Search

Objectives: To compare deep learning (True Fidelity, TF) and partial model based Iterative Reconstruction (ASiR-V) algorithm for image texture, low contrast lesion detectability and potential dose reduction.Methods: Anthropomorphic phantoms (mimicking non-overweight and overweight patient), containing lesions of 6 mm in diameter with 20HU contrast, were scanned at five different dose levels (2,6,10,15,20 mGy) on a CT system, using clinical routine protocols for liver lesion detection. Images were reconstructed using ASiR-V 0% (surrogate for FBP), 60 % and TF at low, medium and high strength. Noise texture was characterized by computing a normalized Noise Power Spectrum filtered by an eye filter. The similarity against FBP texture was evaluated using peak frequency difference (PFD) and root mean square deviation (RMSD). Low contrast detectability was assessed using a channelized Hotelling observer and the area under the ROC curve (AUC) was used as figure of merit. Potential dose reduction was calculated to obtain the same AUC for TF and ASiR-V.Results: FBP-like noise texture was more preserved with TF (PFD from -0.043mm-1 to -0.09mm-1, RMSD from 0.12mm-1 to 0.21mm-1) than with ASiR-V (PFD equal to 0.12 mm-1, RMSD equal to 0.53mm-1), resulting in a sharper image. AUC was always higher with TF than ASIR-V. In average, TF compared to ASiR-V, enabled a radiation dose reduction potential of 7%, 25 % and 33 % for low, medium and high strength respectively.Conclusion: Compared to ASIR-V, TF at high strength does not impact noise texture and maintains low contrast liver lesions detectability at significant lower dose. [ABSTRACT FROM AUTHOR]

The Stockwell Transform has the potential to perform multi-resolution texture analysis in magnetic resonance imaging (MRI). However, it is computationally intensive and memory demanding. The polar Stockwell Transform (PST) is rotation-invariant and relatively memory efficient, but still computationally demanding. The new Discrete Orthogonal Stockwell Transform (DOST) appears to have addressed both the computation and storage challenges; however, its utility in localized texture analysis remains unclear. Our goal was to investigate the theory and texture analysis ability of the DOST versus PST using both synthetic and MR images, and explore the relative importance of the associated texture features using a simple classification example based on clinical brain MRI of six multiple sclerosis patients. MRI texture analysis focused on FLAIR images, and the classification used a machine learning algorithm, random forest, that differentiated regions of interest (ROIs) into 2 classes: white matter lesions, and the contralateral normal-appearing white matter (control). Our results showed that the PST features had a greater ability in detecting subtle changes in image structure than the DOST and polar-index DOST (PDOST). Quantitatively, based on 187 lesion and 187 control ROIs, both the PST and the rotation-invariant radial PST performed better in the classification than the DOST and PDOST, where the latter were no better than guessing (p = 0.65 and 0.98). Further analysis using a hierarchical random forest showed that combining MRI signal intensity with the PST or DOST predictions increased the classification performance, with the accuracy, sensitivity, and specificity all improved to >85% in the tests. Collectively, the DOST is less competitive than the PST in localized image texture analysis. The PST features may help with texture-based lesion classification in MS based on clinical brain MRI scans following further verification. [ABSTRACT FROM AUTHOR]

Recently, a concept for a new approach for rapid identification of uranium ore concentrate (UOC) powders using colour, textural and spectroscopy signatures was developed using a sample dataset consisting of 79 industrial uranium ore concentrate powders produced by different production routes at different facilities. The samples were first grouped into six different predefined colour categories using a colour-based classification model previously developed. In this study, a machine learning approach was used to develop supervised texture- and spectroscopy-based classification models for each colour category of uranium ore concentrates. Scanning electron microscopy images of the UOC samples were collected at 100x, 250x, 500x/1000× magnifications. A total of 524 texture features describing sample morphology were extracted from each image using First Order Statistics (FOS), Grey Level Co-Occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Grey Level Size Zone Matrix (GLSZM), Locally Binary Pattern (LBP) and Angle Measure Technique (AMT) textural feature extraction algorithms. Hyperspectral images of the samples were collected in the wavelength range 900–1700 nm and the mean spectrum of each sample was calculated, giving the spectral features of the UOCs. The texture and spectral features were used as input to the machine learning approach which consisted of a feature reduction step to identify discriminatory features and a classification step to provide the texture- and spectroscopy-based models. An average prediction accuracy of 82% and 97% were obtained for the texture- and spectral-based classification models, respectively, on hold-out test sets. The feature reduction step identified two new nuclear forensics signatures, consisting of 3–7 texture (morphological) characteristics and 2–6 characteristic wavelengths, respectively. This new approach enables rapid classification of unknown uranium ore concentrate powders using only their textural and hyperspectral images. By combining the classification results from the two models, the most probable production process and chemical composition can be identified, and the provenience of the seized sample can be narrowed down to a few matching possibilities. Moreover, after the image acquisition, the models can provide results in less than 2 min speeding up the investigation procedure, a key factor when a nuclear security event occurs. • A new approach to classify seized UOCs powders by colour, texture and spectroscopy data is proposed. • 79 commercial UOCs samples were used as training and test samples. • ML approach was used to test several classifiers and to carry out feature reduction/selection. • LDA was selected as the most promising classifier to develop the texture/spectroscopy-based classification models. • An average prediction accuracy of 82% and 97% were obtained for the two models respectively. [ABSTRACT FROM AUTHOR]

• Texture features of hydrophobized concrete contain the information about their absorptivity. • Machine learning algorithms are used to estimate the absorptivity of hydrophobic concrete. • Water absorption of concrete is reliably estimated from conventional SEM. • Application of image texture analysis to characterize the condition of hydrophobic concrete surfaces. The work concerned the study of surface hydrophobized concrete's physical and mechanical properties. An aqueous emulsion based on nano-silicates (A1) and an oligomeric propylsilicate/silicate (A2) concentrate in three dilution states (100%, 70%, and 50%) were used as surface modification agents. The scanning electron microscopy (SEM) images determined three classes of water absorption (WA). A predictive modeling process was performed to automatically identify 1 of the 3 water absorption classes. For the best model, a classification accuracy of 96% was obtained. After 14 days of testing, the hydrophobization efficiency was still high, over 54% for A1 and 45% for A2 for 100% concentration. The samples achieved the best frost resistance with agents A1 and A2 in a 70% concentration. Experimental studies have confirmed the close relationship between concretes' water absorptivity and their surfaces' SEM images. No similar studies of this type are known. [ABSTRACT FROM AUTHOR]