Single source dual energy CT: What is the optimal monochromatic energy level for the analysis of the lung parenchyma?

A randomised controlled trial comparing three clinical administration strategies in spectral detector CT pulmonary angiography with low contrast medium dose

OBJECTIVES

To compare vascular attenuation (VA) with three strategies for administering a low contrast medium (CM) dose in dual-layer spectral detector CT pulmonary angiography (CTPA).

METHODS

Patients were prospectively randomised into control- or one of two experimental groups. Control group patients received CM (350 mgI/mL) diluted 1:1 with saline. Experimental group B received CM (350 mgI/mL) with low flow. Experimental group C received CM with low concentration (140 mgI/mL). Virtual monoenergetic images at 40 and 55 kiloelectron Volt (keV) were reconstructed. Objective examination quality (OEQ) i.e., VA, noise, and signal-to-noise ratio, was measured and subjective examination quality (SEQ) was rated at three anatomical levels: in the pulmonary trunk (PT), the interlobar arteries and the posterior basal segmental arteries.

PRIMARY OUTCOME

VA in PT at 40 keV.

SECONDARY OUTCOMES

OEQ and SEQ across all anatomic levels.

RESULTS

A total of 328 patients were randomised. 112 vs 115 and 101 were analysed in the control (A) vs experimental groups (B and C), respectively. There were no differences in VA in PT between the groups: A vs B (p = 0.96), B vs C (p = 0.14), and A vs C (p = 0.18). Group C showed higher VA across all anatomical levels. There were no differences in SEQ.

CONCLUSION

There was no difference in the attenuation in the PT between the dilution-, low flow-, and low concentration groups. However, the low concentration group showed higher attenuation in the pulmonary arteries when all anatomical levels were assessed.

KEY POINTS

Question Contrast medium reduction may be accomplished with dilution, low flow, or low concentration. However, the effect of the different strategies on vascular attenuation is unknown. Findings There was no difference in pulmonary trunk attenuation between the three strategies on spectral detector CT pulmonary angiography. Clinical relevance Low contrast medium dose spectral detector CT pulmonary angiography may be implemented with the administration strategy of the unit's own choice.

Dual-energy CT-based radiomics for predicting pathological grading of invasive lung adenocarcinoma

AIMS

The purpose of the study was to build a radiomics model using Dual-energy CT (DECT) to predict pathological grading of invasive lung adenocarcinoma.

MATERIALS AND METHODS

The retrospective study enrolled 107 patients (80 low-grade and 27 high-grade) with invasive lung adenocarcinoma before surgery. Clinical features, radiographic characteristics, and quantitative parameters were measured. Virtual monoenergetic images at 50kev and 150kev were reconstructed for extracting DECT radiomics features. To select features for constructing models, Pearson's correlation analysis, intraclass correlation coefficients, and least absolute shrinkage and selection operator penalized logistic regression were performed. Four models, including the DECT radiomics model, the clinical-DECT model, the conventional CT radiomics model, and the mixed model, were established. Area under the curve (AUC) and decision curve analysis were used to measure the performance and the clinical value of the models.

RESULTS

The radiomics model based on DECT exhibited outstanding performance in predicting tumor differentiation, with an AUC of 0.997 and 0.743 in the training and testing sets, respectively. Incorporating tumor density, lobulation, and effective atomic number at AP, the clinical-DECT model showed a comparable performance with an AUC of 0.836 in both the training and testing sets. In comparison to the conventional CT radiomics model (AUC of 0.998 in the training and 0.529 in the testing set) and the mixed model (AUC of 0.988 in the training and 0.707 in the testing set), the DECT radiomics model demonstrated a greater AUC value and provided patients with a more significant net benefit in the testing set.

CONCLUSIONS

In contrast to the conventional CT radiomics model, the DECT radiomics model produced greater predictive performance in pathological grading of invasive lung adenocarcinoma.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study

OBJECTIVES

To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms.

METHODS

An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions.

RESULTS

NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy.

CONCLUSIONS

DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules

Objectives

To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery.

Methods

The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA).

Results

The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model.

Conclusion

DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.

COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation

BACKGROUND

Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians.

OBJECTIVE

We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non-COVID-19 pneumonia and nonpneumonia diseases.

METHODS

A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers.

RESULTS

Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively).

CONCLUSIONS

FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Influence of virtual monochromatic spectral image at different energy levels on motion artifact correction in dual-energy spectral coronary CT angiography

PURPOSE

To investigate the influence of virtual monochromatic spectral (VMS) CT images at different energy levels on the effectiveness of a motion correction technique (SSF) in dual-energy Spectral coronary CT angiography (CCTA).

MATERIALS AND METHODS

29 cases suspected of or diagnosed with coronary artery disease underwent Spectral CCTA using a prospective ECG triggering with 250 ms padding time. SSF was applied to the determined least-motion phase to generate 6 additional sets of VMS images with energy levels from 40 to 100 keV. CT value and standard deviation (SD) in the aortic root and epicardial adipose tissue were measured. Image quality of the RCA, LAD and LCX was evaluated on a per-vessel basis in each patient. Two reviewers evaluated the artery using the score of the segment.

RESULTS

The low energy VMS images increased CT value and image noise compared with higher-energy VMS images, except 90 keV and 100 keV. The CNR of 40-70 keV were higher than those of 80-100 keV (P < 0.05). The image quality scores for images at 50-80 keV were higher than those of 40, 90, and 100 keV (P < 0.05), and the VMS image quality at 50 keV and 60 keV with SSF was the highest.

CONCLUSION

SSF can effectively reduce the motion artifacts when coronary vessels have suitable contrast enhancement which can be achieved by adjusting energy levels of VMS images.

Radiation dose reduction in chest dual-energy computed tomography: effect on image quality and diagnostic information

Objective

To determine whether dual-energy computed tomography (DECT) of the chest can be performed at a reduced radiation dose, with an emphasis on images generated with post-processing techniques.

Materials and Methods

In 21 patients undergoing DECT of the chest in a dual-source scanner, an additional image series was acquired at a reduced radiation dose. Four thoracic radiologists assessed both image series for image quality, normal thoracic structures, as well as pulmonary and mediastinal abnormalities, on virtual monochromatic images at 40 keV and 60 keV. Data were analyzed with Student's t-test, kappa statistics, analysis of variance, and the Wilcoxon signed-rank test.

Results

The overall image quality of 60 keV virtual monochromatic images at a reduced radiation dose was considered optimal in all patients, and no abnormalities were missed. Contrast enhancement and lesion detection performance were comparable between reduced-dose images at 40 keV and standard-of-care images at 60 keV. The intraobserver and interobserver agreement were both good. The mean volumetric CT dose index (CTDIvol), size-specific dose estimate (SSDE), dose-length product (DLP), and effective dose (ED) for reduced-dose DECT were 3.0 ± 0.6 mGy, 4.0 ± 0.6 mGy, 107 ± 30 mGy.cm, and 1.5 ± 0.4 mSv, respectively.

Conclusion

DECT of the chest can be performed at a reduced radiation dose (CTDIvol < 3 mGy) without loss of diagnostic information.

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.