Whole-body voxel-based internal dosimetry using deep learning

In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propose a novel method to perform whole-body personalized organ-level dosimetry taking into account the heterogeneity of activity distribution, non-uniformity of surrounding medium, and patient-specific anatomy using deep learning algorithms

An update on computational anthropomorphic anatomical models

The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.</p

Predictive value of 99mTc-MAA-based dosimetry in personalized 90Y-SIRT planning for liver malignancies

[EN] Background: Selective internal radiation therapy with 90Y radioembolization aims to selectively irradiate liver tumours by administering radioactive microspheres under the theragnostic assumption that the pre-therapy injection of 99mTc labelled macroaggregated albumin (99mTc-MAA) provides an estimation of the 90Y microspheres biodistribution, which is not always the case. Due to the growing interest in theragnostic dosimetry for personalized radionuclide therapy, a robust relationship between the delivered and pre-treatment radiation absorbed doses is required. In this work, we aim to investigate the predictive value of absorbed dose metrics calculated from 99mTc-MAA (simulation) compared to those obtained from 90Y post-therapy SPECT/CT. Results: A total of 79 patients were analysed. Pre- and post-therapy 3D-voxel dosimetry was calculated on 99mTc-MAA and 90Y SPECT/CT, respectively, based on Local Deposition Method. Mean absorbed dose, tumour-to-normal ratio, and absorbed dose distribution in terms of dose-volume histogram (DVH) metrics were obtained and compared for each volume of interest (VOI). Mann-Whitney U-test and Pearson's correlation coefficient were used to assess the correlation between both methods. The effect of the tumoral liver volume on the absorbed dose metrics was also investigated. Strong correlation was found between simulation and therapy mean absorbed doses for all VOIs, although simulation tended to overestimate tumour absorbed doses by 26%. DVH metrics showed good correlation too, but significant differences were found for several metrics, mostly on non-tumoral liver. It was observed that the tumoral liver volume does not significantly affect the differences between simulation and therapy absorbed dose metrics. Conclusion: This study supports the strong correlation between absorbed dose metrics from simulation and therapy dosimetry based on 90Y SPECT/CT, highlighting the predictive ability of 99mTc-MAA, not only in terms of mean absorbed dose but also of the dose distributionEURATO

Development of a library of adult computational phantoms based on anthropometric indexes

Computational phantom libraries have been developed over the years to enhance the accuracy of Monte Carlo-based radiation dose calculations from radiological procedures. In this work, we report on the development of an adult computational anthropomorphic phantom library covering different body morphometries among the 20-80 years old population. The anatomical diversities of different populations are modeled based on anthropometric parameters extracted from the National Health and Nutrition Examination Survey (NHANES) database, including standing height, total weight and body mass index. Organ masses were modified to match the corresponding data. The ICRP reference male and female models were selected as anchor phantoms. A computer code was developed for adjusting standing height and percentage of fat free mass of anchor phantoms by 3D scaling. The waist circumference and total body mass were further adjusted. The diversity of organ masses due to anthropometric differences deviates from the mean values by about 3-21%, while this deviation exceeds 50% for genital organs. Thereafter, organ-level absorbed doses from both internal and external radiation exposure conditions were estimated. A total of 479 phantoms corresponding to seven age groups were constructed for both genders, thus fulfilling the criteria for representing a diverse adult population with different anthropomorphic and anatomical characteristic

Assessment of uncertainties associated with monte carlo-based personalized dosimetry in clinical ct examinations

The clinical value of x-ray computed tomography (CT) has skyrocketed in the last decade while at the same time being the main source of medical exposure to the population. Concerns regarding the potential health hazards associated with the use of ionizing radiation were raised and an appropriate estimation of absorbed dose to patients is highly desired. In this work, we aim to validate our developed Monte Carlo CT simulator using in-phantom dose measurements and further assess the impact of personalized scan-related parameters on dosimetric calculations. We developed a Monte Carlo-based CT simulator for personalized organ level dose calculations, in which the CT source model, patient-specific computational model and personalized scanning protocol were integrated. The CT simulator was benchmarked using an ionization chamber and standard CT Dose Index phantom while the dosimetry methodology was validated through experimental measurements using thermoluminescent dosimeters (TLDs) embedded within an anthropomorphic phantom. Patient-specific scan protocols extracted from CT raw data and DICOM image metadata, respectively, were fed as input into the CT simulator to calculate individualized dose profiles. Thereby, the dosimetric uncertainties associated with using different protocol-related parameters were investigated. The absolute absorbed dose difference between measurements and simulations using the ionization chamber was less than 3%. In the case of the anthropomorphic phantom, the absolute absorbed dose difference between simulations and TLD measurements ranged from  -8.3% to 22%, with a mean absolute difference of 14% while the uncertainties of protocol-related input parameters introduced an extra absolute error of 15% to the simulated results compared with TLD measurements. The developed methodology can be employed for accurate estimation of organ level dose from clinical CT examinations. The validated methodology can be further developed to produce an accurate MC simulation model with a reduced computational burden

Assessment of uncertainties associated with Monte Carlo-based personalized dosimetry in clinical CT examinations

The clinical value of x-ray computed tomography (CT) has skyrocketed in the last decade while at the same time being the main source of medical exposure to the population. Concerns regarding the potential health hazards associated with the use of ionizing radiation were raised and an appropriate estimation of absorbed dose to patients is highly desired. In this work, we aim to validate our developed Monte Carlo CT simulator using in-phantom dose measurements and further assess the impact of personalized scan-related parameters on dosimetric calculations. We developed a Monte Carlo-based CT simulator for personalized organ level dose calculations, in which the CT source model, patient-specific computational model and personalized scanning protocol were integrated. The CT simulator was benchmarked using an ionization chamber and standard CT Dose Index phantom while the dosimetry methodology was validated through experimental measurements using thermoluminescent dosimeters (TLDs) embedded within an anthropomorphic phantom. Patient-specific scan protocols extracted from CT raw data and DICOM image metadata, respectively, were fed as input into the CT simulator to calculate individualized dose profiles. Thereby, the dosimetric uncertainties associated with using different protocol-related parameters were investigated. The absolute absorbed dose difference between measurements and simulations using the ionization chamber was less than 3%. In the case of the anthropomorphic phantom, the absolute absorbed dose difference between simulations and TLD measurements ranged from -8.3% to 22%, with a mean absolute difference of 14% while the uncertainties of protocol-related input parameters introduced an extra absolute error of 15% to the simulated results compared with TLD measurements. The developed methodology can be employed for accurate estimation of organ level dose from clinical CT examinations. The validated methodology can be further developed to produce an accurate MC simulation model with a reduced computational burden

The promise of artificial intelligence and deep learning in PET and SPECT imaging

This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, the underlying limitations/challenges of these imaging modalities are briefly discussed followed by a description of AI-based solutions proposed to address these challenges. This review will focus on mainstream generic fields, including instrumentation, image acquisition/formation, image reconstruction and low-dose/fast scanning, quantitative imaging, image interpretation (computer-aided detection/ diagnosis/prognosis), as well as internal radiation dosimetry. A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the clinic are discussed

Development and validation of an optimal GATE model for proton pencil-beam scanning delivery

Objective: To develop and validate a versatile Monte Carlo (MC)-based dose calculation engine to support MC-based dose verification of treatment planning systems (TPSs) and quality assurance (QA) workflows in proton therapy. Methods: The GATE MC toolkit was used to simulate a fixed horizontal active scan-based proton beam delivery (SIEMENS IONTRIS). Within the nozzle, two primary and secondary dose monitors have been designed to enable the comparison of the accuracy of dose estimation from MC simulations with respect to physical QA measurements. The developed beam model was validated against a series of commissioning measurements using pinpoint chambers and 2D array ionization chambers (IC) in terms of lateral profiles and depth dose distributions. Furthermore, beam delivery module and treatment planning has been validated against the literature deploying various clinical test cases of the AAPM TG-119 (c-shape phantom) and a prostate patient. Results: MC simulations showed excellent agreement with measurements in the lateral depth-dose parameters and spread-out Bragg peak (SOBP) characteristics within a maximum relative error of 0.95 mm in range, 1.83% in entrance to peak ratio, 0.27% in mean point-to-point dose difference, and 0.32% in peak location. The mean relative absolute difference between MC simulations and measurements in terms of absorbed dose in the SOBP region was 0.93% ± 0.88%. Clinical phantom studies showed a good agreement compared to research TPS (relative error for TG-119 planning target volume PTV-D95 ∼ 1.8%; and for prostate PTV-D95 ∼ −0.6%). Conclusion: We successfully developed a MC model for the pencil beam scanning system, which appears reliable for dose verification of the TPS in combination with QA information, prior to patient treatment

Development and validation of an optimal GATE model for proton pencil-beam scanning delivery

Objective: To develop and validate a versatile Monte Carlo (MC)-based dose calculation engine to support MC-based dose verification of treatment planning systems (TPSs) and quality assurance (QA) workflows in proton therapy. Methods: The GATE MC toolkit was used to simulate a fixed horizontal active scan-based proton beam delivery (SIEMENS IONTRIS). Within the nozzle, two primary and secondary dose monitors have been designed to enable the comparison of the accuracy of dose estimation from MC simulations with respect to physical QA measurements. The developed beam model was validated against a series of commissioning measurements using pinpoint chambers and 2D array ionization chambers (IC) in terms of lateral profiles and depth dose distributions. Furthermore, beam delivery module and treatment planning has been validated against the literature deploying various clinical test cases of the AAPM TG-119 (c-shape phantom) and a prostate patient. Results: MC simulations showed excellent agreement with measurements in the lateral depth-dose parameters and spread-out Bragg peak (SOBP) characteristics within a maximum relative error of 0.95 mm in range, 1.83% in entrance to peak ratio, 0.27% in mean point-to-point dose difference, and 0.32% in peak location. The mean relative absolute difference between MC simulations and measurements in terms of absorbed dose in the SOBP region was 0.93% ± 0.88%. Clinical phantom studies showed a good agreement compared to research TPS (relative error for TG-119 planning target volume PTV-D95 ∼ 1.8%; and for prostate PTV-D95 ∼ −0.6%). Conclusion: We successfully developed a MC model for the pencil beam scanning system, which appears reliable for dose verification of the TPS in combination with QA information, prior to patient treatment