Optimization of acquisition parameters for reduced-dose thoracic CT: A phantom study

Evaluation of low-dose pediatric chest CT examination using in-house developed various age-size pediatric chest phantoms

PURPOSE

This study aims to develop Various Age-size Pediatric Chest Phantoms (VAPC) to evaluate low-dose protocol that approximates clinical conditions achieved by low organ-specific doses and optimal image quality among the challenges of pediatric size variations.

METHODS

Three original pediatric data aged 1, 4, and 7 years were used as a reference for developing VAPC phantoms. Six protocols, namely standard dose (STD) and low dose (low mA and low kV) reconstructed using Filtered Back Projection (FBP) and iterative reconstruction (IR) algorithms, were investigated. This study directly measured the lungs, heart, and spinal cord dose using LD-V1 film. Linearity, Modulation Transfer Function (MTF), Contrast to Noise Ratio (CNR), and Noise Power Spectrum (NPS) were evaluated to assess the CT image quality of the VAPC phantom.

RESULTS

This study found that the mean organ-specific dose was higher than CTDIvol. A Comparison of mean lung doses showed VAPC phantom 1 (y.o.) received 74.8% and 137.2% more doses than 4 (y.o.) and 7 (y.o.), respectively. Low kV produces a lower organ dose than low mA. The linearity of CT numbers is not biased at low doses. Differences in age measures significantly influenced organ-specific dose, MTF, CNR, and NPS.

CONCLUSION

Smaller pediatrics are still exposed to higher doses at low-dose examinations, whereas larger pediatrics have lower contrast resolution and increased image noise. CT number linearity is unbiased. The combination of low kV with FBP produces higher spatial resolution, while low mA with IR effectively reduces noise to detect low-contrast objects better.

Artificial intelligence in lung cancer: current applications and perspectives

Artificial intelligence (AI) has been a very active research topic over the last years and thoracic imaging has particularly benefited from the development of AI and in particular deep learning. We have now entered a phase of adopting AI into clinical practice. The objective of this article was to review the current applications and perspectives of AI in thoracic oncology. For pulmonary nodule detection, computer-aided detection (CADe) tools have been commercially available since the early 2000s. The more recent rise of deep learning and the availability of large annotated lung nodule datasets have allowed the development of new CADe tools with fewer false-positive results per examination. Classical machine learning and deep-learning methods were also used for pulmonary nodule segmentation allowing nodule volumetry and pulmonary nodule characterization. For pulmonary nodule characterization, radiomics and deep-learning approaches were used. Data from the National Lung Cancer Screening Trial (NLST) allowed the development of several computer-aided diagnostic (CADx) tools for diagnosing lung cancer on chest computed tomography. Finally, AI has been used as a means to perform virtual biopsies and to predict response to treatment or survival. Thus, many detection, characterization and stratification tools have been proposed, some of which are commercially available.

Size-Specific Dose Estimates for Thoracic and Abdominal Computed Tomography Examinations at Two Moroccan Hospitals

Size-specific dose estimates (SSDE) are the latest topic of interest in patient radiation-dose studies in computed tomography (CT). The aim of this study is to calculate and evaluate the doses (SSDE) by measuring the effective diameter (ED) of cross-sectional images collected during CT examinations of the chest and abdomen in Moroccan hospitals. Doses (SSDE) were calculated based on cross-sectional images by measuring the effective diameters of 75 patients in both examinations (45 for the thorax and 30 for the abdomen). Specific conversion factors for (ED) were used to convert the registered CTDIvol to SSDE, according to the instruction in the American Association of Physicists (AAPM) Report 204. In thoracic CT, the CTDIvol and SSDE values ranged from 5.8 to 10.7 mGy (mean: 8.08) and 9.55 to 15.37 mGy (mean: 12.13), respectively. For abdominal CT, CTDIvol and SSDE values ranged from 4.8 to 12.2 mGy (mean: 7.95) and 8.01 to 14.15 mGy (mean: 11.31), respectively. The results show that the SSDE is a useful tool and could potentially educate CT operators on its effective use as a way to optimize radiation dose instead of CTDIvol, in particular to establish diagnostic reference levels.

Early results of ultra-low-dose CT-scan for extremity traumas in emergency room

Background

Ultra-low dose computed tomography (ULD-CT) was shown to be a good alternative to digital radiographs in various locations. This study aimed to assess the diagnostic sensitivity and specificity of ULD-CT versus digital radiographs in patients consulting for extremity traumas in emergency room.

Methods

Digital radiography and ULD-CT scan were performed in patients consulting at the emergency department (February-August 2018) for extremity traumas. Fracture detection was evaluated retrospectively by two blinded independent radiologists. Sensitivity and specificity were evaluated using best value comparator (BVC) and a Bayesian latent class model (LCM) approaches and clinical follow-up. Image quality, quality diagnostic and diagnostic confidence level were evaluated (Likert scale). The effective dose received was calculated.

Results

Seventy-six consecutive patients (41 men, mean age: 35.2±13.2 years), with 31 wrists/hands and 45 ankles/feet traumas were managed by emergency physicians. According to clinical data, radiography had 3 false positive and 10 false negative examinations, and ULD-CT, 2 of each. Radiography and ULD-CT specificities were similar; sensitivities were lower for radiography, with BVC and Bayesian. With Bayesian, ULD-CT and radiography sensitivities were 90% (95% CI: 87-93%) and 76% (95% CI: 71-81%, P<0.0001) and specificities 96% (95% CI: 93-98%) and 93% (95% CI: 87-97%, P=0.84). The inter-observer agreement was higher for ULD-CT for all subjective indexes. The effective dose for ULD-CT and radiography was 0.84±0.14 and 0.58±0.27 µSv (P=0.002) for hand/wrist, and 1.50±0.32 and 1.44±0.78 µSv (P=NS) for foot/ankle.

Conclusions

With an effective dose level close to radiography, ULD-CT showed better detection of extremities fractures in the emergency room and may allow treatment adaptation. Further studies need to be performed to assess impact of such examination in everyday practice.

Trial Registration

ClinicalTrials.gov Identifier: NCT04832490.

Performance of virtual non-contrast images generated on clinical photon-counting detector CT for emphysema quantification: proof of concept

OBJECTIVE

To evaluate the performance of virtual non-contrast images (VNC) compared to true non-contrast (TNC) images in photon-counting detector computed tomography (PCD-CT) for the evaluation of lung parenchyma and emphysema quantification.

METHODS

65 (mean age 73 years; 48 male) consecutive patients who underwent a three-phase (non-contrast, arterial and venous) chest/abdomen CT on a first-generation dual-source PCD-CT were retrospectively included. Scans were performed in the multienergy (QuantumPlus) mode at 120 kV with 70 ml intravenous contrast agent at an injection rate of 4 ml s-1. VNC were reconstructed from the arterial (VNCart) and venous phase (VNCven). TNC and VNC images of the lung were assessed quantitatively by calculating the global noise index (GNI) and qualitatively by two independent, blinded readers (overall image quality and emphysema assessment). Emphysema quantification was performed using a commercially available software tool at a threshold of -950 HU for all data sets. TNC images served as reference standard for emphysema quantification. Low attenuation values (LAV) were compared in a Bland-Altman plot.

RESULTS

GNI was similar in VNCart (103.0 ± 30.1) and VNCven (98.2 ± 22.2) as compared to TNC (100.9 ± 19.0, p = 0.546 and p = 0.272, respectively). Subjective image quality (emphysema assessment and overall image quality) was highest for TNC (p = 0.001), followed by VNCven and VNCart. Both, VNCart and VNCven showed no significant difference in emphysema quantification as compared to TNC (p = 0.409 vs. p = 0.093; respectively).

CONCLUSION

Emphysema evaluation is feasible using virtual non-contrast images from PCD-CT.

ADVANCES IN KNOWLEDGE

Emphysema quantification is feasible and accurate using VNC images in PCD-CT. Based on these findings, additional TNC scans for emphysema quantification could be omitted in the future.

The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review

INTRODUCTION

Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT.

METHODS

Literature searches of ProQuest, PubMed, Cinahl, ScienceDirect, EbscoHost Ebook Collection and Ovid were carried out to find research articles published between the years 2015 and 2020. In addition, manual search was conducted in SweMed+, SwePub, NORA, Taylor & Francis Online and Medic.

RESULTS

Following a systematic search process, the review comprised of 16 articles. Articles were organised according to the effects of the deep learning networks, e.g. image noise reduction, image restoration. Deep learning can be used in multiple ways to facilitate dose optimisation in low-dose CT. Most articles discuss image noise reduction in low-dose CT.

CONCLUSION

Deep learning can be used in the optimisation of patients' radiation dose. Nevertheless, the image quality is normally lower in low-dose CT (LDCT) than in regular-dose CT scans because of smaller radiation doses. With the help of deep learning, the image quality can be improved to equate the regular-dose computed tomography image quality.

IMPLICATIONS TO PRACTICE

Lower dose may decrease patients' radiation risk but may affect the image quality of CT scans. Artificial intelligence technologies can be used to improve image quality in low-dose CT scans. Radiologists and radiographers should have proper education and knowledge about the techniques used.

First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels

OBJECTIVE

The aim of this study was to evaluate the image quality (IQ) and performance of an artificial intelligence (AI)-based computer-aided detection (CAD) system in photon-counting detector computed tomography (PCD-CT) for pulmonary nodule evaluation at different low-dose levels.

MATERIALS AND METHODS

An anthropomorphic chest-phantom containing 14 pulmonary nodules of different sizes (range, 3-12 mm) was imaged on a PCD-CT and on a conventional energy-integrating detector CT (EID-CT). Scans were performed with each of the 3 vendor-specific scanning modes (QuantumPlus [Q+], Quantum [Q], and High Resolution [HR]) at decreasing matched radiation dose levels (volume computed tomography dose index ranging from 1.79 to 0.31 mGy) by adapting IQ levels from 30 to 5. Image noise was measured manually in the chest wall at 8 different locations. Subjective IQ was evaluated by 2 readers in consensus. Nodule detection and volumetry were performed using a commercially available AI-CAD system.

RESULTS

Subjective IQ was superior in PCD-CT compared with EID-CT (P < 0.001), and objective image noise was similar in the Q+ and Q-mode (P > 0.05) and superior in the HR-mode (PCD 55.8 ± 11.7 HU vs EID 74.8 ± 5.4 HU; P = 0.01). High resolution showed the lowest image noise values among PCD modes (P = 0.01). Overall, the AI-CAD system delivered comparable results for lung nodule detection and volumetry between PCD- and dose-matched EID-CT (P = 0.08-1.00), with a mean sensitivity of 95% for PCD-CT and of 86% for dose-matched EID-CT in the lowest evaluated dose level (IQ5). Q+ and Q-mode showed higher false-positive rates than EID-CT at lower-dose levels (IQ10 and IQ5). The HR-mode showed a sensitivity of 100% with a false-positive rate of 1 even at the lowest evaluated dose level (IQ5; CDTIvol, 0.41 mGy).

CONCLUSIONS

Photon-counting detector CT was superior to dose-matched EID-CT in subjective IQ while showing comparable to lower objective image noise. Fully automatized AI-aided nodule detection and volumetry are feasible in PCD-CT, but attention has to be paid to false-positive findings.

CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma

PURPOSE

The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies.

MATERIALS AND METHODS

Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment.

RESULTS

A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment.

CONCLUSION

Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.

Imaging of postoperative complications following Ivor-Lewis esophagectomy

Postoperative imaging plays a key role in the identification of complications after Ivor-Lewis esophagectomy (ILE). Careful analysis of imaging examinations can help identify the cause of the presenting symptoms and the mechanism of the complication. The complex surgical procedure used in ILE results in anatomical changes that make imaging interpretation challenging for many radiologists. The purpose of this review was to make radiologists more familiar with the imaging findings of normal anatomical changes and those of complications following ILE to enable accurate evaluation of patients with an altered postoperative course. Anastomotic leak, gastric conduit necrosis and pleuropulmonary complications are the most serious complications after ILE. Computed tomography used in conjunction with oral administration of contrast material is the preferred diagnostic tool, although it conveys limited sensitivity for the diagnosis of anastomotic fistula. In combination with early endoscopic assessment, it can also help early recognition of complications and appropriate therapeutic management.

Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data

PURPOSE

The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications.

MATERIAL AND METHODS

Acquisitions on image quality and anthropomorphic phantoms were performed at six dose levels (CTDI_vol: 10/7.5/5/2.5/1/0.5mGy) on two CT scanners equipped with two different DLR algorithms (TrueFidelity^TM and AiCE). Raw data were reconstructed using the filtered back-projection (FBP) and the lowest/intermediate/highest DLR levels (L-DLR/M-DLR/H-DLR) of each algorithm. Noise power spectrum, task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a soft tissue mediastinal nodule, ground-glass opacity, or high-contrast pulmonary lesion. Subjective image quality of anthropomorphic phantom images was analyzed by two radiologists.

RESULTS

For the L-DLR/M-DLR levels, the noise magnitude was lower with TrueFidelity^TM than with AiCE from 2.5 to 10 mGy. For H-DLR, noise magnitude was lower with AiCE . For L-DLR and M-DLR, the average NPS spatial frequency (f_av) values were greater for AiCE except for 0.5 mGy. For H-DLR levels, f_av was greater for TrueFidelity^TM than for AiCE. TTF_50% values were greater with AiCE for the air insert, and lower than TrueFidelity^TM for the polyethylene insert. From 2.5 to10 mGy, d' was greater for AiCE than for TrueFidelity^TM for H-DLR for all lesions, but similar for L-DLR and M-DLR. Image quality was rated clinically appropriate for all levels of both algorithms, for dose from 2.5 to 10 mGy, except for L-DLR of AiCE.

CONCLUSION

DLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.

Impact of Morphotype on Image Quality and Diagnostic Performance of Ultra-Low-Dose Chest CT

Objectives: The image quality of an Ultra-Low-Dose (ULD) chest CT depends on the patient’s morphotype. We hypothesize that there is a threshold beyond which the diagnostic performance of a ULD chest CT is too degraded. This work assesses the influence of morphotype (Body Mass Index BMI, Maximum Transverse Chest Diameter MTCD and gender) on image quality and the diagnostic performance of a ULD chest CT. Methods: A total of 170 patients from three prior prospective monocentric studies were retrospectively included. Renewal of consent was waived by our IRB. All the patients underwent two consecutive unenhanced chest CT acquisitions with a full dose (120 kV, automated tube current modulation) and a ULD (135 kV, fixed tube current at 10 mA). Image noise, subjective image quality and diagnostic performance for nine predefined lung parenchyma lesions were assessed by two independent readers, and correlations with the patient’s morphotype were sought. Results: The mean BMI was 26.6 ± 5.3; 20.6% of patients had a BMI > 30. There was a statistically significant negative correlation of the BMI with the image quality (ρ = −0.32; IC95% = (−0.468; −0.18)). The per-patient diagnostic performance of ULD was sensitivity, 77%; specificity, 99%; PPV, 94% and NPV, 65%. There was no statistically significant influence of the BMI, the MTCD nor the gender on the per-patient and per-lesion diagnostic performance of a ULD chest CT, apart from a significant negative correlation for the detection of emphysema. Conclusions: Despite a negative correlation between the BMI and the image quality of a ULD chest CT, we did not find a correlation between the BMI and the diagnostic performance of the examination, suggesting a possible use of the ULD protocol in obese patients.

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse^® and 14 with corpuls cpr^®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.

Imaging during pregnancy: What the radiologist needs to know

During the last decades, there has been a growing demand for medical imaging in gravid women. Imaging of the pregnant woman is challenging as it involves both the mother and the fetus and, consequently, several medical, ethical, or legal considerations are likely to be raised. Theoretically, all currently available imaging modalities may be used for the evaluation of the pregnant woman; however, in practice, confusion regarding the safety of the fetus often results in unnecessary avoidance of useful diagnostic tests, especially those involving ionizing radiation. This review article is focused on the current safety guidelines and considerations regarding the use of different imaging modalities in the pregnant population; also presented is an imaging work-up for the most common medical conditions of pregnant women, with emphasis on fetal and maternal safety.

Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment

PURPOSE

To assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform.

MATERIALS AND METHODS

Acquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDI_vol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d') was computed to model the detection task of two enhanced iodine lesions as function of keV.

RESULTS

Noise-magnitude was significantly reduced between 40 to 70 keV by -56±0% (SD) (range: -56%--55%) with FBP; -56±0% (SD) (-56%--56%) with A3; and -57±1% (SD) (range: -57%--56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2mg/mL, d' values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d' peaked at 60 keV. For the other simulated lesion, d' values were highest at 40 keV and decreased beyond.

CONCLUSION

At low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.

Radiomics in the evaluation of lung nodules: Intrapatient concordance between full-dose and ultra-low-dose chest computed tomography

PURPOSE

The purpose of this study was to retrospectively evaluate the quantitative and qualitative intrapatient concordance of pulmonary nodule risk assessment by commercially available radiomics software between full-dose (FD) chest-CT and ultra-low-dose (ULD) chest CT.

MATERIALS AND METHODS

Between July 2013 and September 2015, 68 patients (52 men and16 women; mean age, 65.5±10.6 [SD] years; range: 35-87 years) with lung nodules≥5mm and<30mm who underwent the same day FD chest CT (helical acquisition; 120kV; automated tube current modulation) and ULD chest CT (helical acquisition; 135kV; 10mA fixed) were retrospectively included. Each nodule on each acquisition was assessed by a commercial radiomics software providing a similarity malignancy index (mSI), classifying it as "benign-like" (mSI<0.1); "malignant-like" (mSI>0.9) or "undetermined" (0.1≤mSI≤0.9). Intrapatient qualitative agreement was evaluated with weighted Cohen-Kappa test and quantitative agreement with intraclass correlation coefficient (ICC).

RESULTS

Ninety-nine lung nodules with a mean size of 9.14±4.3 (SD) mm (range: 5-25mm) in 68 patients (mean 1.46 nodule per patient; range: 1-5) were assessed; mean mSI was 0.429±0.331 (SD) (range: 0.001-1) with FD chest CT (22/99 [22%] "benign-like", 67/99 [68%] "undetermined" and 10/99 [10%] "malignant-like") and mean mSI was 0.487±0.344 (SD) (range: 0.002-1) with ULD chest CT (20/99 [20%] "benign-like", 59/99 [60%] "undetermined" and 20/99 [20%] "malignant-like"). Qualitative and quantitative agreement of FD chest CT with ULD chest CT were "good" with Kappa value of 0.60 (95% CI: 0.46-0.74) and ICC of 0.82 (95% CI: 0.73-0.87), respectively.

CONCLUSION

A good agreement in malignancy similarity index can be obtained between ULD chest CT and FD chest CT using radiomics software. However, further studies must be done with more case material to confirm our results and elucidate the diagnostic capabilities of radiomics software using ULD chest CT for lung nodule characterization by comparison with FD chest CT.