AIMC Topic: Radiometry
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Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.
Seminars in nuclear medicine
Nov 12, 2022
Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment ...
Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.
Medical physics
Jul 18, 2022
The use of deep learning (DL) to improve cone-beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation...
Dosimetric impact of deep learning-based CT auto-segmentation on radiation therapy treatment planning for prostate cancer.
Radiation oncology (London, England)
Jan 31, 2022
BACKGROUND: The evaluation of automatic segmentation algorithms is commonly performed using geometric metrics. An analysis based on dosimetric parameters might be more relevant in clinical practice but is often lacking in the literature. The aim of t...
Deep learning-based 3Ddose reconstruction with an electronic portal imaging device for magnetic resonance-linear accelerators: a proof of concept study.
Physics in medicine and biology
Dec 2, 2021
To develop a novel deep learning-based 3Ddose reconstruction framework with an electronic portal imaging device (EPID) for magnetic resonance-linear accelerators (MR-LINACs).The proposed method directly back-projected 2D portal dose into 3D patient c...
Predicting cancer outcomes with radiomics and artificial intelligence in radiology.
Nature reviews. Clinical oncology
Oct 18, 2021
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challen...
Deep learning-augmented radioluminescence imaging for radiotherapy dose verification.
Medical physics
Oct 5, 2021
PURPOSE: We developed a novel dose verification method using a camera-based radioluminescence imaging system (CRIS) combined with a deep learning-based signal processing technique.
Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy.
Medical physics
Sep 24, 2021
PURPOSE: The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID).
Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 11, 2021
Advances in artificial intelligence-based methods have led to the development and publication of numerous systems for auto-segmentation in radiotherapy. These systems have the potential to decrease contour variability, which has been associated with ...
Computation of epistemic uncertainty due to limited data samples in small field dosimetry using Fuzzy Set Theory.
The British journal of radiology
Mar 18, 2021
OBJECTIVE: To estimate the epistemic (or fuzzy) uncertainty, arising due to limited data samples in the measurement of the output factors (OFs) of the small fields using Fuzzy Set Theory (FST).
Radiation dose calculation in 3D heterogeneous media using artificial neural networks.
Medical physics
Mar 16, 2021
PURPOSE: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of calculating the dose delivered by a clinical treatment plan. However, existing methods of calculating dose distributions have limitations. Monte Carl...
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