resolution (NR), high-resolution (HR), and super high-resolution (SHR) modes of ultra-high-resolution computed tomography at 120 kV and 75 mAs. All images were reconstructed into a 5-mm thick image slices with filtered back-projection (FBP) and hybrid image reconstruction (HIR), which included normal and enhanced adaptive iterative dose reduction 3D (AIDR and eAIDR, respectively). The modulation transfer function (MTF) and noise power spectrum (NPS) were measured using the circular edge method and radial frequency method, respectively. The signal-to-noise ratio (SNR) was then calculated. High-contrast resolution and low-contrast detectability were evaluated visually by five radiological technologists. The MTFs of HR_eAIDR and HR_FBP images were higher than those of NR_FBP images. However, the NPSs of HR_eAIDR and HR_FBP images were larger than those of NR_FBP images. The SNR of HR_eAIDR images was higher than that of NR_FBP and HR_FBP images. The scores of high-contrast resolution of HR_eAIDR, NR_FBP, and HR_FBP images were 13, 8, and 13 cycles/cm, respectively, and the scores of low-contrast detectability were 5, 5, and 6 mm, respectively. Hence, an improvement in high-contrast resolution of signal more than 400 HU in the axial section can be achieved without increasing the radiation dose and decreasing low-contrast detectability with 10 HU using the HR mode and eAIDR.

Radiation dose reduction techniques for chest CT: Principles and clinical results

Computer tomography plays a major role in the evaluation of thoracic diseases, especially since the advent of the multidetector-row CT (MDCT) technology. However, the increase use of this technique has raised some concerns about the resulting radiation dose. In this review, we will present the various methods allowing limiting the radiation dose exposure resulting from chest CT acquisitions, including the options of image filtering and iterative reconstruction (IR) algorithms. The clinical applications of reduced dose protocols will be reviewed, especially for lung nodule detection and diagnosis of pulmonary thromboembolism. The performance of reduced dose protocols for infiltrative lung disease assessment will also be discussed. Lastly, the influence of using IR algorithms on computer-aided detection and volumetry of lung nodules, as well as on quantitative and functional assessment of chest diseases will be presented and discussed.

Organ doses evaluation for chest computed tomography procedures with TL dosimeters: Comparison with Monte Carlo simulations

Purpose

To evaluate organ doses in routine and low‐dose chest computed tomography (CT) protocols using an experimental methodology. To compare experimental results with results obtained by the National Cancer Institute dosimetry system for CT (NCICT) organ dose calculator. To address the differences on organ dose measurements using tube current modulation (TCM) and fixed tube current protocols.

Methods

An experimental approach to evaluate organ doses in pediatric and adult anthropomorphic phantoms using thermoluminescent dosimeters (TLDs) was employed in this study. Several analyses were performed in order to establish the best way to achieve the main results in this investigation. The protocols used in this study were selected after an analysis of patient data collected from the Institute of Radiology of the School of Medicine of the University of São Paulo (InRad). The image quality was evaluated by a radiologist from this institution. Six chest adult protocols and four chest pediatric protocols were evaluated. Lung doses were evaluated for the adult phantom and lung and thyroid doses were evaluated for the pediatric phantom. The irradiations were performed using both a GE and a Philips CT scanner. Finally, organ doses measured with dosimeters were compared with Monte Carlo simulations performed with NCICT.

Results

After analyzing the data collected from all CT examinations performed during a period of 3 yr, the authors identified that adult and pediatric chest CT are among the most applied protocol in patients in that clinical institution, demonstrating the relevance on evaluating organ doses due to these examinations. With regards to the scan parameters adopted, the authors identified that using 80 kV instead of 120 kV for a pediatric chest routine CT, with TCM in both situations, can lead up to a 28.7% decrease on the absorbed dose. Moreover, in comparison to the standard adult protocol, which is performed with fixed mAs, TCM, and ultra low‐dose protocols resulted in dose reductions of up to 35.0% and 90.0%, respectively. Finally, the percent differences found between experimental and Monte Carlo simulated organ doses were within a 20% interval.

Conclusions

The results obtained in this study measured the impact on the absorbed dose in routine chest CT by changing several scan parameters while the image quality could be potentially preserved.

Radiologist performance in the detection of lung cancer using CT

AIM

To measure the level of radiologists' performance in lung cancer detection, and to explore radiologists' performance in cancer specialised and non-specialised centres.

MATERIALS AND METHODS

Thirty radiologists read 60 chest computed tomography (CT) examinations. Thirty cases had surgically or biopsy-proven lung cancer and 30 were cancer-free cases. The cancer cases were validated by four expert radiologists who located the malignant lung nodules. Reader performance was evaluated by calculating sensitivity, location sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). In addition, sensitivity at fixed specificity (0.794) was computed from each reader's estimated ROC curve.

RESULTS

The radiologists had a mean sensitivity of 0.749, sensitivity at fixed specificity of 0.744, location sensitivity of 0.666, specificity of 0.81 and AUC of 0.846. Radiologists in the specialised and non-specialised cancer centres had the following (specialised, non-specialised) pairs of values: sensitivity=(0.80, 0.719); sensitivity for fixed 0.794 specificity=(0.752, 0.740); location sensitivity=(0.712, 0.637); specificity=(0.794, 0.82) and AUC=(0.846, 0.846).

CONCLUSION

The efficacy of radiologists was comparable to other studies. Furthermore, AUC outcomes were similar for specialised and non-specialised cancer centre radiologists, suggesting they have similar discriminatory ability and that the higher sensitivity and lower specificity for specialised-centre radiologists can be attributed to them being less conservative in interpreting case images.

Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging

The purpose of this study is to (1) develop metrics to characterize the regional anatomical complexity of the lungs, and (2) relate these metrics with lung nodule detection in chest CT. A free-scrolling reader-study with virtually inserted nodules (13 radiologists × 157 total nodules = 2041 responses) is used to characterize human detection performance. Metrics of complexity based on the local density and orientation of distracting vasculature are developed for two-dimensional (2-D) and three-dimensional (3-D) considerations of the image volume. Assessed characteristics included the distribution of 2-D/3-D vessel structures of differing orientation (dubbed "2-D/3-D and dot-like/line-like distractor indices"), contiguity of inserted nodules with local vasculature, mean local gray-level surrounding each nodule, the proportion of lung voxels to total voxels in each section, and 3-D distance of each nodule from the trachea bifurcation. A generalized linear mixed-effects statistical model is used to determine the influence of each these metrics on nodule detectability. In order of decreasing effect size: 3-D line-like distractor index, 2-D line-like distractor index, 2-D dot-like distractor index, local mean gray-level, contiguity with 2-D dots, lung area, and contiguity with 3-D lines all significantly affect detectability ( P < 0.05 ). These data demonstrate that local lung complexity degrades detection of lung nodules.

Association of Focal Radiation Dose Adjusted on Cross Sections with Subsolid Nodule Visibility and Quantification on Computed Tomography Images Using AIDR 3D: Comparison Among Scanning at 84, 42, and 7 mAs

RATIONALE AND OBJECTIVES

The objectives of this study were to compare the visibility and quantification of subsolid nodules (SSNs) on computed tomography (CT) using adaptive iterative dose reduction using three-dimensional processing between 7 and 42 mAs and to assess the association of size-specific dose estimate (SSDE) with relative measured value change between 7 and 84 mAs (RMVC_7-84) and relative measured value change between 42 and 84 mAs (RMVC_42-84).

MATERIALS AND METHODS

As a Japanese multicenter research project (Area-detector Computed Tomography for the Investigation of Thoracic Diseases [ACTIve] study), 50 subjects underwent chest CT with 120 kV, 0.35 second per location and three tube currents: 240 mA (84 mAs), 120 mA (42 mAs), and 20 mA (7 mAs). Axial CT images were reconstructed using adaptive iterative dose reduction using three-dimensional processing. SSN visibility was assessed with three grades (1, obscure, to 3, definitely visible) using CT at 84 mAs as reference standard and compared between 7 and 42 mAs using t test. Dimension, mean CT density, and particular SSDE to the nodular center of 71 SSNs and volume of 58 SSNs (diameter >5 mm) were measured. Measured values (MVs) were compared using Wilcoxon signed-rank tests among CTs at three doses. Pearson correlation analyses were performed to assess the association of SSDE with RMVC_7-84: 100 × (MV at 7 mAs - MV at 84 mAs)/MV at 84 mAs and RMVC_42-84.

RESULTS

SSN visibilities were similar between 7 and 42 mAs (2.76 ± 0.45 vs 2.78 ± 0.40) (P = .67). For larger SSNs (>8 mm), MVs were similar among CTs at three doses (P > .05). For smaller SSNs (<8 mm), dimensions and volumes on CT at 7 mAs were larger and the mean CT density was smaller than 42 and 84 mAs, and SSDE had mild negative correlations with RMVC_7-84 (P < .05).

CONCLUSIONS

Comparable quantification was demonstrated irrespective of doses for larger SSNs. For smaller SSNs, nodular exaggerating effect associated with decreased SSDE on CT at 7 mAs compared to 84 mAs could result in comparable visibilities to CT at 42 mAs.

Application value of iterative reconstruction with CTA to intractable headache patients

Application value of iterative reconstruction with computed tomographic angiography (CTA) in the patients with intractable headache was investigated. One hundred and eighty patients with headache, who were admitted and treated in Cangzhou Central Hospital, were selected to undergo CTA scan. The patients were divided into group A, B and C according to different scanning conditions and data reconstruction techniques. In group A, the scanning parameters were 120 kV and 300 mA, and filtered back projection (FBP) algorithm was used for data reconstruction. In group B, the scan parameters were 100 kV and automatic milliamperes. Further, adaptive iterative dose reduction via three-dimensional processing (AIDR-3D) was used for data reconstruction. In group C, the scan parameters were 80 kV with automatic milliamperes, and AIDR-3D technique was utilized for data reconstruction. The CT value, noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective assessment score of image quality and radiation dose of the three groups of images were compared. There were significant differences in CT values, standard deviation (SD) values, SNRs and CNRs of different vessel segments and muscles among the three groups (P<0.05). The image assessment scores at the levels of the atlas and C7 vertebra as well as those of the brain parenchyma in the three groups had notable differences (P<0.05). However, they showed no differences at the level of the C4 vertebra (P>0.05). Further, significant differences were observed in volume computed tomography dose index (CTDI_vol), dose-length product (DLP) and effective dose (ED) (P<0.05). In conclusion, for patients with intractable headache, the image quality of the CTA scan using AIDR-3D reconstruction method showed better results over FBP reconstruction method. Further, best results were observed when the scan parameters were 100 kV, automatic milliamperes and the data reconstruction was performed by AIDR-3D.

Lung cancer screening: nodule identification and characterization

The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.

Comparison of computer-aided detection (CADe) capability for pulmonary nodules among standard-, reduced- and ultra-low-dose CTs with and without hybrid type iterative reconstruction technique

PURPOSE

To directly compare the effect of a reconstruction algorithm on nodule detection capability of the computer-aided detection (CADe) system using standard-dose, reduced-dose and ultra-low dose chest CTs with and without adaptive iterative dose reduction 3D (AIDR 3D).

MATERIALS AND METHODS

Our institutional review board approved this study, and written informed consent was obtained from each patient. Standard-, reduced- and ultra-low-dose chest CTs (250 mA, 50 mA and 10 mA) were used to examine 40 patients, 21 males (mean age ± standard deviation: 63.1 ± 11.0 years) and 19 females (mean age, 65.1 ± 12.7 years), and reconstructed as 1 mm-thick sections. Detection of nodule equal to more than 4 mm in dimeter was automatically performed by our proprietary CADe software. The utility of iterative reconstruction method for improving nodule detection capability, sensitivity and false positive rate (/case) of the CADe system using all protocols were compared by means of McNemar's test or signed rank test.

RESULTS

Sensitivity (SE: 0.43) and false-positive rate (FPR: 7.88) of ultra-low-dose CT without AIDR 3D was significantly inferior to those of standard-dose CTs (with AIDR 3D: SE, 0.78, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.80, p < .0001, FPR: 2.63, p < .0001), reduced-dose CTs (with AIDR 3D: SE, 0.81, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.62, p < .0001, FPR: 2.95, p < .0001) and ultra-low-dose CT with AIDR 3D (SE, 0.79, p < .0001, FPR, 4.88, p = .0001).

CONCLUSION

The AIDR 3D has a significant positive effect on nodule detection capability of the CADe system even when radiation dose is reduced.

Submillisievert chest dual energy computed tomography: a pilot study

OBJECTIVE

To assess if diagnostic dual energy CT (DECT) of the chest can be achieved at submillisievert (sub-mSv) doses.

METHODS

Our IRB-approved prospective study included 20 patients who were scanned on dual-source multidector CT(MDCT). All patients gave written informed consent for acquisition of additional image series at reduced radiation dose on a dual-source MDCT (80/140 kV) within 10 s after the standard of care acquisition. Dose reduction was achieved by reducing the quality reference milliampere-second, with combined angular exposure control. Four readers, blinded to all clinical data, evaluated the image sets. Image noise, signal-to-noise and contrast-to-noise ratio were assessed. Volumetric CT dose index (CTDI_vol), doselength product (DLP), size specific dose estimate, and effective dose were also recorded.

RESULTS

The mean age and body mass index of the patients were 71 years ± 9 and 24 kg m^-2 ± 3, respectively. Although images became noisier, overall image quality and image sharpness on blended images were considered good or excellent in all cases (20/20). All findings made on the reduced dose images presented with good demarcation. The intraobserver and interobserver agreements were κ = 0.83 and 0.73, respectively. Mean CTDI_vol, size specific dose estimate, DLP and effective dose for reduced dose DECT were: 1.3 ± 0.2 mGy, 1.8 ± 0.2 mGy, 51 ± 9.9 mGy.cm and 0.7 ± 0.1 mSv, respectively.

CONCLUSION

Routine chest DECT can be performed at sub-mSv doses with good image quality and without loss of relevant diagnostic information. Advances in knowledge: (1) Contrast-enhanced DECT of the chest can be performed at sub-mSv doses, down to mean CTDI_vol 1.3 mGy and DLP 51 mGy.cm in patients with body mass index <31 kg m^-2. (2) To our knowledge, this is the first time that sub-mSv doses have been successfully applied in a patient study using a dual source DECT scanner.

Sub-solid Nodule Detection Performance on Reduced-dose Computed Tomography with Iterative Reduction: Comparison Between 20 mA (7 mAs) and 120 mA (42 mAs) Regarding Nodular Size and Characteristics and Association with Size-specific Dose Estimate

RATIONALE AND OBJECTIVES

This study aimed to compare sub-solid nodule detection performances (SSNDP) on chest computed tomography (CT) with Adaptive Iterative Dose Reduction using Three Dimensional Processing (AIDR 3D) between 7 mAs (0.21 mSv) and 42 mAs (1.28 mSv) in total and in subgroups classified by nodular size, characteristics, and location, and analyze the association of SSNDP with size-specific dose estimate (SSDE).

MATERIALS AND METHODS

As part of the Area-detector Computed Tomography for the Investigation of Thoracic Diseases Study, a Japanese multicenter research project, 68 subjects underwent chest CT with 120 kV, 0.35 seconds per rotation, and three tube currents: 240 mA (84 mAs), 120 mA (42 mAs), and 20 mA (7 mAs). The research committee of the study project outlined and approved our study protocols. The institutional review board of each institution approved this study. Axial 2-mm-thick CT images were reconstructed using AIDR 3D. Standard reference was determined by CT images at 84 mAs. Four radiologists recorded SSN presence by continuously distributed rating on CT at 7 mAs and 42 mAs. Receiver operating characteristic analysis was used to evaluate SSNDP at both doses in total and in subgroups classified by nodular longest diameter (LD) (≥5 mm), characteristics (pure and part-solid), and locations (ventral, intermediate, or dorsal; central or peripheral; and upper, middle, or lower). Detection sensitivity was compared among five groups of SSNs classified based on particular SSDE to nodule on CT with AIDR 3D at 7 mAs.

RESULTS

Twenty-two part-solid and 86 pure SSNs were identified. For larger SSNs (LD ≥ 5 mm) as well as subgroups classified by nodular locations and part-solid nodules, SSNDP was similar in both methods (area under the receiver operating characteristics curve: 0.96 ± 0.02 in CT at 7 mAs and 0.97 ± 0.01 in CT at 42 mAs), with acceptable interobserver agreements in five locations. For larger SSNs (LD ≥ 5 mm), on CT at 42 mAs, no significant differences in detection sensitivity were found among the five groups classified by SSDE, whereas on CT with 7 mAs, four groups with SSDE of 0.65 or higher were superior in detection sensitivity to the other group, with SSDE less than 0.65 mGy.

CONCLUSIONS

For SSNs with 5 mm or more in cases with normal range of body habitus, CT at 7 mAs was demonstrated to have comparable SSNDP to CT at 42 mAs regardless of nodular location and characteristics, and SSDE higher than 0.65 mGy is desirable to obtain sufficient SSNDP.

Evaluation of a projection-domain lung nodule insertion technique in thoracic computed tomography

Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Prospective case acquisition can be time-consuming. Inserting lesions into existing cases to simulate positive cases is a promising alternative. The aim was to evaluate a recently developed projection-based lesion insertion technique in thoracic CT. In total, 32 lung nodules of various attenuations were segmented from 21 patient cases, forward projected, inserted into projections, and reconstructed. Two experienced radiologists and two residents independently evaluated these nodules in two substudies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a score from 1 to 10 (1 = absolutely artificial to 10 = absolutely realistic). Second, the inserted and the corresponding original lesions were presented side-by-side to each reader. For the randomized evaluation, discrimination of real versus inserted nodules was poor with areas under the receiver operative characteristic curves being 0.57 [95% confidence interval (CI): 0.46 to 0.68], 0.69 (95% CI: 0.58 to 0.78), and 0.62 (95% CI: 0.54 to 0.69) for the two residents, two radiologists, and all four readers, respectively. Our projection-based lung nodule insertion technique provides a robust method to artificially generate positive cases that prove to be difficult to differentiate from real cases.

A virtual clinical trial using projection-based nodule insertion to determine radiologist reader performance in lung cancer screening CT

Task-based image quality assessment using model observers is promising to provide an efficient, quantitative, and objective approach to CT dose optimization. Before this approach can be reliably used in practice, its correlation with radiologist performance for the same clinical task needs to be established. Determining human observer performance for a well-defined clinical task, however, has always been a challenge due to the tremendous amount of efforts needed to collect a large number of positive cases. To overcome this challenge, we developed an accurate projection-based insertion technique. In this study, we present a virtual clinical trial using this tool and a low-dose simulation tool to determine radiologist performance on lung-nodule detection as a function of radiation dose, nodule type, nodule size, and reconstruction methods. The lesion insertion and low-dose simulation tools together were demonstrated to provide flexibility to generate realistically-appearing clinical cases under well-defined conditions. The reader performance data obtained in this virtual clinical trial can be used as the basis to develop model observers for lung nodule detection, as well as for dose and protocol optimization in lung cancer screening CT.

Lung cancer screening with ultra-low dose CT using full iterative reconstruction

PURPOSE

To investigate the diagnostic capability of ultra-low-dose CT (ULDCT) with full iterative reconstruction (f-IR) for lung cancer screening.

MATERIALS AND METHODS

All underwent ULDCT and/or low-dose CT (LD-CT) on a 320-detector scanner. ULDCT images were reconstructed with f-IR. We qualitatively and quantitatively studied 95 nodules in 69 subjects. Two radiologists classified the nodules on ULDCT images as solid-, part-solid-, and pure ground-glass (PGG) and recorded their mean size. Their findings were compared with the reference standard. The observer performance study included 7 other radiologists and 35 subjects with- and 15 without nodules. The results were analyzed by AFROC analysis.

RESULTS

In the qualitative study, the kappa values between observers 1 and 2, respectively, and the reference standard were 0.70 and 0.83; the intra-class correlation coefficients for the nodule diameter between the reference standard and their measurements were 0.84 and 0.90. The 95% confidence interval (CI) for the area under the curve (AUC) difference for nodule detection on LDCT and ULDCT was -0.03 to 0.07. The 95% CI crossed the 0 difference in the AUC but not the pre-defined non-inferiority margin of -0.08.

CONCLUSION

The diagnostic ability of ULDCT using f-IR is comparable to LDCT.

[What future for chest x-ray against ultra-low-dose computed tomography?]

Technological improvements, with iterative reconstruction at the foreground, have lowered the radiation dose of a chest CT close to that of a PA and lateral chest x-ray. This ultra-low dose chest CT (ULD-CT) has an image quality that is degraded on purpose, yet remains diagnostic in many clinical indications. Thus, its effectiveness is already validated for the detection and the monitoring of solid parenchymal nodules, for the diagnosis and monitoring of infectious lung diseases and for the screening of pleural lesions secondary to asbestos exposure. Its limitations are the analysis of the mediastinal structures, the severe obesity (BMI>35) and the detection of interstitial lesions. If it can replace the standard chest CT in these indications, all the more in situations where radiation dose is a major problem (young patients, repeated exams, screening), it progressively emerges as a first line alternative for chest radiograph, providing more data at a similar radiation cost.

A phantom study for ground-glass nodule detectability using chest digital tomosynthesis with iterative reconstruction algorithm by ten observers: association with radiation dose and nodular characteristics

OBJECTIVE

To compare detectability of simulated ground-glass nodules (GGNs) on chest digital tomosynthesis (CDT) among 12 images obtained at 6 radiation doses using 2 reconstruction algorithms and to analyze its association with nodular size and density.

METHODS

74 simulated GGNs [5, 8 and 10 mm in diameter/-630 and -800 Hounsfield units (HU) in density] were placed in a chest phantom in 14 nodular distribution patterns. 12 sets of coronal images were obtained using CDT at 6 radiation doses: 120 kV-10 mA/20 mA/80 mA/160 mA, 100 kV-80 mA and 80 kV-320 mA with and without iterative reconstruction (IR). 10 radiologists recorded GGN presence and locations by continuously distributed rating. GGN detectability was compared by receiver operating characteristic analysis among 12 images and detection sensitivities (DS) were compared among 12 images in subgroups classified by nodular diameters and densities.

RESULTS

GGN detectability at 120 kV-160 mA with IR was similar to that at 120 kV-80 mA with IR (0.614 mSv), as area under receiver operating characteristic curve was 0.798 ± 0.024 and 0.788 ± 0.025, respectively, and higher than six images acquired at 120 kV (p < 0.05). For nodules of -630 HU/8 mm, DS at 120 kV-10 mA without IR was 73.5 ± 6.0% and was similar to that by the other 11 data acquisition methods (p = 0.157). For nodules of -800 HU/10 mm, DS both at 120 kV-80 mA and 120 kV-160 mA without IR was improved by IR (56.3 ± 11.9%) (p < 0.05).

CONCLUSION

CDT demonstrated sufficient detectability for larger more-attenuated GGNs (>8 mm) even in the lowest radiation dose (0.17 mSv) and improved detectability for less-attenuated GGNs with the diameter of 10 mm at submillisievert with IR. Advances in knowledge: IR improved detectability for larger less-attenuated simulated GGNs on CDT.

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 0.13mSv

OBJECTIVES

To 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.

METHODS

202 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.

RESULTS

The 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).

CONCLUSIONS

Computer-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 a projection-domain lung nodule insertion technique in thoracic CT

Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Inserting lesions into existing cases to simulate positive cases is a promising alternative approach. The aim of this study was to evaluate a recently-developed raw-data based lesion insertion technique in thoracic CT. Lung lesions were segmented from patient CT images, forward projected, and reinserted into the same patient CT projection data. In total, 32 nodules of various attenuations were segmented from 21 CT cases. Two experienced radiologists and 2 residents blinded to the process independently evaluated these inserted nodules in two sub-studies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a rating score from 1 to 10 (1=absolutely artificial to 10=absolutely realistic). Second, the inserted and the corresponding original lesions were presented side-by-side to each reader, who identified the inserted lesion and provided a confidence score (1=no confidence to 5=completely certain). For the randomized evaluation, discrimination of real versus artificial nodules was poor with areas under the receiver operative characteristic curves being 0.69 (95% CI: 0.58-0.78), 0.57 (95% CI: 0.46-0.68), and 0.62 (95% CI: 0.54-0.69) for the 2 radiologists, 2 residents, and all 4 readers, respectively. For the side-by-side evaluation, although all 4 readers correctly identified inserted lesions in 103/128 pairs, the confidence score was moderate (2.6). Our projection-domain based lung nodule insertion technique provides a robust method to artificially generate clinical cases that prove to be difficult to differentiate from real cases.

Reducing Radiation Dose at Chest CT: Comparison Among Model-based Type Iterative Reconstruction, Hybrid Iterative Reconstruction, and Filtered Back Projection

RATIONALE AND OBJECTIVES

The study aimed to evaluate the performances of two iterative reconstruction (IR) algorithms and of filtered back projection (FBP) when using reduced-dose chest computed tomography (RDCT) compared to standard-of-care CT.

MATERIALS AND METHODS

An institutional review board approval was obtained. Thirty-six patients with hematologic malignancies referred for a control chest CT of a known lung disease were prospectively enrolled. Patients underwent standard-of-care scan reconstructed with hybrid IR, followed by an RDCT reconstructed with FBP, hybrid IR, and iterative model reconstruction. Objective and subjective quality measurements, lesion detectability, and evolution assessment on RDCT were recorded.

RESULTS

For RDCT, the CTDIvol (volumetric computed tomography dose index) was 0.43 mGy⋅cm for all patients, and the median [interquartile range] effective dose was 0.22 mSv [0.22-0.24]; corresponding measurements for standard-of-care scan were 3.4 mGy [3.1-3.9] and 1.8 mSv [1.6-2.0]. Noise significantly decreased from FBP to hybrid IR and from hybrid IR to iterative model reconstruction on RDCT, whereas lesion conspicuity and diagnostic confidence increased. Accurate evolution assessment was obtained in all cases with IR. Emphysema identification was higher with iterative model reconstruction.

CONCLUSION

Although iterative model reconstruction offered better diagnostic confidence and emphysema detection, both IR algorithms allowed an accurate evolution assessment with an effective dose of 0.22 mSv.