Nuklearmedizin. Nuclear medicine最新文献

{"title":"Enhancing Interoperability and Harmonisation of Nuclear Medicine Image Data and Associated Clinical Data.","authors":"Timo Fuchs, Lena Kaiser, Dominik Müller, Laszlo Papp, Regina Fischer, Johannes Tran-Gia","doi":"10.1055/a-2187-5701","DOIUrl":"10.1055/a-2187-5701","url":null,"abstract":"<p><p>Nuclear imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) in combination with computed tomography (CT) are established imaging modalities in clinical practice, particularly for oncological problems. Due to a multitude of manufacturers, different measurement protocols, local demographic or clinical workflow variations as well as various available reconstruction and analysis software, very heterogeneous datasets are generated. This review article examines the current state of interoperability and harmonisation of image data and related clinical data in the field of nuclear medicine. Various approaches and standards to improve data compatibility and integration are discussed. These include, for example, structured clinical history, standardisation of image acquisition and reconstruction as well as standardised preparation of image data for evaluation. Approaches to improve data acquisition, storage and analysis will be presented. Furthermore, approaches are presented to prepare the datasets in such a way that they become usable for projects applying artificial intelligence (AI) (machine learning, deep learning, etc.). This review article concludes with an outlook on future developments and trends related to AI in nuclear medicine, including a brief research of commercial solutions.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":" ","pages":"389-398"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71430608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).","authors":"Julian Manuel Michael Rogasch, Kuangyu Shi, David Kersting, Robert Seifert","doi":"10.1055/a-2198-0545","DOIUrl":"10.1055/a-2198-0545","url":null,"abstract":"<p><strong>Aim: </strong>Despite a vast number of articles on radiomics and machine learning in positron emission tomography (PET) imaging, clinical applicability remains limited, partly owing to poor methodological quality. We therefore systematically investigated the methodology described in publications on radiomics and machine learning for PET-based outcome prediction.</p><p><strong>Methods: </strong>A systematic search for original articles was run on PubMed. All articles were rated according to 17 criteria proposed by the authors. Criteria with >2 rating categories were binarized into \"adequate\" or \"inadequate\". The association between the number of \"adequate\" criteria per article and the date of publication was examined.</p><p><strong>Results: </strong>One hundred articles were identified (published between 07/2017 and 09/2023). The median proportion of articles per criterion that were rated \"adequate\" was 65% (range: 23-98%). Nineteen articles (19%) mentioned neither a test cohort nor cross-validation to separate training from testing. The median number of criteria with an \"adequate\" rating per article was 12.5 out of 17 (range, 4-17), and this did not increase with later dates of publication (Spearman's rho, 0.094; p = 0.35). In 22 articles (22%), less than half of the items were rated \"adequate\". Only 8% of articles published the source code, and 10% made the dataset openly available.</p><p><strong>Conclusion: </strong>Among the articles investigated, methodological weaknesses have been identified, and the degree of compliance with recommendations on methodological quality and reporting shows potential for improvement. Better adherence to established guidelines could increase the clinical significance of radiomics and machine learning for PET-based outcome prediction and finally lead to the widespread use in routine clinical practice.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 6","pages":"361-369"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667066/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine.","authors":"Thomas Wendler, Michael C Kreissl, Benedikt Schemmer, Julian Manuel Michael Rogasch, Francesca De Benetti","doi":"10.1055/a-2200-2145","DOIUrl":"10.1055/a-2200-2145","url":null,"abstract":"<p><p>Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 6","pages":"343-353"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies.","authors":"Julia Franziska Brosch-Lenz, Astrid Delker, Fabian Schmidt, Johannes Tran-Gia","doi":"10.1055/a-2179-6872","DOIUrl":"10.1055/a-2179-6872","url":null,"abstract":"<p><p>Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":" ","pages":"379-388"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Applications of Radiomics in Nuclear Medicine.","authors":"Philipp Lohmann, Ralph Alexander Bundschuh, Isabelle Miederer, Felix M Mottaghy, Karl Josef Langen, Norbert Galldiks","doi":"10.1055/a-2191-3271","DOIUrl":"10.1055/a-2191-3271","url":null,"abstract":"<p><p>Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":" ","pages":"354-360"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71490666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.","authors":"Dirk Hellwig, Nils Constantin Hellwig, Steven Boehner, Timo Fuchs, Regina Fischer, Daniel Schmidt","doi":"10.1055/a-2198-0358","DOIUrl":"10.1055/a-2198-0358","url":null,"abstract":"<p><p>Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline fundamentals of PET imaging as well as state-of-the-art in AI-based PET IR with its typical algorithms and DL architectures. Advances improve resolution and contrast recovery, reduce noise, and remove artifacts via inferred attenuation and scatter correction, sinogram inpainting, denoising, and super-resolution refinement. Kernel-priors support list-mode reconstruction, motion correction, and parametric imaging. Hybrid approaches combine AI with conventional IR. Challenges of AI-assisted PET IR include availability of training data, cross-scanner compatibility, and the risk of hallucinated lesions. The need for rigorous evaluations, including quantitative phantom validation and visual comparison of diagnostic accuracy against conventional IR, is highlighted along with regulatory issues. First approved AI-based applications are clinically available, and its impact is foreseeable. Emerging trends, such as the integration of multimodal imaging and the use of data from previous imaging visits, highlight future potentials. Continued collaborative research promises significant improvements in image quality, quantitative accuracy, and diagnostic performance, ultimately leading to the integration of AI-based IR into routine PET imaging protocols.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 6","pages":"334-342"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities.","authors":"Thomas Küstner,&nbsp;Tobias Hepp,&nbsp;Ferdinand Seith","doi":"10.1055/a-2157-6670","DOIUrl":"10.1055/a-2157-6670","url":null,"abstract":"<p><strong>Background: </strong>Machine learning (ML) is considered an important technology for future data analysis in health care.</p><p><strong>Methods: </strong>The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers.</p><p><strong>Results and conclusion: </strong>In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future.</p><p><strong>Key points: </strong>· ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"306-313"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41144225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ectopic Thyroid Tissue in the Uterus Identified by Iodine-131 SPECT/CT.","authors":"Hansol Moon,&nbsp;Eu Jeong Ku,&nbsp;Chulhan Kim","doi":"10.1055/a-2127-8006","DOIUrl":"10.1055/a-2127-8006","url":null,"abstract":"A 45-year-old woman underwent a total thyroidectomy and was subsequently treated with 1110 MBq of radioactive iodine-131 ablation therapy for papillary thyroid cancer. A post-therapy iodine-131 whole-body scan revealed a focal uptake in the mid-pelvic area. To identify the exact anatomical location, SPECT/CT images were taken. The SPECT/CT fusion images revealed that the uptake was in the uterus, which was considered as radioactive iodine-avid ectopic thyroid tissue in the uterus.","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"294-295"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41173533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives of Evidence-Based Therapy Management.","authors":"Fabian Kiessling,&nbsp;Volkmar Schulz","doi":"10.1055/a-2159-6949","DOIUrl":"https://doi.org/10.1055/a-2159-6949","url":null,"abstract":"<p><strong>Background: </strong>Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.</p><p><strong>Method: </strong>Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.</p><p><strong>Results: </strong>Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.</p><p><strong>Conclusion: </strong>Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.</p><p><strong>Key points: </strong>· Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"314-322"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41161261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Oncological Hybrid Imaging.","authors":"Benedikt Feuerecker,&nbsp;Maurice M Heimer,&nbsp;Thomas Geyer,&nbsp;Matthias P Fabritius,&nbsp;Sijing Gu,&nbsp;Balthasar Schachtner,&nbsp;Leonie Beyer,&nbsp;Jens Ricke,&nbsp;Sergios Gatidis,&nbsp;Michael Ingrisch,&nbsp;Clemens C Cyran","doi":"10.1055/a-2157-6810","DOIUrl":"https://doi.org/10.1055/a-2157-6810","url":null,"abstract":"<p><strong>Background: </strong> Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.</p><p><strong>Methods and results: </strong> The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.</p><p><strong>Conclusion: </strong> AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.</p><p><strong>Key points: </strong>  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"296-305"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41166719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}