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CT scan of multi-infarct dementia showing the presence of three large infarcts, two in the chronic stage on the left hemisphere and one subacute (arrows) in the right hemisphere, in a patient with internal bilateral carotid stenosis.
Source publication
Neuroimaging has become part of the required investigations when assessing a patient with dementia. In this brief paper, we summarize the role of computed tomography (CT) in the routine work-up in dementia and provide some information about the role of the CT scan in the field of dementia research.
Although CT is far less sensitive than magnetic re...
Context in source publication
Context 1
... CT, small vessel vascular dementia is characterized by the presence of lacunes and white matter changes; in multi-infarct dementia, multiple infarcts are located in cortical and/or subcortical areas ( Figure 5). In our opinion, the term multi- infarct dementia is best suited to patients with multiple non-lacunar infarcts caused by large vessel disease or cardioembolism, while those with multiple lacunar infarcts are best considered with the group of small vessel disease. In strategic infarct dementia, the single lesion can be either cortical or lacunar. Typical locations of strategic lacunar infarcts are the thalamus (Figure 6), internal capsule, particularly the genu, and head of the caudate nucleus. Strategic non-lacunar infarcts are located in the angular gyrus and the posterior cerebral artery territory involving the medial part of the temporal ...
Citations
... 13 CT-based visual ratings are comparable to those obtained from MR imaging with regard to certain pathomorphological characteristics, and they are correlated significantly with cognitive test results. 13,14 However, MR imaging (MRI) is extensively used for highresolution atrophy assessment and brain volumetry [15][16][17] due to its stronger soft-tissue contrast. Atrophy assessment in CT is conducted through semi-quantitative visual ratings, a subjective, time-consuming approach that requires a trained expert. ...
INTRODUCTION
Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep‐learning–based model that produced accurate and robust cranial CT tissue classification.
MATERIALS AND METHODS
We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT‐based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition.
RESULTS
CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR‐based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration.
DISCUSSION
These findings suggest the potential future use of CT‐based volumetric measures as an informative first‐line examination tool for neurodegenerative disease diagnostics after further validation.
Highlights
Computed tomography (CT)–based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls.
CT‐based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases.
Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature.
Intermodality agreement between our automated CT‐based and established magnetic resonance (MR)–based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.
... These signs include brain atrophy, cerebral microbleeds, and cerebral small vessel disease as potential underlying risk factors of dementia with more speci c signature cerebral topographic regions [127,128]. Although Computed Tomography (CT) is less sensitive and speci c than MRI in detecting changes related to cognitive dysfunction, it may still have a role in detecting secondary, and often potentially treatable causes of cognitive impairment, such as periventricular leucoaraiosis, ventriculomegaly, cortical or subcortical atrophy, intracranial masses and normal pressure hydrocephalus [129]. Due to its availability, lower cost, and shorter acquisition time, brain CT is often used as a rst-line tool in many resource-limited settings where it is the only available option [124,129]. ...
... Although Computed Tomography (CT) is less sensitive and speci c than MRI in detecting changes related to cognitive dysfunction, it may still have a role in detecting secondary, and often potentially treatable causes of cognitive impairment, such as periventricular leucoaraiosis, ventriculomegaly, cortical or subcortical atrophy, intracranial masses and normal pressure hydrocephalus [129]. Due to its availability, lower cost, and shorter acquisition time, brain CT is often used as a rst-line tool in many resource-limited settings where it is the only available option [124,129]. Several hospital-based prevalence studies conducted in SSA have identi ed causes of dementia ranging from degenerative causes to vascular, and infectious (HIV-related) using MRI assessment [93,130,131]. A study conducted in Togo to assess the brain CT features in dementia reported about 17% of normal CT ndings [132]. ...
Dementia is a global public health problem with increasing prevalence and incidence worldwide. The African continent is expected to bear the biggest brunt of the burden of dementia by 2050 because of the rapid demographic changes, including rapid population growth, an increase in life expectancy, and ageing. However, French-speaking Sub-Saharan African (FS-SSA) countries are underrepresented in research on dementia in Africa. While the reasons are diverse and complex, linguistic and cultural barriers to research, disproportionately affect these countries and may be significant factors. Any efforts, therefore, to redress the burden of dementia in Africa must consider the specific demographic, cultural, and linguistic characteristics of FS-SSA countries. This scoping review explores the current state of knowledge in dementia and cognitive impairment in Sub-Saharan Africa, highlighting research gaps and specific patterns unique to FS-SSA Africa. We identify pathways for research to bridge the knowledge gaps on dementia in FS-SSA as part of the global endeavor to tackle dementia worldwide.
... CT is not recommended for first-line imaging as it is less sensitive in detecting changes associated with cognitive impairment compared to MRI. However, there are still many advantages such as lower cost, shorter acquisition time, and wider availability (Pasi et al., 2011;Health Quality Ontario, 2014). ...
Alzheimer’s disease (AD) is a progressive, neurodegenerative disorder that affects memory, thinking, behavior, and other cognitive functions. Although there is no cure, detecting AD early is important for the development of a therapeutic plan and a care plan that may preserve cognitive function and prevent irreversible damage. Neuroimaging, such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), has served as a critical tool in establishing diagnostic indicators of AD during the preclinical stage. However, as neuroimaging technology quickly advances, there is a challenge in analyzing and interpreting vast amounts of brain imaging data. Given these limitations, there is great interest in using artificial Intelligence (AI) to assist in this process. AI introduces limitless possibilities in the future diagnosis of AD, yet there is still resistance from the healthcare community to incorporate AI in the clinical setting. The goal of this review is to answer the question of whether AI should be used in conjunction with neuroimaging in the diagnosis of AD. To answer the question, the possible benefits and disadvantages of AI are discussed. The main advantages of AI are its potential to improve diagnostic accuracy, improve the efficiency in analyzing radiographic data, reduce physician burnout, and advance precision medicine. The disadvantages include generalization and data shortage, lack of in vivo gold standard, skepticism in the medical community, potential for physician bias, and concerns over patient information, privacy, and safety. Although the challenges present fundamental concerns and must be addressed when the time comes, it would be unethical not to use AI if it can improve patient health and outcome.
... Generally, the diagnosis of dementia is made by magnetic resonance imaging (MRI) [10,11], a technique that uses magnetic fields and radio waves to produce a 3D image of the brain. In a computed tomography (CT) scan, the machine images the brain with X-rays at different angles, and the image is processed to find if any remarkable changes are present [12]. The PET (Positron Emission Tomography) test uses radioactive traces to map the areas of the brain. ...
Dementia affects the patient’s memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.
... Cognitive and functional impairment is a typical feature of neurodegenerative disorders, including AD [34]. In the diagnostic setting of AD, brain imaging moved from a minor to central role [35,36]. ...
... As a matter of fact, only a few studies showed that MTA scales are applicable to CT in clinical practice [40][41][42][43][44][45][46][47], and even fewer demonstrated the use of MTA score by CT scan in diagnosing AD [36,45,47,48]. ...
Our study aims to investigate the relationship between medial temporal lobe atrophy (MTA) score, assessed by computed tomography (CT) scans, and functional impairment, cognitive deficit, and psycho-behavioral disorder severity. Overall, 239 (M = 92, F = 147; mean age of 79.3 ± 6.8 years) patients were evaluated with cognitive, neuropsychiatric, affective, and functional assessment scales. MTA was evaluated from 0 (no atrophy) to 4 (severe atrophy). The homocysteine serum was set to two levels: between 0 and 10 µmol/L, and > 10 µmol/L. The cholesterol and glycemia blood concentrations were measured. Hypertension and atrial fibrillation presence/absence were collected. A total of 14 patients were MTA 0, 44 patients were MTA 1, 63 patients were MTA 2, 79 patients were MTA 3, and 39 patients were MTA 4. Cognitive (p < 0.0001) and functional (p < 0.0001) parameters decreased according to the MTA severity. According to the diagnosis distribution, AD patient percentages increased by MTA severity (p < 0.0001). In addition, the homocysteine levels increased according to MTA severity (p < 0.0001). Depression (p < 0.0001) and anxiety (p = 0.001) increased according to MTA severity. This study encourages and supports the potential role of MTA score and CT scan in the field of neurodegenerative disorder research and diagnosis.
... The utility of non-MRI modalities in the identification of ARIA represents a current gap in the literature. This is a key area of interest owing to the elevated risk for patients with dementia of developing comorbidities that require the implantation of medical or biostimulation devices (50). Many of these devices are contraindications of MRI, including pacemakers, vagus nerve stimulators, i m p l a n t a b l e c a rd i o v e r t e r-d e fi b r i l l a t o r s , l o o p recorders, insulin pumps, and cochlear implants (50). ...
... This is a key area of interest owing to the elevated risk for patients with dementia of developing comorbidities that require the implantation of medical or biostimulation devices (50). Many of these devices are contraindications of MRI, including pacemakers, vagus nerve stimulators, i m p l a n t a b l e c a rd i o v e r t e r-d e fi b r i l l a t o r s , l o o p recorders, insulin pumps, and cochlear implants (50). Consequently, the use of non-MRI methods for ARIA needs to be validated to ensure that adequate detection and monitoring procedures can be performed in all patients. ...
... Consequently, the use of non-MRI methods for ARIA needs to be validated to ensure that adequate detection and monitoring procedures can be performed in all patients. A possible candidate is computed tomography (CT); however, the use of this modality is limited by its relatively low spatial definition and resolution in comparison with MRI (50). Therefore, CT would not be expected to detect milder forms of ARIA-E and is insensitive to the detection of microhemorrhages and siderosis. ...
Amyloid-related imaging abnormalities (ARIA) are adverse events reported in Alzheimer's disease trials of anti-amyloid beta (Aβ) therapies. This review summarizes the existing literature on ARIA, including bapineuzumab, gantenerumab, donanemab, lecanemab, and aducanumab studies, with regard to potential risk factors, detection, and management. The pathophysiology of ARIA is unclear, but it may be related to binding of antibodies to accumulated Aβ in both the cerebral parenchyma and vasculature, resulting in loss of vessel wall integrity and increased leakage into surrounding tissues. Radiographically, ARIA-E is identified as vasogenic edema in the brain parenchyma or sulcal effusions in the leptomeninges/sulci, while ARIA-H is hemosiderin deposits presenting as microhemorrhages or superficial siderosis. ARIA tends to be transient and asymptomatic in most cases, typically occurring early in the course of treatment, with the risk decreasing later in treatment. Limited data are available on continued dosing following radiographic findings of ARIA; hence, in the event of ARIA, treatment should be continued with caution and regular monitoring. Clinical trials have implemented management approaches such as temporary suspension of treatment until symptoms or radiographic signs of ARIA have resolved or permanent discontinuation of treatment. ARIA largely resolves without concomitant treatment, and there are no systematic data on potential treatments for ARIA. Given the availability of an anti-Aβ therapy, ARIA monitoring will now be implemented in routine clinical practice. The simple magnetic resonance imaging sequences used in clinical trials are likely sufficient for effective detection of cases. Increased awareness and education of ARIA among clinicians and radiologists is vital.
... X-ray CT and MRI are the most frequently used modalities for structural assessment in neurodegenerative disorders (Wattjes et al., 2009;Pasi et al., 2011). MRI scans are commonly used for image-based tissue classification to quantify and extract atrophy-related measures from structural neuroimaging modalities (Despotović et al., 2015). ...
Brain tissue segmentation plays a crucial role in feature extraction, volumetric quantification, and morphometric analysis of brain scans. For the assessment of brain structure and integrity, CT is a non-invasive, cheaper, faster, and more widely available modality than MRI. However, the clinical application of CT is mostly limited to the visual assessment of brain integrity and exclusion of copathologies. We have previously developed two-dimensional (2D) deep learning-based segmentation networks that successfully classified brain tissue in head CT. Recently, deep learning-based MRI segmentation models successfully use patch-based three-dimensional (3D) segmentation networks. In this study, we aimed to develop patch-based 3D segmentation networks for CT brain tissue classification. Furthermore, we aimed to compare the performance of 2D- and 3D-based segmentation networks to perform brain tissue classification in anisotropic CT scans. For this purpose, we developed 2D and 3D U-Net-based deep learning models that were trained and validated on MR-derived segmentations from scans of 744 participants of the Gothenburg H70 Cohort with both CT and T1-weighted MRI scans acquired timely close to each other. Segmentation performance of both 2D and 3D models was evaluated on 234 unseen datasets using measures of distance, spatial similarity, and tissue volume. Single-task slice-wise processed 2D U-Nets performed better than multitask patch-based 3D U-Nets in CT brain tissue classification. These findings provide support to the use of 2D U-Nets to segment brain tissue in one-dimensional (1D) CT. This could increase the application of CT to detect brain abnormalities in clinical settings.
... CT uses dedicated X-ray equipment to generate 3D image volumes of the body, whereas MR uses electromagnetic fields to record and map the spatial variation of images according to the properties of the tissues (Jacobs et al., 2007;Westbrook and Talbot, 2018). In clinical settings, CT and MR imaging are the main examination tools for the structural assessment of brain abnormalities in dementia disorders (Ashburner et al., 1997;Pasi et al., 2011;Wattjes et al., 2009). ...
Automatic methods for feature extraction, volumetry, and morphometric analysis in clinical neuroscience typically operate on images obtained with magnetic resonance (MR) imaging equipment. Although CT scans are less expensive to acquire and more widely available than MR scans, their application is currently limited to the visual assessment of brain integrity and the exclusion of co-pathologies. CT has rarely been used for tissue classification because the contrast between grey matter and white matter was considered insufficient. In this study, we propose an automatic method for segmenting grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and intracranial volume (ICV) from head CT images. A U-Net deep learning model was trained and validated on CT images with MRI-derived segmentation labels. We used data from 744 participants of the Gothenburg H70 Birth Cohort Studies for whom CT and T1-weighted MR images had been acquired on the same day. Our proposed model predicted brain tissue classes accurately from unseen CT images (Dice coefficients of 0.79, 0.82, 0.75, 0.93 and 0.98 for GM, WM, CSF, brain volume and ICV, respectively). To contextualize these results, we generated benchmarks based on established MR-based methods and intentional image degradation. Our findings demonstrate that CT-derived segmentations can be used to delineate and quantify brain tissues, opening new possibilities for the use of CT in clinical practice and research.
... Furthermore, MRI is more useful when combined with a detailed clinical assessment for Alzheimer disease and might not be necessary in an emergency setting [21]. Additionally, whereas MRI tends to be the preferred imaging modality in the diagnosis and evaluation of individuals with dementia, CT is more cost-effective and accessible and can be used to rule out other potential causes that may explain patients' presentation to the ED [22][23][24]. Other contributing reasons potentially include out-of-pocket costs associated with diagnostic tests, physicians' practice variations, and patients' concerns about radiation and safety [21]. ...
OBJECTIVE. This article aimed to assess changing use of brain imaging tests among patients with Alzheimer disease and vascular dementia who visited U.S. emergency departments (EDs) between 2006 and 2014. MATERIALS AND METHODS. Using the largest publicly available all-payer ED database, the Nationwide Emergency Department Sample, we identified a weighted cohort of 427,705 individuals with Alzheimer disease and 33,743 individuals with vascular dementia who visited U.S. EDs between 2006 and 2014. Logistic regression analyses were performed to identify factors associated with use. RESULTS. Between 2006 and 2014, ED visits among patients with Alzheimer disease and vascular dementia declined by 24.7% and 20.3%, respectively. However, there was a significant increase in utilization rates of head CT (from 4.4% to 11.1% in patients with Alzheimer disease and from 1.5% to 2.9% in patients with vascular dementia) and brain MRI (from 0.04% to 0.5% in patients with Alzheimer disease and 0.0% to 0.1% in those with vascular dementia) in the same time period. Among patients with Alzheimer disease, age, median income in patient ZIP code, day of the week of the ED visit, hospital teaching status, and hospital geographic region were significant predictors of imaging use. Among patients with vascular dementia, insurance type and hospital classification (urban vs rural) were significant predictors of imaging use. CONCLUSION. Despite declining ED visits, ED brain imaging in patients with Alzheimer disease and vascular dementia has increased. Various patient-specific and hospital-specific factors contribute to differential utilization rates.
... CT is equally accurate as MRI for pathologies such as brain tumours, subdural haematomas, many larger infarcts, acute haemorrhages, and can show brain atrophy, moderate to severe white matter lesions (leukoaraiosis), and lacunes. 28 Nevertheless, differentiation of old haemorrhages from old infarcts, identification of microbleeds and cSS and some small acute infarcts is not reliable on CT. MRI is much more Acute or old cortical infarcts; Acute or old subcortical infarcts; acute or old brain haemorrhage; WMH, lacunes, microbleeds, cortical siderosis; brain atrophy including regional distribution T1-weighted, T2-weighted, FLAIR, SWI and DWI sequences are all essential to assess for the range of cerebrovascular disease lesions. ...
Purpose
Practical suggestions on clinical decisions about vascular disease management in patients with cognitive impairment are proposed.
Methods
The document was produced by the Dementia Committee of the European Stroke Organisation (ESO) based on the evidence from the literature where available and on the clinical experience of the Committee members. This paper was endorsed by the ESO.
Findings
Vascular risk factors and cerebrovascular disease are frequent in patients with cognitive impairment. While acute stroke treatment has evolved substantially in last decades, evidence of management of cerebrovascular pathology beyond stroke in patients with cognitive impairment and dementia is quite limited. Additionally, trials to test some daily-life clinical decisions are likely to be complex, difficult to undertake and take many years to provide sufficient evidence to produce recommendations. This document was conceived to provide some suggestions until data from field trials are available. It was conceived for the use of clinicians from memory clinics or involved specifically in cognitive disorders, addressing practical aspects on diagnostic tools, vascular risk management and suggestions on some therapeutic options.
Discussion and conclusions
The authors did not aim to do an exhaustive or systematic review or to cover all current evidence. The document approach in a very practical way frequent issues concerning cerebrovascular disease in patients with known cognitive impairment.
