171 research outputs found

    Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

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    Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities

    IDENTIFICATION OF OCCULT CEREBRAL MICROBLEEDS IN ADULTS WITH IMMUNE THROMBOCYTOPENIA

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    Management of symptoms and prevention of life-threatening hemorrhage in immune thrombocytopenia (ITP) must be balanced against adverse effects of therapies. Because current treatment guidelines based on platelet count are confounded by variable bleeding phenotypes, there is a need to identify new objective markers of disease severity for treatment stratification. In this cross-sectional prospective study of 49 patients with ITP and nadir platelet counts <30 × 109/L and 18 aged-matched healthy controls, we used susceptibility-weighted magnetic resonance imaging to detect cerebral microbleeds (CMBs) as a marker of occult hemorrhage. CMBs were detected using a semiautomated method and correlated with clinical metadata using multivariate regression analysis. No CMBs were detected in health controls. In contrast, lobar CMBs were identified in 43% (21 of 49) of patients with ITP; prevalence increased with decreasing nadir platelet count (0/4, ≥15 × 109/L; 2/9, 10-14 × 109/L; 4/11, 5-9 × 109/L; 15/25 <5 × 109/L) and was associated with longer disease duration (P = 7 × 10−6), lower nadir platelet count (P = .005), lower platelet count at time of neuroimaging (P = .029), and higher organ bleeding scores (P = .028). Mucosal and skin bleeding scores, number of previous treatments, age, and sex were not associated with CMBs. Occult cerebral microhemorrhage is common in patients with moderate to severe ITP. Strong associations with ITP duration may reflect CMB accrual over time or more refractory disease. Further longitudinal studies in children and adults will allow greater understanding of the natural history and clinical and prognostic significance of CMBs

    DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

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    Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies

    Accelerated development of cerebral small vessel disease in young stroke patients.

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    OBJECTIVE: To study the long-term prevalence of small vessel disease after young stroke and to compare this to healthy controls. METHODS: This prospective cohort study comprises 337 patients with an ischemic stroke or TIA, aged 18-50 years, without a history of TIA or stroke. In addition, 90 age- and sex-matched controls were included. At follow-up, lacunes, microbleeds, and white matter hyperintensity (WMH) volume were assessed using MRI. To investigate the relation between risk factors and small vessel disease, logistic and linear regression were used. RESULTS: After mean follow-up of 9.9 (SD 8.1) years, 337 patients were included (227 with an ischemic stroke and 110 with a TIA). Mean age of patients was 49.8 years (SD 10.3) and 45.4% were men; for controls, mean age was 49.4 years (SD 11.9) and 45.6% were men. Compared with controls, patients more often had at least 1 lacune (24.0% vs 4.5%, p < 0.0001). In addition, they had a higher WMH volume (median 1.5 mL [interquartile range (IQR) 0.5-3.7] vs 0.4 mL [IQR 0.0-1.0], p < 0.001). Compared with controls, patients had the same volume WMHs on average 10-20 years earlier. In the patient group, age at stroke (β = 0.03, 95% confidence interval [CI] 0.02-0.04) hypertension (β = 0.22, 95% CI 0.04-0.39), and smoking (β = 0.18, 95% CI 0.01-0.34) at baseline were associated with WMH volume. CONCLUSIONS: Patients with a young stroke have a higher burden of small vessel disease than controls adjusted for confounders. Cerebral aging seems accelerated by 10-20 years in these patients, which may suggest an increased vulnerability to vascular risk factors.This is the final version of the article. It first appeared from Wolters Kluwer via https://doi.org/10.​1212/​WNL.​0000000000003123

    New horizons in cognitive and functional impairment as a consequence of cerebral small vessel disease

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    Cerebral small vessel disease (cSVD) is a frequent finding in imaging of the brain in older adults, especially in the concomitance of cardiovascular disease risk factors. Despite the well-established link between cSVD and (vascular) cognitive impairment (VCI), it remains uncertain how and when these vascular alterations lead to cognitive decline. The extent of acknowledged markers of cSVD is at best modestly associated with the severity of clinical symptoms, but technological advances increasingly allow to identify and quantify the extent and perhaps also the functional impact of cSVD more accurately. This will facilitate a more accurate diagnosis of VCI, against the backdrop of concomitant other neurodegenerative pathology, and help to identify persons with the greatest risk of cognitive and functional deterioration. In this study, we discuss how better assessment of cSVD using refined neuropsychological and comprehensive geriatric assessment as well as modern image analysis techniques may improve diagnosis and possibly the prognosis of VCI. Finally, we discuss new avenues in the treatment of cSVD and outline how these contemporary insights into cSVD can contribute to optimise screening and treatment strategies in older adults with cognitive impairment and multimorbidity.</p

    Assessment of Post-Treatment Imaging Changes Following Radiotherapy using Magnetic Susceptibility Techniques

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    Radiation therapy (RT) is a common treatment for brain neoplasms and is used alone or in combination with other therapies. The use of RT has been found to be successful in controlling tumors and extending the overall survival of patients; however, there are many unanswered questions regarding radiotherapy effects in the normal brain surrounding or infiltrated by tumor. Changes to the vascular and parenchyma have been documented, and more recently inflammatory mechanisms have been postulated to play a role in radiation injury. Traditional imaging techniques used within the clinic (CT and MRI) are often lacking in their ability to differentiate between recurrent tumor, transient treatment effects, or radiation necrosis. The primary goal of this thesis is to demonstrate an MRI acquisition method that has been shown to be sensitive to deoxygenated blood and iron content as a potential biomarker of radiation effect on the normal brain. Specifically, post-processing techniques are used to determine the applicability of qualitative images such as Susceptibility-Weighted Imaging (SWI) and quantitative methods such as Quantitative Susceptibility Mapping (QSM) and apparent traverse relaxation (R2*) using the same sequence. These methods are potential surrogate markers for vascular changes and neuroinflammatory components that could predict sub-acute and long-term radiation effects. Within this thesis, R2* is shown to be a promising marker for the prediction of radiation necrosis, whereas SWI and QSM are shown to be excellent modalities for detecting longterm effects such as microbleeds. Additionally, R2 * is shown to be a potentially useful technique in identifying post-imaging treatment changes (pseudoprogression) following chemoradiotherapy for malignant glioma. Finally, the use of this non-contrast method shows promise for integration within a clinical setting and the potential for expansion to multicenter clinical trials

    Characterizing and revealing biomarkers on patients with Cerebral Amyloid Angiopathy using artificial intelligence

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    Dissertação de mestrado em BioinformáticaCerebral Amyloid Angiopathy is a cerebrovascular disorder resulting from the deposition of an amyloidogenic protein in small and medium sized cortical and leptomeningeal vessels. A primary cause of spontaneous intracerebral haemorrhages, it manifests predominantly in the elder population. Although CAA is a common neuropathological finding on itself, it is also known to frequently occur in conjunction with Alzheimer’s disease, being sometimes misdiagnosed. Currently, CAA diagnosis is generally conducted by post-mortem examination or, in live patients by the examination of an evacuated hematoma or brain biopsy samples, which are typically unavailable. Therefore, a reliable and non-invasive method for diagnosing CAA would facilitate the clinical decision making and accelerate the clinical intervention. The main goal of this dissertation is to study the application of Machine Learning (ML) to reveal possible biomarkers to aid the diagnosis and early medical intervention, and better understand the disease. Therefore, three scenarios were tested: Classification of four neurodegenerative diseases with annotation data obtained from visual rating scores, age and gender; Classification of the diseases with radiomic data derived from the patient’s MRI; and a combination of the previous experiments. The results show that the application of Artificial intelligence in the medical field brings advantages to support the physicians in the decision making process and, at some point, make a correct prediction of the disease label. Although the results are satisfactory, there are still improvements to be done. For instance, image segmentation of cerebral lesions or brain regions and additional clinical information of the patients would be of value.Angiopatia Amiloide Cerebral (AAC) é uma doença vascular cerebral resultante da deposição de matéria amiloide. Principal causa de hemorragias cerebral espontâneas, a AAC manifesta se predominantemente na população idosa. Embora a AAC seja uma doença que por si só tem um grande impacto no grupo etário referido, ocorre em simultâneo com inúmeras outras doenças neurodegenerativas, como a doença de Alzheimer. Atualmente, o diagnóstico de AAC realiza-se quer em post-mortem, quer em pacientes vivos. No entanto, o diagnóstico em vida é conseguido por meio de biópsias de tecidos cerebrais, sendo um método invasivo, o que dificulta a intervenção clínica. Deste modo, torna-se imperativa a procura de alternativas fiáveis e não invasivas em vida para auxiliar o diagnóstico da doença e permitir a melhoria da qualidade de vida do paciente. Perante os progressos na área da tecnologia e medicina, esta dissertação propõe o estudo da aplicação de algoritmos de Machine Learning (ML) para revelar possíveis biomarcadores para auxiliar o diagnóstico e permitir uma intervenção precoce. Deste modo, foram testados três cenários distintos: a classificação de quatro doenças neurodegenerativas com dados anotados obtidos a partir de métricas visuais de avaliação da atrofia, idade e sexo; a classificação das doenças com dados gerados a partir de métodos radiómicos; e uma combinação das duas abordagens anteriores. Neste documento apresenta-se e discute-se os resultados obtidos com a aplicação de quatro diferentes algoritmos de ML que visam a deteção automática da doença associada à imagem testada. Adicionalmente, é feita uma análise crítica de quais as características mais relevantes que levaram à tomada de decisão por parte do algoritmo. Os resultados demonstram que através de aplicação de metodologias automáticas é possível o auxílio ao diagnostico médico por especialistas e, no limite, a realização de diagnostico automático com elevada precisão. Finalmente, são apresentadas possíveis alternativas de trabalho futuro para que os resultados possam ser aperfeiçoados, como por exemplo, a segmentação das regiões de interesse, i.e., identificação das lesões, aquando da anotação por especialistas. Mediante a inclusão dessa segmentação, uma vez que será mais especifica, os resultados serão, por sua vez, aprimorados
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