| 1. |
- Almhagen, Erik, et al.
(författare)
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A beam model for focused proton pencil beams
- 2018
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Ingår i: Physica medica (Testo stampato). - : Elsevier. - 1120-1797 .- 1724-191X. ; 52, s. 27-32
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: We present a beam model for Monte Carlo simulations of the IBA pencil beam scanning dedicated nozzle installed at the Skandion Clinic. Within the nozzle, apart from entrance and exit windows and the two ion chambers, the beam traverses vacuum, allowing for a beam that is convergent downstream of the nozzle exit. Materials and methods: We model the angular, spatial and energy distributions of the beam phase space at the nozzle exit with single Gaussians, controlled by seven energy dependent parameters. The parameters were determined from measured profiles and depth dose distributions. Verification of the beam model was done by comparing measured and GATE acquired relative dose distributions, using plan specific log files from the machine to specify beam spot positions and energy. Results: GATE-based simulations with the acquired beam model could accurately reproduce the measured data. The gamma index analysis comparing simulated and measured dose distributions resulted in > 95% global gamma index pass rates (3%/2 mm) for all depths. Conclusion: The developed beam model was found to be sufficiently accurate for use with GATE e.g. for applications in quality assurance (QA) or patient motion studies with the IBA pencil beam scanning dedicated nozzles.
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| 2. |
- Almhagen, Erik, et al.
(författare)
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Plan robustness and RBE influence for proton dose painting by numbers for head and neck cancers
- 2023
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Ingår i: Physica medica (Testo stampato). - : Elsevier. - 1120-1797 .- 1724-191X. ; 115, s. 103157-
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo investigate the feasibility of dose painting by numbers (DPBN) with respect to robustness for proton therapy for head and neck cancers (HNC), and to study the influence of variable RBE on the TCP and OAR dose burden.Methods and materialsData for 19 patients who have been scanned pretreatment with PET-FDG and subsequently treated with photon therapy were used in the study. A dose response model developed for photon therapy was implemented in a TPS, allowing DPBN plans to be created. Conventional homogeneous dose and DPBN plans were created for each patient, optimized with either fixed RBE = 1.1 or a variable RBE model. Robust optimization was used to create clinically acceptable plans. To estimate the maximum potential loss in TCP due to actual SUV variations from the pre-treatment imaging, we applied a test case with randomized SUV distribution.ResultsRegardless of the use of variable RBE for optimization or evaluation, a statistically significant increase (p < 0.001) in TCP was found for DPBN plans as compared to homogeneous dose plans. Randomizing the SUV distribution decreased the TCP for all plans. A correlation between TCP increase and variance of the SUV distribution and target volume was also found.ConclusionDPBN for protons and HNC is feasible and could lead to a TCP gain. Risks associated with the temporal variation of SUV distributions could be mitigated by imposing minimum doses to targets. The correlation found between TCP increase and SUV variance and target volume may be used for patient selection.
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| 3. |
- Andersson, Jonas, 1975-, et al.
(författare)
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Artificial intelligence and the medical physics profession-A Swedish perspective
- 2021
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Ingår i: Physica Medica-European Journal of Medical Physics. - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 88, s. 218-225
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is a continuous and dynamic discussion on artificial intelligence (AI) in present-day society. AI is expected to impact on healthcare processes and could contribute to a more sustainable use of resources allocated to healthcare in the future. The aim for this work was to establish a foundation for a Swedish perspective on the potential effect of AI on the medical physics profession. Materials and methods: We designed a survey to gauge viewpoints regarding AI in the Swedish medical physics community. Based on the survey results and present-day situation in Sweden, a SWOT analysis was performed on the implications of AI for the medical physics profession. Results: Out of 411 survey recipients, 163 responded (40%). The Swedish medical physicists with a professional license believed (90%) that AI would change the practice of medical physics but did not foresee (81%) that AI would pose a risk to their practice and career. The respondents were largely positive to the inclusion of AI in educational programmes. According to self-assessment, the respondents' knowledge of and workplace preparedness for AI was generally low. Conclusions: From the survey and SWOT analysis we conclude that AI will change the medical physics profession and that there are opportunities for the profession associated with the adoption of AI in healthcare. To overcome the weakness of limited AI knowledge, potentially threatening the role of medical physicists, and build upon the strong position in Swedish healthcare, medical physics education and training should include learning objectives on AI.
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| 4. |
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| 5. |
- Andersson, Patrik, et al.
(författare)
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Effects of lung tissue characterization in radiotherapy of breast cancer under deep inspiration breath hold when using Monte Carlo dosimetry
- 2021
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Ingår i: Physica medica (Testo stampato). - : Associazione Italiana di Fisica Medica. - 1120-1797 .- 1724-191X. ; 90, s. 83-90
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To investigate the sensitivity of Monte Carlo (MC) calculated lung dose distributions to lung tissue characterization in external beam radiotherapy of breast cancer under Deep Inspiration Breath Hold (DIBH). Methods: EGSnrc based MC software was employed. Mean lung densities for one hundred patients were analysed. CT number frequency and clinical dose distributions were calculated for 15 patients with mean lung density below 0.14 g/cm3. Lung volume with a pre-defined CT numbers was also considered. Lung tissue was characterized by applying different CT calibrations in the low-density region and air-lung tissue thresholds. Dose impact was estimated by Dose Volume Histogram (DVH) parameters. Results: Mean lung densities below 0.14 g/cm3 were found in 10% of the patients. CT numbers below −960 HU dominated the CT frequency distributions with a high rate of CT numbers at −990 HU. Mass density conversion approach influenced the DVH shape. V4Gy and V8Gy varied by 7% and 5% for the selected patients and by 9% and 3.5% for the pre-defined lung volume. V16Gy and V20Gy, were within 2.5%. Regions above 20 Gy were affected. Variations in air- lung tissue differentiation resulted in DVH parameters within 1%. Threshold at −990 HU was confirmed by the CT number frequency distributions. Conclusions: Lung dose distributions were more sensitive to variations in the CT calibration curve below lung (inhale) density than to air-lung tissue differentiation. Low dose regions were mostly affected. The dosimetry effects were found to be potentially important to 10% of the patients treated under DIBH.
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| 6. |
- Ardenfors, Oscar, et al.
(författare)
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Organ doses from a proton gantry-mounted cone-beam computed tomography system characterized with MCNP6 and GATE
- 2018
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 53, s. 56-61
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo determine organ doses from a proton gantry-mounted cone-beam computed tomography (CBCT) system using two Monte Carlo codes and to study the influence on organ doses from different acquisition modes and repeated imaging.MethodsThe CBCT system was characterized with MCNP6 and GATE using measurements of depth doses in water and spatial profiles in air. The beam models were validated against absolute dose measurements and used to simulate organ doses from CBCT imaging with head, thorax and pelvis protocols. Anterior and posterior 190° scans were simulated and the resulting organ doses per mAs were compared to those from 360° scans. The influence on organ doses from repeated imaging with different imaging schedules was also investigated.ResultsThe agreement between MCNP6, GATE and measurements with regard to depth doses and beam profiles was within 4% for all protocols and the corresponding average agreement in absolute dose validation was 4%. Absorbed doses for in-field organs from 360° scans ranged between 6 and 8 mGy, 15–17 mGy and 24–54 mGy for the head, thorax and pelvis protocols, respectively. Cumulative organ doses from repeated CBCT imaging ranged between 0.04 and 0.32 Gy for weekly imaging and 0.2–1.6 Gy for daily imaging. The anterior scans resulted in an average increase in dose per mAs of 24% to the organs of interest relative to the 360° scan, while the posterior scan showed a 37% decrease.ConclusionsA proton gantry-mounted CBCT system was accurately characterized with MCNP6 and GATE. Organ doses varied greatly depending on acquisition mode, favoring posterior scans.
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| 7. |
- Ardenfors, Oscar, et al.
(författare)
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Out-of-field doses from secondary radiation produced in proton therapy and the associated risk of radiation-induced cancer from a brain tumor treatment
- 2018
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 53, s. 129-136
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo determine out-of-field doses produced in proton pencil beam scanning (PBS) therapy using Monte Carlo simulations and to estimate the associated risk of radiation-induced second cancer from a brain tumor treatment.MethodsSimulations of out-of-field absorbed doses were performed with MCNP6 and benchmarked against measurements with tissue-equivalent proportional counters (TEPC) for three irradiation setups: two irradiations of a water phantom using proton energies of 78-147 MeV and 177-223 MeV, and one brain tumor irradiation of a whole-body phantom. Out-of-field absorbed and equivalent doses to organs in a whole-body phantom following a brain tumor treatment were subsequently simulated and used to estimate the risk of radiation-induced cancer. Additionally, the contribution of absorbed dose originating from radiation produced in the nozzle was calculated from simulations.ResultsOut-of-field absorbed doses to the TEPC ranged from 0.4 to 135 mu Gy/Gy. The average deviation between simulations and measurements of the water phantom irradiations was about 17%. The absorbed dose contribution from radiation produced in the nozzle ranged between 0 and 70% of the total dose; the contribution was however small in absolute terms. The absorbed and equivalent doses to the organs ranged between 0.2 and 60 mu Gy/Gy and 0.5-151 mu Sv/Gy. The estimated lifetime risk of radiation-induced second cancer was approximately 0.01%.ConclusionsThe agreement of out-of-field absorbed doses between measurements and simulations was good given the sources of uncertainties. Calculations of out-of-field organ doses following a brain tumor treatment indicated that proton PBS therapy of brain tumors is associated with a low risk of radiation-induced cancer.
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| 8. |
- Astaraki, Mehdi, PhD Student, 1984-, et al.
(författare)
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Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features
- 2021
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 83, s. 146-153
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Low-Dose Computed Tomography (LDCT) is the most common imaging modality for lung cancer diagnosis. The presence of nodules in the scans does not necessarily portend lung cancer, as there is an intricate relationship between nodule characteristics and lung cancer. Therefore, benign-malignant pulmonary nodule classification at early detection is a crucial step to improve diagnosis and prolong patient survival. The aim of this study is to propose a method for predicting nodule malignancy based on deep abstract features.Methods: To efficiently capture both intra-nodule heterogeneities and contextual information of the pulmonary nodules, a dual pathway model was developed to integrate the intra-nodule characteristics with contextual attributes. The proposed approach was implemented with both supervised and unsupervised learning schemes. A random forest model was added as a second component on top of the networks to generate the classification results. The discrimination power of the model was evaluated by calculating the Area Under the Receiver Operating Characteristic Curve (AUROC) metric. Results: Experiments on 1297 manually segmented nodules show that the integration of context and target supervised deep features have a great potential for accurate prediction, resulting in a discrimination power of 0.936 in terms of AUROC, which outperformed the classification performance of the Kaggle 2017 challenge winner.Conclusion: Empirical results demonstrate that integrating nodule target and context images into a unified network improves the discrimination power, outperforming the conventional single pathway convolutional neural networks.
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| 9. |
- Astaraki, Mehdi, PhD Student, 1984-, et al.
(författare)
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Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method
- 2019
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 60, s. 58-65
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Tidskriftsartikel (refereegranskat)abstract
- PurposeTo explore prognostic and predictive values of a novel quantitative feature set describing intra-tumor heterogeneity in patients with lung cancer treated with concurrent and sequential chemoradiotherapy.MethodsLongitudinal PET-CT images of 30 patients with non-small cell lung cancer were analysed. To describe tumor cell heterogeneity, the tumors were partitioned into one to ten concentric regions depending on their sizes, and, for each region, the change in average intensity between the two scans was calculated for PET and CT images separately to form the proposed feature set. To validate the prognostic value of the proposed method, radiomics analysis was performed and a combination of the proposed novel feature set and the classic radiomic features was evaluated. A feature selection algorithm was utilized to identify the optimal features, and a linear support vector machine was trained for the task of overall survival prediction in terms of area under the receiver operating characteristic curve (AUROC).ResultsThe proposed novel feature set was found to be prognostic and even outperformed the radiomics approach with a significant difference (AUROCSALoP = 0.90 vs. AUROCradiomic = 0.71) when feature selection was not employed, whereas with feature selection, a combination of the novel feature set and radiomics led to the highest prognostic values.ConclusionA novel feature set designed for capturing intra-tumor heterogeneity was introduced. Judging by their prognostic power, the proposed features have a promising potential for early survival prediction.
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| 10. |
- Berthon, Beatrice, et al.
(författare)
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PETSTEP : generation of synthetic PET lesions for fast evaluation of segmentation methods
- 2015
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Ingår i: Physica medica (Testo stampato). - : Elsevier BV. - 1120-1797 .- 1724-191X. ; 31:8, s. 969-980
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.Methods: PETSTEP was implemented within Matlab as open source software. It allows generating threedimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.Results: PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.Conclusions: PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.
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