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Protons

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Deep learning-based in vivo dose verification from proton-induced secondary-electron-bremsstrahlung images with various count level.

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)
PURPOSE: Proton-induced secondary-electron-bremsstrahlung (SEB) imaging is a promising method for estimating the ranges of particle beam. However, SEB images do not directly represent dose distributions of particle beams. In addition, the ranges esti...

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy.

Medical physics
BACKGROUND: Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton d...

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach.

Physics in medicine and biology
. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning me...

3Ddose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.

Physics in medicine and biology
Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-time3D dose verification. Howev...

Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy.

Physics in medicine and biology
. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DEC...

Analysis and prediction of anion- and temperature responsive behaviours of luminescent Ru(II)-terpyridine complexes by using Boolean, fuzzy logic, artificial neural network and adapted neuro fuzzy inference models.

Dalton transactions (Cambridge, England : 2003)
Anion- and temperature responsive behaviors of three luminescent Ru(II)-terpyridine complexes are utilized here to demonstrate multiple Boolean (BL) and fuzzy logic (FL) operations. Taking advantage of the imidazole NH protons, anion-promoted alterat...

Uncertainty-aware physics-driven deep learning network for free-breathing liver fat and R * quantification using self-gated stack-of-radial MRI.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning-based method for rapid liver proton-density fat fraction (PDFF) and R * quantification with built-in uncertainty estimation using self-gated free-breathing stack-of-radial MRI.

A Dual-Channel Deep Learning Approach for Lung Cavity Estimation From Hyperpolarized Gas and Proton MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and str...

Water irradiation devoid pulses enhance the sensitivity of H,H nuclear Overhauser effects.

Journal of biomolecular NMR
The nuclear Overhauser effect (NOE) is one of NMR spectroscopy's most important and versatile parameters. NOE is routinely utilized to determine the structures of medium-to-large size biomolecules and characterize protein-protein, protein-RNA, protei...