Single Photon Emission Computed Tomography Research Articles

Advancements in molecular imaging probes for precision diagnosis and treatment of prostate cancer.

Prostate cancer is the second most common cancer in men, accounting for 14.1% of new cancer cases in 2020. The aggressiveness of prostate cancer is highly variable, depending on its grade and stage at the time of diagnosis. Despite recent advances in prostate cancer treatment, some patients still experience recurrence or even progression after undergoing radical treatment. Accurate initial staging and monitoring for recurrence determine patient management, which in turn affect patient prognosis and survival. Classical imaging has limitations in the diagnosis and treatment of prostate cancer, but the use of novel molecular probes has improved the detection rate, specificity, and accuracy of prostate cancer detection. Molecular probe-based imaging modalities allow the visualization and quantitative measurement of biological processes at the molecular and cellular levels in living systems. An increased understanding of tumor biology of prostate cancer and the discovery of new tumor biomarkers have allowed the exploration of additional molecular probe targets. The development of novel ligands and advances in nano-based delivery technologies have accelerated the research and development of molecular probes. Here, we summarize the use of molecular probes in positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and ultrasound imaging, and provide a brief overview of important target molecules in prostate cancer.

Computationally efficient collimator-detector response compensation in high energy SPECT using 1D convolutions and rotations

Objective. Modeling of the collimator-detector response (CDR) in single photon emission computed tomography (SPECT) reconstruction enables improved resolution and accuracy, and is thus important for quantitative imaging applications such as dosimetry. The implementation of CDR modeling, however, can become a computational bottleneck when there are substantial components of septal penetration and scatter in the acquired data, since a direct convolution-based approach requires large 2D kernels. This work proposes a 1D convolution and rotation-based CDR model that reduces reconstruction times but maintains consistency with models that employ 2D convolutions. To enable open-source development and use of these models in image reconstruction, we release a SPECTPSFToolbox repository for the PyTomography project on GitHub.Approach. A 1D/rotation-based CDR model was formulated and subsequently fit to Monte Carlo (MC) point source data representative of177Lu,131I, and225Ac imaging. Computation times of (i) the proposed 1D/rotation-based model and (ii) a traditional model that uses 2D convolutions were compared for typical SPECT matrix sizes. Both CDR models were then used in the reconstruction of MC, physical phantom, and patient data; the models were compared by quantifying total counts in hot regions of interest (ROIs) and activity contrast between hot ROIs and background regions.Results. For typical matrix sizes in SPECT reconstruction, application of the 1D/rotation-based model provides a two-fold computational speed-up over the 2D model when running on GPU. Only small differences between the 1D/rotation-based and 2D models (order of 1%) were obtained for count and contrast quantification in select ROIs.Significance. A technique for CDR modeling in SPECT was proposed that (i) significantly speeds up reconstruction times, and (ii) yields nearly identical reconstructions to traditional 2D convolution based CDR techniques. The released toolbox will permit open-source development of similar models for different isotopes and collimators.

The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.

The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field. This narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models. Among the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks. This review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.

Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes.

Development of 111In-Labeled Monoclonal Antibodies Targeting SFTSV Structural Proteins for Molecular Imaging of SFTS Infectious Diseases by SPECT.

No effective vaccines or treatments are currently available for severe fever with thrombocytopenia syndrome (SFTS), a fatal tick-borne infectious disease caused by the SFTS virus (SFTSV). This study evaluated the potential of 111In-labeled anti-SFTSV antibodies targeting SFTSV structural proteins as single-photon emission computed tomography (SPECT) imaging agents for the selective visualization of SFTSV-infected sites. This study used nuclear medicine imaging to elucidate the pathology of SFTS and assess its therapeutic efficacy. Immunostaining experiments confirmed that the anti-SFTSV antibody (N-mAb), which targets the N protein, specifically accumulated in SFTSV-infected Vero E6 cells. 111In-labeled N-mAb was successfully prepared using a diethylenetriaminepentaacetic acid (DTPA) chelator, resulting in [111In]In-DTPA-N-mAb with high radiochemical purity exceeding 95% and a radiochemical yield of 55%. Cell-binding assays using SFTSV-infected Vero E6 cells demonstrated that [111In]In-DTPA-N-mAb binding was detectable even without membrane permeabilization, with the binding intensity correlating with infection levels. In vivo studies using SFTSV-infected A129 mice showed high spleen accumulation of [111In]In-DTPA-N-mAb (87.5% ID/g), consistent with SFTSV tropism, compared to 12.3% ID/g in mock-infected mice. SPECT/CT imaging clearly revealed high radioactivity in these regions. Although nonspecific accumulation was noted in the liver and spleen, this issue may be mitigated through antibody modifications such as fragmentation or PEGylation. Overall, [111In]In-DTPA-N-mAb is a promising imaging agent for non-invasive visualization of SFTSV-infected sites and may aid in elucidating SFTS pathology and assessing therapeutic efficacy.

First comparison between artificial intelligence-guided coronary computed tomography angiography versus single-photon emission computed tomography testing for ischemia in clinical practice.

Noninvasive cardiac testing with coronary computed tomography angiography (CCTA) and single-photon emission computed tomography (SPECT) are becoming alternatives to invasive angiography for the evaluation of obstructive coronary artery disease. We aimed to evaluate whether a novel artificial intelligence (AI)-assisted CCTA program is comparable to SPECT imaging for ischemic testing. CCTA images were analyzed using an artificial intelligence convolutional neural network machine-learning-based model, atherosclerosis imaging-quantitative computed tomography (AI-QCT)ISCHEMIA. A total of 183 patients (75 females and 108 males, with an average age of 60.8 years ± 12.3 years) were selected. All patients underwent AI-QCTISCHEMIA-augmented CCTA, with 60 undergoing concurrent SPECT and 16 having invasive coronary angiograms. Eight studies were excluded from analysis due to incomplete data or coronary anomalies. A total of 175 patients (95%) had CCTA performed, deemed acceptable for AI-QCTISCHEMIA interpretation. Compared to invasive angiography, AI-QCTISCHEMIA-driven CCTA showed a sensitivity of 75% and specificity of 70% for predicting coronary ischemia, versus 70% and 53%, respectively for SPECT. The negative predictive value was high for female patients when using AI-QCTISCHEMIA compared to SPECT (91% vs. 68%, P = 0.042). Area under the receiver operating characteristic curves were similar between both modalities (0.81 for AI-CCTA, 0.75 for SPECT, P = 0.526). When comparing both modalities, the correlation coefficient was r = 0.71 (P < 0.04). AI-powered CCTA is a viable alternative to SPECT for detecting myocardial ischemia in patients with low- to intermediate-risk coronary artery disease, with significant positive and negative correlation in results. For patients who underwent confirmatory invasive angiography, the results of AI-CCTA and SPECT imaging were comparable. Future research focusing on prospective studies involving larger and more diverse patient populations is warranted to further investigate the benefits offered by AI-driven CCTA.

Application of the U-Net Deep Learning Model for Segmenting Single-Photon Emission Computed Tomography Myocardial Perfusion Images.

Myocardial perfusion imaging (MPI) is a type of single-photon emission computed tomography (SPECT) used to evaluate patients with suspected or confirmed coronary artery disease (CAD). Detection and diagnosis of CAD are complex processes requiring precise and accurate image processing. Proper segmentation is critical for accurate diagnosis, but segmentation issues can pose significant challenges, leading to diagnostic difficulties. Machine learning (ML) algorithms have demonstrated superior performance in addressing segmentation problems. In this study, a deep learning (DL) algorithm, U-Net, was employed to enhance segmentation accuracy for image segmentation in MPI. Data were collected from 1100 patients who underwent MPI studies at Al-Jahra Hospital between 2015 and 2024. To train the U-Net model, 100 studies were segmented by nuclear medicine (NM) experts to create a ground truth (gold-standard coordinates). The dataset was divided into a training set (n = 100 images) and a validation set (n = 900 images). The performance of the U-Net model was evaluated using multiple cross-validation metrics, including accuracy, precision, intersection over union (IOU), recall, and F1 score. A dataset of 4560 images and corresponding masks was generated. Both holdout and k-fold (k = 5) validation strategies were applied, utilizing cross-entropy and Dice score as evaluation metrics. The best results were achieved with the holdout split and cross-entropy loss function, yielding a test accuracy of 98.9%, a test IOU of 89.6%, and a test Dice coefficient of 94%. The k-fold validation scenario provided a more balanced true positive and false positive rate. The U-Net segmentation results were comparable to those produced by expert nuclear medicine technologists, with no significant difference (p = 0.1). The findings demonstrate that the U-Net model effectively addresses some segmentation challenges in MPI, facilitating improved diagnosis and analysis of mega data.

Asymptomatic multifocal avascular necrosis, a commonly overlooked finding in patients with systemic lupus erythematosus

BackgroundIn patients with Systemic lupus erythematosus (SLE), osteonecrosis of various joints is a debilitating complication associated with the disease and its treatment, in which a considerable proportion of osteonecrosis may be asymptomatic. Recognizing the crucial role of early and timely detection, as well as appropriate management of asymptomatic osteonecrosis, in preventing joint destruction, we conducted a study to evaluate the prevalence of asymptomatic osteonecrosis in SLE patients who have already been diagnosed with symptomatic osteonecrosis. Additionally, we aimed to examine the relationship between proposed risk factors of osteonecrosis and the development of asymptomatic osteonecrosis.MethodsIn this cross-sectional study, Patients with recently diagnosed symptomatic osteonecrosis of at least one joint were selected by reviewing data from the digital medical record system of the Rheumatology Research Center. The patients underwent three-phase Single Photon Emission Computed Tomography (SPECT) bone scintigraphy to screen for other asymptomatic osteonecrotic joints. MRI was subsequently performed on the asymptomatic osteonecrotic sites for further diagnostic confirmation. The study evaluated the prevalence of asymptomatic osteonecrosis, the extent of joint involvement, the specific locations of osteonecrosis, the most commonly affected joints, and the risk factors for asymptomatic osteonecrosis.ResultsEight out of the 17 patients (47%) who participated in our research were found to have asymptomatic osteonecrosis. The most commonly affected joint without symptoms was the left knee (25%), while the most frequently affected joint with symptoms was the left hip (23.07%). The only statistically significant difference observed between patients with and without asymptomatic osteonecrosis in this study was the age at which the disease first appeared (p = 0.046) and this age was higher among patients with asymptomatic osteonecrosis.ConclusionsOur research provides further evidence of the high incidence of asymptomatic osteonecrosis in individuals with SLE due to the nature of the disease and the frequent use of high-dose corticosteroids. It underscores the importance of early detection through whole-body SPECT bone scintigraphy and MRI, as well as prompt intervention in order to avert the incapacitating effects of osteonecrosis.

Molecular Positron Emission Tomography and Single-Photon Emission Computed Tomography Imaging for Understanding the Neurobiological Mechanisms of Electroconvulsive Therapy: A Scoping Review.

Electroconvulsive therapy (ECT) effectively treats severe psychiatric disorders such as depression, mania, catatonia, and schizophrenia. Although its exact mechanism remains unclear, ECT is thought to induce neurochemical and neuroendocrine changes. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) have provided vital insights into ECT's neurobiological effects. This scoping review investigates the role of molecular imaging in understanding these effects. A systematic search across PubMed, EMBASE, Web of Science, Cochrane, and Scopus databases yielded 857 unique records, from which 45 peer-reviewed articles in English with longitudinal PET or SPECT measures in ECT patients were included. The review identifies 2 main research directions: ECT's impact on brain activity and neurotransmitters. Initial research assessed regional cerebral blood flow and regional glucose metabolism during ictal (during ECT), postictal (within 24 hours), short-term (within a week), and long-term (beyond a week) follow-up as markers of brain activity. Initial findings showed an anterior-posterior regional cerebral blood flow gradient during the ictal phase, with subsequent normalization of hypoperfusion in frontal and parietal regions, and persistent long-term effects. Later, research shifted to the monoamine hypothesis of depression, examining ECT's impact on serotonin and dopamine systems via PET imaging. Results on receptor availability post-ECT were mixed, showing both reductions and no significant changes, indicating variable effects. This scoping review further highlights the need to explore new targets, tailor methodologies for patient populations, and foster multicenter studies. Although SPECT has been valuable, advances in PET imaging now make it preferable, offering unparalleled insights into ECT's molecular and neurobiological mechanisms.

Modern Thyroid Cancer Diagnosis: A Review of AI-Powered Algorithms for Detection and Classification

Thyroid nodules are one of the most common abnormalities in the thyroid gland, which are often harmless in nature (benign), but in a few unfortunate instances, they may be fatal (malignant). This review explores recent advancements in artificial intelligence (AI) applied to thyroid cancer detection and classification, with a focus on machine learning, deep learning, and image processing techniques. We provide a comprehensive evaluation of AI applications across key imaging modalities—Ultrasonography (USG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET/CT)—as well as cytopathological analysis using Fine Needle Aspiration Biopsy (FNAB). By critically examining studies on AI-driven preoperative assessments, we highlight improvements in diagnostic accuracy, sensitivity, specificity and efficiency. This review also identifies current limitations in AI applications, including technical challenges and unresolved issues that hinder widespread clinical adoption. Although significant progress has been achieved, the integration of AI in clinical settings remains limited, as AI-based outputs currently serve as supportive tools rather than definitive diagnostic evidence. We discuss the potential of AI to transform thyroid cancer diagnostics by enhancing reliability and accessibility, while addressing the need for further research to develop a unified, robust and clinically trustworthy AI framework for thyroid cancer diagnosis.

Pharmaceutical development and practical approach to synthesis of tropantiol radiopharmaceutical ([99mTc]Tc-TRODAT-1) for diagnostic of neurodegenerative diseases (review)

Introduction. The symptoms of Parkinson's disease are mainly associated with the formation of intraneuronal protein inclusions with Lewy bodies, and the progressive loss of dopaminergic neurons of the Substantia nigra and their axons. Existing diagnostic criteria for the diagnosis of Parkinson's disease often take into account symptoms occurring in the later stages of the disease. Thus, for a more accurate diagnosis in the early stages, it is necessary to confirm pathologic changes in brain tissue by molecular imaging methods such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT). At the same time SPECT is a more accessible method of diagnostics of neurodegenerative diseases in comparison with PET, because of the possibility to obtain medical radionuclides for SPECT imaging using mobile generator systems, in particular 99Mo/99mTc generator. Among the formulations based on 99mTc and tropane derivatives proposed for dopamine transporter (DAT) imaging, [99mTc]Tc-TRODAT-1 (technetium-99m-labeled tropantiol) is the most effective. Currently, various compositions of the freeze-dried kits for the synthesis of [99mTc]Tc-TRODAT-1 have been proposed, facilitating the process of its production in situ, which, together with the availability of technetium-99m generator in a healthcare facility, as well as favorable pharmacokinetics, makes [99mTc]Tc-TRODAT-1 a drug of choice for routine use in clinical practice.Text. In this review, various approaches to design and optimize the composition of the freeze-dried kits for the synthesis of [99mTc]Tc-TRODAT-1, including the amount and ratio of active ingredient and excipients, synthesis conditions, in particular the temperature regime, synthesis time and pH of the reaction mixture, have been considered.Conclusion. Development and optimization of the composition of the freeze-dried kits for the synthesis of [99mTc]Tc-TRODAT-1 is an urgent task in the context of improving its use in clinical practice. Based on the published data, clear dependencies can be traced, which may form the basis for further development and optimization of the composition of the freeze-dried kits for [99mTc]Tc-TRODAT-1 synthesis for the diagnosis of Parkinson's disease and other neurodegenerative diseases by SPECT in the Russian Federation.

Cerebral blood flow following successful living kidney transplantation: VINTAGE study

Abstract Background Chronic kidney disease (CKD) is a significant risk factor for cerebrovascular disease. However, there is limited research on how successful living donor kidney transplantation (LDKT) affects cerebral blood flow (CBF). This study aims to comprehensively investigate how LDKT influences CBF across various brain levels and regions. Methods Data from 53 recipients between 2016 and 2020 were obtained from the VINTAGE study conducted at our hospital. CBF was measured by level and region using single-photon emission computed tomography (SPECT), according to the Talairach brain atlas. The primary endpoint was the mean difference in CBF before and 1-year post-LDKT. Subgroup analysis using traditional risk factors assessed the heterogeneity of the effect on CBF in the frontal lobe region. Results LDKT improved blood flow in the anterior cerebral artery and middle cerebral artery but had less impact on the posterior cerebral artery. The most consistent improvements were observed in the frontal lobe region (left frontal lobe: -0.12 [95% CI -0.18 to -0.05, P &amp;lt; 0.001]; right frontal lobe: -0.13 [95% CI -0.21 to -0.05, P = 0.001]. Subgroup analysis showed a consistent effect of LDKT on frontal lobe CBF improvement, with no qualitative interaction observed. Conclusions LDKT contributes to the normalization of CBF, with improvement in anterior circulation and frontal lobe blood flow. To clarify the clinical significance of KT's CBF-improving effect, future studies should investigate the relationship between specific cognitive impairments (e.g. short-term memory, visuospatial ability, executive function) and CBF in each perfusion region.

99mTc-DTPA-Collagen Radiotracer for the Noninvasive Detection of Infective Endocarditis.

Infective endocarditis (IE) represents a significant concern among hospital-acquired infections, frequently caused by the Gram-positive bacterium Staphylococcus aureus. Nuclear imaging is emerging as a noninvasive and precise diagnostic tool. However, the gold standard radiotracer [18F]-FDG cannot distinguish between infection and inflammation, resulting in false positives. Based on the presence of collagen-binding proteins in the cell wall of S. aureus, we propose the radiolabeling of collagen for its evaluation in IE animal models by single-photon emission computed tomography (SPECT) imaging. We radiolabeled rat tail collagen I using DTPA chelator and [99mTc]NaTcO4. Selectivity was evaluated in vitro using 3 Gram-positive bacteria, 1 Gram-negative bacteria and 1 yeast. In vivo SPECT/computed tomography (CT) imaging was conducted on 8 SD rat models of IE and 8 sterile sham model as controls. Ex vivo biodistribution and autoradiography were performed following imaging. Diagnosis of IE was confirmed through microbiological studies and H&E histopathology. [99mTc]-DTPA-Collagen was synthesized successfully with a yield of 42.86 ± 6.35%, a purity of 95.84 ± 1.85% and a stability higher than 90% after 50 h postincubation. In vitro uptake demonstrated the selectivity for Gram-positive bacteria (63.85 ± 15.15%). Ex vivo analysis confirmed hepato-splenic excretion. In vivo SPECT/CT imaging revealed highly localized uptake within the aortic valve with a sensitivity of 62.5% and specificity of 87.5%. We successfully synthesized and characterized a new SPECT radiotracer based on [99mTc]Tc-radiolabeled collagen. In vitro studies demonstrated the selectivity of the radiotracer for Gram-positive bacteria. In vivo SPECT/CT-based assessment in an IE model confirmed the potential of this approach to detect active IE.

FDG PET/CT imaging and circulating biomarkers of inflammation in desmoplakin cardiomyopathy.

Inflammation has been implicated in the pathogenesis of desmoplakin (DSP) cardiomyopathy, and retrospective studies have described abnormal myocardial fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) findings in symptomatic patients eventually diagnosed with DSP cardiomyopathy. We aimed to prospectively investigate if ambulatory patients with DSP cardiomyopathy had myocardial FDG uptake PET/CT imaging indicative of myocardial inflammation and if they had circulating biomarker evidence of inflammation. We prospectively recruited participants with DSP cardiomyopathy and participants with titin cardiomyopathy as a comparator group. Blood samples for clinical labs and proteomic profiling, myocardial perfusion single-photon emission computed tomography (SPECT) and myocardial FDG PET/CT were obtained for all participants. Ten participants with DSP cardiomyopathy (median age 36.5years (28, 60); 80% female; 100% White and non-Hispanic) and four participants with titin cardiomyopathy (median age 55.5years [38.5, 64]; 50% female; 100% White and non-Hispanic) were recruited. There were no significant differences between the groups in white blood cell count, ESR, hsCRP or hsTn. Three participants with DSP cardiomyopathy and two participants with titin cardiomyopathy had non-specific myocardial FDG uptake on PET/CT. All other participants had no myocardial FDG uptake. Integration of miRNA differential expression and their predicted targets from the differential expression proteomics data identified a total of 11 inverse miRNA-mRNA pairs potentially involved in the regulation of top 20 significantly enriched pathways, including pathways involved in metabolism, inflammasome/inflammatory signalling and cell death/pyroptosis. In a group of ambulatory patients with DSP and titin cardiomyopathy, we found no differences in FDG PET/CT findings or clinical circulating biomarkers of inflammation. However, miRNA-seq/proteomics analyses identified several enriched pathways and unique miRNA-protein pairs between DSP and titin cardiomyopathy, including pathways involved in inflammasome/inflammatory signalling. Future work will centre on evaluation during myocarditis-like episodes.

Evaluation of myocardial perfusion imaging techniques and artificial intelligence (AI) tools in coronary artery disease (CAD) diagnosis through multi-criteria decision-making method.

Cardiovascular diseases (CVDs) continue to be the world's greatest cause of death. To evaluate heart function and diagnose coronary artery disease (CAD), myocardial perfusion imaging (MPI) has become essential. Artificial intelligence (AI) methods have been incorporated into diagnostic methods such as MPI to improve patient outcomes in recent years. This study aims to employ a novel approach to examine how parameters/criteria and performance metrics affect the prioritization of selected MPI techniques and AI tools in CAD diagnosis. Identifying the most effective method in these two interconnected areas will increase the CAD diagnosis rate. The study includes an in-depth investigation of popular convolutional neural network (CNN) models, including InceptionV3, VGG16, ResNet50, and DenseNet121, in addition to widely used machine learning (ML) models, including random forests (RF), K-nearest neighbor (KNN), support vector machine (SVM), and Naïve Bayes (NB). In addition, it includes the evaluation of nuclear MPI techniques, including positron emission tomography (PET) and single photon emission computed tomography (SPECT), with the non-nuclear MPI technique of cardiovascular magnetic resonance imaging (CMR). Various performance metrics were used to evaluate AI tools. They are F1-score, recall, specificity, precision, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). For MPI techniques, the evaluation criteria include specificity, sensitivity, radiation dose, cost of scan, and study duration. The analysis was evaluated and compared using the fuzzy-based preference ranking organization method for enrichment evaluation (PROMETHEE), the multi-criteria decision-making method (MDCM). According to the study's findings, considering selected performance metrics or criteria, RF is the most efficient AI tool for SPECT MPI in the diagnosis of CAD with a net flow (Φnet ) of 0.3778, and it's revealed that CMR is the most efficient MPI technique for CAD diagnosis with a net flow of 0.3666. By expanding this study, more comprehensive evaluations can be made in the diagnosis of CAD. It was concluded that CMR outperformed the nuclear MPI techniques. SPECT, as the least advantageous technique, remained below average on other criteria except for the cost of the scan. Integrating the RF algorithm, which stands out as the most effective AI tool in diagnosing CAD, with SPECT MPI may contribute to SPECT becoming a superior alternative.

Improving Treatment Quality of Gynecologic Cancers with Online Adaptive Brachytherapy Using Deep Learning-Based CT Imaging

This study proposes a framework for online adaptive three-dimensional (3D) brachytherapy, a type of radiation therapy used to treat gynecological cancers such as cervical and endometrial cancers. Intracavitary brachytherapy is used to deliver a high dose of radiation directly to the tumor, while minimizing exposure to the surrounding normal tissue. Maintaining accurate reproducibility of radiation dose delivery during each treatment session is important. The proposed framework uses a C-arm-based integrated online imaging system for computed tomography (CT) and single-photon emission computed tomography (SPECT) to detect changes in the treatment conditions and improve treatment quality. The C-arm CT system acquires 3D images by obtaining two-dimensional image data at limited rotation angles, which are less than the complete 360° range of rotation. However, images obtained with limited angular rotation have poor image quality and many artifacts. To overcome this limitation, a deep learning technique was applied to eliminate these artifacts and improve image quality. This study evaluated the auto-segmentation performance of ground truth (GT) images, limited-angle C-arm cone-beam CT images, and C-arm CT images corrected using deep learning. We used the Dice similarity coefficient and Hausdorff distance to quantitatively evaluate the auto-segmentation performance of major organs in the pelvic region. Although slight differences were observed in certain areas, the auto-segmentation results demonstrated an overall high performance, similar to that of the GT image. The results showed that the proposed framework can establish an optimal treatment plan by quickly and accurately calculating the patient's internal radiation dose distribution. This study demonstrates the potential of deep learning-based high-quality CT imaging techniques and auto-contouring techniques for major organs within images to improve the treatment quality of intracavitary brachytherapy.

Lewy Body Dementia.

Lewy body dementia (LBD) is an umbrella term describing two closely related conditions: Parkinson disease dementia (PDD) and dementia with Lewy bodies (DLB). LBD is the second most common cause of neurodegenerative dementia but is often underrecognized in clinical practice. This review covers the key epidemiologic, clinical, cognitive, behavioral, and biomarker features of LBD and discusses current treatment options. Indicative biomarkers of LBD improve the ability to make a diagnosis and include single-photon emission computed tomography (SPECT) of the dopamine system (brain) and the noradrenergic system (cardiac), and polysomnography. α-Synuclein-specific biomarkers in spinal fluid, skin, plasma, and brain imaging are in active development with some available for clinical use. Prodromal stages of PDD and DLB have been contextualized, and diagnostic criteria have been published. An emerging theme is whether an integrated staging system focusing on protein aggregation, rather than clinical symptoms, may advance research efforts. LBD is a common cause of cognitive impairment in older adults but is often subject to significant delays in diagnosis and treatment, increasing the burden on patients and family care partners. Understanding key features of disease and the use of biomarkers will improve recognition. Earlier detection may also facilitate the development of new therapeutics and enrollment in clinical trials.

Evaluation of the SwiftScan mode in bone single-photon emission computed tomography (SPECT) imaging: effect on imaging quality and a semi-quantitative analysis.

99mTc-stannous methylene diphosphonate (99mTc-MDP) bone single-photon emission computed tomography/computed tomography (SPECT/CT) imaging plays a crucial role in various clinical applications. Many strategies have been developed to reduce the injection activity and procedure time, improve the patient experience and reduce their anxiety prior to and during SPECT imaging. This study aimed to evaluate the SwiftScan mode and its effect on image quality, and diagnostic performance of malignant skeletal lesions in bone SPECT image. 99mTc-MDP SPECT/CT scans were acquired from the National Electrical Manufacturers Association (NEMA) phantom with a sphere-to-background ratio of 10:1, and 69 patients were enrolled in this retrospective study. The patients who showed abnormal uptake of focal 99mTc-MDP on whole-body bone planar scans, underwent local SPECT/CT scans. The image comparison and analysis were performed in three acquisition modes: SwiftScan; SwiftScan-50% (the SwiftScan mode with a 50% reduction in acquisition time); and step-and-shoot (SS). The image mean count, coefficient of variation (COV), radioactive concentration, and signal-to-background ratio (SBR) were measured for each phantom image. Visual assessment (using a 5-point Likert scale), semi-quantitative analysis [to determine the COV, mean of the standard uptake value (SUVmean), maximum of the standard uptake value (SUVmax), and SBR], and receiver operating characteristics (ROC) curve analysis were performed for clinical assessment. The SwiftScan mode obtained better quality images than the SwiftScan-50% and SS modes, and it also had better sphere clarity and lower background COVs (P<0.05). The mean counts of the SwiftScan, and the corresponding radioactive concentration and SBR were significantly higher than those of the SwiftScan-50% and SS (P<0.05), except in relation to the 13-10 mm spheres. A total of 101 abnormal 99mTc-MDP uptake skeletal lesions were included in the clinical study. The SwiftScan had significantly higher visual scores than both the SwiftScan-50% and SS (P<0.001). Additionally, there were significantly higher visual scores in the SwiftScan-50% than SS (P<0.05). The SwiftScan also had a lower COV, indicating reduced image noise. The SUVmax, SUVmean, and SBR for the skeletal lesions were significantly higher in the SwiftScan than the other two modes (P<0.05). The optimal diagnostic cut-off values of the SUVmax for identifying malignant skeletal lesions in the SwiftScan, SwiftScan-50%, and SS were 17.95, 14.75, and 9.94, respectively, but there was no significant difference among the three modes in terms of their differential diagnostic performance (all P>0.05). The SwiftScan showed significant improvements in image quality and the semi-quantitative analysis of bone SPECT imaging. It reduced the scanning time by 50% without compromising the image quality or diagnostic performance. These findings provide valuable insights that may inform the clinical application of the SwiftScan, enhancing qualitative and quantitative diagnosis in bone SPECT imaging.

A Survey of Crystals for SPECT Imaging

Single-photon emission computed tomography (SPECT) is an important nuclear medicine imaging tool for diagnosis and drug research. The gamma-ray detector is the core component of the SPECT system and influences the overall system performance. The detector crystals, which can be divided into scintillation crystals and semiconductor crystals, are among the main determinants of the detector’s performance. The development of these crystal materials plays an important role in improving SPECT imaging. This paper provides a survey of the technological development and applications of several crystals currently used in SPECT detectors. Furthermore, it explores future research directions for the development of detector crystals.

Tuned Manganese-Impregnated Mesoporous Silica Nanoparticles as a pH-Responsive Dual Imaging Probe.

The limitations of individual imaging modalities have led to significant interest in hybrid imaging methods that combine the advantages of multiple techniques. The development of diverse dual imaging agents, which offer the exceptional sensitivity of single-photon emission computed tomography (SPECT) and the high spatial resolution of magnetic resonance imaging (MRI), has been addressing the demand for more advanced diagnostic pharmaceuticals. In this study, 99mTc-labeled manganese oxide-loaded mesoporous silica nanoparticles (MSNs), conjugated with folic acid as the targeting moiety and the chelating agent H2pentapa-en-NH2 (99mTc-MnOx-MSN-FA-pa), were developed for targeted SPECT-MRI dual imaging. The toxicity of the nanoparticles was confirmed through an MTT assay, showing >90% viability in HEK-293 and MDA-MB-231 cells at concentrations up to 200 μg/mL, indicating nonsignificant toxicity. Cellular uptake studies showed that folic acid functionalization effectively accentuated tumor-specific intracellular uptake of nanoparticles in MDA-MB-231 cells through folate receptor-mediated endocytosis. Additionally, the radiolabeling yield of 99mTc-MnOx-MSN-FA-pa was found to be 99.6 ± 0.8% (n = 3), and the pH-responsive release of paramagnetic manganese ions increased the r1 relaxivity of the nanoprobe to 11.37 mM-1 s-1. In vivo SPECT imaging demonstrated rapid tracer accumulation in MDA-MB-231 xenografts, with a tumor-to-muscle ratio of 6.01 ± 0.51 at 2 h, and minimal uptake in nontargeted organs. In vivo MRI studies indicated the strongest tumor contrast at 2 h postinjection. Given its desirable contrast enhancement in T1 MRI and SPECT imaging, along with low toxicity, MnOx-MSN-FA-pa shows potential as an effective multifunctional nanoprobe for precise tumor imaging.