A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules

Combining Low-energy Images in Dual-energy Spectral CT With Deep Learning Image Reconstruction Algorithm to Improve Inferior Vena Cava Image Quality

OBJECTIVE

To explore the application of low-energy image in dual-energy spectral CT (DEsCT) combined with deep learning image reconstruction (DLIR) to improve inferior vena cava imaging.

MATERIALS AND METHODS

Thirty patients with inferior vena cava syndrome underwent contrast-enhanced upper abdominal CT with routine dose, and the 40, 50, 60, 70, and 80 keV images in the delayed phase were first reconstructed with the ASiR-V40% algorithm. Image quality was evaluated both quantitatively [CT value, SD, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for inferior vena cava] and qualitatively to select an optimal energy level with the best image quality. Then, the optimal-energy images were reconstructed again using deep learning image reconstruction medium strength (DLIR-M) and DLIR-H (high strength) algorithms and compared with that of ASiR-V40%.

RESULTS

The objective CT value, SD, SNR, and CNR increased with the decrease in energy level, with statistically significant differences (all P<0.05). The 40 keV images had the highest CT values, SNR, and CNR and good diagnostic acceptability, and 40 keV was selected as the best energy level. Compared with ASiR-V40% and DLIR-M, DLIR-H had the lowest SD, highest SNR and CNR, and subjective score (all P<0.001) with good consistencies between the 2 physicians (all k ≥0.75). The 40 keV images with DLIR-H had the highest overall image quality, showing sharper edges of inferior vena cava vessels and clearer lumen in patients with Budd-Chiari syndrome.

CONCLUSIONS

Compared with the ASiR-V algorithm, DLIR-H significantly reduces image noise and provides the highest CNR and best diagnostic image quality for the 40 keV DEsCT images in imaging inferior vena cava.

Virtual monoenergetic imaging predicting Ki-67 expression in lung cancer

This study aimed to optimize slope and energy levels for evaluating Ki-67 expression in lung cancer using virtual monoenergetic imaging and compare the predictive efficiency of different energy spectrum slopes (λHU) for Ki-67. Forty-three patients with primary lung cancer confirmed via pathological examination were enrolled in this study. They underwent baseline arterial-phase (AP) and venous-phase (VP) energy spectrum computed tomography (CT) scanning before surgery. The CT values were 40-190 keV, with 40-140 keV indicating pulmonary lesions at AP and VP, and P < 0.05 indicating a statistically significant difference. An immunohistochemical examination was conducted, and receiver operating characteristic curves were used to analyze the prediction performance of λHU for Ki-67 expression. SPSS Statistics 22.0 (IBM Corp., NY, USA) was used for statistical analysis, and χ², t, and Mann-Whitney U tests were used for quantitative and qualitative analyses of data. Significant differences were observed at the corresponding CT values of 40 keV (as 40-keV is considered the best for single-energy image for evaluating Ki-67 expression) and 50 keV in AP and at 40, 60, and 70 keV in VP between high- and low-Ki-67 expression groups (P < 0.05). In addition, the λHU values of three-segment energy spectrum curve in both AP and VP were quite different between two groups (P < 0.05). However, the VP data had greater predictive values for Ki-67. The areas under the curve were 0.859, 0.856, and 0.859, respectively. The 40-keV single-energy sequence was the best single-energy sequence to evaluate the expression of Ki-67 in lung cancer and to obtain λHU values using the energy spectrum curve in the VP. The CT values had better diagnostic efficiency.

The application of dual-layer spectral detector computed tomography in solitary pulmonary nodule identification

BACKGROUND

Differentiating between malignant solitary pulmonary nodules (SPNs) and other lung diseases remains a substantial challenge. The latest generation of dual-energy computed tomography (CT), which realizes dual-energy technology at the detector level, has clinical potential for distinguishing lung cancer from other benign SPNs. This study aimed to evaluate the performance of dual-layer spectral detector CT (SDCT) for the differentiation of SPNs.

METHODS

Spectral images of 135 SPNs confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP). Patients were classified into two groups [the malignant group (n=93) and the benign group (n=42)], with the malignant group further divided into small cell lung cancer (SCLC, n=30) and non-small cell lung cancer (NSCLC, n=63) subtypes. The slope of the spectral Hounsfield Unit (HU) curve (λHU), normalized iodine concentration (NIC), CT values of 40 keV monochromatic images (CT40keV), and normalized arterial enhancement fraction (NAEF) in contrast-enhanced images were calculated and compared between the benign and malignant groups, as well as between the SCLC and NSCLC subgroups. ROC curve analysis was performed to assess the diagnostic performance of the above parameters. Seventy cases were randomly selected and independently measured by two radiologists, and intraclass correlation coefficient (ICC) and Bland-Altman analyses were performed to calculate the reliability of the measurements.

RESULTS

Except for NAEF (P=0.23), the values of the parameters were higher in the malignant group than in the benign group (all P<0.05). NIC, λHU, and CT40keV performed better in the VP (NICVP, λVPHU, and CTVP40keV) (P<0.001), with an area under the ROC curve (AUC) of 0.93, 0.89, and 0.89 respectively. With respective cutoffs of 0.31, 1.83, and 141.00 HU, the accuracy of NICVP, λVPHU, and CTVP40keV was 91.11%, 85.19%, and 88.15%, respectively. In the subgroup differentiating NSCLC and SCLC, the diagnostic performances of NICAP (AUC =0.89) were greater than other parameters. NICAP had an accuracy of 86.02% when the cutoff was 0.14. ICC and Bland-Altman analyses indicated that the measurement of SDCT has great reproducibility.

CONCLUSIONS

Quantitative measures from SDCT can help to differentiate benign from malignant SPNs and may help with the further subclassification of malignant cancer into SCLC and NSCLC.

Application of Dual-Energy Spectral Computed Tomography to Thoracic Oncology Imaging

Computed tomography (CT) is an important imaging modality in evaluating thoracic malignancies. The clinical utility of dual-energy spectral computed tomography (DESCT) has recently been realized. DESCT allows for virtual monoenergetic or monochromatic imaging, virtual non-contrast or unenhanced imaging, iodine concentration measurement, and effective atomic number (Z_eff map). The application of information gained using this technique in the field of thoracic oncology is important, and therefore many studies have been conducted to explore the use of DESCT in the evaluation and management of thoracic malignancies. Here we summarize and review recent DESCT studies on clinical applications related to thoracic oncology.

Does dual-energy CT differentiate benign and malignant ovarian tumours?

AIM

To assess the ability of dual-energy computed tomography (DECT) to distinguish benign from malignant ovarian tumours (OTs).

MATERIALS AND METHODS

Following approval of the institutional review board, the institutional database was mined for treatment-naive patients who underwent primary cytoreduction for OT. Thirty-seven patients were included and divided into those with benign OTs (n = 11) and malignant OTs (n = 26), including high-grade (n = 20) and low-grade (n = 6) malignant OTs. Advanced processing and region of interest delineation on the ovarian mass were performed using the preoperative staging DECT examination using the Advantage Workstation. The pixel-level data of the CT attenuation values at 50, 70, and 120 keV and the effective atomic number (Zeff), water content (WC), and iodine content (IC) in the ovarian mass were recorded. The Wilcoxon rank-sum test was used to compare CT attenuation data at different voltages, Zeff, and WC and IC levels between benign and malignant OTs and between high- and low-grade malignant OTs. Simple logistic regression was used to correlate the imaging characteristics with malignant status and grade.

RESULTS

Malignant OTs had significantly higher Zeff and IC compared with benign OTs. The threshold values for the diagnosis of malignant OT were IC≥9.74 (100 μg/cm3) with 81% sensitivity and 73% specificity and Zeff ≥8.16 with 85% sensitivity and 73% specificity. High-grade OTs had significantly higher WC compared with low-grade OTs, and a threshold of ≥1,013.92 mg/cm3 differentiated them with 80% sensitivity and 83% specificity.

CONCLUSION

DECT may be a tool to help distinguish malignant and benign OTs and predict tumour grade.

Is there any correlation between spectral CT imaging parameters and PD-L1 expression of lung adenocarcinoma?

Background

The aim of this study was to explore whether spectral computed tomography (CT) imaging parameters are associated with PD‐L1 expression of lung adenocarcinoma.

Methods

Spectral CT imaging parameters (iodine concentrations [IC] of lesion in arterial phase [ICLa] and venous phase [ICLv], normalized IC [NICa/NICv]‐normalized to the IC in the aorta, slope of the spectral HU curve [λHUa/λHUv] and enhanced monochromatic CT number [CT40keVa/v, CT70keVa/v] on 40 and 70 keV images) were analyzed in 34 prospectively enrolled lung adenocarcinoma patients with common molecular pathological markers including PD‐L1 expression detected with immunohistochemistry. Patients were divided into two groups: positive PD‐L1 expression and negative PD‐L1 expression groups. Two‐sample Mann‐Whitney U test was used to test the difference of spectral CT imaging parameters between the two groups.

Results

The CT40keVa (127.03 ± 37.92 vs. −54.69 ± 262.04), CT40keVv (124.39 ± 34.71 vs. −45.73 ± 238.97), CT70keVa (49.56 ± 11.76 vs. −136.51 ± 237.08) and CT70keVv (46.13 ± 15.81 vs. −133.10 ± 230.72) parameters in the positive PD‐L1 expression group of lung adenocarcinoma were significantly higher than the negative PD‐L1 expression group (all P < 0.05). There was no difference detected in IC, NIC and λHU of the arterial and venous phases between both groups (all P > 0.05).

Conclusion

CT40keVa, CT40keVv, CT70keVa and CT70keVv were increased in positive PD‐L1 expression. These parameters may be used to distinguish the PD‐L1 expression state of lung adenocarcinoma.

Effect of a New Model-Based Reconstruction Algorithm for Evaluating Early Peripheral Lung Cancer With Submillisievert Chest Computed Tomography

OBJECTIVE

The aim of this study was to compare a new model-based iterative reconstruction algorithm with either spatial and density resolution balance (MBIRSTND) or spatial resolution preference (MBIRRP20) with the adaptive statistical iterative reconstruction (ASIR) in evaluating early small peripheral lung cancer (SPLC) with submillisievert chest computed tomography (CT).

METHODS

Low-contrast and spatial resolutions were assessed in a phantom and with 30 pathologically confirmed SPLC patients. Images were reconstructed using 40% ASIR, MBIRSTND, and MBIRRP20. Computed tomography value and image noise were measured by placing the regions of interest on back muscle and subcutaneous fat at 3 levels. Two radiologists used a 4-point scale (1, worst, and 4, best) to rate subjective image quality in 3 aspects: image noise, nodule imaging signs, and nodule internal clarity.

RESULTS

The phantom study revealed an improved detectability of low-contrast targets and small objects for MBIRSTND and MBIRRP20 compared with ASIR. The effective dose for patient scans was 0.88 ± 0.83 mSv. There was no significant difference in CT value between the 3 reconstructions (P > 0.05), but MBIRSTND and MBIRRP20 significantly reduced image noise compared with ASIR (P < 0.05): 15.69 ± 1.83 HU and 29.97 ± 3.84 HU versus 51.06 ± 11.02 HU in the back muscle, and 15.96 ± 3.07 HU and 27.37 ± 3.88 HU versus 38.04 ± 8.87 HU in subcutaneous fat, respectively. Among the 3 reconstructions, MBIRSTND was the best in reducing image noise and identifying the internal compositions of cancer nodules, and MBIRRP20 was the best in analyzing the internal and external signs of pulmonary nodules.

CONCLUSIONS

Submillisievert chest CT reconstructed with MBIRSTND and MBIRRP20 provides superior images for the detailed analyses of SPLC compared with ASIR.

Spectral Computed Tomography Imaging in the Differential Diagnosis of Lung Cancer and Inflammatory Myofibroblastic Tumor

OBJECTIVE

The aim of this study was to explore the value of spectral computed tomography (CT) imaging in differentiating lung cancer from inflammatory myofibroblastic tumor (IMT).

METHODS

One hundred twelve patients with 96 lung cancers and 16 IMTs underwent spectral CT during arterial phase (AP) and venous phase (VP). The normalized iodine concentration in AP (NICAP) and VP (NICVP), slope of spectral Hounsfield unit curve in AP (λAP) and VP (λVP), and normalized iodine concentration difference between AP and VP (ICD) were calculated. The 2-sample t test compared quantitative parameters. Two readers qualitatively assessed lesion types according to imaging features. Receiver operating characteristic curves were generated to calculate sensitivity and specificity. Sensitivity and specificity of the qualitative and quantitative studies were compared.

RESULTS

The patients with IMT had significantly higher NICAP, NICVP, λAP, λVP, and ICD than did the patients with lung cancer (P < 0.05). The threshold NICVP of 0.425 would yield the highest sensitivity and specificity of 92.7% and 81.3%, respectively, for differentiating lung cancer from IMT. The logistic regression model produced from combining quantitative parameters NICAP, NICVP, λAP, and λVP provided a sensitivity and specificity of 100% and 81.3%, respectively, for differentiating lung cancer from IMT.

CONCLUSIONS

Spectral CT imaging with the quantitative analysis may help to increase the accuracy of differentiating lung cancer from IMT.

Can spectral computed tomography imaging improve the differentiation between malignant and benign pulmonary lesions manifesting as solitary pure ground glass, mixed ground glass, and solid nodules?

BACKGROUND

This study quantitatively assessed the efficacy of spectral computed tomography (CT) imaging parameters for differentiating the malignancy and benignity of solitary pulmonary nodules (SPNs) manifesting as ground glass nodules (GGNs) and solid nodules (SNs).

METHODS

The study included 114 patients with SPNs (61 GGNs, and 53 SNs) who underwent CT plain and enhanced scans in the arterial (a) and venous (v) phases using the spectral imaging mode. The spectral CT imaging parameters included: iodine concentrations (IC) of lesions in the arterial (ICLa) and venous (ICLv) phases; normalized IC (NICa/NICv, normalized to the IC in the aorta); the slope of the spectral Hounsfield unit (HU) curve (λHUa/λHUv); and monochromatic CT number (CT40keVa/v, CT70keVa/v) enhancement on 40 and 70 keV images. The two-sample Mann-Whitney U test was used to compare quantitative parameters between malignant and benign SPNs, SNs, and GGNs.

RESULTS

Pathology revealed 75 lung cancer cases, 3 metastatic nodules, 14 benign nodules, and 22 inflammatory nodules. Among the 53 SNs there were 37 malignant and 16 benign nodules. Among the 61 GGNs there were 41 malignant and 20 benign nodules. Overall, the CT40keVa, λHUa, CT40keVv, λHUv, and ICLv of benign SPNs were all greater than those of malignant SPNs (all P < 0.05). For GGNs, CT40keVa/v, CT70keVa/v, λHUa/λHUv, and ICLv of malignant GGNs were all lower than those of benign GGNs.

CONCLUSION

Spectral CT imaging is a more promising method for distinguishing malignant from benign nodules, especially in nodules manifesting as GGNs in contrast-enhanced scanning.

Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference

Objective

The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT.

Methods

Sixty patients with 39 malignant nodules and 21 benign nodules underwent chest contrast CT scans using spectral imaging mode during pulmonary arterial phase (PP), arterial phase (AP), and venous phase (VP). Iodine concentrations of proximal and distal regions in pulmonary nodules on iodine-based material decomposition images were recorded. Normalized iodine concentration (NIC) and the differences in NIC between the proximal and the distal regions (dNIC) were calculated. The two-sample t-test and Mann-Whitney U-test were performed to compare the multiple parameters generated from spectral CT between malignant and benign nodules. Receiver operating characteristic (ROC) curves were generated to calculate sensitivity and specificity.

Results

NIC in the proximal region (NIC_pro) and NIC in the distal region (NIC_dis) between malignant and benign nodules at AP (NIC_pro, P=0.012; NIC_dis, P=0.024), and VP (NIC_pro, P=0.005; NIC_dis, P =0.004) were significantly different. NIC_pro at PP (P = 0.037) was also found significantly different between malignant and benign nodules; however, no significant differences were found in NIC_dis at PP (P = 0.093). In addition, the dNIC of malignant nodules was significantly higher than that of benign ones at PP (median and interquartiles (0.31, 0.11, 0.57 versus -0.26, -0.5, -0.1); p≤0.001), AP (mean dNIC, 0.093 ±0.094 versus -0.075±0.060; p≤0.001), and VP (mean dNIC, 0.171±0.137 versus -0.183±0.127; p≤0.001). The sensitivity and specificity (93%, 95%, respectively) of dNIC during VP were higher than other parameters, with a threshold value of -0.07.

Conclusions

Spectral CT imaging with multiple parameters such as NIC_pro, NIC_dis, and dNIC may be a new method for differentiating malignant SPNs from benign ones.

Low-dose spectral CT reconstruction using image gradient ℓ 0-norm and tensor dictionary

Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient ℓ ₀-norm, which is named as ℓ ₀TDL. The ℓ ₀TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the ℓ ₀-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The split-bregman method is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed ℓ ₀TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.

Effects of low-dose energy spectrum scanning combined with adaptive statistical iterative reconstruction on the quality of imaging in Budd-Chiari syndrome

OBJECTIVE

To assess the quality and diagnostic accuracy of monochromatic images combined with adaptive statistical iterative reconstruction (ASIR) performed via spectral computed tomography (CT) in patients with Budd-Chiari syndrome (BCS).

METHODS

Sixty-two patients with BCS underwent pectral CT with upper abdominal two-phase contrast-enhanced scanning to generate a 60keV monochromatic energy level combined with ASIR (ranging from 0% -100%) during the portal venous phase (PVP) and the hepatic venous phase (HVP). One-way ANOVA was used to compare vessel-to-liver contrast-to-noise ratio (CNR) for the portal vein (PV), hepatic vein (HV), and inferior vena cava (IVC). Subjective evaluations of the images in the three groups were conducted by image quality assessors and compared via Kruskal-Wallis H test.

RESULTS

The CNR values of the PV trunk, HV, IVC, liver parenchyma and pancreas were within ASIR (ranging from 0% - 100%) weight, and the difference were statistically significant (p <0.05). The highest overall image score was distributed at 50% ASIR weight value. Higher CNR values of HV, hepatic parenchyma and pancreas were obtained in the IVC type than in mixed and HV types (respective p values = 0.035, 0.019 and 0.042). Higher CNR values of the IVC were obtained in the HV type than in mixed and IVC types (p = 0.032). The CNR value of the IVC in the mixed type was less than that of the HV type (p = 0.028). The CNR values of the HV and liver parenchyma in mixed type were lower than those of the IVC type (p = 0.016 and 0.038, respectively). The CNR value of pancreas in IVC type was higher than that of the HV type (p = 0.037). The diagnostic value of CNR in patients with the IVC type was higher than that in patients with mixed and HV type, while the diagnostic value of CNR was found to be the lowest for the HV type (p = 0.043).

CONCLUSION

A monochromatic energy level of 60 keV with 50% ASIR can significantly improve image quality in cases of BCS.

Effect of CT Acquisition Parameters on Iodine Density Measurement at Dual-Layer Spectral CT

OBJECTIVE

We aimed to evaluate the effect of tube voltage, tube current-time product, and iterative reconstruction on iodine quantification using a dual-layer spectral CT scanner.

MATERIALS AND METHODS

Two mediastinal iodine phantoms, each containing six tubes of different iodine concentrations (0, 1, 2.5, 5, 10, and 20 mg I/mL; the two phantoms had tubes with contrast media diluted in water and in 10% amino acid solution, respectively), were inserted into an anthropomorphic chest phantom and scanned with varying acquisition parameters (120 and 140 kVp; 20, 40, 60, 80, 100, 150, and 200 mAs; and spectral reconstruction levels 0 and 6). Thereafter, iodine density was measured (in milligrams of iodine per milliliter) using a dedicated software program, and the effect of acquisition parameters on iodine density and on its relative measurement error (RME) was analyzed using a linear mixed-effects model.

RESULTS

Tube voltages (all, p < 0.001) and tube current-time products (p < 0.05, depending on the interaction terms for iodine density; p = 0.023 for RME) had statistically significant effects on iodine density and RME. However, the magnitude of their effects was minimal. That is, estimated differences between tube voltage settings ranged from 0 to 0.8 mg I/mL for iodine density and from 1.0% to 4.2% for RME. For tube current-time product, alteration of 100 mAs caused changes in iodine density and RME of approximately 0.1 mg I/mL and 0.6%, respectively. Spectral level was not an affecting factor for iodine quantification (p = 0.647 for iodine density and 0.813 for RME).

CONCLUSION

Iodine quantification using dual-layer spectral CT was feasible irrespective of CT acquisition parameters because their effects on iodine density and RME were minimal.

Risk factors for pulmonary nodules in north China: A prospective cohort study

OBJECTIVES

Pulmonary nodules have become common incidental findings with the widespread use of computed tomography (CT) technology. Such nodules have the potential to become early lung cancer lesions, so understanding more about factors that may be associated with them is important.

MATERIALS AND METHODS

The present work was based on a large prospective cohort comprising 32,438 participants in Hebei Province (China) between January 2014 and March 2016. Participants aged 40-75 years completed a questionnaire, underwent low-dose CT (LDCT), and were followed up to March 2017. Grouped by the results of LDCT, normal participants and those with pulmonary nodules were included in the data analysis.

RESULTS

In total 7752 subjects were included in this study, of whom 2040 (26.32%) were pulmonary nodule patients. Older age, current smoking status (hazard ratio (HR) = 1.43, 95% confidence interval (95%CI): 1.21, 1.68), exposure to second-hand smoke (SHS) at work (HR = 1.17, 95%CI: 1.01, 1.35), dust exposure (HR = 1.49, 95%CI: 1.06, 2.11), history of lung disease (HR = 1.44, 95%CI: 1.16, 1.77), and family history of cancer (HR = 1.28, 95%CI: 1.12, 1.48) were associated with pulmonary nodules. However, consumption of vegetables (HR = 0.82, 95%CI: 0.68, 0.99), tea (HR = 0.88, 95%CI: 0.78, 0.99) and legumes reduced the risk. Approximately 10.09% and 8.58% of pulmonary nodule incidences were attributed to tobacco smoking and low fruit intake, respectively. An estimated 6.36% and 3.88% of patients with pulmonary nodules attributable to family history of cancer and history of lung disease were detected.

CONCLUSION

The results of this study suggest that age, smoking, SHS, dietary factors, occupational exposures, history of disease and family history of cancer may affect the incidence of pulmonary nodules.

Assessment of quantification accuracy and image quality of a full-body dual-layer spectral CT system

The performance of a recently introduced spectral computed tomography system based on a dual-layer detector has been investigated. A semi-anthropomorphic abdomen phantom for CT performance evaluation was imaged on the dual-layer spectral CT at different radiation exposure levels (CTDIvol of 10 mGy, 20 mGy and 30 mGy). The phantom was equipped with specific low-contrast and tissue-equivalent inserts including water-, adipose-, muscle-, liver-, bone-like materials and a variation in iodine concentrations. Additionally, the phantom size was varied using different extension rings to simulate different patient sizes. Contrast-to-noise (CNR) ratio over the range of available virtual mono-energetic images (VMI) and the quantitative accuracy of VMI Hounsfield Units (HU), effective-Z maps and iodine concentrations have been evaluated. Central and peripheral locations in the field-of-view have been examined. For all evaluated imaging tasks the results are within the calculated theoretical range of the tissue-equivalent inserts. Especially at low energies, the CNR in VMIs could be boosted by up to 330% with respect to conventional images using iDose/spectral reconstructions at level 0. The mean bias found in effective-Z maps and iodine concentrations averaged over all exposure levels and phantom sizes was 1.9% (eff. Z) and 3.4% (iodine). Only small variations were observed with increasing phantom size (+3%) while the bias was nearly independent of the exposure level (±0.2%). Therefore, dual-layer detector based CT offers high quantitative accuracy of spectral images over the complete field-of-view without any compromise in radiation dose or diagnostic image quality.

The Effects of Iodine Attenuation on Pulmonary Nodule Volumetry using Novel Dual-Layer Computed Tomography Reconstructions

OBJECTIVES

To assess the effect of iodine attenuation on pulmonary nodule volumetry using virtual non-contrast (VNC) and mono-energetic reconstructions.

METHODS

A consecutive series of patients who underwent a contrast-enhanced chest CT scan were included. Images were acquired on a novel dual-layer spectral CT system. Conventional reconstructions as well as VNC and mono-energetic images at different keV levels were used for nodule volumetry.

RESULTS

Twenty-four patients with a total of 63 nodules were included. Conventional reconstructions showed a median (interquartile range) volume and diameter of 174 (87 - 253) mm3 and 6.9 (5.4 - 9.9) mm, respectively. VNC reconstructions resulted in a significant volume reduction of 5.5% (2.6 - 11.2%; p<0.001). Mono-energetic reconstructions showed a correlation between nodule attenuation and nodule volume (Spearman correlation 0.77, (0.49 - 0.94)). Lowering the keV resulted in increased volumes while higher keV levels resulted in decreased pulmonary nodule volumes compared to conventional CT.

CONCLUSIONS

Novel dual-layer spectral CT offers the possibility to reconstruct VNC and mono-energetic images. Those reconstructions show that higher pulmonary nodule attenuation results in larger nodule volumes. This may explain the reported underestimation in nodule volume on non-contrast enhanced compared to contrast-enhanced acquisitions.

KEY POINTS

• Pulmonary nodule volumes were measured on virtual non-contrast and mono-energetic reconstructions • Mono-energetic reconstructions showed that higher attenuation results in larger volumes • This may explain the reported nodule volume underestimation on non-contrast enhanced CT • Mostly metastatic pulmonary nodules were evaluated, results might differ for benign nodules.

Hybrid FDG-PET/MR compared to FDG-PET/CT in adult lymphoma patients

PURPOSE

The goal of this study is to evaluate the diagnostic performance of simultaneous FDG-PET/MR including diffusion compared to FDG-PET/CT in patients with lymphoma.

METHODS

Eighteen patients with a confirmed diagnosis of non-Hodgkin's (NHL) or Hodgkin's lymphoma (HL) underwent an IRB-approved, single-injection/dual-imaging protocol consisting of a clinical FDG-PET/CT and subsequent FDG-PET/MR scan. PET images from both modalities were reconstructed iteratively. Attenuation correction was performed using low-dose CT data for PET/CT and Dixon-MR sequences for PET/MR. Diffusion-weighted imaging was performed. SUVmax was measured and compared between modalities and the apparent diffusion coefficient (ADC) using ROI analysis by an experienced radiologist using OsiriX. Strength of correlation between variables was measured using the Pearson correlation coefficient (r p).

RESULTS

Of the 18 patients included in this study, 5 had HL and 13 had NHL. The median age was 51 ± 14.8 years. Sixty-five FDG-avid lesions were identified. All FDG-avid lesions were visible with comparable contrast, and therefore initial and follow-up staging was identical between both examinations. SUVmax from FDG-PET/MR [(mean ± sem) (21.3 ± 2.07)] vs. FDG-PET/CT (mean 23.2 ± 2.8) demonstrated a strongly positive correlation [r s = 0.95 (0.94, 0.99); p < 0.0001]. There was no correlation found between ADCmin and SUVmax from FDG-PET/MR [r = 0.17(-0.07, 0.66); p = 0.09].

CONCLUSION

FDG-PET/MR offers an equivalent whole-body staging examination as compared with PET/CT with an improved radiation safety profile in lymphoma patients. Correlation of ADC to SUVmax was weak, understating their lack of equivalence, but not undermining their potential synergy and differing importance.