Artificial intelligence in single photon emission computed tomography (SPECT) imaging: a narrative review

Verification of image quality improvement of low-count bone scintigraphy using deep learning

To improve image quality for low-count bone scintigraphy using deep learning and evaluate their clinical applicability. Six hundred patients (training, 500; validation, 50; evaluation, 50) were included in this study. Low-count original images (75%, 50%, 25%, 10%, and 5% counts) were generated from reference images (100% counts) using Poisson resampling. Output (DL-filtered) images were obtained after training with U-Net using reference images as teacher data. Gaussian-filtered images were generated for comparison. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) to the reference image were calculated to determine image quality. Artificial neural network (ANN) value, bone scan index (BSI), and number of hotspots (Hs) were computed using BONENAVI analysis to assess diagnostic performance. Accuracy of bone metastasis detection and area under the curve (AUC) were calculated. PSNR and SSIM for DL-filtered images were highest in all count percentages. BONENAVI analysis values for DL-filtered images did not differ significantly, regardless of the presence or absence of bone metastases. BONENAVI analysis values for original and Gaussian-filtered images differed significantly at ≦25% counts in patients without bone metastases. In patients with bone metastases, BSI and Hs for original and Gaussian-filtered images differed significantly at ≦10% counts, whereas ANN values did not. The accuracy of bone metastasis detection was highest for DL-filtered images in all count percentages; the AUC did not differ significantly. The deep learning method improved image quality and bone metastasis detection accuracy for low-count bone scintigraphy, suggesting its clinical applicability.

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.

Scintigraphic Diagnosis of Acute Pulmonary Embolism: From Basics to Best Practices

In this article the technique, interpretation, and diagnostic performance of scintigraphy for the diagnosis of acute pulmonary embolism (PE) are reviewed. Lung scintigraphy has stood the test of time as a reliable and validated examination for the determination of PE. Ventilation/perfusion (V/Q) lung scintigraphy assesses the functional consequences of the clot on its downstream vascular bed in conjunction with the underlying ventilatory status of the affected lung region, in contrast to CT pulmonary angiography (CTPA), which visualizes presence of the clot within affected vessels. Most-commonly used ventilation radiopharmaceuticals are Technetium-99m labeled aerosols (such as 99mTechnetium-DTPA), or ultrafine particle suspensions (99mTc-Technegas) which reach the distal lung in proportion to regional distribution of ventilation. Perfusion images are obtained after intravenous administration 99mTc-labeled macro-aggregated albumin particles which lodge in the distal pulmonary capillaries. Both planar and tomographic methods of imaging, each favored in different geographical regions, will be described. Guidelines for interpretation of scintigraphy have been issues by both the Society of Nuclear Medicine and Molecular Imaging, and by the European Association of Nuclear Medicine. Breast tissue is particularly radiosensitive during pregnancy due to its highly proliferative state and many guidelines recommend use of lung scintigraphy rather than CTPA in this population. Several maneuvers are available in order to further reduce radiation exposure including reducing radiopharmaceutical dosages or omitting ventilation altogether, functionally converting the study to a low-dose screening examination; if perfusion defects are present, further testing is necessary. Several groups have also performed perfusion-only studies during the COVID epidemic in order to reduce risk of respiratory contagion. In patients where perfusion defects are present, further testing is again necessary to avoid false-positive results. Improved availability of personal protective equipment, and reduced risk of serious infection, have rendered this maneuver moot in most practices. First introduced 60 years ago, subsequent advances in radiopharmaceutical development and imaging methods have positioned lung scintigraphy to continue to play an important clinical and research role in the diagnosis of acute PE.

Neuro-Vulnerability in Energy Metabolism Regulation: A Comprehensive Narrative Review

This comprehensive narrative review explores the concept of neuro-vulnerability in energy metabolism regulation and its implications for metabolic disorders. The review highlights the complex interactions among the neural, hormonal, and metabolic pathways involved in the regulation of energy metabolism. The key topics discussed include the role of organs, hormones, and neural circuits in maintaining metabolic balance. The review investigates the association between neuro-vulnerability and metabolic disorders, such as obesity, insulin resistance, and eating disorders, considering genetic, epigenetic, and environmental factors that influence neuro-vulnerability and subsequent metabolic dysregulation. Neuroendocrine interactions and the neural regulation of food intake and energy expenditure are examined, with a focus on the impact of neuro-vulnerability on appetite dysregulation and altered energy expenditure. The role of neuroinflammation in metabolic health and neuro-vulnerability is discussed, emphasizing the bidirectional relationship between metabolic dysregulation and neuroinflammatory processes. This review also evaluates the use of neuroimaging techniques in studying neuro-vulnerability and their potential applications in clinical settings. Furthermore, the association between neuro-vulnerability and eating disorders, as well as its contribution to obesity, is examined. Potential therapeutic interventions targeting neuro-vulnerability, including pharmacological treatments and lifestyle modifications, are reviewed. In conclusion, understanding the concept of neuro-vulnerability in energy metabolism regulation is crucial for addressing metabolic disorders. This review provides valuable insights into the underlying neurobiological mechanisms and their implications for metabolic health. Targeting neuro-vulnerability holds promise for developing innovative strategies in the prevention and treatment of metabolic disorders, ultimately improving metabolic health outcomes.

Activity recovery for differently shaped objects in quantitative SPECT

Objective.The aim was to theoretically and experimentally investigate recovery in SPECT images with objects of different shapes. Furthermore, the accuracy of volume estimation by thresholding was studied for those shapes.Approach.Nine spheres, nine oblate spheroids, and nine prolate spheroids phantom inserts were used, of which the six smaller spheres were part of the NEMA IEC body phantom and the rest of the inserts were 3D-printed. The inserts were filled with99mTc and177Lu. When filled with99mTc, SPECT images were acquired in a Siemens Symbia Intevo Bold gamma camera and when filled with177Lu in a General Electric NM/CT 870 DR gamma camera. The signal rate per activity (SRPA) was determined for all inserts and represented as a function of the volume-to-surface ratio and of the volume-equivalent radius using VOIs defined according to the sphere dimensions and VOIs defined using thresholding. Experimental values were compared with theoretical curves obtained analytically (spheres) or numerically (spheroids), starting from the convolution of a source distribution with a point-spread function. Validation of the activity estimation strategy was performed using four 3D-printed ellipsoids. Lastly, the threshold values necessary to determine the volume of each insert were obtained.Main results.Results showed that SRPA values for the oblate spheroids diverted from the other inserts, when SRPA were represented as a function of the volume-equivalent radius. However, SRPA values for all inserts followed a similar behaviour when represented as a function of the volume-to-surface ratio. Results for ellipsoids were in agreement with those results. For the three types of inserts the volume could be accurately estimated using a threshold method for volumes larger than 25 ml.Significance.Determination of SRPA independently of lesion or organ shape should decrease uncertainties in estimated activities and thereby, in the long term, be beneficial to patient care.

The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update

The incidence of thyroid nodules diagnosed is increasing every year, leading to a greater risk of unnecessary procedures being performed or wrong diagnoses being made. In our paper, we present the latest knowledge on the use of artificial intelligence in diagnosing and classifying thyroid nodules. We particularly focus on the usefulness of artificial intelligence in ultrasonography for the diagnosis and characterization of pathology, as these are the two most developed fields. In our search of the latest innovations, we reviewed only the latest publications of specific types published from 2018 to 2022. We analyzed 930 papers in total, from which we selected 33 that were the most relevant to the topic of our work. In conclusion, there is great scope for the use of artificial intelligence in future thyroid nodule classification and diagnosis. In addition to the most typical uses of artificial intelligence in cancer differentiation, we identified several other novel applications of artificial intelligence during our review.

Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging

While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson’s disease (PD), they require a large amount of data for training. It is often difficult to collect a large population of patient data to support the ML research, and the ground truth of lesion is also unknown. This paper leverages a generative adversarial network (GAN) to generate digital brain phantoms for training ML-based PD SPECT algorithms. A total of 594 PET 3D brain models from 155 patients (113 male and 42 female) were reviewed and 1597 2D slices containing the full or a portion of the striatum were selected. Corresponding attenuation maps were also generated based on these images. The data were then used to develop a GAN for generating 2D brain phantoms, where each phantom consisted of a radioactivity image and the corresponding attenuation map. Statistical methods including histogram, Fréchet distance, and structural similarity were used to evaluate the generator based on 10,000 generated phantoms. When the generated phantoms and training dataset were both passed to the discriminator, similar normal distributions were obtained, which indicated the discriminator was unable to distinguish the generated phantoms from the training datasets. The generated digital phantoms can be used for 2D SPECT simulation and serve as the ground truth to develop ML-based reconstruction algorithms. The cumulated experience from this work also laid the foundation for building a 3D GAN for the same application.