Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection

Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images

OBJECTIVE

Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness.

METHODS

In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).

RESULTS

The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.

Use of "Diagnostic Yield" in Imaging Research Reports: Results from Articles Published in Two General Radiology Journals

OBJECTIVE

"Diagnostic yield," also referred to as the detection rate, is a parameter positioned between diagnostic accuracy and diagnosis-related patient outcomes in research studies that assess diagnostic tests. Unfamiliarity with the term may lead to incorrect usage and delivery of information. Herein, we evaluate the level of proper use of the term "diagnostic yield" and its related parameters in articles published in Radiology and Korean Journal of Radiology (KJR).

MATERIALS AND METHODS

Potentially relevant articles published since 2012 in these journals were identified using MEDLINE and PubMed Central databases. The initial search yielded 239 articles. We evaluated whether the correct definition and study setting of "diagnostic yield" or "detection rate" were used and whether the articles also reported companion parameters for false-positive results. We calculated the proportion of articles that correctly used these parameters and evaluated whether the proportion increased with time (2012-2016 vs. 2017-2022).

RESULTS

Among 39 eligible articles (19 from Radiology and 20 from KJR), 17 (43.6%; 11 from Radiology and 6 from KJR) correctly defined "diagnostic yield" or "detection rate." The remaining 22 articles used "diagnostic yield" or "detection rate" with incorrect meanings such as "diagnostic performance" or "sensitivity." The proportion of correctly used diagnostic terms was higher in the studies published in Radiology than in those published in KJR (57.9% vs. 30.0%). The proportion improved with time in Radiology (33.3% vs. 80.0%), whereas no improvement was observed in KJR over time (33.3% vs. 27.3%). The proportion of studies reporting companion parameters was similar between journals (72.7% vs. 66.7%), and no considerable improvement was observed over time.

CONCLUSION

Overall, a minority of articles accurately used "diagnostic yield" or "detection rate." Incorrect usage of the terms was more frequent without improvement over time in KJR than in Radiology. Therefore, improvements are required in the use and reporting of these parameters.

Observer Performance for Detection of Pulmonary Nodules at Chest CT over a Large Range of Radiation Dose Levels

Background There is a wide variation in radiation dose levels that can be used with chest CT in order to detect indeterminate pulmonary nodules. Purpose To compare the performance of lower-radiation-dose chest CT with that of routine dose in the detection of indeterminate pulmonary nodules 5 mm or greater. Materials and Methods In this retrospective study, CT projection data from 83 routine-dose chest CT examinations performed in 83 patients (120 kV, 70 quality reference mAs [QRM]) were collected between November 2013 and April 2014. Reference indeterminate pulmonary nodules were identified by two nonreader thoracic radiologists. By using validated noise insertion, five lower-dose data sets were reconstructed with filtered back projection (FBP) or iterative reconstruction (IR; 30 QRM with FBP, 10 QRM with IR, 5 QRM with FBP, 5 QRM with IR, and 2.5 QRM with IR). Three thoracic radiologists circled pulmonary nodules, rating confidence that the nodule was a 5-mm-or-greater indeterminate pulmonary nodule, and graded image quality. Analysis was performed on a per-nodule basis by using jackknife alternative free-response receiver operating characteristic figure of merit (FOM) and noninferiority limit of -0.10. Results There were 66 indeterminate pulmonary nodules (mean size, 8.6 mm ± 3.4 [standard deviation]; 21 part-solid nodules) in 42 patients (mean age, 51 years ± 17; 21 men and 21 women). Compared with the FOM for routine-dose CT (size-specific dose estimate, 6.5 mGy ± 1.8; FOM, 0.86 [95% confidence interval: 0.80, 0.91]), FOM was noninferior for all lower-dose configurations except for 2.5 QRM with IR. The sensitivity for subsolid nodules at 70 QRM was 60% (range, 48%-72%) and was significantly worse at a dose of 5 QRM and lower, whether or not IR was used (P < .05). Diagnostic image quality decreased with decreasing dose (P < .001) and was better with IR at 5 QRM (P < .05). Conclusion CT images reconstructed at dose levels down to 10 quality reference mAs (size-specific dose estimate, 0.9 mGy) had noninferior performance compared with routine dose in depicting pulmonary nodules. Iterative reconstruction improved subjective image quality but not performance at low dose levels. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by White and Kazerooni in this issue.

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.

Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT

OBJECTIVE

To evaluate the clinical value of low-dose chest CT combined with the new generation adaptive statistical iterative reconstruction (ASIR-V) algorithm in the diagnosis of pulmonary nodule.

METHODS

30 patients with pulmonary nodules underwent chest CT using Revolution CT. The patients were first scanned with standard-dose at a noise index (NI) of 14, and the images were reconstructed with filtered back projection (FBP) algorithm. If pulmonary nodules were found, a low-dose targeted scan, with NI of 24, was performed localized on the nodules, and the images were reconstructed with 60% ASIR-V. The detection rate of pulmonary nodules in the two scanning modes was recorded. The size of nodules, CT value and standard deviation of nodules were measured. The signal-to-noise ratio and contrast-to-noise ratio were also calculated. Two experienced radiologists used a 5-point method to score the image quality. The volumetric CT dose index, and dose-length product were recorded and the effective dose (ED) was calculated of the two scanning modes.

RESULTS

Volumetric CT dose index (ED) of the standard-dose scan covering the entire lungs was 7.29 ± 2.38 mGy (3.52 ± 1.09 mSv), and that of low-dose targeted scan was 2.56 ± 1.87 mGy (0.51 ± 0.32 mSv). However, the ED of the virtual low-dose scan for the entire lungs was 1.44 ± 0.15 mSv, which would mean a dose reduction of 59.1% compared with the standard-dose scan. 85 of the 87 pulmonary nodules were detected in the low-dose targeted scan, with 2 of the ground-glass density nodules with size less than 1 cm missed, resulting in 97.7% overall detection rate. There was no difference between the low-dose ASIR-V images and standard-dose FBP images for the size (1.49 ± 0.74 cm vs 1.48 ± 0.75 cm), CT value [33.02 ± 1.95 Hounsfield unit (HU) vs 34.6 ± 3.07 HU], standard deviation (27.64 ± 14.42 HU vs 30.38 ± 20.04 HU), signal-to-noise ratio (1.44 ± 0.88 vs 1.43 ± 1.31) and contrast-to-noise ratio (38.95 ± 18.43 vs 38.23 ± 14.99) of nodules (all p > 0.05). There was no difference in the subjective scores between the two scanning modes.

CONCLUSION

The low-dose CT scan combined with ASIR-V algorithm is of comparable value in the detection and the display of pulmonary nodules when compared with the FBP images obtained by standard-dose scan.

ADVANCES IN KNOWLEDGE

This is a clinical study to evaluate the clinical value of pulmonary nodules using ASIR-V algorithm in the same patients in the low-dose chest CT scans. It suggests that ASIR-V provides similar image quality and detection rate for pulmonary nodules at much reduced radiation dose.

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.

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).

Pulmonary Emphysema Quantification on Ultra-Low-Dose Computed Tomography Using Model-Based Iterative Reconstruction With or Without Lung Setting

OBJECTIVES

To evaluate the influence of model-based iterative reconstruction (MBIR) with lung setting and conventional setting on pulmonary emphysema quantification by ultra-low-dose computed tomography (ULDCT) compared with standard-dose CT (SDCT).

METHODS

Forty-five patients who underwent ULDCT (0.18 ± 0.02 mSv) and SDCT (6.66 ± 2.69 mSv) were analyzed in this retrospective study. Images were reconstructed using filtered back projection (FBP) with smooth and sharp kernels and MBIR with conventional and lung settings. Extent of emphysema was evaluated using fully automated software. Correlation between ULDCT and SDCT was assessed by interclass correlation coefficiency (ICC) and Bland-Altman analysis.

RESULTS

Excellent correlation was seen between MBIR with conventional setting on ULDCT and FBP with smooth kernel on SDCT (ICC, 0.97; bias, -0.31%) and between MBIR with lung setting on ULDCT and FBP with sharp kernel on SDCT (ICC, 0.82; bias, -2.10%).

CONCLUSION

Model-based iterative reconstruction improved the agreement between ULDCT and SDCT on emphysema quantification.

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.

Quantitative and qualitative evaluation of hybrid iterative reconstruction, with and without noise power spectrum models: A phantom study

The purpose of this phantom study was to investigate the feasibility of dose reduction with hybrid iterative reconstruction, with and without a noise power spectrum (NPS) model, using both quantitative and qualitative evaluations. Standard dose (SD), three‐quarter dose (TQD), and half‐dose (HD) of radiation were used. Images were reconstructed with filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR 3D) (MILD, STR), and AIDR 3D enhanced (eAIDR 3D) (eMILD, eSTR). An NPS analysis, task‐based modulation transfer function (MTF_task) analysis, and comparisons of low‐contrast detectability and image texture were performed. Although the eAIDR 3D had a higher NPS value in the high‐frequency range and improved image texture and resolution as compared with AIDR 3D at the same radiation dose and iteration levels, it yielded higher noise than AIDR 3D. Additionally, although there was no statistically significant difference between SD‐FBP and the TQD series in the comparison of the mean area under the curve (AUC), the mean AUC was statistically significantly different between SD‐FBP and the HD series. NPS values in the high‐frequency range, 10% MTF_task values, low‐contrast detectability, and image textures of TQD‐eMILD were comparable to those of SD‐FBP. Our findings suggested that using eMILD can reduce the radiation dose by 25%, while potentially maintaining diagnostic performance, spatial resolution, and image texture; this could support selecting the appropriate protocol in a clinical setting.

Effect of an Arm Traction Device on Image Quality and Radiation Exposure during Neck CT: A Prospective Study

BACKGROUND AND PURPOSE

The image quality of neck CT is frequently disturbed by streak artifact from the shoulder girdles. Our aim was to determine the effects of an arm traction device on image quality and radiation exposure in neck CT.

MATERIALS AND METHODS

Patients with lymphoma with complete remission who were scheduled to undergo 2 consecutive follow-up neck CT scans for surveillance within a 1-year interval were enrolled in this prospective study. They underwent 2 consecutive neck CT scans (intervention protocol: patients with an arm traction device; standard protocol: no positioning optimization) on the same CT system. The primary outcome measures were image noise in the lower neck and dose-length product. Secondary outcomes were streak artifacts in the supraclavicular fossa, volume CT dose index, and the extent of the biacromial line shift.

RESULTS

Seventy-three patients were enrolled and underwent 2 consecutive CT scans with a mean interval of 155 days. In the intervention protocol, a mean noise reduction in the lower neck of 25.2%-28.5% (P < .001) was achieved, and a significant decrease in dose-length product (413 versus 397, P < .001) was observed. The intervention protocol significantly decreased streak artifacts (P < .001) and volume CT dose index (13.9 versus 13.4, P < .001) and could lower the biacromial line an average of 2.1 cm.

CONCLUSIONS

An arm traction device can improve image quality and reduce radiation exposure during neck CT. The device can be simply applied in cooperative patients with suspected lower neck lesions, and the approach offers distinct advantages over the conventional imaging protocol.

Reduced dose CT with model-based iterative reconstruction compared to standard dose CT of the chest, abdomen, and pelvis in oncology patients: intra-individual comparison study on image quality and lesion conspicuity

PURPOSE

To compare image quality and lesion conspicuity of reduced dose (RD) CT with model-based iterative reconstruction (MBIR) compared to standard dose (SD) CT in patients undergoing oncological follow-up imaging.

METHODS

Forty-four cancer patients who had a staging SD CT within 12 months were prospectively included to undergo a weight-based RD CT with MBIR. Radiation dose was recorded and tissue attenuation and image noise of four tissue types were measured. Reproducibility of target lesion size measurements of up to 5 target lesions per patient were analyzed. Subjective image quality was evaluated for three readers independently utilizing 4- or 5-point Likert scales.

RESULTS

Median radiation dose reduction was 46% using RD CT (P < 0.01). Median image noise across all measured tissue types was lower (P < 0.01) in RD CT. Subjective image quality for RD CT was higher (P < 0.01) in regard to image noise and overall image quality; however, there was no statistically significant difference regarding image sharpness (P = 0.59). There were subjectively more artifacts on RD CT (P < 0.01). Lesion conspicuity was subjectively better in RD CT (P < 0.01). Repeated target lesion size measurements were highly reproducible both on SD CT (ICC = 0.987) and RD CT (ICC = 0.97).

CONCLUSIONS

RD CT imaging with MBIR provides diagnostic imaging quality and comparable lesion conspicuity on follow-up exams while allowing dose reduction by a median of 46% compared to SD CT imaging.

[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.

Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening

RATIONALE AND OBJECTIVES

This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening.

MATERIALS AND METHODS

We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms.

RESULTS

The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP (P < .001).

CONCLUSIONS

The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.

The Impact of Iterative Reconstruction in Low-Dose Computed Tomography on the Evaluation of Diffuse Interstitial Lung Disease

OBJECTIVE

To evaluate the impact of iterative reconstruction (IR) on the assessment of diffuse interstitial lung disease (DILD) using CT.

MATERIALS AND METHODS

An American College of Radiology (ACR) phantom (module 4 to assess spatial resolution) was scanned with 10-100 effective mAs at 120 kVp. The images were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), with blending ratios of 0%, 30%, 70% and 100%, and model-based iterative reconstruction (MBIR), and their spatial resolution was objectively assessed by the line pair structure method. The patient study was based on retrospective interpretation of prospectively acquired data, and it was approved by the institutional review board. Chest CT scans of 23 patients (mean age 64 years) were performed at 120 kVp using 1) standard dose protocol applying 142-275 mA with dose modulation (high-resolution computed tomography [HRCT]) and 2) low-dose protocol applying 20 mA (low dose CT, LDCT). HRCT images were reconstructed with FBP, and LDCT images were reconstructed using FBP, ASIR, and MBIR. Matching images were randomized and independently reviewed by chest radiologists. Subjective assessment of disease presence and radiological diagnosis was made on a 10-point scale. In addition, semi-quantitative results were compared for the extent of abnormalities estimated to the nearest 5% of parenchymal involvement.

RESULTS

In the phantom study, ASIR was comparable to FBP in terms of spatial resolution. However, for MBIR, the spatial resolution was greatly decreased under 10 mA. In the patient study, the detection of the presence of disease was not significantly different. The values for area under the curve for detection of DILD by HRCT, FBP, ASIR, and MBIR were as follows: 0.978, 0.979, 0.972, and 0.963. LDCT images reconstructed with FBP, ASIR, and MBIR tended to underestimate reticular or honeycombing opacities (-2.8%, -4.1%, and -5.3%, respectively) and overestimate ground glass opacities (+4.6%, +8.9%, and +8.5%, respectively) compared to the HRCT images. However, the reconstruction methods did not differ with respect to radiologic diagnosis.

CONCLUSION

The diagnostic performance of LDCT with MBIR was similar to that of HRCT in typical DILD cases. However, caution should be exercised when comparing disease extent, especially in follow-up studies with IR.

Does the Reporting Quality of Diagnostic Test Accuracy Studies, as Defined by STARD 2015, Affect Citation?

Objective

To determine the rate with which diagnostic test accuracy studies that are published in a general radiology journal adhere to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015, and to explore the relationship between adherence rate and citation rate while avoiding confounding by journal factors.

Materials and Methods

All eligible diagnostic test accuracy studies that were published in the Korean Journal of Radiology in 2011–2015 were identified. Five reviewers assessed each article for yes/no compliance with 27 of the 30 STARD 2015 checklist items (items 28, 29, and 30 were excluded). The total STARD score (number of fulfilled STARD items) was calculated. The score of the 15 STARD items that related directly to the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 was also calculated. The number of times each article was cited (as indicated by the Web of Science) after publication until March 2016 and the article exposure time (time in months between publication and March 2016) were extracted.

Results

Sixty-three articles were analyzed. The mean (range) total and QUADAS-2-related STARD scores were 20.0 (14.5–25) and 11.4 (7–15), respectively. The mean citation number was 4 (0–21). Citation number did not associate significantly with either STARD score after accounting for exposure time (total score: correlation coefficient = 0.154, p = 0.232; QUADAS-2-related score: correlation coefficient = 0.143, p = 0.266).

Conclusion

The degree of adherence to STARD 2015 was moderate for this journal, indicating that there is room for improvement. When adjusted for exposure time, the degree of adherence did not affect the citation rate.

Monochromatic Spectral Computed Tomography with Low Iodine Concentration Contrast Medium in a Rabbit VX2 Liver Model:: Investigation of Image Quality and Detection Rate

RATIONALE AND OBJECTIVES

This study aimed to validate the feasibility of using virtual monochromatic spectral computed tomography (CT) with isotonic low iodine concentration contrast medium for VX2 hepatic tumors.

MATERIALS AND METHODS

Sixty New Zealand white rabbits with implanted VX2 hepatic tumors underwent two-phase contrast-enhanced spectral CT imaging on the 14th day after tumor implantation. They were randomly divided into groups A, B, and C, with 20 rabbits each (group A: 270 mg I/mL, monochromatic spectral images; group B: 370 mg I/mL, conventional 120 kVp images, 100% filtered back projection [FBP]; group C: 270 mg I/mL, conventional 120 kVp images, 100% FBP). Group A was further divided into two subgroups (subgroup A1: 100% FBP; subgroup A2: 50% FBP + 50% adaptive statistical iterative reconstruction). Objective evaluation (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and image noise), subjective rating score (image noise score, anatomical details score, overall image quality score, and lesion conspicuity score), CT dose index volume, and dose length product were compared between groups during two-phase contrast enhancement. The detection rates of the four groups were calculated as percentages.

RESULTS

Image noise (SNR and CNR) among the four groups was statistically significant (P <0.05). The image noise in group A2 was lower than in group A1, but higher than that in groups B and C (P <0.05). SNR and CNR in group A2 were the highest, followed by group A1, and group C was the lowest (P <0.05 for all). The image noise score of group A2 was higher than that of the other three groups. In terms of the anatomic details score, the overall image quality score, and the lesion conspicuity score, the images of group A2 were superior to that of groups A1 and C. For hepatic tumor diameters more than or equal to 1.0 cm and less than 3.0 cm, group A achieved a higher detection rate than groups B and C. The CT dose index volume, dose length product, and effective dose in group A were significantly lower than that in groups B and C (P <0.05). On average, group A reduced the effective radiation dose by 27.2% compared to group B, whereas group B reduced the effective radiation dose by 28% compared to group C. Group A reduced the iodine load by 22.86% compared to group B.

CONCLUSIONS

The use of monochromatic images combined with 50% adaptive statistical iterative reconstruction with an isotonic low concentration contrast medium of 270 mg I/mL can optimize image quality, reduce image noise, increase detection rate for small tumors, and decrease radiation dose and iodine load in hepatic tumor CT examinations.