Variability in CT lung-nodule volumetry: Effects of dose reduction and reconstruction methods
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TL;DR: Lung-nodule volumetry was extremely robust to the radiation-dose level, down to the minimum scanner-supported dose settings, which included both conventional filtered backprojection and iterative methods.
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Abstract: Purpose:
Measuring the size of nodules on chest CT is important for lung
cancer staging and measuring therapy response. 3D volumetry has been proposed as a more robust alternative to 1D and 2D sizing methods. There have also been substantial advances in methods to reduce radiation
dose in CT. The purpose of this work was to investigate the effect of dose reduction and reconstruction methods on variability in 3D lung-nodule volumetry.
Methods:
Reduced-dose CT scans were simulated by applying a noise-addition tool to the raw (sinogram) data from clinically indicated patient scans acquired on a multidetector-row CT
scanner (Definition Flash, Siemens Healthcare). Scans were simulated at 25%, 10%, and 3% of the dose of their clinical protocol (CTDIvol of 20.9 mGy), corresponding to CTDIvol values of 5.2, 2.1, and 0.6 mGy. Simulated reduced-dose data were reconstructed with both conventional filtered backprojection (B45 kernel) and iterative reconstruction methods (SAFIRE: I44 strength 3 and I50 strength 3). Three lab technologist readers contoured “measurable” nodules in 33 patients under each of the different acquisition/reconstruction conditions in a blinded study design. Of the 33 measurable nodules, 17 were used to estimate repeatability with their clinical reference protocol, as well as interdose and inter-reconstruction-method reproducibilities. The authors compared the resulting distributions of proportional differences across dose and reconstruction methods by analyzing their means, standard deviations (SDs), and t-test and F-test results.
Results:
The clinical-dose repeatability experiment yielded a mean proportional difference of 1.1% and SD of 5.5%. The interdose reproducibility experiments gave mean differences ranging from −5.6% to −1.7% and SDs ranging from 6.3% to 9.9%. The inter-reconstruction-method reproducibility experiments gave mean differences of 2.0% (I44 strength 3) and −0.3% (I50 strength 3), and SDs were identical at 7.3%. For the subset of repeatability cases, inter-reconstruction-method mean/SD pairs were (1.4%, 6.3%) and (−0.7%, 7.2%) for I44 strength 3 and I50 strength 3, respectively. Analysis of representative nodules confirmed that reader variability appeared unaffected by dose or reconstruction method.
Conclusions:
Lung-nodule volumetry was extremely robust to the radiation-dose level, down to the minimum scanner-supported dose settings. In addition, volumetry was robust to the reconstruction methods used in this study, which included both conventional filtered backprojection and iterative methods.
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Citations
Lung nodules: size still matters
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TL;DR: Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.
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TL;DR: Most of the radiomic features were significantly affected by the reconstruction algorithms and inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.
Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets
Michael F. McNitt-Gray,Sandy Napel,Akshay Jaggi,Sarah A. Mattonen,Sarah A. Mattonen,Lubomir M. Hadjiiski,Mark Muzi,Dmitry B. Goldgof,Yoganand Balagurunathan,Larry Pierce,Paul E. Kinahan,Ella F. Jones,A. Nguyen,A. Virkud,Heang Ping Chan,Nastaran Emaminejad,M Wahi-Anwar,M. Daly,Mahmoud A. Abdalah,Hao Yang,Lin Lu,Wenbing Lv,Arman Rahmim,Aimilia Gastounioti,Sarthak Pati,Spyridon Bakas,Despina Kontos,Binsheng Zhao,Jayashree Kalpathy-Cramer,Keyvan Farahani +29 more
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TL;DR: Assessment of radiomic features when computed by several groups by using different software packages under very tightly controlled conditions highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.
A convolutional neural network for ultra-low-dose CT denoising and emphysema screening.
TL;DR: This work provides a tailored DnCNN for (ultra-)low-dose CT denoising, and demonstrates significant improvement on both the image quality and the clinical emphysema quantification accuracy over various dose levels.
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Technical Note: FreeCT_wFBP: A robust, efficient, open-source implementation of weighted filtered backprojection for helical, fan-beam CT.
TL;DR: A freely available, open-source software package implementing a common reconstruction method, weighted filtered backprojection (wFBP), for helical fan-beam CT applications, which shows good performance with both clinical and simulated data.
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