Image quality and radiation dose reduction in chest CT in pulmonary infection

Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study

RATIONALE AND OBJECTIVES

To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.

MATERIALS AND METHODS

Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard. Objective assessment, including image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), and subjective assessment using five-point scales by five radiologists were performed. Detection and false-positive rate of subsolid nodules, and morphologic features of nodules were recorded.

RESULTS

102 patients (mean age, 57.0 ± 12.3 years) with 358 subsolid nodules in SDCT were enrolled. The mean effective dose of SDCT and LDCT were 5.37 ± 0.80mSv and 0.86 ± 0.14mSv, respectively (P < 0.001). DLIR-H showed the lowest noise, highest CNRs, SNRs, and subjective scores among LDCT groups (all P < 0.001), almost approaching comparability with SDCT. The detection rates for DLIR-H, DLIR-M, ASIR-V-50%, and FBP were 76.5%, 76.3%, 83.8%, and 72.1%, respectively (P < 0.001), with false-positive rate of 2.5%, 2.2%, 8.3%, and 1.1%, respectively (P < 0.001). DLIR-H showed the highest detection rates for morphologic features (79.4%-95.2%) compared to DLIR-M (74.6%-88.9%), ASIR-V-50% (72.0%-88.4%), and FBP (66.1%-84.1%) (all P ≤ 0.001).

CONCLUSION

Sub-milliSievert LDCT with DLIR-H offers substantial dose reduction without compromising image quality. It is promising for evaluating subsolid nodules with a high detection rate and better identification of morphologic features.

Impact of Simulated Reduced-Dose Chest CT on Diagnosing Pulmonary T1 Tumors and Patient Management

To determine the diagnostic performance of simulated reduced-dose chest CT scans regarding pulmonary T1 tumors and assess the potential impact on patient management, a repository of 218 patients with histologically proven pulmonary T1 tumors was used. Virtual reduced-dose images were simulated at 25%- and 5%-dose levels. Tumor size, attenuation, and localization were scored by two experienced chest radiologists. The impact on patient management was assessed by comparing hypothetical LungRADS scores. The study included 210 patients (41% females, mean age 64.5 ± 9.2 years) with 250 eligible T1 tumors. There were differences between the original and the 5%-but not the 25%-dose simulations, and LungRADS scores varied between the dose levels with no clear trend. Sensitivity of Reader 1 was significantly lower using the 5%-dose vs. 25%-dose vs. original dose for size categorization (0.80 vs. 0.85 vs. 0.84; p = 0.007) and segmental localization (0.81 vs. 0.86 vs. 0.83; p = 0.018). Sensitivities of Reader 2 were unaffected by a dose reduction. A CT dose reduction may affect the correct categorization and localization of pulmonary T1 tumors and potentially affect patient management.

Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity - 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

A Novel Laser Angle Selection System for Computed Tomography-Guided Percutaneous Transthoracic Needle Biopsies

Purpose: To evaluate a novel laser angle selection system (LASS) for improving the efficiency of a computed tomography (CT)–guided percutaneous transthoracic needle biopsy (PTNB). Methods: Thirty-eight patients referred for CT-guided PTNB were randomly separated into a LASS-assisted puncture group (18 patients) or conventional freehand control group (20 patients). The puncture time, number of control CT scans, and patients’ radiation dose were compared for each group. Results: The lesion size, target-to-pleural distance, planned puncture depth, and angle of the two groups were not significantly different. LASS-assisted PTNB significantly reduced the number of control scans (1.7 ± 0.8 vs 3.5 ± 1.5, P < .001) and the mean operation time (12.0 ± 4.3 min vs 28.8 ± 13.3 min, P < .001) compared with the conventional method. The corresponding room time (27.1 ± 6.6 min vs 44.1 ± 14.4 min, P < .001) and total radiation dose (7.9 ± 1.0 mSv vs 10.1 ± 1.7 mSv, P < .001) of each procedure also decreased significantly. Fifty-six percent (10/18) of the operations hit the target on the first needle pass when using LASS compared with 10% (2/20) using the conventional method. Conclusions: Compared with a conventional method, this novel laser angle simulator improves puncture efficiency with fewer needle readjustments and reduces patient radiation dose.

Analysis of a monocentric computed tomography dosimetric database using a radiation dose index monitoring software: dose levels and alerts before and after the implementation of the adaptive statistical iterative reconstruction on CT images

OBJECTIVE

To analyze dosimetric data of a single center by a radiation dose index monitoring software evaluating quantitatively the dose reduction obtained with the implementation of the adaptive statistical iterative reconstruction (ASIR) on Computed Tomography in terms of both the value of the dose length product (DLP) and the alerts provided by the dose tool.

METHODS

Dosimetric quantities were acquired using Qaelum DOSE tool (QAELUM NV, Leuven-Heverlee, Belgium). Dose data pertaining to CT examinations were performed using a General Electric Healthcare CT tomography with 64 detectors. CT dose data were collected over 4 years (January 1, 2017 to December 31, 2020) and included CT dose length product (DLP). Moreover, all CT examinations that triggered a high radiation dose (twice the median for that study description), termed alerts on Dose tool, were retrieved for the analysis. Two radiologists retrospectively assessed CT examinations in consensus for the images quality and for the causes of the alerts issued. A Chi-square test was used to assess whether there were any statistically significant differences among categorical variable while a Kruskal Wallis test was considered to assess differences statistically significant for continuous variables.

RESULTS

Differences statistically significant were found for the DLP median values between the dosimetric data recorded on 2017-2018 versus 2019-2020. The differences were linked to the implementation of ASIR technique at the end of 2018 on the CT scanner. The highest percentage of alerts was reported in the CT study group "COMPLETE ABDOMEN + CHEST + HEAD" (range from 1.26% to 2.14%). A reduction year for year was relieved linked to the CT protocol optimization with a difference statistically significant. The highest percentage of alerts was linked to wrong study label/wrong study protocol selection with a range from 29 to 40%.

CONCLUSIONS

Automated methods of radiation dose data collection allowed for detailed radiation dose analysis according to protocol and equipment over time. The use of CT ASIR technique could determine considerable reduction in radiation dose.

Radiomics Metrics Combined with Clinical Data in the Surgical Management of Early-Stage (cT1-T2 N0) Tongue Squamous Cell Carcinomas: A Preliminary Study

Objective: To predict the risk of metastatic lymph nodes and the tumor grading related to oral tongue squamous cell carcinoma (OTSCC) through the combination of clinical data with radiomics metrics by computed tomography, and to develop a supportive approach in the management of the lymphatic cervical areas, with particular attention to the early stages (T1−T2). Between March 2016 and February 2020, patients with histologically confirmed OTSCC, treated by partial glossectomy and ipsilateral laterocervical lymphadenectomy and subjected to computed tomography (CT) before surgery, were identified by two centers: 81 patients (49 female and 32 male) with 58 years as the median age (range 19−86 years). Univariate analysis with non-parametric tests and multivariate analysis with machine learning approaches were used. Clinical, hematological parameters and radiological features extracted by CT were considered individually and in combination. All clinical parameters showed statistically significant differences (p < 0.05) for the Kruskal−Wallis test when discriminating both the tumor grading and the metastatic lymph nodes. DOI, PLR, SII, and SIRI showed an accuracy of 0.70 (ROC analysis) when identifying the tumor grading, while an accuracy ≥ 0.78 was shown by DOI, NLR, PLR, SII, and SIRI when discriminating metastatic lymph nodes. In the context of the analysis of radiomics metrics, the original_glszm_HighGrayLevelZoneEmphasis feature was selected for identifying the tumor grading (accuracy of 0.70), while the wavelet_HHH_glrlm_LowGrayLevelRunEmphasis predictor was selected for determining metastatic lymph nodes (accuracy of 0.96). Remarkable findings were also obtained when classifying patients with a machine learning approach. Radiomics features alone can predict tumor grading with an accuracy of 0.76 using a logistic regression model, while an accuracy of 0.82 can be obtained by running a CART algorithm through a combination of three clinical parameters (SIRI, DOI, and PLR) with a radiomics feature (wavelet_LLL_glszm_SizeZoneNonUniformityNormalized). In the context of predicting metastatic lymph nodes, an accuracy of 0.94 was obtained using 15 radiomics features in a logistic regression model, while both CART and CIDT achieved an asymptotic accuracy value of 1.00 using only one radiomics feature. Radiomics features and clinical parameters have an important role in identifying tumor grading and metastatic lymph nodes. Machine learning approaches can be used as an easy-to-use tool to stratify patients with early-stage OTSCC, based on the identification of metastatic and non-metastatic lymph nodes.

Automated Quantitative Lung CT Improves Prognostication in Non-ICU COVID-19 Patients beyond Conventional Biomarkers of Disease

(1) Background: COVID-19 continues to represent a worrying pandemic. Despite the high percentage of non-severe illness, a wide clinical variability is often reported in real-world practice. Accurate predictors of disease aggressiveness, however, are still lacking. The purpose of our study was to evaluate the impact of quantitative analysis of lung computed tomography (CT) on non-intensive care unit (ICU) COVID-19 patients' prognostication; (2) Methods: Our historical prospective study included fifty-five COVID-19 patients consecutively submitted to unenhanced lung CT. Primary outcomes were recorded during hospitalization, including composite ICU admission for the need of mechanical ventilation and/or death occurrence. CT examinations were retrospectively evaluated to automatically calculate differently aerated lung tissues (i.e., overinflated, well-aerated, poorly aerated, and non-aerated tissue). Scores based on the percentage of lung weight and volume were also calculated; (3) Results: Patients who reported disease progression showed lower total lung volume. Inflammatory indices correlated with indices of respiratory failure and high-density areas. Moreover, non-aerated and poorly aerated lung tissue resulted significantly higher in patients with disease progression. Notably, non-aerated lung tissue was independently associated with disease progression (HR: 1.02; p-value: 0.046). When different predictive models including clinical, laboratoristic, and CT findings were analyzed, the best predictive validity was reached by the model that included non-aerated tissue (C-index: 0.97; p-value: 0.0001); (4) Conclusions: Quantitative lung CT offers wide advantages in COVID-19 disease stratification. Non-aerated lung tissue is more likely to occur with severe inflammation status, turning out to be a strong predictor for disease aggressiveness; therefore, it should be included in the predictive model of COVID-19 patients.

Accurate and efficient pulmonary CT imaging workflow for COVID-19 patients by the combination of intelligent guided robot and automatic positioning technology

Background

The ongoing coronavirus disease 2019 (COVID-19) pandemic has put radiologists at a higher risk of infection during the computer tomography (CT) examination for the patients. To help settling these problems, we adopted a remote-enabled and automated contactless imaging workflow for CT examination by the combination of intelligent guided robot and automatic positioning technology to reduce the potential exposure of radiologists to 2019 novel coronavirus (2019-nCoV) infection and to increase the examination efficiency, patient scanning accuracy and better image quality in chest CT imaging .

Methods

From February 10 to April 12, 2020, adult COVID-19 patients underwent chest CT examinations on a CT scanner using the same scan protocol except with the conventional imaging workflow (CW group) or an automatic contactless imaging workflow (AW group) in Wuhan Leishenshan Hospital (China) were retrospectively and prospectively enrolled in this study. The total examination time in two groups was recorded and compared. The patient compliance of breath holding, positioning accuracy, image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and compared between the two groups.

Results

Compared with the CW group, the total positioning time of the AW group was reduced ((118.0 ± 20.0) s vs. (129.0 ± 29.0) s, P = 0.001), the proportion of scanning accuracy was higher (98% vs. 93%), and the lung length had a significant difference ((0.90±1.24) cm vs. (1.16±1.49) cm, P = 0.009). For the lesions located in the pulmonary centrilobular and subpleural regions, the image noise in the AW group was significantly lower than that in the CW group (centrilobular region: (140.4 ± 78.6) HU vs. (153.8 ± 72.7) HU, P = 0.028; subpleural region: (140.6 ± 80.8) HU vs. (159.4 ± 82.7) HU, P = 0.010). For the lesions located in the peripheral, centrilobular and subpleural regions, SNR was significantly higher in the AW group than in the CW group (centrilobular region: 6.6 ± 4.3 vs. 4.9 ± 3.7, P = 0.006; subpleural region: 6.4 ± 4.4 vs. 4.8 ± 4.0, P < 0.001).

Conclusions

The automatic contactless imaging workflow using intelligent guided robot and automatic positioning technology allows for reducing the examination time and improving the patient's compliance of breath holding, positioning accuracy and image quality in chest CT imaging.

Impact of coronavirus disease 2019 (COVID-19) outbreak on radiology research: An Italian survey

PURPOSE

To understand how COVID-19 pandemic has changed radiology research in Italy.

METHODS

A questionnaire (n = 19 questions) was sent to all members of the Italian Society of Radiology two months after the first Italian national lockdown was lifted.

RESULTS

A total of 327 Italian radiologists took part in the survey (mean age: 49 ± 12 years). After national lockdown, the working-flow came back to normal in the vast majority of cases (285/327, 87.2%). Participants reported that a total of 462 radiological trials were recruiting patients at their institutions prior to COVID-19 outbreak, of which 332 (71.9%) were stopped during the emergency. On the other hand, 252 radiological trials have been started during the pandemic, of which 156 were non-COVID-19 trials (61.9%) and 96 were focused on COVID-19 patients (38.2%). The majority of radiologists surveyed (61.5%) do not conduct research. Of the radiologists who carried on research activities, participants reported a significant increase of the number of hours per week spent for research purposes during national lockdown (mean 4.5 ± 8.9 h during lockdown vs. 3.3 ± 6.8 h before lockdown; p = .046), followed by a significant drop after the lockdown was lifted (3.2 ± 6.5 h per week, p = .035). During national lockdown, 15.6% of participants started new review articles and completed old papers, 14.1% completed old works, and 8.9% started new review articles. Ninety-six surveyed radiologists (29.3%) declared to have submitted at least one article during COVID-19 emergency.

CONCLUSION

This study shows the need to support radiology research in challenging scenarios like COVID-19 emergency.

Increased Radiation Dose Exposure in Thoracic Computed Tomography in Patients with Covid-19

The CT manifestation of COVID-19 patients is now well known and essentially reflects pathological changes in the lungs. Actually, there is insufficient knowledge on the long-term outcomes of this new disease, and several chest CTs might be necessary to evaluate the outcomes. The aim of this study is to evaluate the radiation dose for chest CT scans in COVID-19 patients compared to a cohort with pulmonary infectious diseases at the same time of the previous year to value if there is any modification of exposure dose. The analysis of our data shows an increase in the overall mean dose in COVID-19 patients compared with non-COVID-19 patients. In our results, the higher dose increase occurs in the younger age groups (+86% range 21–30 years and +67% range 31–40 years). Our results show that COVID-19 patients are exposed to a significantly higher dose of ionizing radiation than other patients without COVID infectious lung disease, and especially in younger age groups, although some authors have proposed the use of radiotherapy in these patients, which is yet to be validated. Our study has limitations: the use of one CT machine in a single institute and a limited number of patients.