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

Harder, Annemarie M. den; Bangert, F.; Hamersvelt, Robbert W. van; Leiner, Tim; Milles, Julien; Schilham, Arnold M. R.; Willemink, Martin J.; Jong, Pim A. de

doi:10.1007/s00330-017-4938-1

Cited by 12 publications

(8 citation statements)

References 26 publications

(34 reference statements)

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“…A slight shift in the position of this border can have a large impact on volume calculation: e.g., shifting the border of the LV myocardium one voxel inwards can reduce its volume by as much as 9%. A similar effect of underestimating volumes in NCCT with respect to CCTA has previously been found for pulmonary nodule volumes 32 . Second, physiological differences in heart rate, blood pressure, and respiratory state between acquisitions with and without contrast enhancement might affect the volume of individual chambers and the whole heart.…”

Section: Discussionsupporting

confidence: 77%

“…A similar effect of underestimating volumes in NCCT with respect to CCTA has previously been found for pulmonary nodule volumes. 32 Second, physiological differences in heart rate, blood pressure, and respiratory state between acquisitions with and without contrast enhancement might affect the volume of individual chambers and the whole heart. This could partly be caused by the administration of nitroglycerine before the CCTA acquisition.…”

Section: Structurementioning

confidence: 99%

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Deep learning from dual‐energy information for whole‐heart segmentation in dual‐energy and single‐energy non‐contrast‐enhanced cardiac CT

et al. 2020

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Purpose: Deep learning-based whole-heart segmentation in coronary computed tomography angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable, but defining a manual reference standard that would allow training a deep learning-based method for whole-heart segmentation in NCCT is challenging, if not impossible. In this work, we leverage dual-energy information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a three-dimensional (3D) convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Methods: Eighteen patients were scanned with and without contrast enhancement on a dual-layer detector CT scanner. Contrast-enhanced acquisitions were reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA image, manual reference segmentations of the left ventricular (LV) myocardium, LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and pulmonary artery trunk were obtained and propagated to the corresponding VNC image. These VNC images and reference segmentations were used to train 3D CNNs in a sixfold cross-validation for automatic segmentation in either VNC images or NCCT images reconstructed from the non-contrastenhanced acquisition. Automatic segmentation in VNC images was evaluated using the Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD). Automatically determined volumes of the cardiac chambers and LV myocardium in NCCT were compared to reference volumes of the same patient in CCTA by Bland-Altman analysis. An additional independent multivendor multicenter set of single-energy NCCT images from 290 patients was used for qualitative analysis, in which two observers graded segmentations on a five-point scale. Results: Automatic segmentations in VNC images showed good agreement with reference segmentations, with an average DSC of 0.897 AE 0.034 and an average ASSD of 1.42 AE 0.45 mm. Volume differences [95% confidence interval] between automatic NCCT and reference CCTA segmentations were À19 [À67; 30] mL for LV myocardium, À25 [À78; 29] mL for LV cavity, À29 [À73; 14] mL for right ventricle, À20 [À62; 21] mL for left atrium, and À19 [À73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from the independent multivendor multicenter set, both observers agreed that the automatic segmentation was mostly accurate (grade 3) or better. Conclusion: Our automatic method produced accurate whole-heart segmentations in NCCT images using a CNN trained with VNC images from a dual-layer detector CT scanner. This method might enable quantification of additional cardiac measures from NCCT images for improved cardiovascular risk prediction.

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Section: Discussionsupporting

confidence: 77%

Section: Structurementioning

confidence: 99%

Deep learning from dual‐energy information for whole‐heart segmentation in dual‐energy and single‐energy non‐contrast‐enhanced cardiac CT

et al. 2020

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“…However, there was a correlation between attenuation and volume changes at different energy levels of VMI (15). VMI with high energy resulted in low attenuation and nodule volumes, while VMI with low energy resulted in higher attenuation and nodule volumes.…”

Section: Contrast Enhancementmentioning

confidence: 90%

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

Kim

Park

et al. 2020

Korean J Radiol

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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 (Zeff 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.

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“…Furthermore, the spectral base image (SBI) datasets reconstructed with an iterative reconstruction (IR) algorithm contain relevant information for retrospective analysis and eliminate the need for further reconstruction or post-processing (12). To date, only image quality and iodine attenuation have been used to investigate chest disease using SDCT (13,14), and the incremental value of spectral parameters in the differential diagnosis of LN metastasis of lung cancer has not yet been reported. Therefore, the purpose of this study was to investigate the added value of spectral quantitative parameters for the evaluation of LN metastasis of lung cancer using SDCT.…”

Section: Introductionmentioning

confidence: 99%

“…LNs in group 4 were metastatic and LNs in group 10 were non-metastatic. For group 4 LNs: short-axis diameter: 0.56 cm, AEF: 89.54%, AP NIC: 0.24, VP NIC: 0.40, AP λ HU : 3 13,. VP λ HU : 2.49.…”

mentioning

confidence: 99%

Added value of spectral parameters for the assessment of lymph node metastasis of lung cancer with dual-layer spectral detector computed tomography

Gao

Lü

Wen

et al. 2021

Quant Imaging Med Surg

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Background: Lymph node (LN) metastasis is an important factor affecting the treatment of lung cancer. The purpose of this article was to investigate the benefits of dual-layer spectral detector computed tomography (SDCT) for the evaluation of metastatic LNs in lung cancer.Methods: Data from 93 patients with lung cancer who underwent dual-phase enhanced scanning with SDCT were retrospectively analyzed. According to the pathological findings, 166 LNs were grouped as metastatic (n=80) or non-metastatic (n=86). LNs in station 4 (n=80) and station 7 (n=35) accounted for the majority of the LNs (approximately 69.23%). The short-axis diameter of the LN, arterial enhancement fraction (AEF), normalized iodine concentration (NIC), and the slope of the spectral Hounsfield unit curve (λ HU ) during the arterial phase (AP) and venous phase (VP) were measured. The Mann-Whitney U test was used to statistically compare these quantitative parameters. Receiver operating characteristic (ROC) curves were plotted to identify the cutoff values, and decision curve analysis (DCA) was performed to determine the net benefit of each parameter. The diagnostic performance, obtained by combining the short-axis diameter with each of the above parameters, was also studied. Results:The short-axis LN diameter, AEF, NIC, and λ HU during the AP and VP all showed significant differences between the metastatic and non-metastatic groups (P<0.05). Of the parameters, the AEF had the greatest diagnostic efficiency for metastatic LNs [area under the ROC curve (AUC) AEF =0.885] with a threshold of 86.40%. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and 95% confidence interval were 90.00%, 89.53%, 88.89%, 90.59%, and 0.830-0.944, respectively.When the quantitative parameters were combined with the short-axis diameter, the AUCs of the parameters, except the AEF, were significantly improved (P<0.05). Conclusions:The iodine quantitative parameters from SDCT, such as the AEF, demonstrated high diagnostic performances in the differentiation of metastatic and non-metastatic LNs.

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

Cited by 12 publications

References 26 publications

Deep learning from dual‐energy information for whole‐heart segmentation in dual‐energy and single‐energy non‐contrast‐enhanced cardiac CT

Deep learning from dual‐energy information for whole‐heart segmentation in dual‐energy and single‐energy non‐contrast‐enhanced cardiac CT

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

Added value of spectral parameters for the assessment of lymph node metastasis of lung cancer with dual-layer spectral detector computed tomography

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