Aiming for [18F]FDG-PET acquisition time reduction in clinical practice for neurological patients

PURPOSE

Positron emission tomography (PET) imaging with [18F]FDG provides valuable information regarding the underlying pathological processes in neurodegenerative disorders. PET imaging for these populations should be as short as possible to limit head movements and improve comfort. This study aimed to validate an optimized [18F]FDG-PET image reconstruction protocol aiming to reduce acquisition time while maintaining adequate quantification accuracy and image quality.

METHODS

A time-reduced reconstruction protocol (5 min) was evaluated in [18F]FDG-PET retrospective data from healthy individuals and Alzheimer's disease (AD) patients. Standard (8 min) and time-reduced protocols were compared by means of image quality and quantification accuracy metrics, as well as standardized uptake value ratio (SUVR) and Z-scores (pons was used as reference). Images were randomly and blindly presented to experienced physicians and scored in terms of image quality.

RESULTS

No differences between protocols were identified during the visual assessment. Small differences (p < 0.01) in the pons SUVR were observed between the standard and time-reduced protocols for healthy individuals (-0.002 ± 0.011) and AD patients (-0.007 ± 0.013). Likewise, incorporating the PSF correction in the reconstruction algorithm resulted in small differences (p < 0.01) in SUVR between protocols (healthy individuals: -0.003 ± 0.011; AD patients: -0.007 ± 0.014).

CONCLUSION

Quality metrics were similar between time-reduced and standard protocols. In the visual assessment of the images, the physicians did not consider the use of PSF adequate, as it degraded the quality image. Shortening the acquisition time is possible by optimizing the image reconstruction parameters while maintaining adequate quantification accuracy and image quality.

Oral administration of PET tracers: Current status

The oral route is the most widely used and preferable way of drug administration. Several pharmacokinetic processes play a role in the distribution of administered drugs. Therefore, accurate quantification of absorption, distribution, metabolism, excretion, and characterisation of drug kinetics after oral administration is extremely important for developing new human drugs. In vivo methods, such as gamma-scintigraphy, magnetic resonance imaging (MRI), and positron emission tomography (PET), have been used to analyse gastrointestinal tract (GIT) absorption behaviour. This scoping review provides an overview of PET studies that used oral tracer administration. A systematic literature search was performed using PubMed, EMBASE, Scopus, Science Direct, and Web of Science databases. Extensive variation between these studies was seen concerning acquisition protocols, quantification methods, and pharmacokinetic outcome parameters. Studies in humans indicate that it takes 10 to 30 min for the tracer to be in the intestine and about 100 min to reach its maximum concentration in the brain. In rodent studies, different pharmacokinetic parameters for the brain, blood, and GIT were estimated, showing the potential of PET to measure the absorption and distribution of drugs and pharmaceuticals non-invasively. Finally, regarding radiation protection, oral administration has a higher absorbed dose in GIT and, consequently, a higher effective dose. However, with the recent introduction of Long Axial Field of View (LAFOV) PET scanners, it is possible to reduce the administered dose, making oral administration feasible for routine clinical studies.

A new model for classification of medical CT images using CNN: a COVID-19 case study

SARS-CoV-2 is the causative agent of COVID-19 and leaves characteristic impressions on chest Computed Tomography (CT) images in infected patients and this analysis is performed by radiologists through visual reading of lung images, and failures may occur. In this article, we propose a classification model, called Wavelet Convolutional Neural Network (WCNN) that aims to improve the differentiation of images of patients with COVID-19 from images of patients with other lung infections. The WCNN model was based on a Convolutional Neural Network (CNN) and wavelet transform. The model proposes a new input layer added to the neural network, which was called Wave layer. The hyperparameters values were defined by ablation tests. WCNN was applied to chest CT images to images from two internal and one external repositories. For all repositories, the average results of Accuracy (ACC), Sensitivity (Sen) and Specificity (Sp) were calculated. Subsequently, the average results of the repositories were consolidated, and the final values were ACC = 0.9819, Sen = 0.9783 and Sp = 0.98. The WCNN model uses a new Wave input layer, which standardizes the network input, without using data augmentation, resizing and segmentation techniques, maintaining the integrity of the tomographic image analysis. Thus, applications developed based on WCNN have the potential to assist radiologists with a second opinion in the analysis.1.

Discriminating Aging Cognitive Decline Spectrum Using PET and Magnetic Resonance Image Features

Background: The population aging increased the prevalence of brain diseases, like Alzheimer’s disease (AD), and early identification of individuals with higher odds of cognitive decline is essential to maintain quality of life. Imaging evaluation of individuals at risk of cognitive decline includes biomarkers extracted from brain positron emission tomography (PET) and structural magnetic resonance imaging (MRI). Objective: We propose investigating ensemble models to classify groups in the aging cognitive decline spectrum by combining features extracted from single imaging modalities and combinations of imaging modalities (FDG+AMY+MRI, and a PET ensemble). Methods: We group imaging data of 131 individuals into four classes related to the individuals’ cognitive assessment in baseline and follow-up: stable cognitive non-impaired; individuals converting to mild cognitive impairment (MCI) syndrome; stable MCI; and Alzheimer’s clinical syndrome. We assess the performance of four algorithms using leave-one-out cross-validation: decision tree classifier, random forest (RF), light gradient boosting machine (LGBM), and categorical boosting (CAT). The performance analysis of models is evaluated using balanced accuracy before and after using Shapley Additive exPlanations with recursive feature elimination (SHAP-RFECV) method. Results: Our results show that feature selection with CAT or RF algorithms have the best overall performance in discriminating early cognitive decline spectrum mainly using MRI imaging features. Conclusion: Use of CAT or RF algorithms with SHAP-RFECV shows good discrimination of early stages of aging cognitive decline, mainly using MRI image features. Further work is required to analyze the impact of selected brain regions and their correlation with cognitive decline spectrum.

Percepção de cirurgiões ortopédicos sobre os riscos da exposição à radiação na fluoroscopia

Objective  The present study aims to understand the perceptions of orthopedists and traumatologists regarding the risk of exposure to ionizing radiation in fluoroscopy procedures.

Methods  An objective, structured, self-administered questionnaire with sociodemographic, professional, and occupational variables was developed, available through an invitation sent to orthopedist physicians whose contacts were made publicly available.

Results  A total of 141 questionnaires were answered and analyzed. Most respondents (99%) use fluoroscopy in their surgeries, and only 34.8% of the participants feel safe with the use of the equipment. It was observed that the knowledge about ionizing radiation is inadequate, because 22.6% of the participants are unaware of the type of radiation emitted in fluoroscopy and its biological effects. In addition, 52% of the participants did not know or do not understand the principles of radiological protection and their relationship with surgical practices.

Conclusion We concluded that the radiological protection of most orthopedists in surgical procedures is inadequate, and initial and continued training programs of professionals are necessary, bringing health benefits to orthopedists and their patients.

Women in Medical Physics and Biomedical Engineering: past, present and future

Women in Medical Physics and Biomedical Engineering (WiMPBME) is a Task Group established in 2014 under the International Union of Physical and Engineering Scientists in Medicine (IUPESM). The group’s main role is to identify, develop, implement, and coordinate various tasks and projects related to women’s needs and roles in medical physics and biomedical engineering around the world.

The current paper summarizes the past, present and future goals and activities undertaken or planned by the Task group in order to motivate, nurture and support women in medical physics and biomedical engineering throughout their professional careers. In addition, the article includes the historical pathway followed by various women’s groups and subcommittees from 2004 up to the present day and depicts future aims to further these professions in a gender-balanced manner.

The Biggest Challenges Resulting from the COVID-19 Pandemic on Gender-Related Work from Home in Biomedical Fields—World-Wide Qualitative Survey Analysis

(1) Background: This paper aims to present and discuss the most significant challenges encountered by STEM professionals associated with remote working during the COVID-19 lockdowns. (2) Methods: We performed a qualitative analysis of 921 responses from professionals from 76 countries to the open-ended question: “What has been most challenging during the lockdown for you, and/or your family?” (3) Findings: Participants reported challenges within the immediate family to include responsibilities for school, childcare, and children’s wellbeing; and the loss of social interactions with family and friends. Participants reported increased domestic duties, blurred lines between home and work, and long workdays. Finding adequate workspace was a problem, and adaptations were necessary, especially when adults shared the same setting for working and childcare. Connectivity issues and concentration difficulties emerged. While some participants reported employers’ expectations did not change, others revealed concerns about efficiency. Mental health issues were expressed as anxiety and depression symptoms, exhaustion and burnout, and no outlets for stress. Fear of becoming infected with COVID-19 and uncertainties about the future also emerged. Pressure points related to gender, relationship status, and ethnicities were also evaluated. Public policies differed substantially across countries, raising concerns about the adherence to unnecessary restrictions, and similarly, restrictions being not tight enough. Beyond challenges, some benefits emerged, such as increased productivity and less time spent getting ready for work and commuting. Confinement resulted in more quality time and stronger relationships with family. (4) Interpretation: Viewpoints on positive and negative aspects of remote working differed by gender. Females were more affected professionally, socially, and personally than males. Mental stress and the feeling of inadequate work efficiency in women were caused by employers’ expectations and lack of flexibility. Working from home turned out to be challenging, primarily due to a lack of preparedness, limited access to a dedicated home-office, and lack of previous experience in multi-layer/multi-scale environments.

Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

COVID-19 classification in X-ray chest images using a new convolutional neural network: CNN-COVID

Purpose

COVID-19 causes lung inflammation and lesions, and chest X-ray and computed tomography images are remarkably suitable for differentiating the new disease from patients with other lung diseases. In this paper, we propose a computer model to classify X-ray images of patients diagnosed with COVID-19. Chest X-ray exams were chosen over computed tomography scans because they are low cost, results are quickly obtained, and X-ray equipment is readily available.

Methods

A new CNN network, called CNN-COVID, has been developed to classify X-ray patient’s images. Images from two different datasets were used. The images of Dataset I is originated from the COVID-19 image data collection and the ChestXray14 repository, and the images of Dataset II belong to the BIMCV COVID-19+ repository. To assess the accuracy of the network, 10 training and testing sessions were performed in both datasets. A confusion matrix was generated to evaluate the model’s performance and calculate the following metrics: accuracy (ACC), sensitivity (SE), and specificity (SP). In addition, Receiver Operating Characteristic (ROC) curves and Areas Under the Curve (AUCs) were also considered.

Results

After running 10 tests, the average accuracy for Dataset I and Dataset II was 0.9787 and 0.9839, respectively. Since the weights of the best test results were applied in the validation, it was obtained the accuracy of 0.9722 for Dataset I and 0.9884 for Dataset II.

Conclusions

The results showed that the CNN-COVID is a promising tool to help physicians classify chest images with pneumonia, considering pneumonia caused by COVID-19 and pneumonia due to other causes.

Amyloid-β PET Classification on Cognitive Aging Stages Using the Centiloid Scale

PROPOSE

This study aims to explore the use of the Centiloid (CL) method in amyloid-β PET quantification to evaluate distinct cognitive aging stages, investigating subjects' mismatch classification using different cut-points for amyloid-β positivity.

PROCEDURES

The CL equation was applied in four groups of individuals: SuperAgers (SA), healthy age-matched controls (AC), healthy middle-aged controls (MC), and Alzheimer's disease (AD). The amyloid-β burden was calculated and compared between groups and quantitative variables. Three different cut-points (Jack CR, Wiste HJ, Weigand SD, et al., Alzheimer's Dement 13:205-216, 2017; Salvadó G, Molinuevo JL, Brugulat-Serrat A, et al., Alzheimer's Res Ther 11:27, 2019; and Amadoru S, Doré V, McLean CA, et al., Alzheimer's Res Ther 12:22, 2020) were applied in CL values to differentiate the earliest abnormal pathophysiological accumulation of Aβ and the established Aβ pathology.

RESULTS

The AD group exhibited a significantly increased Aβ burden compared to the MC, but not AC groups. Both healthy control (MC and AC) groups were not significantly different. Visually, the SA group showed a diverse distribution of CL values compared with MC; however, the difference was not significant. The CL values have a moderate and significant relationship between Aβ visual read, RAVLT DR and MMSE. Depending on the cut-point used, 10 CL, 19 CL, or 30 CL, 7.5% of our individuals had a different classification in the Aβ positivity. For the AC group, we obtained about 40 to 60% of the individuals classified as positive.

CONCLUSION

SuperAgers exhibited a similar Aβ load to AC and MC, differing in cognitive performance. Independently of cut-point used (10 CL, 19 CL, or 30 CL), three SA individuals were classified as Aβ positive, showing the duality between the individual's clinics and the biological definition of Alzheimer's. Different cut-points lead to Aβ positivity classification mismatch in individuals, and an extra care is needed for individuals who have a CL value between 10 and 30 CL.

Cognitive Decline Assessment: A Review From Medical Imaging Perspective

Aging is a complex process that involves changes at both molecular and morphological levels. However, our understanding of how aging affects brain anatomy and function is still poor. In addition, numerous biomarkers and imaging markers, usually associated with neurodegenerative diseases such as Alzheimer's disease (AD), have been clinically used to study cognitive decline. However, the path of cognitive decline from healthy aging to a mild cognitive impairment (MCI) stage has been studied only marginally. This review presents aspects of cognitive decline assessment based on the imaging differences between individuals cognitively unimpaired and in the decline spectrum. Furthermore, we discuss the relationship between imaging markers and the change in their patterns with aging by using neuropsychological tests. Our goal is to delineate how aging has been studied by using medical imaging tools and further explore the aging brain and cognitive decline. We find no consensus among the biomarkers to assess the cognitive decline and its relationship with the cognitive decline trajectory. Brain glucose hypometabolism was found to be directly related to aging and indirectly to cognitive decline. We still need to understand how to quantify an expected hypometabolism during cognitive decline during aging. The Aβ burden should be longitudinally studied to achieve a better consensus on its association with changes in the brain and cognition decline with aging. There exists a lack of standardization of imaging markers that highlight the need for their further improvement. In conclusion, we argue that there is a lot to investigate and understand cognitive decline better and seek a window for a suitable and effective treatment strategy.

Increased Glucose Activity in Subgenual Anterior Cingulate and Hippocampus of High Performing Older Adults, Despite Amyloid Burden

BACKGROUND

Individuals at 80 years of age or above with exceptional memory are considered SuperAgers (SA), an operationalized definition of successful cognitive aging. SA showed increased thickness and altered functional connectivity in the anterior cingulate cortex as a neurobiological signature. However, their metabolic alterations are yet to be uncovered.

OBJECTIVE

Herein, a metabolic (FDG-PET), amyloid (PIB-PET), and functional (fMRI) analysis of SA were conducted.

METHODS

Ten SA, ten age-matched older adults (C80), and ten cognitively normal middle-aged (C50) adults underwent cognitive testing and multimodal neuroimaging examinations. Anterior and posterior regions of the cingulate cortex and hippocampal areas were primarily examined, then subregions of anterior cingulate were segregated.

RESULTS

The SA group showed increased metabolic activity in the left and right subgenual anterior cingulate cortex (sACC, p < 0.005 corrected, bilateral) and bilateral hippocampi (right: p < 0.0005 and left: p < 0.005, both corrected) as compared to that in the C80 group. Amyloid deposition was above threshold in 30% of SA and C80 (p > 0.05). The SA group also presented decreased connectivity between right sACC and posterior cingulate (p < 0.005, corrected) as compared to that of the C80 group.

CONCLUSION

These results support the key role of sACC and hippocampus in SA, even in the presence of amyloid deposition. It also suggests that sACC may be used as a potential biomarker in older adults for exceptional memory ability. Further longitudinal studies measuring metabolic biomarkers may help elucidate the interaction between these areas in the cognitive aging process.

The impact of COVID-19 pandemic on gender-related work from home in STEM fields-Report of the WiMPBME Task Group

The COVID-19 pandemic has forced many people, including those in the fields of science and engineering, to work from home. The new working environment caused by the pandemic is assumed to have a different impact on the amount of work that women and men can do from home. Particularly, if the major burden of child and other types of care is still predominantly on the shoulders of women. As such, a survey was conducted to assess the main issues that biomedical engineers, medical physicists (academics and professionals), and other similar professionals have been facing when working from home during the pandemic. A survey was created and disseminated worldwide. It originated from a committee of International Union for Physical and Engineering Sciences in Medicine (IUPESM; Women in Medical Physics and Biomedical Engineering Task Group) and supported by the Union. The ethics clearance was received from Carleton University. The survey was deployed on the Survey Monkey platform and the results were analyzed using IBM SPSS software. The analyses mainly consisted of frequency of the demographic parameters and the cross-tabulation of gender with all relevant variables describing the impact of work at home. A total of 921 responses from biomedical professions in 76 countries were received: 339 males, 573 females, and nine prefer-not-to-say/other. Regarding marital/partnership status, 85% of males were married or in partnership, and 15% were single, whereas 72% of females were married or in partnership, and 26% were single. More women were working from home during the pandemic (68%) versus 50% of men. More men had access to an office at home (68%) versus 64% for women. The proportion of men spending more than 3 h on child care and schooling per day was 12%, while for women it was 22%; for household duties, 8% of men spent more than 3 h; for women, this was 12.5%. It is interesting to note that 44% of men spent between 1 and 3 h per day on household duties, while for women, it was 55%. The high number of survey responses can be considered excellent. It is interesting to note that men participate in childcare and household duties in a relatively high percentage; although this corresponds to less hours daily than for women. It is far more than can be found 2 and 3 decades ago. This may reflect the situation in the developed countries only-as majority of responses (75%) was received from these countries. It is evident that the burden of childcare and household duties will have a negative impact on the careers of women if the burden is not more similar for both sexes. It is important to recognize that a change in policies of organizations that hire them may be required to provide accommodation and compensation to minimize the negative impact on the professional status and career of men and women who work in STEM fields.

Pharmacokinetic modeling of [11C]flumazenil kinetics in the rat brain

Background

Preferred models for the pharmacokinetic analysis of [¹¹C]flumazenil human studies have been previously established. However, direct translation of these models and settings to animal studies might be sub-optimal. Therefore, this study evaluates pharmacokinetic models for the quantification of [¹¹C]flumazenil binding in the rat brain.

Dynamic (60 min) [¹¹C]flumazenil brain PET scans were performed in two groups of male Wistar rats (tracer dose (TD), n = 10 and pre-saturated (PS), n = 2). Time-activity curves from five regions were analyzed, including the pons (pseudo-reference region). Distribution volume (V_T) was calculated using one- and two-tissue compartment models (1TCM and 2TCM) and spectral analysis (SA). Binding potential (BP_ND) was determined from full and simplified reference tissue models with one or two compartments for the reference tissue (FRTM, SRTM, and SRTM-2C). Model preference was determined by Akaike information criterion (AIC), while parameter agreement was assessed by linear regression, repeated measurements ANOVA and Bland-Altman plots.

Results

1TCM and 2TCM fits of regions with high specific binding showed similar AIC, a preference for the 1TCM, and good V_T agreement (0.1% difference). In contrast, the 2TCM was markedly preferred and necessary for fitting low specific-binding regions, where a worse V_T agreement (17.6% difference) and significant V_T differences between the models (p < 0.005) were seen. The PS group displayed results similar to those of low specific-binding regions. All reference models (FRTM, SRTM, and SRTM-2C) resulted in at least 13% underestimation of BP_ND.

Conclusions

Although the 1TCM was sufficient for the quantification of high specific-binding regions, the 2TCM was found to be the most adequate for the quantification of [¹¹C]flumazenil in the rat brain based on (1) higher fit quality, (2) lower AIC values, and (3) ability to provide reliable fits for all regions. Reference models resulted in negatively biased BP_ND and were affected by specific binding in the pons of the rat.

A Case-Based Study with Radiologists Performing Diagnosis Tasks in Virtual Reality

In radiology diagnosis, medical images are most often visualized slice by slice. At the same time, the visualization based on 3D volumetric rendering of the data is considered useful and has increased its field of application. In this work, we present a case-based study with 16 medical specialists to assess the diagnostic effectiveness of a Virtual Reality interface in fracture identification over 3D volumetric reconstructions. We developed a VR volume viewer compatible with both the Oculus Rift and handheld-based head mounted displays (HMDs). We then performed user experiments to validate the approach in a diagnosis environment. In addition, we assessed the subjects' perception of the 3D reconstruction quality, ease of interaction and ergonomics, and also the users opinion on how VR applications can be useful in healthcare. Among other results, we have found a high level of effectiveness of the VR interface in identifying superficial fractures on head CTs.

Primary progressive aphasia patients evaluated using diffusion tensor imaging and voxel based volumetry-preliminary results

There are individuals who have a progressive language deficit without presenting cognitive deficits in other areas. One of the diseases related to this presentation is primary progressive aphasia (PPA).

OBJECTIVE

Identify by means of diffusion tensor imaging (DTI) and measurements of cortical volume, brain areas that lead to dysphasia when presenting signs of impaired connectivity or reduced volume.

METHOD

Four patients with PPA were evaluated using DTI, and measurements of cortical volumes in temporal areas. These patients were compared with two normal volunteers.

RESULTS

There is a trend to a difference in the number and volume of related fibers between control group and patients with PPA. Comparing cortical volumes in temporal areas between groups yielded a trend to a smaller volume in PPA patients.

CONCLUSION

Patients with PPA have a trend to impairment in cortical and subcortical levels regarding relevant areas.

Ambiente colaborativo para formação de pessoal em medicina nuclear

OBJETIVO: Validar a proposta do desenvolvimento de um ambiente colaborativo virtual para formação de pessoal em medicina nuclear. MATERIAIS E MÉTODOS: No desenvolvimento inicial do ambiente foram levantadas as premissas, restrições e funcionalidades que deveriam ser oferecidas aos profissionais da área. O protótipo foi desenvolvido no ambiente Moodle, incluindo funcionalidades de armazenamento de dados e interação. Um estudo piloto de interação no ambiente foi realizado com uma amostra de profissionais especialistas em medicina nuclear. Análises quantitativas e de conteúdo foram realizadas a partir de um questionário semiestruturado de opinião dos usuários. RESULTADOS: A proposta do ambiente colaborativo foi validada por uma comunidade de profissionais que atuam nesta área e considerada relevante visando a auxiliar na formação de pessoal. Sugestões de melhorias e novas funcionalidades foram indicadas. Observou-se a necessidade de estabelecer um programa de formação dos moderadores no ambiente, visto que são necessárias características de interação distintas do ensino presencial. CONCLUSÃO: O ambiente colaborativo poderá permitir a troca de experiências e a discussão de casos entre profissionais localizados em instituições de diferentes regiões do País, possibilitando uma aproximação e colaboração entre esses profissionais. Assim, o ambiente pode contribuir para formação inicial e continuada de profissionais que atuam em medicina nuclear.