AIMC Topic: Radiometry

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A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

Journal of applied clinical medical physics
PURPOSE: Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties...

Generation of tissues outside the field of view (FOV) of radiation therapy simulation imaging based on machine learning and patient body outline (PBO).

Radiation oncology (London, England)
BACKGROUND: It is not unusual to see some parts of tissues are excluded in the field of view of CT simulation images. A typical mitigation is to avoid beams entering the missing body parts at the cost of sub-optimal planning.

Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.

Physics in medicine and biology
. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnife. The objective of this work is to demonstrate the use of a plastic scintillator imaged with a commercial cam...

Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study.

Journal of applied clinical medical physics
PURPOSE: Accurate and fast multiorgan segmentation is essential in image-based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time-consuming as well as subject to hu...

Deep learning-based tools to distinguish plan-specific from generic deviations in EPID-based in vivo dosimetry.

Medical physics
BACKGROUND: Dose distributions calculated with electronic portal imaging device (EPID)-based in vivo dosimetry (EIVD) differ from planned dose distributions due to generic and plan-specific deviations. Generic deviations are characteristic to a class...

Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.

International journal of radiation oncology, biology, physics
PURPOSE: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractions is sometimes considered controversial, as the nominal biologically effective dose (BED) of 100 Gy is felt by some to be insufficient for long-term ...

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies.

Nuklearmedizin. Nuclear medicine
Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workf...

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

Journal of applied clinical medical physics
PURPOSE: The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor...

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques.

Physics in medicine and biology
A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for a...

Rapid estimation of patient-specific organ doses using a deep learning network.

Medical physics
BACKGROUND: Patient-specific organ-dose estimation in diagnostic CT examinations can provide useful insights on individualized secondary cancer risks, protocol optimization, and patient management. Current dose estimation techniques mainly rely on ti...