A phantom study investigating the relationship between ground-glass opacity visibility and physical detectability index in low-dose chest computed tomography

Assessing the effect of scanning parameter on the size and density of pulmonary nodules: a phantom study

BACKGROUND

Lung cancer remains a leading cause of death among cancer patients. Computed tomography (CT) plays a key role in lung cancer screening. Previous studies have not adequately quantified the effect of scanning protocols on the detected tumor size. The aim of this study was to assess the effect of various CT scanning parameters on tumor size and densitometry based on a phantom study and to investigate the optimal energy and mA image quality for screening assessment.

METHODS

We proposed a new model using the LUNGMAN N1 phantom multipurpose anthropomorphic chest phantom (diameters: 8, 10, and 12 mm; CT values: - 100, - 630, and - 800 HU) to evaluate the influence of changes in tube voltage and tube current on the size and density of pulmonary nodules. In the LUNGMAN N1 model, three types of simulated lung nodules representing solid tumors of different sizes were used. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used to evaluate the image quality of each scanning combination. The consistency between the calculated results based on segmentation from two physicists was evaluated using the interclass correlation coefficient (ICC).

RESULTS

In terms of nodule size, the longest diameters of ground-glass nodules (GGNs) were closest to the ground truth on the images measured at 100 kVp tube voltage, and the longest diameters of solid nodules were closest to the ground truth on the images measured at 80 kVp tube voltage. In respect to density, the CT values of GGNs and solid nodules were closest to the ground truth when measured at 80 kVp and 100 kVp tube voltage, respectively. The overall agreement demonstrates that the measurements were consistent between the two physicists.

CONCLUSIONS

Our proposed model demonstrated that a combination of 80 kVp and 140 mA scans was preferred for measuring the size of the solid nodules, and a combination of 100 kVp and 100 mA scans was preferred for measuring the size of the GGNs when performing lung cancer screening. The CT values at 80 kVp and 100 kVp were preferred for the measurement of GGNs and solid nodules, respectively, which were closest to the true CT values of the nodules. Therefore, the combination of scanning parameters should be selected for different types of nodules to obtain more accurate nodal data.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia

BACKGROUND

The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model.

METHODS

LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model.

RESULTS

Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81.

CONCLUSIONS

Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts.

KEY POINTS

We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Quantum Iterative Reconstruction for Low-Dose Ultra-High-Resolution Photon-Counting Detector CT of the Lung

The aim of this study was to characterize image quality and to determine the optimal strength levels of a novel iterative reconstruction algorithm (quantum iterative reconstruction, QIR) for low-dose, ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) of the lung. Images were acquired on a clinical dual-source PCD-CT in the UHR mode and reconstructed with a sharp lung reconstruction kernel at different strength levels of QIR (QIR-1 to QIR-4) and without QIR (QIR-off). Noise power spectrum (NPS) and target transfer function (TTF) were analyzed in a cylindrical phantom. 52 consecutive patients referred for low-dose UHR chest PCD-CT were included (CTDIvol: 1 ± 0.6 mGy). Quantitative image quality analysis was performed computationally which included the calculation of the global noise index (GNI) and the global signal-to-noise ratio index (GSNRI). The mean attenuation of the lung parenchyma was measured. Two readers graded images qualitatively in terms of overall image quality, image sharpness, and subjective image noise using 5-point Likert scales. In the phantom, an increase in the QIR level slightly decreased spatial resolution and considerably decreased noise amplitude without affecting the frequency content. In patients, GNI decreased from QIR-off (202 ± 34 HU) to QIR-4 (106 ± 18 HU) (p < 0.001) by 48%. GSNRI increased from QIR-off (4.4 ± 0.8) to QIR-4 (8.2 ± 1.6) (p < 0.001) by 87%. Attenuation of lung parenchyma was highly comparable among reconstructions (QIR-off: -849 ± 53 HU to QIR-4: -853 ± 52 HU, p < 0.001). Subjective noise was best in QIR-4 (p < 0.001), while QIR-3 was best for sharpness and overall image quality (p < 0.001). Thus, our phantom and patient study indicates that QIR-3 provides the optimal iterative reconstruction level for low-dose, UHR PCD-CT of the lungs.

A phantom study comparing low-dose CT physical image quality from five different CT scanners

BACKGROUND

To systematically evaluate the physical image quality of low-dose computed tomography (LDCT) on CT scanners from 5 different manufacturers using a phantom model.

METHODS

CT images derived from a Catphan 500 phantom were acquired using manufacturer-specific iterative reconstruction (IR) algorithms and deep learning image reconstruction (DLIR) on CT scanners from 5 different manufacturers and compared using filtered back projection with 2 radiation doses of 0.25 and 0.75 mGy. Image high-contrast spatial resolution and image noise were objectively characterized by modulation transfer function (MTF) and noise power spectrum (NPS). Image high-contrast spatial resolution and image low-contrast detectability were compared directly by visual evaluation. CT number linearity and image uniformity were compared with intergroup differences using one-way analysis of variance (ANOVA).

RESULTS

The CT number linearity of 4 insert materials were as follows: acrylic (95% CI: 120.35 to 121.27; P=0.134), low-density polyethylene (95% CI: -98.43 to -97.43; P=0.070), air (95% CI: -996.16 to -994.51; P=0.018), and Teflon (95% CI: 984.40 to 986.87; P=0.883). The image uniformity values of GE Healthcare (95% CI: 3.24 to 3.83; P=0.138), Philips (95% CI: 2.62 to 3.70; P=0.299), Siemens (95% CI: 2.10 to 3.59; P=0.054), Minfound (95% CI: 2.35 to 3.65; P=0.589), and Neusoft (95% CI: 2.63 to 3.37; P=0.900) were evaluated and found to be within ±4 Hounsfield units (HU), with a range of 0.99-2.76 HU for standard deviations. There was no statistically significant difference in CT number linearity and image uniformity across the 5 CT scanners under different radiation doses with IR and DLIR algorithms (P>0.05). The resolution level at 10% MTF was 6.98 line-pairs-per-centimeter (lp/cm) on average, which was similar to the subjective evaluation results (mostly up to 7 lp/cm). DLIR at all 3 levels had the highest 50% MTF values among all reconstruction algorithms. For image low-contrast detectability, the minimum diameter of distinguishable contrast holes reached 4 mm at a 0.5% resolution. Increasing the radiation dose and IR strength reduced the image noise and NPS curve peak frequency while improving image low-contrast detectability.

CONCLUSIONS

This study demonstrated that the image quality of CT scanners from 5 different manufacturers in LDCT is comparable and that the CT number linearity is unbiased and can contribute to accurate bone mineral density quantification.

Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study

Purpose: To assess the physical performance of deep learning image reconstruction (DLIR) compared with those of filtered back projection (FBP) and iterative reconstruction (IR) and to estimate the dose reduction potential of the technique. Approach: A cylindrical water bath phantom with a diameter of 300 mm including two rods composed of acrylic and soft tissue-equivalent material was scanned using a clinical computed tomography (CT) scanner at four dose levels (CT dose index of 20, 15, 10, and 5 mGy). Phantom images were reconstructed using FBP, DLIR, and IR. The in-plane and z axis task transfer functions (TTFs) and in-plane noise power spectrum (NPS) were measured. The dose reduction potential was estimated by evaluating the system performance function calculated from TTF and NPS. The visibilities of a bar pattern phantom placed in the same water bath phantom were compared. Results: The use of DLIR resulted in a notable decrease in noise magnitude. The shift in peak NPS frequency was reduced compared with IR. Preservation of in-plane TTF was superior using DLIR than using IR. The estimated dose reduction potentials of DLIR and IR were 39% to 54% and 19% to 29%, respectively. However, the z axis resolution was decreased with DLIR by 6% to 21% compared with FBP. The bar pattern visibilities were approximately consistent with the TTF results in both planes. Conclusions: The in-plane edge-preserving noise reduction performance of DLIR is superior to that of IR. Moreover, DLIR enables approximately half-dose acquisitions with no deterioration in noise texture in cases that permit some z axis resolution reduction.

Comparison of physical image qualities and artifact indices for head computed tomography in the axial and helical scan modes

This study aimed to validate the clinically demonstrated equivalency of the axial and helical scan modes (AS and HS, respectively) for head computed tomography (CT) using physical image quality measures and artifact indices (AIs). Two 64-row multi-detector row CT systems (CT-A and CT-B) were used for comparing AS and HSs with detector rows of 64 and 32. The modulation transfer function (MTF), noise power spectrum (NPS), and slice sensitivity profile were measured using a CT dose index corresponding to clinical use. The system performance function (SPF) was calculated as MTF²/NPS. The AI of streak artifacts in the skull base was measured using an image obtained of a head phantom, while the AI of motion artifacts was measured from images obtained during the head phantom was in motion. For CT-A, the 50%MTFs were 7% to 9% higher in the HS than the AS, and the higher MTFs of HS associated NPS increases. For CT-B, the MTFs and NPSs were almost equivalent between the AS and HS, respectively. Consequently, the SPFs of AS and HS were nearly identical for both CT systems. For both CT systems, the skull base AI did not differ significantly between AS and HS, while the motion AIs of HS were significantly better than of AS. The superior motion AI in the HS indicated the effectiveness of HS on moving patients.

Transbronchial biopsy of peripheral lung lesions using fluoroscopic guidance combined with an enhanced ray-summation display

The aim of this study was to investigate the effectiveness of guidance assistance during transbronchial biopsy (TBB) to achieve an appropriate pathway to small and peripheral pulmonary lesions (PPLs) using a combination of fluoroscopy and specialized ray-summation (Ray-sum_TBB) images, which were processed from preprocedural lung computed tomography (CT) images. To improve the visibility of the correct pathway to the PPLs, three-dimensional spatial resolution enhancement and CT number conversion processes were applied to the original CT images. The Ray-sum_TBB images reconstructed from the processed CT images were used as additional guides. We compared the rates of successful tumor localization and biopsy (arrival rate) between the trial (with Ray-sum_TBB) and control (without Ray-sum_TBB) groups. The fluoroscopy and examination times were also compared. The arrival rate of the trial group (73.1%) was significantly better than that of the control group (42.3%) (p = 0.048). The fluoroscopy and examination times did not differ significantly between the trial and control groups. No complications were identified in the trial group. Our results suggest that Ray-sum_TBB improves the diagnostic accuracy of TBB.

Quality evaluation of image-based iterative reconstruction for CT: Comparison with hybrid iterative reconstruction

The purpose of this study is to evaluate the physical image quality of a commercially available image‐based iterative reconstruction (IIR) system for two object contrasts to resemble a soft tissue (60 HU) and an enhanced vessel (270 HU), and compare the results with those of filtered back projection (FBP) and iterative reconstruction (IR). A 192‐slice computed tomography (CT) scanner was used for data acquisitions. IIR images were processed from the FBP images. Task‐based in‐plane transfer function (TTF) and slice sensitivity profile (SSP_task) were measured from rod objects inside of a 25‐cm diameter water phantom at four dose levels (2.5, 5, 10, and 20 mGy). Noise power spectrum (NPS) was measured from the water‐only part. System performance (SP) function was calculated as TTF²/NPS over FBP, IR, and IIR for comparison. In addition, an image subtraction was performed using images of rod objects, a bar‐pattern phantom, and a clinical abdomen case to observe the noise reduction performance of IIR. As a results, IIR mostly preserved TTF and SSP_task of FBP, whereas IR exhibited enhanced TTF at 10 and 20 mGy for 60 HU contrast and at all doses for 270 HU contrast. SP of IIR at 2.5, 5, 10 mGy (half doses) were similar to those of FBP at 5, 10, 20 mGy, respectively. IR exhibited enhanced SP at medium to high frequencies. The subtracted images showed weak remained edge signals in the bar‐pattern and abdominal images. In conclusion, IIR uniformly improved the task‐based image quality of FBP over the entire frequency range, whereas IR improved the characteristics over medium to high frequencies. The dose reduction potential of IIR estimated from SP is approximately 50%, when allowing the slight signal reductions.

Use of an Anthropomorphic Chest Model to Evaluate Multiple Scanning Protocols for High-Definition and Standard-Definition Computed Tomography to Detect Small Pulmonary Nodules

BACKGROUND This study aimed to use the LUNGMAN N1 anthropomorphic chest model to evaluate protocols for high-definition computed tomography (HDCT) and standard-definition CT (SDCT) to detect and compare small pulmonary nodules and determine the most appropriate low-dose scanning protocols. MATERIAL AND METHODS HDCT imaging used the Discovery HD750 scanner (80, 100, 120 and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA), and SDCT imaging used the Lightspeed VCT scanner (80, 120, and 140 kVp; 360, 320, 280, 240, 200, 160, 120, 80, 40, and 20 mA). The LUNGMAN N1 anthropomorphic chest model contained artificial pulmonary nodules (diameter: 5, 8, 10, and 12 mm). Low-dose scanning protocols were used in image acquisition. Two experienced radiologists evaluated the image quality. The combinations of voltage, tube current, image noise, and radiation dose were recorded. Consistency of the image quality between raters was assessed by kappa statistical analysis. RESULTS Seventy CT scans of pulmonary nodules (diameter, 5-12 mm) were performed. There was a high degree of consistency for image quality between the two observers (K=0.929 for 5 mm nodules; K=0.819 for overall image quality). For 8 mm nodules, 100% were detected on both SDCT and HDCT. HDCT outperformed SDCT by 5%, in terms of effective dose. There was no significant difference in image quality between the SDCT and HDCT scanners. CONCLUSIONS Using an anthropomorphic chest model, the identification and image quality using SDCT was similar to that of HDCT for small pulmonary nodules between 5-12 mm.

Comparative evaluation of image quality among different detector configurations using area detector computed tomography

The 320-detector row computed tomography (CT) system, i.e., the area detector CT (ADCT), can perform helical scanning with detector configurations of 4-, 16-, 32-, 64-, 80-, 100-, and 160-detector rows for routine CT examinations. This phantom study aimed to compare the quality of images obtained using helical scan mode with different detector configurations. The image quality was measured using modulation transfer function (MTF) and noise power spectrum (NPS). The system performance function (SP), based on the pre-whitening theorem, was calculated as MTF²/NPS, and compared between configurations. Five detector configurations, i.e., 0.5 × 16 mm (16 row), 0.5 × 64 mm (64 row), 0.5 × 80 mm (80 row), 0.5 × 100 mm (100 row), and 0.5 × 160 mm (160 row), were compared using a constant volume CT dose index (CTDI_vol) of 25 mGy, simulating the scan of an adult abdomen, and with a constant effective mAs value. The MTF was measured using the wire method, and the NPS was measured from images of a 20-cm diameter phantom with uniform content. The SP of 80-row configuration was the best, for the constant CTDI_vol, followed by the 64-, 160-, 16-, and 100-row configurations. The decrease in the rate of the 100- and 160-row configurations from the 80-row configuration was approximately 30%. For the constant effective mAs, the SPs of the 100-row and 160-row configurations were significantly lower, compared with the other three detector configurations. The 80- and 64-row configurations were adequate in cases that required dose efficiency rather than scan speed.

Value of window technique in diagnosis of the ground glass opacities in patients with non-small cell pulmonary cancer

The aim of the present study was to examine the value of window technique in qualitative diagnosis of the ground glass opacities (GGO) in patients with non-small cell pulmonary cancer. A total of 124 clinically suspected pulmonary cancer patients were analyzed retrospectively. The lesions were affirmed by puncture biopsy, and were GGO on pulmonary window while were invisible on mediastinal window. Sixty-four multi-detector spiral computed tomography with the window width and window level of 1,500 Hounsfield units (HU) and −450 HU on pulmonary window, while the window width and window level of 400 and 40 HU on mediastinal window, was used in the study. The window adjustment technique was used to analyze the window width and window level of lesion on pulmonary window and mediastinal window, for searching invisible threshold on 3-megapixel medical displays. The diagnostic accuracy and the cut-off value were compared on receiver operating characteristic (ROC) curve. The results showed that the window width and window level on pulmonary window and mediastinal window of malignant lesions were significantly less than those of benign ones (P<0.05). The cut-off value on pulmonary window was the window width and window level of 1,300 and −220 HU, the area under the ROC was 0.830 [sensitivity was 72.5%, specificity was 84.3%; 95% confidence interval (CI), 0.712–0.945]. The cut-off value on mediastinal window was the window width and window level of 360 and 30 HU, and the area under the ROC was 0.623 (was 62.0%, specificity was 55.7%; 95% CI, 0.541–0.745). In conclusion, the window technique has high sensitivity and accuracy in qualitative diagnosis of the GGO.