Ignacio Scarinci
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ignacio Scarinci has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Mauro Valente
Instituto de Física Enrique Gaviola (IFEG), CONICET-UNC, Córdoba, X5000HUA, Argentina; Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes por Rayos X (LIIFAMIR(X)), FAMAF-UNC, Córdoba, X5000HUA, Argentina; Centro de Excelencia de Física e Ingeniería en Salud- CFIS & Departamento de Ciencias Físicas, Universidad de La Frontera, 4811230, Chile
2 shared publications - Pedro Pérez
Instituto de Física E. Gaviola (IFEG), CONICET, Ciudad Universitaria, Córdoba 5000, Argentina, Argentina
2 shared publications - Adlin López-Díaz
Universidad de La Frontera
1 shared publication - Francisco Malano
Centro de Excelencia de Física e Ingeniería en Salud (CFIS), Universidad de La Frontera, Temuco 4811230, Chile
1 shared publication - Miguel Sofo Haro
Comisión Nacional de Energía Atómica (CNEA) y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Córdoba, Córdoba 5000
1 shared publication - Nicolás Martín
Instituto de Física E. Gaviola (IFEG), CONICET, Ciudad Universitaria, Córdoba, 5000, Argentina; Universidad Nacional de Córdoba (UNC), Facultad de Matemática, Astronomía, Física y Computación (FAMAF), Laboratorio de Investigación e Instrumentación en Física Aplicada a La Medicina e Imágenes por Rayos X (LIIFAMIRx), Ciudad Universitaria, Córdoba, Córdoba, 5000, Argentina
1 shared publication
Latest Publications
- Open Access
- 21 February 2025
- Published by MDPI AG in Preprints.org
Computational Method to Characterise the SNR-Dose ratio of X-Ray Fluorescence in Complex Biomedical Samples Biomarked for microCT and XFCT
This study proposes an innovative methodology that combines experimental microCT imaging with Monte Carlo irradiation simulations using the PENELOPE code aimed at enabling detailed analyses of different features involved in the irradiation processes along with elemental mapping, as required for X-ray fluorescence imaging of in vivo samples. By examining the relationship between signal-to-noise ratio (SNR) and absorbed dose (D), a mathematical approach was proposed to determine critical points where their rates of change are equal. Thus, allowing to identify optimal concentrations of gold dispersions to the specific simulated system of interest, where the signal quality efficiently improves as weighted in relation to the absorbed dose. The proposed methodology has been applied to a typical small animal (rat) microCT that was further used by the computationally implemented model to infuse the animal kidneys by different amounts of gold. For the K_(α_1 ), K_(α_2 ), and K_(β_1 ) lines, these critical concentrations were found to be 0.78 %, 1.32 %, and 0.32 % w/w, respectively, while no such behavior was identified for the K_(β_2 ) line under the given configuration considered as a representative/typical small animal infused with Au-based agents. The obtained results highlight the methodology's ability to optimize the balance between the absorbed dose and corresponding SNR, obtaining high-quality images without compromising in vivo samples due to excessive radiation exposure. Moreover, the proposed methodology provides high-resolution structural imaging and detailed elemental mapping, facilitating the analysis of detection limits along with the overall system performance. These findings confirm the robustness and reliability of the approach, offering a valuable tool for refining X-ray spectroscopy imaging processes and advancing X-ray fluorescence-based tomography techniques.
- 21 February 2025
- Published by MDPI AG in Preprints.org
Open AccessComputational Method to Characterise the SNR-Dose ratio of X-Ray Fluorescence in Complex Biomedical Samples Biomarked for microCT and XFCT
This study proposes an innovative methodology that combines experimental microCT imaging with Monte Carlo irradiation simulations using the PENELOPE code aimed at enabling detailed analyses of different features involved in the irradiation processes along with elemental mapping, as required for X-ray fluorescence imaging of in vivo samples. By examining the relationship between signal-to-noise ratio (SNR) and absorbed dose (D), a mathematical approach was proposed to determine critical points where their rates of change are equal. Thus, allowing to identify optimal concentrations of gold dispersions to the specific simulated system of interest, where the signal quality efficiently improves as weighted in relation to the absorbed dose. The proposed methodology has been applied to a typical small animal (rat) microCT that was further used by the computationally implemented model to infuse the animal kidneys by different amounts of gold. For the K_(α_1 ), K_(α_2 ), and K_(β_1 ) lines, these critical concentrations were found to be 0.78 %, 1.32 %, and 0.32 % w/w, respectively, while no such behavior was identified for the K_(β_2 ) line under the given configuration considered as a representative/typical small animal infused with Au-based agents. The obtained results highlight the methodology's ability to optimize the balance between the absorbed dose and corresponding SNR, obtaining high-quality images without compromising in vivo samples due to excessive radiation exposure. Moreover, the proposed methodology provides high-resolution structural imaging and detailed elemental mapping, facilitating the analysis of detection limits along with the overall system performance. These findings confirm the robustness and reliability of the approach, offering a valuable tool for refining X-ray spectroscopy imaging processes and advancing X-ray fluorescence-based tomography techniques.
- Open Access
- 26 June 2023
- Published by Springer Nature in EJNMMI Physics
- Vol. 10 (1) , 1-18
A machine learning-based model for a dose point kernel calculation
Purpose: Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters.Methods: DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with $$^{90}$$ Y.Results: The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than $$10\%$$ in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than $$7 \%$$ were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations.Conclusion: An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.- 26 June 2023
- Published by Springer Nature in EJNMMI Physics
- Vol. 10 (1) , 1-18
Open AccessA machine learning-based model for a dose point kernel calculation
Purpose: Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters.Methods: DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with $$^{90}$$ Y.Results: The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than $$10\%$$ in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than $$7 \%$$ were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations.Conclusion: An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.