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

Robins, Marthony; Solomon, Justin; Hoye, Jocelyn; Smith, Taylor Brunton; Zheng, Yuese; Ebner, Lukas; Choudhury, Kingshuk Roy; Samei, Ehsan

doi:10.1117/1.jmi.5.3.035504

Cited by 3 publications

(1 citation statement)

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“…There have been several projects that aimed to synthesize tumors in CT images, including a method which involved inserting actual patient lesions into regions of interest 8 . Additionally, others used a computational iterative fitting routine 9 to generate lesions in order to test segmentation algorithms for CT volumetry 10,11 . In contrast to these studies, the task outlined in our work is to learn how to generate realistic looking tumors based on pre‐existing data using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Synthesizing of lung tumors in computed tomography images

O'Briain

Bazalova‐Carter

2020

Medical Physics

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Purpose: When investigating new radiation therapy techniques in the treatment planning stage, it can be extremely time consuming to locate multiple patient scans that match the desired characteristics for the treatment. With the help of machine learning, we propose to bypass the difficulty in finding patient computed tomography (CT) scans that match the treatment requirements. Furthermore, we aim to provide the developed method as a tool that is easily accessible to interested researchers. Methods: We propose a generative adversarial network (GAN) to edit individual volumes of interest (VOIs) in pre-existing CT scans, translating features of the healthy VOIs into features of cancerous volumes. Training and testing was done using VOIs from a dataset of 460 diagnostic and lung cancer screening CT scans. Agreement between real tumors and those produced by the editor was tested by comparing the distributions of several histogram parameters and second-order statistics as well as using qualitative analysis. Results: After training, the network was successfully able to map healthy CT segments to realistic looking cancerous volumes. Based on visual inspection, tumors produced by the editor were found to be both realistic and visually consistent with the surrounding anatomy when placed back into the original CT scan. Furthermore, the network was found to be able to extrapolate well beyond the upper size limit of the training set. Lastly, a graphical user interface (GUI) was developed to easily interact with the resulting network. Conclusion: The trained network and associated GUI can serve as a tool to develop an abundance of lung cancer patient data to be used in treatment planning. In addition, this method can be extended to a variety of cancer types if given an appropriate baseline dataset.

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Section: Introductionmentioning

confidence: 99%

Technical Note: Synthesizing of lung tumors in computed tomography images

O'Briain

Bazalova‐Carter

2020

Medical Physics

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Development, validation, and relevance of in vivo low‐contrast task transfer function to estimate detectability in clinical CT images

et al. 2021

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The current state-of -the-art calculation of detectability index (d') is largely phantom-based, with the latest being based on a hybrid phantom noise power spectrum (NPS) combined with patient-specific noise magnitude and high-contrast air-skin interface. The purpose of this study was to develop and assess the use of fully patient-specific measurements of noise and low-contrast resolution, derived entirely from patient images on d'. Methods: This study developed a d' calculation that is patient-and task-specific, employing newly developed algorithms for estimating patient-specific NPS and low-contrast task transfer function (TTF).The TTF estimation methodology used a trained regression support vector machine (SVM) to estimate a fitted form of the TTF given a variance-normalized estimate of the NPS (referred to as the TTF NPS ). The regression SVM was trained and tested using five-fold crossvalidation on 192 scans (4 dose levels x 6 reconstruction kernels x 4 repeats) of a phantom with low-contrast polyethylene insert and reconstructed with filtered backprojection and iterative reconstructions across 12 clinically relevant kernels (FBP: B20f, B31f, B45f; SAFIRE: I26f, I31f, J45f with strengths: 2, 3, 5). To test the low-contrast TTF estimation method, the estimated TTF NPS measurements were compared to (1) TTF measurements from the air-phantom interface (referred to as the TTF air , representing the most patient-specific clinical alternative) and (2) TTF measurements from the edge of the low-contrast polyethylene insert (referred to as the TTF poly ), which represented the gold standard of lowcontrast TTF measurement. Patient-specific NPS, patient-specific noise magnitude, and patient-specific low-contrast TTF were further combined with a reference task function to calculate a d' (according to a non-prewhitening matched filter model) across 1120 lesions previously evaluated in 2AFC human observer detection of liver lesions. The resulting values were compared to the observer results using a generalized linear mixed-effects statistical model. The correlations between the model and observer results were also compared with previously reported values (using a hybrid method with phantom-derived NPS and TTF air ). Results:The TTF NPS more accurately represented resolution across the considered reconstruction settings, compared with the TTF air . The out-of -fold predictions of the TTF NPS had statistically better root-mean-square error concordance (p < 0.05, one-tailed Wilcoxon rank-sum test) to gold standard than the TTF air (the alternative, measured from the air-phantom interface). Detectability indices informed by purely patient-specific NPS and TTF were strongly correlated with 2AFC outcomes (p < 0.05). R 2 between human detection accuracy 7698

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CT-Based Quantification

Samei

Hoye

2019

Computed Tomography

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

Cited by 3 publications

References 33 publications

Technical Note: Synthesizing of lung tumors in computed tomography images

Technical Note: Synthesizing of lung tumors in computed tomography images

Development, validation, and relevance of in vivo low‐contrast task transfer function to estimate detectability in clinical CT images

CT-Based Quantification

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