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In this paper we are analyzing the influence of the non-binary (i.e. multi-level) changes in a fractal phase map (FPM) of a diffractive element (DE) upon the diffracted intensity. The diffraction efficiency dependence on the modified phase shift is presented. The fractal, bi-dimensional structures are obtained on an opto-electronic device: spatial light modulator (SLM). The FPMs were obtained via a recursive algorithm that generates regular gratings with spatial constants obeying a fractal rule. The spatial distribution of the diffracted intensity is exhibiting also a fractal structure. The contrast of the fractal structure in the screen plane increases significantly in some areas and decreases in others, for a selective phase shift in DE plane. The phase shifting rule obeys a non linear dependence of 1/q type with respect to the fractal stage of growth q. We also study the inverse problem which states the logical frame between the self-similar intensity distribution in the screen plane and the corresponding fractal phase map. The results of the numerical evaluation for the spatial intensity distribution of diffraction patterns are compared with those obtained experimentally.

Analysis of optical diffractive pattern of natural or virtual objects is an actually problem which has important applications. We realized the correlation between diffractive patterns from imprinted objects with different shapes, structures and sizes. An iterative algorithm based on Fourier transform for far field written in MATLAB is used to design computer generated hologram (CGH) in a different number of equidistant phase-levels patterns, which produces the desired image in far field. Spatial light modulators (SLM), two-dimensional electrically addressable devices, are used in experimental setup in order to modulate the phase of a wave front from a monochromatic plane wave. Through the diffractive pattern obtained experimentally with CGH and SLM, we show that the shape of the objects is of less importance than the structure. The shape is modified by different geometrical figures, and the structure is periodical, with or without defects. We use this method to study different imprint objects. By correlation profile we remark the distinction in the shape and structure of different sample.

Background The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. Methods LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. Results Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. Conclusions Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. Key points We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.

Background We describe preoperative computed tomography (CT)guided injection of radiotracer technetium 99m macroaggregates ( 99mTc-MAA) in challenging small lung nodules, intraoperative localization, and resection. Methods Between November 2007 and February 2010, 44 patients with 47 lung nodules which were detected incidentally or at screening and that were 18F-fluorodeoxyglucose positron emission tomography (18F FDG-PET) positive or increasing in size at subsequent CT scans were candidates for surgical biopsy. Inclusion criteria for preoperative percutaneous CT-guided (low-dose technique) 99mTc-MAA localization included having at least one of the following characteristics: nodule size less than 1 cm, subsolid morphology, or distance from the pleura greater than 1 cm. Results Mean nodule size was 11 mm (range, 5 to 24 mm); 24 nodules were nonsolid, 15 nodules were partially solid, and 8 nodules had a solid morphology. Mean distance from the pleura was 11 mm (range, 0 to 35 mm). Localization complications included 13 minor asymptomatic pneumothoraces, 9 parenchymal hemorrhage suffusions, 1 mild allergic reaction to contrast medium, and 2 patients with chest pain after the procedure. Nine patients had mild extravasation of radiotracer into the pleura. In 2 cases, there was an extravasation of a significant quantity of radiotracer into the pleural cavity. Thoracoscopic biopsy was performed in 30 cases, 2 cases were converted to thoracotomy, and 12 patients underwent intentional thoracotomy. Conclusions Asymptomatic subjects with suspicious nodules detected by screening or incidental CT are best candidates due to small lesion size and high percentage of nonsolid morphology, making thoracoscopic biopsy potentially difficult. Radiotracer localization is a safe, versatile, simple technique to help perform diagnosis with a minimally invasive approach in nonpalpable lung lesions. © 2010 The Society of Thoracic Surgeons.

Radiomics focuses on extracting a large number of quantitative imaging features and testing both their correlation with clinical characteristics and their prognostic and predictive values. We propose a radiomic approach using magnetic resonance imaging (MRI) to decode tumor phenotype and local recurrence in oropharyngeal squamous cell carcinoma (OPSCC). The contrast-enhanced T1-weighted sequences from baseline MRI examinations of OPSCC patients treated between 2008 and 2016 were retrospectively selected. Radiomic features were extracted using the IBEX software, and hierarchical clustering was applied to reduce features redundancy. The association of each radiomic feature with tumor grading and stage, HPV status, loco-regional recurrence within 2 years, considered as main endpoints, was assessed by univariate analysis and then corrected for multiple testing. Statistical analysis was performed with SAS/STAT® software. Thirty-two eligible cases were identified. For each patient, 1286 radiomic features were extracted, subsequently grouped into 16 clusters. Higher grading (G3 vs. G1/G2) was associated with lower values of GOH/65Percentile and GOH/85Percentile features (p = 0.04 and 0.01, respectively). Positive HPV status was associated with higher values of GOH/10Percentile (p = 0.03) and lower values of GOH/90Percentile (p = 0.03). Loco-regional recurrence within 2 years was associated with higher values of GLCM3/4-7Correlation (p = 0.04) and lower values of GLCM3/2-1InformationMeasureCorr1 (p = 0.04). Results lost the statistical significance after correction for multiple testing. T stage was significantly correlated with 9 features, 4 of which (GLCM25/180-4InformationMeasureCorr2, Shape/MeanBreadth, GLCM25/90-1InverseDiffMomentNorm and GLCM3/6-1InformationMeasureCorr1) retained statistical significance after False Discovery Rate correction. MRI-based radiomics is a feasible and promising approach for the prediction of tumor phenotype and local recurrence in OPSCC. Some radiomic features seem to be correlated with tumor characteristics and oncologic outcome however, larger collaborative studies are warranted in order to increase the statistical power and to obtain robust and validated results.