AIMC Topic: Radiometry
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Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system.
Physics in medicine and biology
Feb 9, 2021
This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate...
Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.
Scientific reports
Feb 4, 2021
Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, pathology, and neurosurgery. It is expected that AI will soon be used to optimize strategies for the clinical management of patients based on intensiv...
Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.
Medical physics
Jan 27, 2021
PURPOSE: We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy...
A semantic database for integrated management of image and dosimetric data in low radiation dose research in medical imaging.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Medical ionizing radiation procedures and especially medical imaging are a non negligible source of exposure to patients. Whereas the biological effects of high absorbed doses are relatively well known, the effects of low absorbed doses are still deb...
[Not Available].
Zeitschrift fur medizinische Physik
Oct 20, 2020
BACKGROUND: Currently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuro-endocrine and prostate tumours. For a patient-specific treatment, an individual dosimetry based on SPECT/CT imaging is necessary....
Whole-body voxel-based internal dosimetry using deep learning.
European journal of nuclear medicine and molecular imaging
Sep 1, 2020
PURPOSE: In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propo...
Robustness study of noisy annotation in deep learning based medical image segmentation.
Physics in medicine and biology
Aug 27, 2020
Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of ...
Identifying sarcopenia in advanced non-small cell lung cancer patients using skeletal muscle CT radiomics and machine learning.
Thoracic cancer
Aug 6, 2020
BACKGROUND: Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ...
DoseGAN: a generative adversarial network for synthetic dose prediction using attention-gated discrimination and generation.
Scientific reports
Jul 6, 2020
Deep learning algorithms have recently been developed that utilize patient anatomy and raw imaging information to predict radiation dose, as a means to increase treatment planning efficiency and improve radiotherapy plan quality. Current state-of-the...
Boosting radiotherapy dose calculation accuracy with deep learning.
Journal of applied clinical medical physics
Jun 19, 2020
In radiotherapy, a trade-off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil-beam convolution can be much faster than Monte-Carlo methods, but less accurate. The dose difference, mostly cause...
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