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Total 224 results found since Jan 2013.
Defining reperfusion post endovascular therapy in ischemic stroke using MR-dynamic contrast enhanced perfusion.
CONCLUSION: MR perfusion following EVT provides accurate physiological approach to understanding the relationship of CBF, clinical outcome, and DWI growth.
ADVANCES IN KNOWLEDGE: MR perfusion CBF acquired is a robust, objective reperfusion measurement providing following recanalization of the target occlusion which is critical to distinguish potential therapeutic harm from the failed technical success of EVT as well as improve the responsiveness of clinical trial outcomes to disease modification.
PMID: 32941770 [PubMed - as supplied by publisher]
Source: The British Journal of Radiology - September 16, 2020 Category: Radiology Authors: d'Esterre CD, Sah RG, Assis Z, Talai AS, Demchuk AM, Hill MD, Goyal M, Lee TY, Forkert ND, Barber PA Tags: Br J Radiol Source Type: research
CT radiomics predicts esophageal cancer outcomes
Machine-learning models that assess both peritumoral and intratumoral radiomics...Read more on AuntMinnie.comRelated Reading:
PET radiomics tailor head/neck cancer treatment
CT radiomics can predict COVID-19 pneumonia outcomes
AI, radiomics can predict stroke treatment success
Can radiomics improve CT lung cancer screening?
AI can predict if COVID-19 patients will need ventilators
Source: AuntMinnie.com Headlines - September 11, 2020 Category: Radiology Source Type: news
AI, radiomics can predict stroke treatment success
Artificial intelligence (AI) and radiomics can help to predict if a particular...Read more on AuntMinnie.comRelated Reading:
ECR 2020: COVID-19's nonpulmonary manifestations
COVID-19 neuro findings marked by mental status, stroke
fMRI-based machine learning helps predict coma outcomes
Stroke scans drop by 40% during COVID-19 outbreak
Source: AuntMinnie.com Headlines - July 28, 2020 Category: Radiology Source Type: news
Brain lesions on DWI-MRI linked to poor outcomes for some
Lesions that appear on diffusion-weighted imaging (DWI) MRI scans represent...Read more on AuntMinnie.comRelated Reading:
Deep-learning model detects cerebral microbleeds on MRI
Key MRI markers link vascular brain injury to health risks
5 risk factors help predict brain hemorrhage on CT
Combination of ultrasound, tPA may boost stroke treatment
CT guidance helps brain hemorrhage outcomes
Source: AuntMinnie.com Headlines - July 21, 2020 Category: Radiology Source Type: news
Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm
AbstractTo investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologi st. Manual volumetric analysis was performed by two radiology trainees. Vol...
Source: The International Journal of Cardiovascular Imaging - July 15, 2020 Category: Radiology Source Type: research
Double-contrast technique could boost MRI for cancer
A new double-contrast MRI technique in development could help the modality...Read more on AuntMinnie.comRelated Reading:
COVID-19 neuro findings marked by mental status, stroke
fMRI-based machine learning helps predict coma outcomes
ISMRM annual meeting goes virtual
MRI illuminates neurologic manifestations of COVID-19
MRI could help predict efficacy of stem cell therapy
Source: AuntMinnie.com Headlines - May 28, 2020 Category: Radiology Source Type: news
Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers ’ performance
ConclusionThe ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.
Source: Japanese Journal of Radiology - May 11, 2020 Category: Radiology Source Type: research
Machine learning volumetry of ischemic brain lesions on CT after thrombectomy —prospective diagnostic accuracy study in ischemic stroke patients
ConclusionNCCT ILV measured automatically by the Brainomix software might be considered a valuable radiological outcome measure.
Source: Neuroradiology - April 21, 2020 Category: Radiology Source Type: research
Deep Learning Predicts Stroke-Lesion Changes at 1 Week Deep Learning Predicts Stroke-Lesion Changes at 1 Week
Armed with only baseline MRI findings, a new deep learning algorithm may accurately estimate size and location of acute stroke lesions at 3 to 7 days.Medscape Medical News
Source: Medscape Radiology Headlines - March 20, 2020 Category: Radiology Tags: Neurology & Neurosurgery News Source Type: news
AI comparable to rads for interpreting wrist x-rays
An artificial intelligence (AI) deep-learning algorithm performs comparably...Read more on AuntMinnie.comRelated Reading:
Ultrasound can benefit daily rheumatology practice
Skip ultrasound for treatment decisions in rheumatoid arthritis
AI accurately estimates bone age on 3D hand MRI
AI can detect, localize fractures on wrist x-rays
Ultrasound imaging gauges muscle tightness after stroke
Source: AuntMinnie.com Headlines - March 17, 2020 Category: Radiology Source Type: news
Abstract No. 720 Identification of irreversibly damaged brain tissue on computed tomography perfusion using convolutional neural network to assist selection for mechanical thrombectomy in ischemic stroke patients
Endovascular treatment of ischemic stroke has shown positive clinical outcomes. Further optimization requires identifying patients who will benefit from reperfusion. We propose using deep learning, specifically 3D convolutional neural networks (CNN), to identify infarcted tissue (core) on CT perfusion (CTP) with diffusion weighted imaging (DWI) MRI as gold standard for irreversible brain infarction and evaluate lesion size impact on the network ’s performance.
Source: Journal of Vascular and Interventional Radiology : JVIR - February 20, 2020 Category: Radiology Authors: R. Wang, K. Chang, H. Zhou, J. Wu, G. Cohan, M. Jayaraman, R. Huang, J. Boxerman, L. Yang, F. Hui, J. Woo, H. Bai Tags: Scientific Traditional Poster Source Type: research
Radiation exposure and fluoroscopy time in mechanical thrombectomy of anterior circulation ischemic stroke depending on the interventionalist ’s experience—a retrospective single center experience
ConclusionThis retrospective analysis demonstrates a significant influence of interventionalist ’s experience on procedure time, fluoroscopy time, and radiation exposure in mechanical thrombectomy in the anterior circulation.Key Points• There is a significant influence of interventionalist’s experience on procedure time, fluoroscopy time, and radiation exposure in mechanical thrombectomy in the anterior circulation.• Interventionalists’ learning curve is steepest during the first 25 cases.• These circumstances should be considered when reference levels or guide values are established and in training of physicia...
Source: European Radiology - February 20, 2020 Category: Radiology Source Type: research
CT radiomics unlocks basal ganglia stroke onset time
The combination of radiomics and a machine-learning algorithm can determine...Read more on AuntMinnie.comRelated Reading:
AI may help improve management of stroke patients
AI finds infarction in stroke patients on unenhanced CT
CT plus CT perfusion predicts stroke surgery outcomes
CTA lowers costs, improves outcomes for minor stroke
Can AI find brain hemorrhage as well as radiologists?
Source: AuntMinnie.com Headlines - February 11, 2020 Category: Radiology Source Type: news
A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.
CONCLUSION: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.
PMID: 31975663 [PubMed - in process]
Source: Current Medical Imaging Reviews - January 26, 2020 Category: Radiology Tags: Curr Med Imaging Rev Source Type: research
Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training
Publication date: Available online 30 December 2019Source: NeuroImage: ClinicalAuthor(s): Geneviève Richard, Knut Kolskår, Kristine M. Ulrichsen, Tobias Kaufmann, Dag Alnæs, Anne-Marthe Sanders, Erlend S. Dørum, Jennifer Monereo Sánchez, Anders Petersen, Hege Ihle-Hansen, Jan Egil Nordvik, Lars T. WestlyeAbstractCognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but...
Source: NeuroImage: Clinical - December 30, 2019 Category: Radiology Source Type: research
