AIMC Topic: Radiometry

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Personalized Models for Injected Activity Levels in SPECT Myocardial Perfusion Imaging.

IEEE transactions on medical imaging
We propose a patient-specific ("personalized") approach for tailoring the injected activities to individual patients in order to achieve dose reduction in SPECT-myocardial perfusion imaging (MPI). First, we develop a strategy to determine the minimum...

Feasibility of photon beam profile deconvolution using a neural network.

Medical physics
PURPOSE: Ionization chambers are the detectors of choice for photon beam profile scanning. However, they introduce significant volume averaging effect (VAE) that can artificially broaden the penumbra width by 2-3 mm. The purpose of this study was to ...

Assessment of specific versus combined purpose knowledge based models in prostate radiotherapy.

Journal of applied clinical medical physics
Knowledge-based planning (KBP) can be used to improve plan quality, planning speed, and reduce the inter-patient plan variability. KPB may also identify and reduce systematic variations in VMAT plans, something very important in multi-institutional c...

Temporal separation of Cerenkov radiation and scintillation using artificial neural networks in Clinical LINACs.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
The irradiation of scintillator-fiber optic dosimeters by clinical LINACs results in the measurement of scintillation and Cerenkov radiation. In scintillator-fiber optic dosimetry, the scintillation and Cerenkov radiation responses are separated to d...

ScatterNet: A convolutional neural network for cone-beam CT intensity correction.

Medical physics
PURPOSE: To demonstrate a proof-of-concept for fast cone-beam CT (CBCT) intensity correction in projection space by the use of deep learning.

Automated prediction of dosimetric eligibility of patients with prostate cancer undergoing intensity-modulated radiation therapy using a convolutional neural network.

Radiological physics and technology
The quality of radiotherapy has greatly improved due to the high precision achieved by intensity-modulated radiation therapy (IMRT). Studies have been conducted to increase the quality of planning and reduce the costs associated with planning through...

IMRT QA using machine learning: A multi-institutional validation.

Journal of applied clinical medical physics
PURPOSE: To validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.

Characterization and prediction of the backscattered form function of an immersed cylindrical shell using hybrid fuzzy clustering and bio-inspired algorithms.

Ultrasonics
The acoustic scattering of a plane wave by an elastic cylindrical shell is studied. A new approach is developed to predict the form function of an immersed cylindrical shell of the radius ratio b/a ('b' is the inner radius and 'a' is the outer radius...

Vector-model-supported approach in prostate plan optimization.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was deve...

Vector-model-supported optimization in volumetric-modulated arc stereotactic radiotherapy planning for brain metastasis.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Long planning time in volumetric-modulated arc stereotactic radiotherapy (VMA-SRT) cases can limit its clinical efficiency and use. A vector model could retrieve previously successful radiotherapy cases that share various common anatomic features wit...