Geometrical parameters of tracks registered by collimated alpha particles on CR-39 detector

Joshirao, Pranav M.; Shin, Jae Won; Kim, Do Yoon; Hong, Seung-Woo; Kolekar, R.V.; Manchanda, V. Κ.

doi:10.1016/j.nimb.2015.03.085

Cited by 9 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GEANT4 is a particle transport code and is widely used for simulations in diverse scientific fields, such as medical physics [24][25][26], accelerator induced radiation [17,18,27], neutron shielding [28][29][30], and environment radiation [31][32][33][34]. The details of the GEANT4 code can be found on the web [35].…”

Section: Simulation Of Neutron Spectra From Mc-50mentioning

confidence: 99%

Neutron spectra produced by 30, 35 and 40 MeV proton beams at KIRAMS MC-50 cyclotron with a thick beryllium target

Shin

Bak

Ham

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

Self Cite

View full text Add to dashboard Cite

Section: Simulation Of Neutron Spectra From Mc-50mentioning

confidence: 99%

Neutron spectra produced by 30, 35 and 40 MeV proton beams at KIRAMS MC-50 cyclotron with a thick beryllium target

Shin

Bak

Ham

et al. 2015

Nuclear Instruments and Methods in Physics Research Section A:

Self Cite

View full text Add to dashboard Cite

“…It has been thoroughly tested and is being widely used in many different scientific fields, such as medical physics [32][33][34], accelerator-based radiation studies [35][36][37][38], neutron shielding studies [39][40][41], environmental radiation detection studies [42][43][44][45], etc. Additional information, validations and detailed descriptions of the GEANT4 can be found on the GEANT4 website [46].…”

Section: Simulation Toolsmentioning

confidence: 99%

A data-based photonuclear reaction model for GEANT4

Shin

2015

Nuclear Instruments and Methods in Physics Research Section B:

Self Cite

View full text Add to dashboard Cite

“…The study of the observable characteristics of the nuclear tracks is of interest for a further understanding of the experimental process behind their formation. In fact, the use of morphological parameters for the identification and quantification of BNC events has been reported [ 9 , 15 , 16 ]. Considering these aspects, the number of valid events (absence of artifacts) is expected to be greater in images acquired under adequate conditions as opposed to those obtained, for example, under poor illumination.…”

Section: Introductionmentioning

confidence: 99%

From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy

Viglietti,

Espain,

Díaz

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Knowledge of the 10B microdistribution is of great relevance in BNCT studies. Since 10B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging.

show abstract

Geometrical parameters of tracks registered by collimated alpha particles on CR-39 detector

Cited by 9 publications

References 15 publications

Neutron spectra produced by 30, 35 and 40 MeV proton beams at KIRAMS MC-50 cyclotron with a thick beryllium target

Neutron spectra produced by 30, 35 and 40 MeV proton beams at KIRAMS MC-50 cyclotron with a thick beryllium target

A data-based photonuclear reaction model for GEANT4

From nuclear track characterization to machine learning based image classification in neutron autoradiography for boron neutron capture therapy

Contact Info

Product

Resources

About