Evaluation of patients' radiation doses and establishment of institutional diagnostic reference levels in nuclear medicine in Oman.

This study aimed to develop diagnostic reference levels (DRLs) in Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) and Positron Emission Tomography/Computed Tomography (PET/CT) imaging for the most frequent SPECT/CT and PET/CT examinations performed at our institution. A total of 1134 adult patients, who have undergone SPECT/CT and PET/CT scanning over a period of 4 years (2018-2021), were included. The scans consisted of 401 PET/CT and 733 SPECT/CT scans. The CT dosimetry data [CT-dose-index (CTDIvol), dose-length-product (DLP)] and administered activities were collected. The DRLs were calculated for CTDIvol, DLP and administrated activity. The estimated DRLs are given as [median CTDIvol (mGy):median DLP (mGy.cm):median administrated activity (MBq)]: whole body PET/CT: 1.88:175:259; brain PET/CT: 12.9:300:239; cardiac PET/CT: 1.34:32:368; bone SPECT/CT: 2.68:116:763; MPI SPECT/CT (stress-rest): 1.49:52:751-721; parathyroid SPECT/CT: 3.1:126:779; thyroid uptake SPECT: 3.52:147:195; thyroid post-ablation SPECT/CT: 3.85:160:NA. The derived DRLs have allowed careful monitoring of doses delivered to patients and could act as a trigger to investigate doses that systematically exceeds the derived DRLs.

Technological Advances in SPECT and SPECT/CT Imaging.

Single photon emission tomography/computed tomography (SPECT/CT) is a mature imaging technology with a dynamic role in the diagnosis and monitoring of a wide array of diseases. This paper reviews the technological advances, clinical impact, and future directions of SPECT and SPECT/CT imaging. The focus of this review is on signal amplifier devices, detector materials, camera head and collimator designs, image reconstruction techniques, and quantitative methods. Bulky photomultiplier tubes (PMTs) are being replaced by position-sensitive PMTs (PSPMTs), avalanche photodiodes (APDs), and silicon PMs to achieve higher detection efficiency and improved energy resolution and spatial resolution. Most recently, new SPECT cameras have been designed for cardiac imaging. The new design involves using specialised collimators in conjunction with conventional sodium iodide detectors (NaI(Tl)) or an L-shaped camera head, which utilises semiconductor detector materials such as CdZnTe (CZT: cadmium-zinc-telluride). The clinical benefits of the new design include shorter scanning times, improved image quality, enhanced patient comfort, reduced claustrophobic effects, and decreased overall size, particularly in specialised clinical centres. These noticeable improvements are also attributed to the implementation of resolution-recovery iterative reconstructions. Immense efforts have been made to establish SPECT and SPECT/CT imaging as quantitative tools by incorporating camera-specific modelling. Moreover, this review includes clinical examples in oncology, neurology, cardiology, musculoskeletal, and infection, demonstrating the impact of these advancements on clinical practice in radiology and molecular imaging departments.

A systematic review and bayesian meta-analysis of medical devices used in chronic pain management.

Whilst. pharmacological therapies remain the cornerstone of pain management in chronic pain, factors including the current opioid epidemic have led to non-pharmacological techniques becoming a more attractive proposition. We explored the prevalence of medical device use and their treatment efficacy in non-cancer pain management. A systematic methodology was developed, peer reviewed and published in PROSPERO (CRD42021235384). Key words of medical device, pain management devices, chronic pain, lower back pain, back pain, leg pain and chronic pelvic pain using Science direct, PubMed, Web of Science, PROSPERO, MEDLINE, EMBASE, PorQuest and ClinicalTrials.gov. All clinical trials, epidemiology and mixed methods studies that reported the use of medical devices for non-cancer chronic pain management published between the 1st of January 1990 and the 30th of April 2022 were included. 13 studies were included in systematic review, of these 6 were used in the meta-analysis. Our meta-analysis for pain reduction showed that transcutaneous electrical nerve stimulation combined with instrument-assisted soft tissue mobilization treatment and pulsed electromagnetic therapy produced significant treatment on chronic lower back pain patients. Pooled evidence revealed the use of medical device related interventions resulted in 0.7 degree of pain reduction under a 0-10 scale. Significant improvement in disability scores, with a 7.44 degree reduction in disability level compared to a placebo using a 50 score range was also seen. Our analysis has shown that the optimal use of medical devices in a sustainable manner requires further research, needing larger cohort studies, greater gender parity, in a more diverse range of geographical locations.

Synthetic data & the future of Women's Health: A synergistic relationship.

OBJECTIVES: The aim of this perspective is to report the use of synthetic data as a viable method in women's health given the current challenges linked to obtaining life-course data within a short period of time and accessing electronic healthcare data. METHODS: We used a 3-point perspective method to report an overview of data science, common applications, and ethical implications. RESULTS: There are several ethical challenges linked to using real-world data, consequently, generating synthetic data provides an alternative method to conduct comprehensive research when used effectively. The use of clinical characteristics to develop synthetic data is a useful method to consider. Aligning this data as closely as possible to the clinical phenotype would enable researchers to provide data that is very similar to that of the real-world. DISCUSSION: Population diversity and disease characterisation is important to optimally use data science. There are several artificial intelligence techniques that can be used to develop synthetic data. CONCLUSION: Synthetic data demonstrates promise and versatility when used efficiently aligned to clinical problems. Therefore, exploring this option as a viable method in women's health, in particular for epidemiology may be useful.

Evaluation of institutional whole-body and extremity occupational radiation doses in nuclear medicine.

This study evaluated nuclear medicine occupational radiation doses at Sultan Qaboos University Hospital, a 700-bed tertiary care teaching hospital in Oman. Personal effective whole-body doses, Hp(10), and extremity doses, Hp(0.07), were collected for 19 medical radiation workers over a 7-year period (2015-2021). Personal doses for four professional groups were measured using calibrated thermo-luminescence dosemeters ((LiF:Mg,Ti) TLD-100). The average, median and maximum cumulative doses were compared against the annual whole-body and extremity dose limits (20 mSv and 500 mSv y-1, respectively) and local dose investigation level (DIL; 6 mSv y-1). Personal whole-body doses (average:median:maximum) for technologists, medical physicists, nuclear medicine physicians and nurses were 1.8:1.1:7.8, 0.3:0.3:0.4, 0.1:0.1:0.2 and 0.1:0.1:0.2 mSv, respectively. Personal extremity doses for left and right hand (average and maximum doses) follow similar trends. Average annual effective whole-body and extremity doses were well below the recommended annual dose limits. The findings suggest lowering local DIL for all staff except for technologists.

Establishment of diagnostic reference levels in computed tomography in two large hospitals in Oman.

This study aimed to estimate diagnostic reference levels (DRLs) for the most frequent computed tomography (CT) imaging examinations to monitor and better control radiation doses delivered to patients. Seven CT imaging examinations: Head, Chest, Chest High Resolution (CHR), Abdomen Pelvis (AP), Chest Abdomen Pelvis (CAP), Kidneys Ureters Bladder (KUB) and Cardiac, were considered. CT dosimetric quantities and patient demographics were collected from data storage systems. Local typical values for DRLs were calculated for CTDIvol (mGy), dose length product (DLP) (mGy·cm) and effective doses (mSv) were estimated for each examination. The calculated DRLs were given as (median CTDIvol (mGy):median DLP (mGy·cm)): Head: 39:657; Chest: 13:451; CHR: 6:228; AP: 12:578; CAP: 20:807; KUB: 7:315, and Cardiac: 2:31. Estimated effective doses for Head, Chest, CHR, AP, CAP, KUB and Cardiac were 1.3, 12.7, 6.3, 12.5, 18.1, 5.8 and 0.8 mSv, respectively. The estimated DRLs will act as guidance doses to prevent systematic excess of patient doses.

Optimization of BMI-Based Images for Overweight and Obese Patients - Implications on Image Quality, Quantification, and Radiation Dose in Whole Body 18F-FDG PET/CT Imaging.

Purpose: In PET/CT imaging, the activity of the 18F-FDG activity is injected either based on patient body weight (BW) or body mass index (BMI). The purpose of this study was to optimise BMI-based whole body 18F-FDG PET images obtained from overweight and obese patients and assess their image quality, quantitative value and radiation dose in comparison to BW-based images. Methods: The NEMA-IEC-body phantom was scanned using the mCT 128-slice scanner. The spheres and background were filed with F-18 activity. Spheres-to-background ratio was 4:1. Data was reconstructed using the OSEM-TOF-PSF routine reconstruction. The optimization was performed by varying number of iterations and subsets, filter's size and type, and matrix size. The optimized reconstruction was applied to 17 patients' datasets. The optimized BMI-, routine BMI- and the BW-based images were compared visually and using contrast-to-noise ratio (CNR) and standardized uptake values (SUV) measurements. Results: The visual assessment of the optimized phantom images showed better image quality and contrast-recovery-coefficients (CRCs) values compared to the routine reconstruction. Using patient data, the optimized BMI-based images provided better image quality compared to BW-based images in 87.5% of the overweight cases and 66.7% for obese cases. The optimized BMI-based images resulted in more than 50% reduction of radiation dose. No significant differences were found between the three series of images in SUV measurements. Conclusion: The optimized BMI-based approach using 1 iteration, 21 subsets, and 3 mm Hamming filter improves image quality, reduces radiation dose, and provides, at least, similar quantification compared to the BW-based approach for overweight and obese patients.

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics.

OBJECTIVE: To develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. METHODS: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%) followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation, followed by testing using the test set. RESULTS: Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. CONCLUSION: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients.

ASSESSMENT OF OCCUPATIONAL RADIATION DOSES IN DIFFERENT DIAGNOSTIC, INTERVENTIONAL AND THERAPEUTIC RADIOLOGY AND MOLECULAR IMAGING SERVICES IN OMAN.

This study aimed at assessing occupational radiation doses in different diagnostic, interventional and therapeutic services. Personal dose equivalent, Hp(10), of 116 medical radiation workers, all with 3 y of dose records (2015-18), were collected from the TLD dosimetry service at the Sultan Qaboos University Hospital-a 700-bed tertiary care teaching hospital in Oman. The doses were measured using calibrated thermo-luminescence dosemeters (TLD-100 (LiF:Mg,Ti)). Five occupational groups, diagnostic radiology, interventional radiology, nuclear medicine, medical physicists and nurses, were considered. Average, maximum and median cumulative doses were estimated and compared against the annual dose limit (20 mSv per y) and the local dose investigation level (DIL) (6 mSv per y). Personal doses (average:maximum:median) for diagnostic radiology, interventional radiology, nuclear medicine, medical physicists and nurses group were found to be 0.05:0.90:0.00, 0.05:0.50:0.00, 1.20:7.40:0.40, 0.16:1.40:0.00 and 0.10:2.10:0.00 mSv, respectively. The findings of this study suggest, at the exception of nuclear medicine, lower DILs for all occupational groups.

Artificial intelligence-driven assessment of radiological images for COVID-19.

Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

Metabolic Imaging of Pain Matrix Using 18 F Fluoro-deoxyglucose Positron Emission Tomography/Computed Tomography for Patients Undergoing L2 Dorsal Root Ganglion Stimulation for Low Back Pain.

INTRODUCTION: Nociceptive signals from lumbar intervertebral discs ascend in the sympathetic chain via the L2 dorsal root ganglion (L2 DRG), a potential target for discogenic low back pain in neuromodulation. Positron Emission Tomography/Computed Tomography (PET-CT) measures functional changes in the brain metabolic activity, identified by the changes in the regional cerebral blood flow (rCBF) as determined by the changes of F-18 Fluoro-deoxyglucose (18 F FDG) tracer within brain tissues. METHODS AND MATERIALS: Nine patients were recruited to explore the changes in PET-CT imaging at baseline and four-weeks post implantation of bilateral L2 DRG neurostimulation leads and implantable pulse generator (IPG). PET-CT scans were performed 30 min following an IV injection of 250±10% MBq of 18 F FDG tracer. Fifteen frames were acquired in 15 min. PET list-mode raw data were reconstructed and normalized appropriately to a brain anatomical atlas. RESULTS: Nine patients were recruited to the study, where PET-CT imaging data for five patients were analyzed. The right and left insular cortex, primary and secondary somato-sensory cortices, prefrontal cortex, anterior cingulate cortex, thalamus, amygdala, hippocampus and the midline periaqueductal areas, were assessed for any changes in the metabolic activity. A total of 85 pain matrix regions were delineated SUV (standardized uptake value)MAX , SUV MEAN ± SD, and SUVPEAK were calculated for each of these regions of the brain and were compared pre- and post-L2 DRG stimulation. Sixty-one of the 85 matrices showed an increase in metabolic activity whereas 24 matrices showed a reduction in metabolic activity. CONCLUSION: This is the first ever study reporting the changes in cerebral metabolic activity and multi-frame static brain 18 F FDG PET imaging after L2 DRG stimulation for discogenic low back pain. Predominantly an increased metabolic activity in nociceptive brain matrices are seen with an increased in F18 F FDG uptake following L2 DRG stimulation.

Incremental Value of SPECT/CT in the Detection of Femoral Head Viability in Slipped Capital Femoral Epiphysis.

In this article, we discuss the role of 99mTc-methylene diphosphonate SPECT/CT in 2 cases of slipped capital femoral epiphysis. We describe the incremental value of SPECT/CT in determining the viability of the femoral head and the implications in management of patients with slipped epiphysis.

Impact of PET - CT motion correction in minimizing the gross tumor volume in non-small cell lung cancer.

OBJECTIVES: To investigate the impact of respiratory motion on localization, and quantification of lung lesions for the Gross Tumor Volume utilizing a fully automated Auto3Dreg program and dynamic NURBS-based cardiac-torso digitized phantom (NCAT). METHODS: Respiratory motion may result in more than 30% underestimation of the SUV values of lung, liver and kidney tumor lesions. The motion correction technique adopted in this study was an image-based motion correction approach using, a voxel-intensity-based and a multi-resolution multi-optimization (MRMO) algorithm. The NCAT phantom was used to generate CT attenuation maps and activity distribution volumes for the lung regions. All the generated frames were co-registered to a reference frame using a time efficient scheme. Quantitative assessment including Region of Interest (ROI), image fidelity and image correlation techniques, as well as semi-quantitative line profile analysis and qualitatively overlaying non-motion and motion corrected image frames were performed. RESULTS: The largest motion was observed in the Z-direction. The greatest translation was for the frame 3, end inspiration, and the smallest for the frame 5 which was closet frame to the reference frame at 67% expiration. Visual assessment of the lesion sizes, 20-60mm at 3 different locations, apex, mid and base of lung showed noticeable improvement for all the foci and their locations. The maximum improvements for the image fidelity were from 0.395 to 0.930 within the lesion volume of interest. The greatest improvement in activity concentration underestimation was 7.7% below the true activity for the 20 mm lesion in comparison to 34.4% below, prior to correction. The discrepancies in activity underestimation were reduced with increasing the lesion sizes. Overlaying activity distribution on the attenuation map showed improved localization of the PET metabolic information to the anatomical CT images. CONCLUSION: The respiratory motion correction for the lung lesions has led to an improvement in the lesion size, localization and activity quantification with a potential application in reducing the size of the PET GTV for radiotherapy treatment planning applications and hence improving the accuracy of the regime in treatment of lung cancer.