Laser beam shaping for enhanced Zero-Group Velocity Lamb modes generation

Optimization of Lamb modes induced by laser can be achieved by adjusting the spatial source distribution to the mode wavelength ($\lambda$). The excitability of Zero-Group Velocity (ZGV) resonances in isotropic plates is investigated both theoretically and experimentally for axially symmetric sources. Optimal parameters and amplitude gains are derived analytically for spot and annular sources of either Gaussian or rectangular energy profiles. For a Gaussian spot source, the optimal radius is found to be $\lambda_{ZGV}/\pi$. Annular sources increase the amplitude by at least a factor of 3 compared to the optimal Gaussian source. Rectangular energy profiles provide higher gain than Gaussian ones. These predictions are confirmed by semi-analytical simulation of the thermoelastic generation of Lamb waves, including the effect of material attenuation. Experimentally, Gaussian ring sources of controlled width and radius are produced with an axicon-lens system. Measured optimal geometric parameters obtained for Gaussian and annular beams are in good agreement with theoretical predictions. A ZGV resonance amplification factor of 2.1 is obtained with the Gaussian ring. Such source should facilitate the inspection of highly attenuating plates made of low ablation threshold materials like composites.Comment: 11 pages, 12 figure

Evacuateurs de surface et dissipation d’énergie

Cet article aborde principalement les points suivants : Le retour d’expérience (au sens large) sur des ouvrages existants, principalement la dissipation d’énergie

Optimization of Circular Symmetric Laser Source for Enhanced Generation of Zero Group Velocity Lamb Modes

The laser based ultrasound technique is a convenient non-contact tool for generation and detection of guided modes in plate. This technique is well suited to observe the specific Lamb modes having a Zero Group Velocity (ZGV) and a finite wavelength. For these modes, the energy deposited by a laser pulse remains trapped under the source which results in a local and narrow resonance. The frequencies of these modes depend on the bulk velocities and the thickness, and for isotropic the local Poisson’s ratio is provided by measurements of ZGV resonances[1]. The amplitude of the resonance depends on the laser pulse energy which is limited by the ablation threshold for non-destructive testing applications. To maximize the amplitude of resonance, the laser source needs to be matched with spatial distribution of the mode. The normal surface displacement of a ZGV mode of wavelength λ follows the Bessel function of the first kind of order zero u(r)=J0(2πr/λ). A first optimization was performed by adjusting the radius of a Gaussian beam in function of the wavelength of the ZGV mode. The theory predicts that the optimal source radius at constant maximum surface energy is /. For the S1S2 mode in duralumin plate, the wavelength is about 4 times the plate thickness. This result is confirmed by semi-analytic simulations and by measurements on a 1-mm thick duralumin plate. The ZGV resonance amplitude is increased by a factor close to 5 when the radius increases from 0.5 mm to the optimized source radius (1.3 mm). A second optimization is performed on using annular sources. Experimentally, the annular beam is produced by a simple axicon-lens system. Both radius and width of the ring are controlled by varying the distances between the axicon, the lens and the sample and controlled on a ccd camera. We observe that for parameters well adapted to the ZGV mode’s wavelength, the first annular source enhances ZGV mode’s amplitude by a factor greater than 2 compared to the optimized Gaussian source at the same maximal surface energy. Moreover, simulations show that for a fixed width, ZGV amplitude increases pseudo periodically with the square- root of the ring radius. Besides, these annular sources are also interesting to enhance the generation of propagating modes.</p

Esthesioneuroblastoma in children, adolescents and young adults

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Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment

International audiencePurpose :The purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra.Materials and methods :An ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference.Results :A total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm2 respectively.Conclusion :Deep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow

Semiautomated segmentation of hepatocellular carcinoma tumors with MRI using convolutional neural networks

OBJECTIVE To assess the performance of convolutional neural networks (CNNs) for semiautomated segmentation of hepatocellular carcinoma (HCC) tumors on MRI. METHODS This retrospective single-center study included 292 patients (237 M/55F, mean age 61 years) with pathologically confirmed HCC between 08/2015 and 06/2019 and who underwent MRI before surgery. The dataset was randomly divided into training (n = 195), validation (n = 66), and test sets (n = 31). Volumes of interest (VOIs) were manually placed on index lesions by 3 independent radiologists on different sequences (T2-weighted imaging [WI], T1WI pre-and post-contrast on arterial [AP], portal venous [PVP], delayed [DP, 3 min post-contrast] and hepatobiliary phases [HBP, when using gadoxetate], and diffusion-weighted imaging [DWI]). Manual segmentation was used as ground truth to train and validate a CNN-based pipeline. For semiautomated segmentation of tumors, we selected a random pixel inside the VOI, and the CNN provided two outputs: single slice and volumetric outputs. Segmentation performance and inter-observer agreement were analyzed using the 3D Dice similarity coefficient (DSC). RESULTS A total of 261 HCCs were segmented on the training/validation sets, and 31 on the test set. The median lesion size was 3.0 cm (IQR 2.0-5.2 cm). Mean DSC (test set) varied depending on the MRI sequence with a range between 0.442 (ADC) and 0.778 (high b-value DWI) for single-slice segmentation; and between 0.305 (ADC) and 0.667 (T1WI pre) for volumetric-segmentation. Comparison between the two models showed better performance in single-slice segmentation, with statistical significance on T2WI, T1WI-PVP, DWI, and ADC. Inter-observer reproducibility of segmentation analysis showed a mean DSC of 0.71 in lesions between 1 and 2 cm, 0.85 in lesions between 2 and 5 cm, and 0.82 in lesions > 5 cm. CONCLUSION CNN models have fair to good performance for semiautomated HCC segmentation, depending on the sequence and tumor size, with better performance for the single-slice approach. Refinement of volumetric approaches is needed in future studies. KEY POINTS • Semiautomated single-slice and volumetric segmentation using convolutional neural networks (CNNs) models provided fair to good performance for hepatocellular carcinoma segmentation on MRI. • CNN models' performance for HCC segmentation accuracy depends on the MRI sequence and tumor size, with the best results on diffusion-weighted imaging and T1-weighted imaging pre-contrast, and for larger lesions

18F]FDG PET/CT for predicting triple-negative breast cancer outcomes after neoadjuvant chemotherapy with or without pembrolizuma

International audiencePurpose: To determine if pretreatment [18F]FDG PET/CT could contribute to predicting complete pathological complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy with or without pembrolizumab. Methods: In this retrospective bicentric study, we included TNBC patients who underwent [18F]FDG PET/CT before neoadjuvant chemotherapy (NAC) or chemo-immunotherapy (NACI) between March 2017 and August 2022. Clinical, biological, and pathological data were collected. Tumor SUVmax and total metabolic tumor volume (TMTV) were measured from the PET images. Cut-off values were determined using ROC curves and a multivariable model was developed using logistic regression to predict pCR. Results: N = 191 patients were included. pCR rates were 53 and 70% in patients treated with NAC (N = 91) and NACI (N = 100), respectively (p 12.3), and low TMTV (≤ 3.0 cm3) were predictors of pCR in the NAC cohort while tumor staging classification ( 17.2), and low TMTV (≤ 7.3 cm3) correlated with pCR in the NACI cohort. In multivariable analysis, only high tumor SUVmax (NAC: OR 8.8, p < 0.01; NACI: OR 3.7, p = 0.02) and low TMTV (NAC: OR 6.6, p < 0.01; NACI: OR 3.5, p = 0.03) were independent factors for pCR in both cohorts, albeit at different thresholds. Conclusion: High tumor metabolism (SUVmax) and low tumor burden (TMTV) could predict pCR after NAC regardless of the addition of pembrolizumab. Further studies are warranted to validate such findings and determine how these biomarkers could be used to guide neoadjuvant therapy in TNBC patients

Infantile Rhabdomyosarcomas With VGLL2 Rearrangement Are Not Always an Indolent Disease: A Study of 4 Aggressive Cases With Clinical, Pathologic, Molecular, and Radiologic Findings.

VGLL2-rearranged rhabdomyosarcomas (RMS) are rare low-grade tumors with only favorable outcomes reported to date. We describe 4 patients with VGLL2-rearranged RMS confirmed by molecular studies, who experienced local progression and distant metastases, including 2 with fatal outcomes. Tumors were diagnosed at birth (n=3) or at 12 months of age (n=1), and were all localized at initial diagnosis, but unresectable and therefore managed with chemotherapy and surveillance. Metastatic progression occurred from 1 to 8 years from diagnosis (median, 3.5 y). Three patients experienced multimetastatic spread and one showed an isolated adrenal metastasis. At initial diagnosis, 3 tumors displaying bland morphology were misdiagnosed as fibromatosis or infantile fibrosarcoma and initially managed as such, while 1 was a high-grade sarcoma. At relapse, 3 tumors showed high-grade morphology, while 1 retained a low-grade phenotype. Low-grade primary tumors showed only very focal positivity for desmin, myogenin, and/or MyoD1, while high-grade tumors were heterogenously or diffusely positive. Whole-exome sequencing, performed on primary and relapse samples for 3 patients, showed increased genomic instability and additional genomic alterations (eg, TP53, CDKN2A/B, FGFR4) at relapse, but no recurrent events. RNA sequencing confirmed that high-grade tumors retained VGLL2 fusion transcripts and transcriptomic profiles consistent with VGLL2-rearranged RMS. High-grade samples showed a high expression of genes encoding cell cycle proteins, desmin, and some developmental factors. These 4 cases with distinct medical history imply the importance of complete surgical resection, and suggest that RMS-type chemotherapy should be considered in unresectable cases, given the risk of high-grade transformation. They also emphasize the importance of correct initial diagnosis