Development of an automated scanning system for the analysis of heavy ions' fragmentation reaction by nuclear track detectors

Coppola, Fulvio; Durante, Marco; Gialanella, G.; Grossi, G. F.; Manti, Lorenzo; Pugliese, M.; Scampoli, P.

doi:10.1016/j.radmeas.2009.10.014

Cited by 5 publications

(5 citation statements)

References 4 publications

(4 reference statements)

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“…Similar methods have been employed to separate events. Eroding the image, for example, allows a better detection of central pixels [ 35 ].…”

Section: Resultsmentioning

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

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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.

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“…Similar methods have been employed to separate events. Eroding the image, for example, allows a better detection of central pixels [ 35 ].…”

Section: Resultsmentioning

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

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“…After exposure to protons, SSTDs are scored by manually counting the number of tracks, made visible through an etching process [27,28] of 90 min in NaOH 6N solution at 80 • C, on at least 20 random fields observed by a light microscope using a 32X objective, corresponding to a field area of 1.25 • 10 −4 cm 2 . Using the obtained value of the R 60 • /15 • and the count values of the spectrum at 60 • , the expected dose on the SSBD 15 • or the SSTD 15 • , and therefore the expected nominal dose given to the cells, can be calculated using the formulas:…”

Section: Cell Culturementioning

confidence: 99%

“…This is determined before each experimental run. During irradiation of the samples, proton fluence and energy are measured by the SSDB 60 • .Particle fluence and beam spatial uniformity, before and after irradiation, can be also measured and monitored by means of the CR-39 detectors placed at the same position of the sample-holder (SSTD 15 • ) using a special centering tool (Figure2b).After exposure to protons, SSTDs are scored by manually counting the number of tracks, made visible through an etching process[27,28] of 90 min in NaOH 6N solution at 80 • C, on at least 20 random fields observed by a light microscope using a 32X objective, corresponding to a field area of 1.25 • 10 −4 cm 2 . Using the obtained value of the R 60 • /15 • and the count values of the spectrum at 60 • , the expected dose on the SSBD 15 • or the SSTD 15 • , and therefore the expected nominal dose given to the cells, can be calculated using the formulas:…”

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confidence: 99%

A New Low-Energy Proton Irradiation Facility to Unveil the Mechanistic Basis of the Proton-Boron Capture Therapy Approach

et al. 2021

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Protontherapy (PT) is a fast-growing cancer therapy modality thanks to much-improved normal tissue sparing granted by the charged particles’ inverted dose-depth profile. Protons, however, exhibit a low biological effectiveness at clinically relevant energies. To enhance PT efficacy and counteract cancer radioresistance, Proton–Boron Capture Therapy (PBCT) was recently proposed. PBCT exploits the highly DNA-damaging α-particles generated by the p + 11B→3α (pB) nuclear reaction, whose cross-section peaks for proton energies of 675 keV. Although a significant enhancement of proton biological effectiveness by PBCT has been demonstrated for high-energy proton beams, validation of the PBCT rationale using monochromatic proton beams having energy close to the reaction cross-section maximum is still lacking. To this end, we implemented a novel setup for radiobiology experiments at a 3-MV tandem accelerator; using a scattering chamber equipped with an Au foil scatterer for beam diffusion on the biological sample, uniformity in energy and fluence with uncertainties of 2% and 5%, respectively, was achieved. Human cancer cells were irradiated at this beamline for the first time with 685-keV protons. The measured enhancement in cancer cell killing due to the 11B carrier BSH was the highest among those thus far observed, thereby corroborating the mechanistic bases of PBCT.

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“…In most cases the track analysis process is to replace the human eye and its recognition capability, however, operator practice and training is needed even if sophisticated shape recognition method is available. Several custom made automated track counting and related software are reported in the literature, Espinosa et al (1996), Palfalvi et al (1997), Patiris et al (2006), Coppola et al (2009). A general overview on this subject is given by Hulber (2009).…”

Section: Automated Track Counting and Softwarementioning

confidence: 99%

Boron Studies in Interdisciplinary Fields Employing Nuclear Track Detectors (NTDs)

Sajó-Bohus¹,

Greaves²,

Palfálvi³

2011

Radioisotopes - Applications in Bio-Medical Science

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Development of an automated scanning system for the analysis of heavy ions' fragmentation reaction by nuclear track detectors

Cited by 5 publications

References 4 publications

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

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

A New Low-Energy Proton Irradiation Facility to Unveil the Mechanistic Basis of the Proton-Boron Capture Therapy Approach

Boron Studies in Interdisciplinary Fields Employing Nuclear Track Detectors (NTDs)

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