Systematic analysis of bias and variability of texture measurements in computed tomography.

Texture is a key radiomics measurement for quantification of disease and disease progression. The sensitivity of the measurements to image acquisition, however, is uncertain. We assessed bias and variability of computed tomography (CT) texture feature measurements across many clinical image acquisition settings and reconstruction algorithms.

Systematic analysis of bias and variability of morphologic features for lung lesions in computed tomography.

We propose to characterize the bias and variability of quantitative morphology features of lung lesions across a range of computed tomography (CT) imaging conditions. A total of 15 lung lesions were simulated (five in each of three spiculation classes: low, medium, and high). For each lesion, a series of simulated CT images representing different imaging conditions were synthesized by applying three-dimensional blur and adding correlated noise based on the measured noise and resolution properties of five commercial multislice CT systems, representing three dose levels ( of 1.

Interchangeability between real and three-dimensional simulated lung tumors in computed tomography: an interalgorithm volumetry study.

Using hybrid datasets consisting of patient-derived computed tomography (CT) images with digitally inserted computational tumors, we establish volumetric interchangeability between real and computational lung tumors in CT. Pathologically-confirmed malignancies from 30 thoracic patient cases from the RIDER database were modeled. Tumors were either isolated or attached to lung structures.

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

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.

3D task-transfer function representation of the signal transfer properties of low-contrast lesions in FBP- and iterative-reconstructed CT.

Purpose: The purpose of this study was to investigate how accurately the task-transfer function (TTF) models the signal transfer properties of low-contrast features in a non-linear commercial CT system.

Methods: A cylindrical phantom containing 24 anthropomorphic "physical" lesions was 3D printed. Lesions had two sizes (523, 2145 mm ), and two nominal radio-densities (80 and 100 HU at 120 kV).

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

Rationale And Objectives: To evaluate a new approach to establish compliance of segmentation tools with the computed tomography volumetry profile of the Quantitative Imaging Biomarker Alliance (QIBA); and determine the statistical exchangeability between real and simulated lesions through an international challenge.

Materials And Methods: The study used an anthropomorphic phantom with 16 embedded physical lesions and 30 patient cases from the Reference Image Database to Evaluate Therapy Response with pathologically confirmed malignancies. Hybrid datasets were generated by virtually inserting simulated lesions corresponding to physical lesions into the phantom datasets using one projection-domain-based method (Method 1), two image-domain insertion methods (Methods 2 and 3), and simulated lesions corresponding to real lesions into the Reference Image Database to Evaluate Therapy Response dataset (using Method 2).

Estimability index for volume quantification of homogeneous spherical lesions in computed tomography.

Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming.

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

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.

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).

Purpose: Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables.