COVID-19: Findings in nuclear medicine from head to toe.

COVID-19 is a multisystemic disease, and hence its potential manifestations on nuclear medicine imaging can extend beyond the lung. Therefore, it is important for the nuclear medicine physician to recognize these manifestations in the clinic. While FDG-PET/CT is not indicated routinely in COVID-19 evaluation, its unique capability to provide a functional and anatomical assessment of the entire body means that it can be a powerful tool to monitor acute, subacute, and long-term effects of COVID-19. Single-photon scintigraphy is routinely used to assess conditions such as pulmonary embolism, cardiac ischemia, and thyroiditis, and COVID-19 may present in these studies. The most common nuclear imaging finding of COVID-19 vaccination to date is hypermetabolic axillary lymphadenopathy. This may pose important diagnostic and management dilemmas in oncologic patients, particularly those with malignancies where the axilla constitutes a lymphatic drainage area. This article aims to summarize the relevant literature published since the beginning of the pandemic on the intersection between COVID-19 and nuclear medicine.

Supply Issues in Nuclear Medicine.

ABSTRACT: Issues related to the distribution and availability of supplies and personnel in nuclear medicine are well known and episodic. The combination of COVID-related restrictions and the unprecedented growth of our specialty have acutely exacerbated these supply and demand mismatches.

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images.

COVID-19 is one of the most life-threatening and dangerous diseases caused by the novel Coronavirus, which has already afflicted a larger human community worldwide. This pandemic disease recovery is possible if detected in the early stage. We proposed an automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 positive patients by following a four-phase paradigm for COVID-19 detection: preprocess the CT scan images; remove noise from test image by using anisotropic diffusion techniques; make a different segment for the preprocessed images; and train and test COVID-19 detection using Convolutional Neural Network (CNN) models. This study employed well-known pre-trained models, including AlexNet, ResNet50, VGG16 and VGG19 to evaluate experiments. 80% of images are used to train the network in the detection process, while the remaining 20% are used to test it. The result of the experiment evaluation confirmed that the VGG19 pre-trained CNN model achieved better accuracy (98.06%). We used 4861 real-life COVID-19 CT images for experiment purposes, including 3068 positive and 1793 negative images. These images were acquired from a hospital in Sao Paulo, Brazil and two other different data sources. Our proposed method revealed very high accuracy and, therefore, can be used as an assistant to help professionals detect COVID-19 patients accurately.

The COVID-19 Vaccine Opinions of Women Planning a Pregnancy: International Web Blogs Scanning.

Background: The unprecedented COVID-19 has infected millions of people and killed hundreds of thousands of people. A strategy to contain the spread of the disease was the development of the COVID-19 vaccine. Objective: In our study, it was determined the opinions of women who are planning to become pregnant about the COVID-19 vaccine. Methods: Blogs were used as the data source in the research, which was designed as a descriptive qualitative study. For this purpose, the expressions of 34 women identified between February and March 2021 were evaluated with directed qualitative content analysis. Results: Psychological changes, cognitive changes, and coping methods were determined as the themes of our results. This study demonstrates the value of using qualitative methods to determine the thoughts of women planning to become pregnant regarding the COVID-19 vaccine. Conclusion: For women planning pregnancy, continued research into vaccine safety and efficacy is vital, and results should be carefully investigated and handed in the right channels.

Ventilation Scintigraphy With Radiolabeled Carbon Nanoparticulate Aerosol (Technegas): State-of-the-Art Review and Diagnostic Applications to Pulmonary Embolism During COVID-19 Pandemic.

ABSTRACT: Invented and first approved for clinical use in Australia 36 years ago, Technegas is the technology that enabled ventilation scintigraphy with 99m Tc-labeled carbon nanoparticles ( 99m Tc-CNP). The US Food and Drug Administration (FDA) has considered this technology for more than 30 years but only now is getting close to approving it. Meanwhile, more than 4.4 million patients benefited from this technology in 64 countries worldwide. The primary application of 99m Tc-CNP ventilation imaging is the diagnostic evaluation for suspicion of pulmonary embolism using ventilation-perfusion quotient (V/Q) imaging. Because of 99m Tc-CNP's long pulmonary residence, tomographic imaging emerged as the preferred V/Q methodology. The FDA-approved ventilation imaging agents are primarily suitable for planar imaging, which is less sensitive. After the FDA approval of Technegas, the US practice will likely shift to tomographic V/Q. The 99m Tc-CNP use is of particular interest in the COVID-19 pandemic because it offers an option of a dry radioaerosol that takes approximately only 3 to 5 tidal breaths, allowing the shortest exposure to and contact with possibly infected patients. Indeed, countries where 99m Tc-CNP was approved for clinical use continued using it throughout the COVID-19 pandemic without known negative viral transmission consequences. Conversely, the ventilation imaging was halted in most US facilities from the beginning of the pandemic. This review is intended to familiarize the US clinical nuclear medicine community with the basic science of 99m Tc-CNP ventilation imaging and its clinical applications, including common artifacts and interpretation criteria for tomographic V/Q imaging for pulmonary embolism.

Simulated daily readout for maintaining nuclear medicine education in residency training amidst declining case volume: evidence from the COVID-19 pandemic.

BACKGROUND: Simulated daily readout (SDR) is a teaching initiative in radiology and nuclear medicine developed to simulate a resident's experience during periods of case volume reduction. SDR was employed by many training centers during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to evaluate the perception of radiology residents on the effectiveness of SDR. METHOD: The SDR was conducted in the nuclear medicine rotations from 2019 to 2020 during the shutdown of the radionuclide imaging facilities using a combination of strategies including case selection, assignment, reporting and feedback. A brief 8-item questionnaire with Likert scale values was completed by radiology residents who participated in the SDR-based nuclear medicine rotations. RESULTS: Thirty-five of 54 residents returned the questionnaire. The majority of residents affirmed the negative impact of the reduction in case volume on their training experiences and perceived that SDR could alleviate the effects. The SDR strategies perceived as more effective were targeted case selection, in-advanced assignment, verbal interpretation and reporting, and verbal feedback. CONCLUSION: The radiology residents perceived the SDR as an effective tool to preserve their training experiences. The SDR has the potential to be a useful initiative when teaching centers face the threat of declining case volume.

Lesion segmentation in lung CT scans using unsupervised adversarial learning.

Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.

COVID-19 pneumonia detected by parathyroid scintigraphy.

We report a case of incidental diagnosis of COVID-19 pneumonia by parathyroid scintigraphy. A 53-year-old woman who had severe fatigue, and mild dyspnea underwent parathyroid scintigraphy due to increased serum parathyroid hormone (PTH) and serum calcium levels. Parathyroid scan was negative for abnormal parathyroid tissue. Although the patient had three negative results of COVID-19 PCR tests, significant 99m Tchexakis-2-methoxyisobutylisonitrile ([99mTc]MIBI) uptake is noticed in both lungs that was suspicious for Covid-19 pneumonia. The patient underwent CT scan of the chest for further evaluation. Diffuse groundglass opacities were identified in both lungs which were interpreted as typical feature for COVID-19 pneumonia.

Human-level COVID-19 diagnosis from low-dose CT scans using a two-stage time-distributed capsule network.

Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

Supraclavicular and Axillary Lymphadenopathy Induced by COVID-19 Vaccination on 18F-Fluorthanatrace, 68Ga-DOTATATE, and 18F-Fluciclovine PET/CT.

ABSTRACT: COVID-19 vaccination has started in most countries, and postvaccination imaging is inevitable in the oncologic population. The immune response to the vaccination in the form of reactive lymphadenopathy has been well documented on 18F-FDG PET/CT. We present the imaging findings of 3 patients who have undergone non-FDG PET/CT imaging including 18F-fluorthanatrace, 68Ga-DOTATATE, and 18F-fluciclovine PET/CT. It is crucial to recognize the timing and laterality of immunization to avoid false-positive findings.

Artifact from 131I-Contaminated Mask in Postradioiodine Therapy Scintigraphy.

A patient wearing the mandatory face mask because of the ongoing coronavirus disease 2019 pandemic underwent postradioiodine therapy scintigraphy. The spot view of the neck showed an area of uptake that was later demonstrated to be caused by contamination of the mask. This finding has led to updating the scan procedure for posttherapy scintigraphy by replacing the patients' masks before the scan acquisition.

75SeHCAT whole body retention for unexplained chronic diarrhoea based on early first whole body counting.

BACKGROUND: There are many protocol variations in the whole-body 75SeHCAT retention test [whole-body retention (WBR)] for investigation of bile acid diarrhoea. The time between capsule consumption and first count, however, is widely taken, without debate, to be 3 h. In the Covid-19 era, it is desirable to limit the time patients spend in the department. We, therefore, questioned the need for a 3 h interval between capsule administration and the initial count. METHODS: Using an uncollimated gamma camera, whole-body counting was performed at 5, 30 and 180 min after capsule ingestion in 24 patients with chronic diarrhoea. Geometric mean was taken of counts acquired from posterior and anterior projections. WBR was expressed as the ratio of 7 day-to-initial whole-body counts (%) to give WBR5, WBR30 and WBR180. A small meal was given at 60 min after capsule ingestion. RESULTS: There was a close correlation between WBR30 and WBR180 (y = 1.0x - 0.29%; r = 0.99). For WBR180 values of <15% (lower limit of normal), there was close agreement between WBR30 and WBR180 (bias 0.03%; precision 0.7%). WBR5 overestimated WBR180. However, cWBR5, obtained by multiplication of WBR5 by 0.75, also correlated closely with WBR180 (y = 1.2x - 4.5%; r = 0.97), and there was close agreement between cWBR5 and WBR180 for WBR180 values <15% (bias 0.08%, precision 1.3%). CONCLUSION: The first whole-body count in the 75SeHCAT test can be undertaken at 30 min postcapsule without loss of accuracy, or even 5 min if only subnormal values are considered relevant. No food is required after capsule consumption.

Incidental detection of COVID-19 associated pneumonia by thyroid scintigraphy.

This report presents a case of a 49-year-old woman with complaint of sore throat and front neck pain, who referred to a hospital for thyroid scan due to suppressed TSH level (0.005 mU/L). Diffuse and bilateral lungs uptake in the scan was noticed incidentally. The patient had positive history of COVID-19 symptoms. Multifocal and bilateral ground-glass opacities (GGOs) in both lungs were compatible with typical features of lung involvement in COVID-19-associated pneumonia.

Incidental detection of COVID-19 associated pneumonia by [99mTc]UBI scintigraphy.

A 31-year-old woman who had multiple orthopedic surgeries on the left lower limb and recently suffered from pain and redness in the lateral left lower thigh was referred to the hospital to rule out osteomyelitis by [99mTc]UBI scintigraphy. Except soft tissue inflammation in the mentioned region, the scan showed significant and diffuse both lungs uptake incidentally. The patient had experienced symptoms of COVID-19 disease recently. Chest HRCT scan also revealed multiple segmental ground-glass opacities (GGOs) which were typical features for lung involvement of COVID-19 associated pneumonia.

DOTATATE Uptake in an Axillary Lymph Node After COVID-19 Vaccination.

ABSTRACT: A 58-year-old man underwent DOTATATE PET/CT scan for follow-up of pulmonary neuroendocrine tumor after resection and adjuvant chemotherapy. On screening paperwork, the patient indicated having received the Johnson & Johnson/Janssen COVID-19 vaccine (Janssen Biotech, Inc) 1 day previously, administered in the right deltoid muscle. Reactive changes in regional lymph nodes is a known response for all 3 currently Food and Drug Administration-approved COVID-19 vaccines. Recent published data have demonstrated FDG PET-avid axillary lymphadenopathy subsequent to COVID-19 vaccination, and included here is a report of DOTATATE PET-avid axillary lymph node after injection of the Johnson & Johnson COVID-19 vaccine.

Safe Pulmonary Scintigraphy in the Era of COVID-19.

One of the major effects of the COVID-19 pandemic within nuclear medicine was to halt performance of lung ventilation studies, due to concern regarding spread of contaminated secretions into the ambient air. A number of variant protocols for performing lung scintigraphy emerged in the medical literature which minimized or eliminated the ventilation component, due to the persistent need to provide this critical diagnostic service without compromising the safety of staff and patients. We have summarized and reviewed these protocols, many of which are based on concepts developed earlier in the history of lung scintigraphy. It is possible that some of these interim remedies may gain traction and earn a more permanent place in the ongoing practice of nuclear medicine.