Monte Carlo Simulations of an Innovative Molecular Breast Imaging System for the Small Breast Cancer Diagnosis Using GATE

G. E. Poma,F. Garibaldi,F. Giuliani,T. Insero,M. Lucentini,A. Marcucci,P. Musico,C. Petta,F. Santavenere,C. Sutera,E. Cisbani

Radiation Effects and Defects in Solids（2019）SCI 4区

Univ Catania | Ist Super Sanita | Osped Pediat Bambin Gesu | Aeronaut Mil Italiana | Ist Nazl Fis Nucl

Cited 4|Views78

Abstract

This paper draws attention to the study of performance of a new Molecular Breast Imaging (MBI) device, whose purpose is the early diagnosis of breast cancer, using Monte Carlo simulations. MBI provides functional and specific information that are more appropriated to dense breasts. Two asymmetric heads with different types of collimators, facing each other in anti-parallel viewing direction, characterize the system. Detectors and phantoms, together with the data taking procedure, are shortly reported. Monte Carlo simulations using the GATE (GEANT4 Application for Tomographic Emission) simulation toolkit have been implemented to evaluate the optimal detector configuration, in terms of sensitivity and spatial resolution, and also to reproduce the real experimental data. The device can be used both in spot compression and in Limited Angle Tomography (LAT); in the latter configuration one detector head with pinhole collimator is able to rotate around the breast in order to diagnose and localized the small tumors.

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Key words

Molecular breast imaging,Monte Carlo simulations,gate toolkit,breast phantoms,limited angle tomography

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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