Pediatric MDCT

Li, Xiang; Samei, Ehsan; DeLong, David M.; Jones, Robert; Gaca, A; Hollingsworth, Caroline L.; Maxfield, Charles M.; Colsher, James G.; Frush, Donald P.

doi:10.1016/j.acra.2009.01.028

Cited by 25 publications

(8 citation statements)

References 30 publications

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“…We note that the maximum diagnostic accuracy in this study (AUC $ 0.94 6 0.04) was close to but slightly lower than that in our pilot study 11 (AUC $ 0.95 6 0.02), which had a similar study design. This slight decrease in performance may be explained by the fact that the nodules in this study had slightly lower contrast (200-500 HU) than those in the pilot study (250-550 HU).…”

Section: Discussionsupporting

confidence: 65%

“…We have previously evaluated and calibrated the accuracy of this software. 11 We showed that the texture (spatial correlation properties) of the simulated noise matches very well with that of the real noise. However, discrepancies existed between the magnitudes of the simulated and real noise.…”

Section: Iib Noise Simulation and Estimationmentioning

confidence: 62%

“…This is consistent with the results of our pilot study in 13 patients. 11 The noise in the three lung zones was found to be similar: an average of 17, 16, and 17 HU for the upper, middle, and lower lung zones, respectively. Therefore, the difference in diagnostic accuracy cannot be explained by noise.…”

Section: Discussionmentioning

confidence: 82%

“…As a result, the task of nodule detection was rendered easier than that in the actual clinical scenario, where nodules with various appearances must be detected on a complex noise background (spatially correlated noise). To assess radiologist performance in a more realistic setting, we recently conducted a pilot receiver operating characteristic (ROC) study, 11 in which simulated noise with proper texture (i.e., spatial correlation properties) and calibrated magnitude was used in combination with a validated and variable model of three-dimensional lung nodules. 12 However, similar to many other dose reduction studies, [13][14][15][16][17] this pilot study evaluated observer performance at different tube current levels, making it difficult to generalize research results to other scan protocols and CT scanner models.…”

Section: Introductionmentioning

confidence: 99%

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Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance

Samei

Barnhart

et al. 2011

Med. Phys.

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Section: Discussionsupporting

confidence: 65%

Section: Iib Noise Simulation and Estimationmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

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Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance

Samei

Barnhart

et al. 2011

Med. Phys.

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“…Along this same line, it is possible to use computer simulations to fuse real patient CT data with simulated data, resulting in so-called ‘hybrid’ CT images. Li et al and Solomon et al both generated hybrid images by inserting virtual lesion models into reconstructed CT images (Li et al 2009, Solomon and Samei 2014). Although those image based insertion methods produced realistic results they may not be valid to properly assess the impact of reconstruction on nodule volumes.…”

Section: Introductionmentioning

confidence: 99%

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

Robins

Solomon

Sahbaee³

et al. 2017

Phys. Med. Biol.

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Virtual nodule insertion paves the way towards the development of standardized databases of hybrid CT images with known lesions. The purpose of this study was to assess three methods (an established and two newly developed techniques) for inserting virtual lung nodules into CT images. Assessment was done by comparing virtual nodule volume and shape to the CT-derived volume and shape of synthetic nodules. 24 synthetic nodules (three sizes, four morphologies, two repeats) were physically inserted into the lung cavity of an anthropomorphic chest phantom (KYOTO KAGAKU). The phantom was imaged with and without nodules on a commercial CT scanner (SOMATOM Definition Flash, Siemens) using a standard thoracic CT protocol at two dose levels (1.4 and 22 mGy CTDIvol). Raw projection data were saved and reconstructed with filtered back-projection and sinogram affirmed iterative reconstruction (SAFIRE, strength 5) at 0.6 mm slice thickness. Corresponding 3D idealized, virtual nodule models were co-registered with the CT images to determine each nodule’s location and orientation. Virtual nodules were voxelized, partial volume corrected, and inserted into nodule-free CT data (accounting for system imaging physics) using two methods: projection-based Technique A, and image-based Technique B. Also a third Technique C based on cropping a region of interest from the acquired image of the real nodule and blending it into the nodule-free image was tested. Nodule volumes were measured using a commercial segmentation tool (iNtuition, TeraRecon, Inc.) and deformation was assessed using the Hausdorff distance. Nodule volumes and deformations were compared between the idealized, CT-derived and virtual nodules using a linear mixed effects regression model which utilized the mean, standard deviation, and coefficient of variation (MeanRHD, and STDRHD CVRHD) of the regional Hausdorff distance. Overall, there was a close concordance between the volumes of the CT-derived and virtual nodules. Percent differences between them were less than 3% for all insertion techniques and were not statistically significant in most cases. Correlation coefficient values were greater than 0.97. The deformation according to the Hausdorff distance was also similar between the CT-derived and virtual nodules with minimal statistical significance in the (CVRHD) for Techniques A, B, and C. This study shows that both projection-based and image-based nodule insertion techniques yield realistic nodule renderings with statistical similarity to the synthetic nodules with respect to nodule volume and deformation. These techniques could be used to create a database of hybrid CT images containing nodules of known size, location and morphology.

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Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases

et al. 2019

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Purpose: To make available to the medical imaging community a computed tomography (CT) image database composed of hybrid datasets (patient CT images with digitally inserted anthropomorphic lesions) where lesion ground truth is known a priori. It is envisioned that such a dataset could be a resource for the assessment of CT image quality, machine learning, and imaging technologies [e.g., computer aided detection (CAD) and segmentation algorithms]. Acquisition and validation methods: This HIPPA compliant, IRB waiver of approval study consisted of utilizing 120 chest and 100 abdominal clinically acquired adult CT exams. One image series per patient exam was utilized based on coverage of the anatomical region of interest (either the thorax or abdomen). All image series were de-identified. Simulated lesions were derived from a library of anatomically informed digital lesions (93 lung and 50 liver lesions) where six and four digital lesions with nominal diameters ranging from 4 to 20 mm were inserted into lung and liver image series, respectively. Locations for lesion insertion were randomly chosen. A previously validated lesion simulation and virtual insertion technique were utilized. The resulting hybrid images were reviewed by three experienced radiologists to assure similarity with routine clinical imaging in a diverse adult population. Data format and usage notes: The database is composed of four datasets that contain 100 patient cases each, for a total of 400 image series accompanied by Matlab.mat tables that provide descriptive information about the virtually inserted lesions (i.e., size, shape, opacity, and insertion location in physical (world) coordinates and voxel indices). All image and metadata are stored in DICOM format on the Quantitative Imaging Data Warehouse (https://qidw.rsna.org/#collection/57d463471cac0a4ec 8ff8f46/folder/5b23dceb1cac0a4ec800a770?dialog=login), in two sets: (a) QIBA CT Hybrid Dataset I which contains Lung I and Liver I datasets, and (b) QIBA CT Hybrid Dataset II which contains Lung II and Liver II datasets. The QIDW is supported by the Radiological Society of North America (RSNA). Registration is required upon initial log in. Potential applications: By simulating lesion opacity (full solid, part solid and ground glass), size, and texture, the relationship between lesion morphology and segmentation or CAD algorithm performance can be investigated without the need for repetitive patient exams. This database can also serve as a reference standard for device and reader performance studies.

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Pediatric MDCT

Cited by 25 publications

References 30 publications

Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance

Lung nodule detection in pediatric chest CT: Quantitative relationship between image quality and radiologist performance

Techniques for virtual lung nodule insertion: volumetric and morphometric comparison of projection-based and image-based methods for quantitative CT

Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases

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