AIMC Topic: Radiometry

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Geometric and dosimetric evaluation of deep learning based auto-segmentation for clinical target volume on breast cancer.

Journal of applied clinical medical physics
BACKGROUND: Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radi...

Dicentric chromosome assay using a deep learning-based automated system.

Scientific reports
The dicentric chromosome assay is the "gold standard" in biodosimetry for estimating radiation exposure. However, its large-scale deployment is limited owing to its time-consuming nature and requirement for expert reviewers. Therefore, a recently dev...

Machine learning-based predictions of gamma passing rates for virtual specific-plan verification based on modulation maps, monitor unit profiles, and composite dose images.

Physics in medicine and biology
Machine learning (ML) methods have been implemented in radiotherapy to aid virtual specific-plan verification protocols, predicting gamma passing rates (GPR) based on calculated modulation complexity metrics because of their direct relation to dose d...

X-ray dose profiles using artificial neural networks.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15 MV at different depths and field sizes using Artificial Neural Networks within the error margin req...

Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.

Seminars in nuclear medicine
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
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)
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
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
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
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.