Výsledky vyhledávání - "Giovanni Pirro"

A hierarchical approach for modelling X-ray beamlines. Application to a coherent beamline

Autor: Juan Reyes Herrera, Peter Cloetens, Xianbo Shi, Manuel Sanchez del Rio, Ray Barrett, Luca Rebuffi, Mark Glass, Julio Cesar da Silva, Rafael Celestre, Giovanni Pirro

Publikováno v: Journal of Synchrotron Radiation
Journal of Synchrotron Radiation, International Union of Crystallography, 2019, 26, pp.1887-1901. ⟨10.1107/S160057751901213X⟩

Different approaches to simulate a modern X-ray beamline are considered. Several methodologies with increasing complexity are applied to discuss the relevant parameters that quantify the beamline performance. Parameters such as flux, dimensions and i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::469916749795feef79ca3873fc182aee
https://hal.archives-ouvertes.fr/hal-02187919

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Akademický článek

Artificial Intelligence-Based Patient Selection for Deep Inspiration Breath-Hold Breast Radiotherapy from Respiratory Signals

Autor: Alessandra Vendrame, Cristina Cappelletto, Paola Chiovati, Lorenzo Vinante, Masud Parvej, Angela Caroli, Giovanni Pirrone, Loredana Barresi, Annalisa Drigo, Michele Avanzo

Publikováno v: Applied Sciences, Vol 13, Iss 8, p 4962 (2023)

Purpose: to predict eligibility for deep inspiration breath-hold (DIBH) radiotherapy (RT) treatment of patients with left breast cancer from analysis of respiratory signal, using Deep Bidirectional Long Short-Term Memory (BLSTM) recurrent neural netw

Externí odkaz: https://doaj.org/article/309c928cffcf44e2b52b7ad188c061e0

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Akademický článek

Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

Autor: Michele Avanzo, Giovanni Pirrone, Lorenzo Vinante, Angela Caroli, Joseph Stancanello, Annalisa Drigo, Samuele Massarut, Mario Mileto, Martina Urbani, Marco Trovo, Issam el Naqa, Antonino De Paoli, Giovanna Sartor

Publikováno v: Frontiers in Oncology, Vol 10 (2020)

Purpose: to predict the occurrence of late subcutaneous radiation induced fibrosis (RIF) after partial breast irradiation (PBI) for breast carcinoma by using machine learning (ML) models and radiomic features from 3D Biologically Effective Dose (3D-B

Externí odkaz: https://doaj.org/article/89bd94bfeaa34e9e9154248812a28a34

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