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Your search keyword '"Broussaud, Thomas"' showing total 2 results
Start Over Author "Broussaud, Thomas" Topic computed tomography
2 results on '"Broussaud, Thomas"'

1. Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach

Author

Abascal, Juan F P J, Abascal, Juan Fpj, Ducros, Nicolas, Pronina, Valeriya, Rit, Simon, Rodesch, Pierre-Antoine, Broussaud, Thomas, Bussod, Suzanne, Douek, Philippe, Hauptmann, Andreas, Arridge, Simon, PEYRIN, Françoise, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
General Computer Science, Computer science, Computed tomography, 02 engineering and technology, Iterative reconstruction, transfer learning, Regularization (mathematics), Convolutional neural network, 030218 nuclear medicine & medical imaging, Spectral CT, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, 0202 electrical engineering, electronic engineering, information engineering, medicine, General Materials Science, Electrical and Electronic Engineering, medicine.diagnostic_test, business.industry, Deep learning, General Engineering, deep learning, Experimental data, Decomposition, Nonlinear system, inverse problem, 020201 artificial intelligence & image processing, lcsh:Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, business, lcsh:TK1-9971, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm
Abstract

International audience; The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomogra-phy is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specic materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. The network is trained to decompose the materials in the projection domain after which we apply any conventional tomographic method to reconstruct the dierent material volumes. The proposed decomposition method is compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data.

Published
2021
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2. Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases.

Author

Si-Mohamed, Salim A., Nasser, Mouhamad, Colevray, Marion, Nempont, Olivier, Lartaud, Pierre-Jean, Vlachomitrou, Anna, Broussaud, Thomas, Ahmad, Kais, Traclet, Julie, Cottin, Vincent, and Boussel, Loic

Subjects
LUNG volume measurements, IDIOPATHIC pulmonary fibrosis, LUNGS, INTERSTITIAL lung diseases, RETROSPECTIVE studies, VITAL capacity (Respiration), COMPUTED tomography
Abstract

Objectives: To compare the lung CT volume (CTvol) and pulmonary function tests in an interstitial lung disease (ILD) population. Then to evaluate the CTvol loss between idiopathic pulmonary fibrosis (IPF) and non-IPF and explore a prognostic value of annual CTvol loss in IPF.Methods: We conducted in an expert center a retrospective study between 2005 and 2018 on consecutive patients with ILD. CTvol was measured automatically using commercial software based on a deep learning algorithm. In the first group, Spearman correlation coefficients (r) between forced vital capacity (FVC), total lung capacity (TLC), and CTvol were calculated. In a second group, annual CTvol loss was calculated using linear regression analysis and compared with the Mann-Whitney test. In a last group of IPF patients, annual CTvol loss was calculated between baseline and 1-year CTs for investigating with the Youden index a prognostic value of major adverse event at 3 years. Univariate and log-rank tests were calculated.Results: In total, 560 patients (4610 CTs) were analyzed. For 1171 CTs, CTvol was correlated with FVC (r: 0.86) and TLC (r: 0.84) (p < 0.0001). In 408 patients (3332 CT), median annual CTvol loss was 155.7 mL in IPF versus 50.7 mL in non-IPF (p < 0.0001) over 5.03 years. In 73 IPF patients, a relative annual CTvol loss of 7.9% was associated with major adverse events (log-rank, p < 0.0001) in univariate analysis (p < 0.001).Conclusions: Automated lung CT volume may be an alternative or a complementary biomarker to pulmonary function tests for the assessment of lung volume loss in ILD.Key Points: • There is a good correlation between lung CT volume and forced vital capacity, as well as for with total lung capacity measurements (r of 0.86 and 0.84 respectively, p < 0.0001). • Median annual CT volume loss is significantly higher in patients with idiopathic pulmonary fibrosis than in patients with other fibrotic interstitial lung diseases (155.7 versus 50.7 mL, p < 0.0001). • In idiopathic pulmonary fibrosis, a relative annual CT volume loss higher than 9.4% is associated with a significantly reduced mean survival time at 2.0 years versus 2.8 years (log-rank, p < 0.0001). [ABSTRACT FROM AUTHOR]

Published
2022
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