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Boron Neutron Capture Therapy (BNCT) is a neutron radiotherapy used to treat tumours cells previously doping with Boron-10. This therapy requires an epithermal neutron beam for the treatment of deep tumours and a thermal beam for shallow…

This work presents a preliminary evaluation of the use of the convolutional neural network nnU-NET to automatically contour the volume of Glioblastoma Multiforme in medical images of patients. The goal is to assist the preparation of the…

Boron Neutron Capture Therapy (BNCT) is a form of radiotherapy based on the irradiation of the tumour with a low energy neutron beam, after the administration of a selective drug enriched in boron-10. The therapy exploits the high cross…

Selective accumulation of B-10 compound in tumour tissue is a fundamental condition for the achievement of BNCT (Boron Neutron Capture Therapy), since the effectiveness of therapy irradiation derives just from neutron capture reaction of…

Boron Neutron Capture Therapy (BNCT), is an advanced cancer therapy that destroys the cancer tumors using the well-known Li(p,n)Be . Because of the highly selectively reaction between a boron and a neutron, BNCT is effective for rapidly…

Dosimetry in BNCT poses significant challenges due to the indirect effect of neutrons interacting with elements within the body and uncertainties associated with the uptake of boron compounds used in clinical practice. Current treatment…

Boron neutron capture therapy (BNCT) leverages the nuclear reaction between thermal neutrons and boron-10 (B-10) atoms to induce selective tumor cell death. The spatial and quantum mechanical overlap between the neutron wavefunction and…

This paper explores the adaptation and application of i-TED Compton imagers for real-time dosimetry in Boron Neutron Capture Therapy (BNCT). The i-TED array, previously utilized in nuclear astrophysics experiments at CERN, is being…

Background and purpose: Accelerator-Based Boron Neutron Capture Therapy is a radiotherapy based on compact accelerator neutron sources requiring an epithermal neutron field for tumour irradiations. Neutrons of 10 keV are considered as the…

Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed a deep convolutional neural network (CNN) for low-dose X-ray CT and won the second…

Protontherapy is hadrontherapy fastest-growing modality and a pillar in the battle against cancer. Hadrontherapy superiority lies in its inverted depth-dose profile, hence tumour-confined irradiation. Protons, however, lack distinct…

Bremsstrahlung photon beams produced by medical linear accelerators are currently the most commonly used method of radiation therapy for cancerous tumors. Photons with energies greater than 8-10 MeV potentially generate neutrons through…

Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios.…

Digital breast tomosynthesis (DBT) exams should utilize the lowest possible radiation dose while maintaining sufficiently good image quality for accurate medical diagnosis. In this work, we propose a convolution neural network (CNN) to…

Proton computed tomography (pCT) aims to facilitate precise dose planning for hadron therapy, a promising and effective method for cancer treatment. Hadron therapy utilizes protons and heavy ions to deliver well focused doses of radiation,…

Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating early- to mid-stage thoracic tumours, yet the associated radiation exposure substantially elevates the risk of secondary…

Adaptive radiotherapy is a growing field of study in cancer treatment due to it's objective in sparing healthy tissue. The standard of care in several institutions includes longitudinal cone-beam computed tomography (CBCT) acquisitions to…

Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a…

Obtaining accurate and reliable images from low-dose computed tomography (CT) is challenging. Regression convolutional neural network (CNN) models that are learned from training data are increasingly gaining attention in low-dose CT…

Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in PET imaging raises concerns due to the risk of radiation…