Zeitschrift fur Medizinische Physik最新文献

{"title":"Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy","authors":"Marcel Nachbar , Monica lo Russo , Cihan Gani , Simon Boeke , Daniel Wegener , Frank Paulsen , Daniel Zips , Thais Roque , Nikos Paragios , Daniela Thorwarth","doi":"10.1016/j.zemedi.2023.05.001","DOIUrl":"10.1016/j.zemedi.2023.05.001","url":null,"abstract":"<div><h3>Background and purpose</h3><p>MR-guided radiotherapy (MRgRT) online plan adaptation accounts for tumor volume changes, interfraction motion and thus allows daily sparing of relevant organs at risk. Due to the high interfraction variability of bladder and rectum, patients with tumors in the pelvic region may strongly benefit from adaptive MRgRT. Currently, fast automatic annotation of anatomical structures is not available within the online MRgRT workflow. Therefore, the aim of this study was to train and validate a fast, accurate deep learning model for automatic MRI segmentation at the MR-Linac for future implementation in a clinical MRgRT workflow.</p></div><div><h3>Materials and methods</h3><p>For a total of 47 patients, T2w MRI data were acquired on a 1.5 T MR-Linac (Unity, Elekta) on five different days. Prostate, seminal vesicles, rectum, anal canal, bladder, penile bulb, body and bony structures were manually annotated. These training data consisting of 232 data sets in total was used for the generation of a deep learning based autocontouring model and validated on 20 unseen T2w-MRIs. For quantitative evaluation the validation set was contoured by a radiation oncologist as gold standard contours (GSC) and compared in MATLAB to the automatic contours (AIC). For the evaluation, dice similarity coefficients (DSC), and 95% Hausdorff distances (95% HD), added path length (APL) and surface DSC (sDSC) were calculated in a caudal-cranial window of <span><math><mrow><mo>±</mo></mrow></math></span> 4 cm with respect to the prostate ends. For qualitative evaluation, five radiation oncologists scored the AIC on the possible usage within an online adaptive workflow as follows: (1) no modifications needed, (2) minor adjustments needed, (3) major adjustments/ multiple minor adjustments needed, (4) not usable.</p></div><div><h3>Results</h3><p>The quantitative evaluation revealed a maximum median 95% HD of 6.9 mm for the rectum and minimum median 95% HD of 2.7 mm for the bladder. Maximal and minimal median DSC were detected for bladder with 0.97 and for penile bulb with 0.73, respectively. Using a tolerance level of 3 mm, the highest and lowest sDSC were determined for rectum (0.94) and anal canal (0.68), respectively.</p><p>Qualitative evaluation resulted in a mean score of 1.2 for AICs over all organs and patients across all expert ratings. For the different autocontoured structures, the highest mean score of 1.0 was observed for anal canal, sacrum, femur left and right, and pelvis left, whereas for prostate the lowest mean score of 2.0 was detected. In total, 80% of the contours were rated be clinically acceptable, 16% to require minor and 4% major adjustments for online adaptive MRgRT.</p></div><div><h3>Conclusion</h3><p>In this study, an AI-based autocontouring was successfully trained for online adaptive MR-guided radiotherapy on the 1.5 T MR-Linac system. The developed model can automatically generate contours accepted by physicians (80%) o","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Pages 197-207"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000533/pdfft?md5=4c8a5787fe97a32ec18b4426b3597127&pid=1-s2.0-S0939388923000533-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9562444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards quality management of artificial intelligence systems for medical applications","authors":"Lorenzo Mercolli,&nbsp;Axel Rominger,&nbsp;Kuangyu Shi","doi":"10.1016/j.zemedi.2024.02.001","DOIUrl":"10.1016/j.zemedi.2024.02.001","url":null,"abstract":"<div><p>The use of artificial intelligence systems in clinical routine is still hampered by the necessity of a medical device certification and/or by the difficulty of implementing these systems in a clinic’s quality management system. In this context, the key questions for a user are how to ensure robust model predictions and how to appraise the quality of a model’s results on a regular basis.</p><p>In this paper we discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment.</p><p>We propose the methodology from AAPM Task Group 100 report No. 283 as a conceptual framework for developing risk-driven a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an example of a clinical workflow. Our analysis shows how the risk evaluation in this framework can accommodate artificial intelligence based systems independently of their robustness evaluation or the user’s in–house expertise. In particular, we highlight how the degree of interpretability of a machine learning system can be systematically accounted for within the risk evaluation and in the development of a quality management system.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Pages 343-352"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388924000242/pdfft?md5=309f6a0c3aedbe399d5a372c060278f6&pid=1-s2.0-S0939388924000242-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PSMA-PET improves deep learning-based automated CT kidney segmentation","authors":"Julian Leube,&nbsp;Matthias Horn,&nbsp;Philipp E. Hartrampf,&nbsp;Andreas K. Buck,&nbsp;Michael Lassmann,&nbsp;Johannes Tran-Gia","doi":"10.1016/j.zemedi.2023.08.006","DOIUrl":"10.1016/j.zemedi.2023.08.006","url":null,"abstract":"<div><p>For dosimetry of radiopharmaceutical therapies, it is essential to determine the volume of relevant structures exposed to therapeutic radiation. For many radiopharmaceuticals, the kidneys represent an important organ-at-risk. To reduce the time required for kidney segmentation, which is often still performed manually, numerous approaches have been presented in recent years to apply deep learning-based methods for CT-based automated segmentation. While the automatic segmentation methods presented so far have been based solely on CT information, the aim of this work is to examine the added value of incorporating PSMA-PET data in the automatic kidney segmentation.</p></div><div><h3><strong>Methods</strong></h3><p>A total of 108 PET/CT examinations (53 [⁶⁸Ga]Ga-PSMA-I&amp;T and 55 [¹⁸F]F-PSMA-1007 examinations) were grouped to create a reference data set of manual segmentations of the kidney. These segmentations were performed by a human examiner. For each subject, two segmentations were carried out: one CT-based (detailed) segmentation and one PET-based (coarser) segmentation. Five different u-net based approaches were applied to the data set to perform an automated segmentation of the kidney: CT images only, PET images only (coarse segmentation), a combination of CT and PET images, a combination of CT images and a PET-based coarse mask, and a CT image, which had been pre-segmented using a PET-based coarse mask. A quantitative assessment of these approaches was performed based on a test data set of 20 patients, including Dice score, volume deviation and average Hausdorff distance between automated and manual segmentations. Additionally, a visual evaluation of automated segmentations for 100 additional (i.e., exclusively automatically segmented) patients was performed by a nuclear physician.</p></div><div><h3><strong>Results</strong></h3><p>Out of all approaches, the best results were achieved by using CT images which had been pre-segmented using a PET-based coarse mask as input. In addition, this method performed significantly better than the segmentation based solely on CT, which was supported by the visual examination of the additional segmentations. In 80% of the cases, the segmentations created by exploiting the PET-based pre-segmentation were preferred by the nuclear physician.</p></div><div><h3><strong>Conclusion</strong></h3><p>This study shows that deep-learning based kidney segmentation can be significantly improved through the addition of a PET-based pre-segmentation. The presented method was shown to be especially beneficial for kidneys with cysts or kidneys that are closely adjacent to other organs such as the spleen, liver or pancreas. In the future, this could lead to a considerable reduction in the time required for dosimetry calculations as well as an improvement in the results.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Pages 231-241"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000958/pdfft?md5=905c071b84bb04d8b4d49a82783a3b94&pid=1-s2.0-S0939388923000958-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10145024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards MR contrast independent synthetic CT generation","authors":"Attila Simkó ,&nbsp;Mikael Bylund ,&nbsp;Gustav Jönsson ,&nbsp;Tommy Löfstedt ,&nbsp;Anders Garpebring ,&nbsp;Tufve Nyholm ,&nbsp;Joakim Jonsson","doi":"10.1016/j.zemedi.2023.07.001","DOIUrl":"10.1016/j.zemedi.2023.07.001","url":null,"abstract":"<div><p>The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.</p><p>To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, <span><math><mrow><mi>T</mi><mn>1</mn></mrow></math></span> and <span><math><mrow><mi>T</mi><mn>2</mn></mrow></math></span> maps (<em>i.e.</em> contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only <span><math><mrow><mi>T</mi><mn>2</mn><mi>w</mi></mrow></math></span> MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose.</p><p>On <span><math><mrow><mi>T</mi><mn>2</mn><mi>w</mi></mrow></math></span> images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on <span><math><mrow><mi>T</mi><mn>1</mn><mi>w</mi></mrow></math></span> images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model.</p><p>Using a dataset of <span><math><mrow><mi>T</mi><mn>2</mn><mi>w</mi></mrow></math></span> MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Pages 270-277"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000831/pdfft?md5=3e1f7674de1352aa91dfccab724c3a83&pid=1-s2.0-S0939388923000831-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9988159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance","authors":"Ghasem Hajianfar ,&nbsp;Maziar Sabouri ,&nbsp;Yazdan Salimi ,&nbsp;Mehdi Amini ,&nbsp;Soroush Bagheri ,&nbsp;Elnaz Jenabi ,&nbsp;Sepideh Hekmat ,&nbsp;Mehdi Maghsudi ,&nbsp;Zahra Mansouri ,&nbsp;Maziar Khateri ,&nbsp;Mohammad Hosein Jamshidi ,&nbsp;Esmail Jafari ,&nbsp;Ahmad Bitarafan Rajabi ,&nbsp;Majid Assadi ,&nbsp;Mehrdad Oveisi ,&nbsp;Isaac Shiri ,&nbsp;Habib Zaidi","doi":"10.1016/j.zemedi.2023.01.008","DOIUrl":"10.1016/j.zemedi.2023.01.008","url":null,"abstract":"<div><h3>Purpose</h3><p>Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers.</p></div><div><h3>Materials and Methods</h3><p>After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers.</p></div><div><h3>Results</h3><p>DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time.</p></div><div><h3>Conclusion</h3><p>Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Pages 242-257"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000089/pdfft?md5=40da3cacf80f682e80f4655f04f990de&pid=1-s2.0-S0939388923000089-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9133122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Board + Consulting Editorial Board","authors":"","doi":"10.1016/S0939-3889(24)00034-5","DOIUrl":"https://doi.org/10.1016/S0939-3889(24)00034-5","url":null,"abstract":"","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 2","pages":"Page iii"},"PeriodicalIF":2.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388924000345/pdfft?md5=874b26dd35f0095e923d023375e4842c&pid=1-s2.0-S0939388924000345-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radium deposition in human brain tissue: A Geant4-DNA Monte Carlo toolkit study","authors":"S.M.J. Mortazavi ,&nbsp;Payman Rafiepour ,&nbsp;S.A.R. Mortazavi ,&nbsp;S.M.T. Razavi Toosi ,&nbsp;Parya Roshan Shomal ,&nbsp;Lembit Sihver","doi":"10.1016/j.zemedi.2023.09.004","DOIUrl":"10.1016/j.zemedi.2023.09.004","url":null,"abstract":"<div><p>NASA has encouraged studies on ²²⁶Ra deposition in the human brain to investigate the effects of exposure to alpha particles with high linear energy transfer, which could mimic some of the exposure astronauts face during space travel. However, this approach was criticized, noting that radium is a bone-seeker and accumulates in the skull, which means that the radiation dose from alpha particles emitted by ²²⁶Ra would be heavily concentrated in areas close to cranial bones rather than uniformly distributed throughout the brain. In the high background radiation areas of Ramsar, Iran, extremely high levels of ²²⁶Ra in soil contribute to a large proportion of the inhabitants' radiation exposure. A prospective study on Ramsar residents with a calcium-rich diet was conducted to improve the dose uniformity due to ²²⁶Ra throughout the cerebral and cerebellar parenchyma. The study found that exposure of the human brain to alpha particles did not significantly affect working memory but was significantly associated with increased reaction times. This finding is crucial because astronauts on deep space missions may face similar cognitive impairments due to exposure to high charge and energy particles. The current study was aimed to evaluate the validity of the terrestrial model using the Geant4 Monte Carlo toolkit to simulate the interactions of alpha particles and representative cosmic ray particles, acknowledging that these radiation types are only a subset of the complete space radiation environment.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 1","pages":"Pages 166-174"},"PeriodicalIF":2.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923001162/pdfft?md5=4a37c050de1ea682a73d9bbaecce0fac&pid=1-s2.0-S0939388923001162-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136160957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks","authors":"Saeed Izadi, Isaac Shiri, Carlos F. Uribe, Parham Geramifar, Habib Zaidi, Arman Rahmim, Ghassan Hamarneh","doi":"10.1016/j.zemedi.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.zemedi.2024.01.002","url":null,"abstract":"<p>In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of <span><math><mrow is=\"true\"><mn is=\"true\">14.30</mn><mo is=\"true\">±</mo><mn is=\"true\">3.88</mn></mrow></math></span>% and a relative error of <span><math><mrow is=\"true\"><mo is=\"true\" linebreak=\"badbreak\">-</mo><mn is=\"true\">2.11</mn><mo is=\"true\">%</mo><mo is=\"true\">±</mo><mn is=\"true\">2.73</mn></mrow></math></span>% in whole-body.</p>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"1 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139661196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of multi-method-multi-model inference to radiation related solid cancer excess risks models for astronaut risk assessment","authors":"Luana Hafner ,&nbsp;Linda Walsh","doi":"10.1016/j.zemedi.2023.06.003","DOIUrl":"10.1016/j.zemedi.2023.06.003","url":null,"abstract":"<div><p>The impact of including model-averaged excess radiation risks (ER) into a measure of radiation attributed decrease of survival (RADS) for the outcome all solid cancer incidence and the impact on the uncertainties is demonstrated. It is shown that RADS applying weighted model averaged ER based on AIC weights result in smaller risk estimates with narrower 95% CI than RADS using ER based on BIC weights. Further a multi-method-multi-model inference approach is introduced that allows calculating one general RADS estimate providing a weighted average risk estimate for a lunar and a Mars mission. For males the general RADS estimate is found to be 0.42% (95% CI: 0.38%; 0.45%) and for females 0.67% (95% CI: 0.59%; 0.75%) for a lunar mission and 2.45% (95% CI: 2.23%; 2.67%) for males and 3.91% (95% CI: 3.44%; 4.39%) for females for a Mars mission considering an age at exposure of 40 years and an attained age of 65 years. It is recommended to include these types of uncertainties and to include model-averaged excess risks in astronaut risk assessment.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 1","pages":"Pages 83-91"},"PeriodicalIF":2.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000806/pdfft?md5=c3e7e327440b0492d75125a9932acf05&pid=1-s2.0-S0939388923000806-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9769766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Light flashes and other sensory illusions perceived in space travel and on ground, including proton and heavy ion therapies","authors":"Livio Narici","doi":"10.1016/j.zemedi.2023.06.004","DOIUrl":"10.1016/j.zemedi.2023.06.004","url":null,"abstract":"<div><p>Most of the astronauts experience visual illusions, apparent flashes of light (LF) in absence of light. The first reported observation of this phenomenon was in July 1969 by Buzz Aldrin, in the debriefing following the Apollo 11 mission. Several ground-based experiments in the 1970s tried to clarify the mechanisms behind these light flashes and to evaluate possible related risks. These works were supported by dedicated experiments in space on the following Apollo flights and in Low Earth Orbit (LEO). It was soon demonstrated that the LF could be caused by charged particles (present in the space radiation) traveling through the eye, and, possibly, some other visual cortical areas. In the 1990s the interest in these phenomena increased again and additional experiments in Low Earth Orbit and others ground-based were started. Recently patients undergoing proton and heavy ion therapy for eye or head and neck tumors have reported the perception of light flashes, opening a new channel to investigate these phenomena.</p><p>In this paper the many LF studies will be reviewed, presenting an historical and scientific perspective consistent with the combined set of observations, offering a single comprehensive summary aimed to provide further insights on these phenomena.</p><p>While the light flashes appear not to be a risk by themselves, they might provide information on the amount of radiation induced radicals in the astronauts’ eyes. Understanding their generation mechanisms might also support radiation countermeasures development. However, even given the substantial progress outlined in this paper, many questions related to their generation are still under debate, so additional studies are suggested. Finally, it is also conceivable that further LF investigations could provide evidence about the possible interaction of single particles in space with brain function, impacting with the crew ability to optimally perform a mission.</p></div>","PeriodicalId":54397,"journal":{"name":"Zeitschrift fur Medizinische Physik","volume":"34 1","pages":"Pages 44-63"},"PeriodicalIF":2.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0939388923000818/pdfft?md5=6fee664558ca86ac12cabfee7b9ee0d4&pid=1-s2.0-S0939388923000818-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9783877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}