Application of deep learning to ischemic and hemorrhagic stroke computed tomography and magnetic resonance imaging
Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been
applied not only to the “downstream” side such as lesion detection, treatment decision …
applied not only to the “downstream” side such as lesion detection, treatment decision …
Artificial intelligence in neuroimaging: clinical applications
Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in
image recognition tasks. Over the past decade, AI has proven its feasibility for applications in …
image recognition tasks. Over the past decade, AI has proven its feasibility for applications in …
Reduction of scan duration and radiation dose in cerebral CT perfusion imaging of acute stroke using a recurrent neural network
MD Moghari, A Sanaat, N Young… - Physics in Medicine …, 2023 - iopscience.iop.org
Objective. Cerebral CT perfusion (CTP) imaging is most commonly used to diagnose acute
ischaemic stroke and support treatment decisions. Shortening CTP scan duration is …
ischaemic stroke and support treatment decisions. Shortening CTP scan duration is …
Machine learning applications of surgical imaging for the diagnosis and treatment of spine disorders: current state of the art
P Karandikar, E Massaad, M Hadzipasic… - …, 2022 - journals.lww.com
Recent developments in machine learning (ML) methods demonstrate unparalleled
potential for application in the spine. The ability for ML to provide diagnostic faculty, produce …
potential for application in the spine. The ability for ML to provide diagnostic faculty, produce …
OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features
W Ye, X Chen, P Li, Y Tao, Z Wang, C Gao… - Frontiers in …, 2023 - frontiersin.org
Background Early stroke prognosis assessments are critical for decision-making regarding
therapeutic intervention. We introduced the concepts of data combination, method …
therapeutic intervention. We introduced the concepts of data combination, method …
Learning non-local perfusion textures for high-quality computed tomography perfusion imaging
Background. Computed tomography perfusion (CTP) imaging plays a critical role in the
acute stroke syndrome assessment due to its widespread availability, speed of image …
acute stroke syndrome assessment due to its widespread availability, speed of image …
Head movement during cerebral CT perfusion imaging of acute ischaemic stroke: Characterisation and correlation with patient baseline features
MD Moghari, N Young, K Moore, RR Fulton… - European Journal of …, 2021 - Elsevier
Purpose To quantitatively characterise head motion prevalence and severity and to identify
patient-based risk factors for motion during cerebral CT perfusion (CTP) imaging of acute …
patient-based risk factors for motion during cerebral CT perfusion (CTP) imaging of acute …
Basis and current state of computed tomography perfusion imaging: a review
D Zeng, C Zeng, Z Zeng, S Li, Z Deng… - Physics in Medicine …, 2022 - iopscience.iop.org
Computed tomography perfusion (CTP) is a functional imaging that allows for providing
capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we …
capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we …
Deep Learning De-Noising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study
ABSTRACT BACKGROUND AND PURPOSE: Considering recent iodinated contrast media
(ICM) shortages, this study compared reduced ICM and standard dose CTP acquisitions …
(ICM) shortages, this study compared reduced ICM and standard dose CTP acquisitions …
A review of deep learning and Generative Adversarial Networks applications in medical image analysis
Nowadays, computer-aided decision support systems (CADs) for the analysis of images
have been a perennial technique in the medical imaging field. In CADs, deep learning …
have been a perennial technique in the medical imaging field. In CADs, deep learning …