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1.
Clin Nucl Med ; 47(1): 43-55, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34874348

RESUMO

ABSTRACT: The introduction of total body (TB) PET/CT instruments over the past 2 years has initiated a new and exciting era in medical imaging. These instruments have substantially higher sensitivity (up to 68 times) than conventional modalities and therefore allow imaging the entire body over a short period. However, we need to further refine the imaging protocols of this instrument for different indications. Total body PET will allow accurate assessment of the extent of disease, particularly, including the entire axial and appendicular skeleton. Furthermore, delayed imaging with this instrument may enhance the sensitivity of PET for some types of cancer. Also, this modality may improve the detection of venous thrombosis, a common complication of cancer and chemotherapy, in the extremities and help prevent pulmonary embolism. Total body PET allows assessment of atherosclerotic plaques throughout the body as a systematic disease. Similarly, patients with widespread musculoskeletal disorders including both oncologic and nononcologic entities, such as degenerative joint disease, rheumatoid arthritis, and osteoporosis, may benefit from the use of TB-PET. Finally, quantitative global disease assessment provided by this approach will be superior to conventional measurements, which do not reflect overall disease activity. In conclusion, TB-PET imaging may have a revolutionary impact on day-to-day practice of medicine and may become the leading imaging modality in the future.


Assuntos
Neoplasias , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Osso e Ossos , Humanos , Neoplasias/diagnóstico por imagem , Tomografia por Emissão de Pósitrons
2.
PET Clin ; 17(1): 13-29, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809862

RESUMO

Almost 1 in 10 individuals can suffer from one of many rare diseases (RDs). The average time to diagnosis for an RD patient is as high as 7 years. Artificial intelligence (AI)-based positron emission tomography (PET), if implemented appropriately, has tremendous potential to advance the diagnosis of RDs. Patient advocacy groups must be active stakeholders in the AI ecosystem if we are to avoid potential issues related to the implementation of AI into health care. AI medical devices must not only be RD-aware at each stage of their conceptualization and life cycle but also should be trained on diverse and augmented datasets representative of the end-user population including RDs. Inability to do so leads to potential harm and unsustainable deployment of AI-based medical devices (AIMDs) into clinical practice.


Assuntos
Inteligência Artificial , Doenças Raras , Ecossistema , Humanos , Tomografia por Emissão de Pósitrons , Radiografia , Doenças Raras/diagnóstico por imagem
3.
PET Clin ; 17(1): 1-12, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809860

RESUMO

Trust in artificial intelligence (AI) by society and the development of trustworthy AI systems and ecosystems are critical for the progress and implementation of AI technology in medicine. With the growing use of AI in a variety of medical and imaging applications, it is more vital than ever to make these systems dependable and trustworthy. Fourteen core principles are considered in this article aiming to move the needle more closely to systems that are accurate, resilient, fair, explainable, safe, and transparent: toward trustworthy AI.


Assuntos
Inteligência Artificial , Ecossistema , Diagnóstico por Imagem , Humanos
4.
PET Clin ; 17(1): 115-135, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809861

RESUMO

This review discusses the current state of artificial intelligence (AI) in 18F-NaF-PET/CT imaging and the potential applications to come in diagnosis, prognostication, and improvement of care in patients with bone diseases, with emphasis on the role of AI algorithms in CT bone segmentation, relying on their prevalence in medical imaging and utility in the extraction of spatial information in combined PET/CT studies.


Assuntos
Doenças Ósseas , Fluoreto de Sódio , Inteligência Artificial , Radioisótopos de Flúor , Humanos , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Tomografia por Emissão de Pósitrons , Compostos Radiofarmacêuticos
5.
PET Clin ; 17(1): 183-212, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809866

RESUMO

Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.


Assuntos
Inteligência Artificial , Neoplasias , Diagnóstico por Imagem , Humanos , Neoplasias/diagnóstico por imagem , Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Prognóstico
6.
PET Clin ; 17(1): 31-39, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809867

RESUMO

Artificial intelligence (AI) can enhance the efficiency of medical imaging quality control and clinical documentation, provide clinical decision support, and increase image acquisition and processing quality. A clear understanding of the basic tenets of these technologies and their impact will enable nuclear medicine technologists to train for performing advanced imaging tasks. AI-enabled medical devices' anticipated role and impact on routine nuclear medicine workflow (scheduling, quality control, check-in, radiotracer injection, waiting room, image planning, image acquisition, image post-processing) is reviewed in this article. With the assistance of AI, newly compiled patient imaging data can be customized to encompass personalized risk assessments of patients' disease burden, along with the development of individualized treatment plans. Nuclear medicine technologists will continue to play a crucial role on the medical team, collaborating with patients and radiologists to improve each patient's imaging experience and supervising the performance of integrated AI applications.


Assuntos
Inteligência Artificial , Medicina Nuclear , Humanos , Tomografia por Emissão de Pósitrons , Fluxo de Trabalho
7.
PET Clin ; 17(1): 51-55, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809869

RESUMO

Artificial intelligence (AI) in medical imaging is in its infancy. However, ongoing advances in hardware and software as well as increasing access to ever-expanding datasets for training, validation, and testing purposes are likely to make AI an increasingly prevalent and powerful tool. Of course issues, such as the need to protect the privacy of sensitive health data, remain; nevertheless, it is likely the average imager will need to develop an evidence-based approach to assessing AI in medical imaging. We hope this article will provide insight into just how this can be conducted by applying 5 simple questions, specifically: (1) Who was in the training sample, (2) How was the model trained, (3) How reliable is the algorithm, (4) How was the model validated, and (5) How useable is the algorithm.


Assuntos
Inteligência Artificial , Software , Humanos , Radiografia
8.
PET Clin ; 17(1): 95-113, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809874

RESUMO

Positron emission tomography (PET) offers an incredible wealth of diverse research applications in vascular disease, providing a depth of molecular, functional, structural, and spatial information. Despite this, vascular PET imaging has not yet assumed the same clinical use as vascular ultrasound, CT, and MR imaging which provides information about late-onset, structural tissue changes. The current clinical utility of PET relies heavily on visual inspection and suboptimal parameters such as SUVmax; emerging applications have begun to harness the tool of whole-body PET to better understand the disease. Even still, without automation, this is a time-consuming and variable process. This review summarizes PET applications in vascular disorders, highlights emerging AI methods, and discusses the unlocked potential of AI in the clinical space.


Assuntos
Inteligência Artificial , Tomografia por Emissão de Pósitrons , Humanos , Imageamento por Ressonância Magnética
10.
PET Clin ; 17(1): 145-174, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34809864

RESUMO

Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.


Assuntos
Inteligência Artificial , Linfoma , Fluordesoxiglucose F18 , Humanos , Linfoma/diagnóstico por imagem
11.
J Nucl Med ; 62(Suppl 3): 12S-22S, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34857617

RESUMO

Radiopharmaceutical therapy (RPT) is defined as the delivery of radioactive atoms to tumor-associated targets. In RPT, imaging is built into the mode of treatment since the radionuclides used in RPT often emit photons or can be imaged using a surrogate. Such imaging may be used to estimate tumor-absorbed dose. We examine and try to elucidate those factors that impact the absorbed dose-versus-response relationship for RPT agents. These include the role of inflammation- or immune-mediated effects, the significance of theranostic imaging, radiobiology, differences in dosimetry methods, pharmacokinetic differences across patients, and the impact of tumor hypoxia on response to RPT.

13.
J Nucl Med ; 62(Suppl 3): 60S-72S, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34857623

RESUMO

The use of radiopharmaceutical therapies (RPTs) in the treatment of cancers is growing rapidly, with more agents becoming available for clinical use in last few years and many new RPTs being in development. Dosimetry assessment is critical for personalized RPT, insofar as administered activity should be assessed and optimized in order to maximize tumor-absorbed dose while keeping normal organs within defined safe dosages. However, many current clinical RPTs do not require patient-specific dosimetry based on current Food and Drug Administration-labeled approvals, and overall, dosimetry for RPT in clinical practice and trials is highly varied and underutilized. Several factors impede rigorous use of dosimetry, as compared with the more convenient and less resource-intensive practice of empiric dosing. We review various approaches to applying dosimetry for the assessment of activity in RPT and key clinical trials, the extent of dosimetry use, the relative pros and cons of dosimetry-based versus fixed activity, and practical limiting factors pertaining to current clinical practice.

14.
J Nucl Med ; 2021 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-34740952

RESUMO

The nuclear medicine field has seen a rapid expansion of academic and commercial interests in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations for technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations followed by descriptions on how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.

15.
Radiol Cardiothorac Imaging ; 3(5): e210102, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34778782

RESUMO

Purpose: To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model. Materials and Methods: An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study. Results: Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: -9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: -19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: -18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (-5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85). Conclusion: Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions.Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment© RSNA, 2021.

16.
Radiol Clin North Am ; 59(6): 1085-1095, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34689876

RESUMO

No one knows what the paradigm shift of artificial intelligence will bring to medical imaging. In this article, we attempt to predict how artificial intelligence will impact radiology based on a critical review of current innovations. The best way to predict the future is to anticipate, prepare, and create it. We anticipate that radiology will need to enhance current infrastructure, collaborate with others, learn the challenges and pitfalls of the technology, and maintain a healthy skepticism about artificial intelligence while embracing its potential to allow us to become more productive, accurate, secure, and impactful in the care of our patients.


Assuntos
Inteligência Artificial/tendências , Diagnóstico por Imagem/métodos , Diagnóstico por Imagem/tendências , Interpretação de Imagem Assistida por Computador/métodos , Radiologia/métodos , Radiologia/tendências , Humanos
17.
PET Clin ; 16(4): 449-469, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34537126

RESUMO

Artificial intelligence has witnessed exponential growth in the past decade. Advances in computing power and the design of sophisticated artificial intelligence algorithms have enabled computers to outperform humans in a variety of tasks. Yet, artificial intelligence's path has never been smooth, having essentially fallen apart twice in its lifetime after periods of popular success. We provide a brief rundown of artificial intelligence's evolution, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another winter.


Assuntos
Inteligência Artificial , Aprendizado de Máquina , Algoritmos , Previsões , Humanos
18.
PET Clin ; 16(4): 493-511, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34537127

RESUMO

Artificial intelligence-based methods are showing promise in medical imaging applications. There is substantial interest in clinical translation of these methods, requiring that they be evaluated rigorously. We lay out a framework for objective task-based evaluation of artificial intelligence methods. We provide a list of available tools to conduct this evaluation. We outline the important role of physicians in conducting these evaluation studies. The examples in this article are proposed in the context of PET scans with a focus on evaluating neural network-based methods. However, the framework is also applicable to evaluate other medical imaging modalities and other types of artificial intelligence methods.


Assuntos
Inteligência Artificial , Médicos , Humanos , Tomografia por Emissão de Pósitrons
19.
PET Clin ; 16(4): 577-596, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34537131

RESUMO

Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks have shown impressive results and potential toward fully automated segmentation in medical imaging, and particularly PET imaging. To cope with the limited access to annotated data needed in supervised AI methods, given tedious and prone-to-error manual delineations, semi-supervised and unsupervised AI techniques have also been explored for segmentation of tumors or normal organs in single- and bimodality scans. This work reviews existing AI techniques for segmentation tasks and the evaluation criteria for translational AI-based segmentation efforts toward routine adoption in clinical workflows.


Assuntos
Inteligência Artificial , Redes Neurais de Computação , Humanos , Tomografia por Emissão de Pósitrons
20.
PET Clin ; 16(4): 627-641, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34537133

RESUMO

We highlight emerging uses of artificial intelligence (AI) in the field of theranostics, focusing on its significant potential to enable routine and reliable personalization of radiopharmaceutical therapies (RPTs). Personalized RPTs require patient-specific dosimetry calculations accompanying therapy. Additionally we discuss the potential to exploit biological information from diagnostic and therapeutic molecular images to derive biomarkers for absorbed dose and outcome prediction; toward personalization of therapies. We try to motivate the nuclear medicine community to expand and align efforts into making routine and reliable personalization of RPTs a reality.


Assuntos
Medicina Nuclear , Compostos Radiofarmacêuticos , Inteligência Artificial , Humanos , Medicina de Precisão , Radiometria
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