Seminars in nuclear medicine
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Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With the technical development of new methods and the expansion to new fields of application, this trend is likely to become even more pronounced in future. Possible means of application range from automated image reading and classification to correlation with clinical outcomes and to technological applications in image processing and reconstruction. ⋯ Most artificial intelligence approaches in nuclear medicine imaging are still in early stages of development, further improvements are necessary for broad clinical applications. In this review, we describe the current trends in the context fields of body oncology, cardiac imaging, and neuroimaging while an additional section puts emphasis on technological trends. Our aim is not only to describe currently available methods, but also to place a special focus on the description of possible future developments.
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The emergence of artificial intelligence (AI) in nuclear medicine has occurred over the last 50 years but more recent developments in machine learning (ML) and deep learning (DL) have driven new capabilities of AI in nuclear medicine. In nuclear medicine, the artificial neural network (ANN) is the backbone of ML and DL. The inputs may be radiomic features that have been extracted from the image files or, if using a convolutional neural network (CNN), may be the images themselves. ⋯ There are also applications of ANNs and ML that allow small datasets (and larger ones) to be analysed in parallel to conventional statistical analysis. AI has assimilated into the clinical and research practice of nuclear medicine with little disruption. The emergence of ML and DL applications, however, has produced a seismic significant shift in the clinical and research landscape that demands at least rudimentary understanding of the principles of AI, ANNs and CNNs among nuclear medicine professionals.
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Radiomics describes the extraction of multiple features from medical images, including molecular imaging modalities, that with bioinformatic approaches, provide additional clinically relevant information that may be invisible to the human eye. This information may complement standard radiological interpretation with data that may better characterize a disease or that may provide predictive or prognostic information. Progressing from predefined image features, often describing heterogeneity of voxel intensities within a volume of interest, there is increasing use of machine learning to classify disease characteristics and deep learning methods based on artificial neural networks that can learn features without a priori definition and without the need for preprocessing of images. ⋯ However, in this relatively early phase of research in the field, there are limited aspects that have been adopted into routine practice. Most of the reports in the molecular imaging field describe radiomic approaches in cancer using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET). In this review, we will describe radiomics in molecular imaging and summarize the pertinent literature in lung cancer where reports are most prevalent and mature.
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Radiomics - the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. ⋯ We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.
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Ventilation/Perfusion (V/Q) positron emission tomography computed tomography (PET/CT) is now possible by substituting Technetium-99m (99mTc) with Gallium-68 (68Ga), using the same carrier molecules as conventional V/Q imaging. Ventilation imaging can be performed with 68Ga-carbon nanoparticles using the same synthesis device as Technegas. Perfusion imaging can be performed with 68Ga-macroaggregated albumin. ⋯ V/Q PET imaging offers an opportunity to improve the accuracy and utility of V/Q imaging in various pulmonary conditions. For pulmonary embolism, V/Q PET/CT scan may improve the diagnostic performance of the test owing to a better characterization of the pattern of defects and allow an accurate quantification of the extent of vascular obstruction. Establishing an accurate functional map of the regional ventilation and perfusion in the lungs may be relevant in many other clinical situations, including preoperative assessment of the lung cancer patients, radiotherapy planning, or presurgical evaluation of patients undergoing lung volume reduction surgery.