Real-time, acquisition parameter-free voxel-wise patient-specific Monte Carlo dose reconstruction in whole-body CT scanning using deep neural networks

Y Salimi, A Akhavanallaf, Z Mansouri, I Shiri… - European Radiology, 2023 - Springer
Objective We propose a deep learning-guided approach to generate voxel-based absorbed
dose maps from whole-body CT acquisitions. Methods The voxel-wise dose maps …

Rapid estimation of patient‐specific organ doses using a deep learning network

M Myronakis, J Stratakis, J Damilakis - Medical Physics, 2023 - Wiley Online Library
Background Patient‐specific organ‐dose estimation in diagnostic CT examinations can
provide useful insights on individualized secondary cancer risks, protocol optimization, and …

A fully automated machine learning-based methodology for personalized radiation dose assessment in thoracic and abdomen CT

E Tzanis, J Stratakis, M Myronakis, J Damilakis - Physica Medica, 2024 - Elsevier
Purpose To develop a machine learning-based methodology for patient-specific radiation
dosimetry in thoracic and abdomen CT. Methods Three hundred and thirty-one …

Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm

W Shao, X Lin, Y Yi, Y Huang, L Qu… - Physics in Medicine & …, 2024 - iopscience.iop.org
Objectives. This study aims to develop a method for predicting patient-specific head organ
doses by training a support vector regression (SVR) model based on radiomics features and …

Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models

W Shao, X Lin, W Zhao, Y Huang, L Qu, W Zhuo… - Scientific Reports, 2024 - nature.com
The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with
the risk tied to patient-specific organ doses. This study aims to establish a new method to …

A machine learning-based pipeline for multi-organ/tissue patient-specific radiation dosimetry in CT

E Tzanis, J Damilakis - European Radiology, 2024 - Springer
Objectives To develop a machine learning-based pipeline for multi-organ/tissue
personalized radiation dosimetry in CT. Materials and methods For the study, 95 chest CT …

Is deep learning-enabled real-time personalized CT dosimetry feasible using only patient images as input?

T Berris, M Myronakis, J Stratakis, K Perisinakis… - Physica Medica, 2024 - Elsevier
Purpose To propose a novel deep-learning based dosimetry method that allows quick and
accurate estimation of organ doses for individual patients, using only their computed …

Organ dose prediction for patients undergoing radiotherapy CBCT chest examinations using artificial intelligence

F Tsironi, M Myronakis, J Stratakis, V Sotiropoulou… - Physica Medica, 2024 - Elsevier
Purpose To propose an artificial intelligence (AI)-based method for personalized and real-
time dosimetry for chest CBCT acquisitions. Methods CT images from 113 patients who …

Comparing fetal phantoms with surrogate organs in female phantoms during CT exposure of pregnant patients

MK Badawy, K Kashish, S Payne… - Physical and engineering …, 2024 - Springer
With the rising use of Computed Tomography (CT) in diagnostic radiology, there are
concerns regarding radiation exposure to sensitive groups, including pregnant patients …

Predicting patient-specific organ doses from thoracic CT examinations using support vector regression algorithm

W Shao, X Lin, Y Huang, L Qu… - Journal of X-Ray …, 2024 - content.iospress.com
PURPOSE: This study aims to propose and develop a fast, accurate, and robust prediction
method of patient-specific organ doses from CT examinations using minimized …