Objective. X-ray-based imaging modalities including mammography and computed
tomography (CT) are widely used in cancer screening, diagnosis, staging, treatment …

Purpose With the rising number of computed tomography (CT) examinations and the trend
toward personalized medicine, patient‐specific dose estimates are becoming more and …

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 …

Purpose This study developed and evaluated a fully convolutional network (FCN) for
pediatric CT organ segmentation and investigated the generalizability of the FCN across …

Purpose To develop a machine learning-based methodology for patient-specific radiation
dosimetry in thoracic and abdomen CT. Methods Three hundred and thirty-one …

Due to the potential risk of ionizing radiation, the assessment of the administered radiation
dose is an important topic in CT. However, dosimetric quantities that are routinely evaluated …

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 …

Accurately estimating patient exposure is a fundamental concern when modeling the
radiation risk from medical imaging. Inaccurate estimation techniques produce misleading …

Purpose Our goal is to develop a model‐based approach for CT dose estimation. We
previously presented a CT dose estimation method that offered good accuracy in soft tissue …

For the purpose of real-time scan-protocol optimisation and patient-specific dose
management in cone-beam computed tomography, we introduce a numerical algorithm that …