20.9.5 Brain, Stroke, Ischemic Stroke

Chapter Contents (Back)
Brain. Cortex. Stroke Detection.

Clay, M.T., Ferree, T.C.,
Weighted regularization in electrical impedance tomography with applications to acute cerebral stroke,
MedImg(21), No. 6, June 2002, pp. 629-637.
IEEE Top Reference. 0208

Schormann, T., Kraemer, M.,
Voxel-guided morphometry ('VGM') and application to stroke,
MedImg(22), No. 1, January 2003, pp. 62-74.
IEEE Top Reference. 0304

Charalampidis, D., Pascotto, M., Kerut, E.K., Lindner, J.R.,
Anatomy and Flow in Normal and Ischemic Microvasculature Based on a Novel Temporal Fractal Dimension Analysis Algorithm Using Contrast Enhanced Ultrasound,
MedImg(25), No. 8, August 2006, pp. 1079-1086.

Tan, T.L., Sim, K.S., Tso, C.P., Chong, A.K.,
Contrast enhancement of computed tomography images by adaptive histogram equalization-application for improved ischemic stroke detection,
IJIST(22), No. 3, September 2012, pp. 153-160.
DOI Link 1208

Bae, K.T.[Kyongtae Ty], Park, S.H.[Sung-Hong], Shim, H.[Hackjoon], Moon, C.H.[Chan-Hong], Kim, J.H.[Jung-Hwan], Nemoto, E.M.[Edwin M.],
Application of compatible dual-echo arteriovenography in stroke: Preliminary observations,
IJIST(23), No. 2, 2013, pp. 152-156.
DOI Link 1307
stroke, compatible dual-echo arteriovenography, vessel enhancement filtering, susceptibility weighted imaging, time-of-flight, blood oxygenation level-dependent, angiogram, venogram BibRef

Mamatjan, Y.[Yasin],
Imaging of hemorrhagic stroke in magnetic induction tomography: An in vitro study,
IJIST(24), No. 2, 2014, pp. 161-166.
DOI Link 1405
magnetic induction tomography BibRef

Wen, B.[Bo], Ma, L.[Lin], Weng, C.S.[Chang-Shui],
The impact of constraint induced movement therapy on brain activation in chronic stroke patients with upper extremity paralysis: An fMRI study,
IJIST(24), No. 3, 2014, pp. 270-275.
DOI Link 1408
fMRI, brain reorganization, CIMT, stroke BibRef

Park, S.I.[Sang-In], Lee, J.H.[Jin-Hee], Chung, Y.A.[Yong-An], Park, M.S.[Moon-Seo], Sunwoo, H.[Hyun], Lee, K.S.[Kwan-Sung], Sunwoo, Y.Y.[Yun-Young],
The neuroprotective effect of a traditional herbal (kyung-ok-ko) on transient middle cerebral artery occlusion-Induced ischemic rat brain,
IJIST(25), No. 2, 2015, pp. 131-138.
DOI Link 1506
stroke, transient ischemia, MCAO, Kyung-ok-ko, herb medicine BibRef

Menze, B.H., van Leemput, K., Lashkari, D., Riklin-Raviv, T., Geremia, E., Alberts, E., Gruber, P., Wegener, S., Weber, M.A., Székely, G., Ayache, N., Golland, P.,
A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation: With Application to Tumor and Stroke,
MedImg(35), No. 4, April 2016, pp. 933-946.
Gaussian processes BibRef

Jang, J.H.[Jin-Hee], Ahn, K.J.[Kook-Jin], Kim, B.Y.[Bom-Yi], Porter, D.[David], Stemmer, A.[Alto], Choi, H.S.[Hyun Seok], Jung, S.L.[So-Lyung], Kim, B.S.[Bum-Soo],
The usefulness of diffusion-weighted readout-segmented EPI and fast spin echo with BLADE (PROPELLER) k-space sampling: A comparison with single-shot EPI for diffusion-weighted imaging in ischemic stroke patients,
IJIST(26), No. 3, 2016, pp. 216-224.
DOI Link 1609
acute ischemic stroke BibRef

Karthik, R., Menaka, R.,
A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries,
ELCVIA(15), No. 3, 2016, pp. 1.
DOI Link 1701
Ischemic Stroke, Watershed transformation, Discrete Wavelet, Feature statistics BibRef

Wang, L.[Lulu],
Electromagnetic induction holography imaging for stroke detection,
JOSA-A(34), No. 2, February 2017, pp. 294-298.
DOI Link 1702
Image reconstruction techniques BibRef

Sivakumar, P., Ganeshkumar, P.,
An efficient automated methodology for detecting and segmenting the ischemic stroke in brain MRI images,
IJIST(27), No. 3, 2017, pp. 265-272.
DOI Link 1708
brain stroke, classification, ischemic stroke, morphological features, , texture, features BibRef

Zhang, R., Zhao, L., Lou, W., Abrigo, J.M., Mok, V.C.T., Chu, W.C.W., Wang, D., Shi, L.,
Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets,
MedImg(37), No. 9, September 2018, pp. 2149-2160.
Lesions, Image segmentation, Biomedical imaging, Solid modeling, deep learning BibRef

Choi, W.J., Li, Y., Wang, R.K.,
Monitoring Acute Stroke Progression: Multi-Parametric OCT Imaging of Cortical Perfusion, Flow, and Tissue Scattering in a Mouse Model of Permanent Focal Ischemia,
MedImg(38), No. 6, June 2019, pp. 1427-1437.
Imaging, Mice, Scattering, Injuries, Blood, Attenuation, Acute ischemic stroke, hemodynamic and tissue scattering responses BibRef

Ho, K.C., Speier, W., Zhang, H., Scalzo, F., El-Saden, S., Arnold, C.W.,
A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging,
MedImg(38), No. 7, July 2019, pp. 1666-1676.
Deep learning, Stroke (medical condition), Feature extraction, Magnetic resonance imaging, Biomedical imaging, Deep learning, MR perfusion imaging BibRef

Anbumozhi, S.[Selladurai],
Computer aided detection and diagnosis methodology for brain stroke using adaptive neuro fuzzy inference system classifier,
IJIST(30), No. 1, 2020, pp. 196-202.
DOI Link 2002
diagnosis, features, impulse noise, skull, stroke BibRef

Doke, P.[Piyush], Shrivastava, D.[Dhiraj], Pan, C.[Chichun], Zhou, Q.H.[Qing-Hua], Zhang, Y.D.[Yu-Dong],
Using CNN with Bayesian optimization to identify cerebral micro-bleeds,
MVA(31), No. 5, July 2020, pp. Article36.
Springer DOI 2006

Xiang, J., Dong, Y., Yang, Y.,
Multi-Frequency Electromagnetic Tomography for Acute Stroke Detection Using Frequency-Constrained Sparse Bayesian Learning,
MedImg(39), No. 12, December 2020, pp. 4102-4112.
Coils, Conductivity, Tomography, Sensitivity, Image reconstruction, Data models, Acute stroke, electromagnetic tomography, sparse Bayesian learning BibRef

Xiao, W.[Wei], Gao, Q.[Qian], Kumar, R.[Rahul], Yu, C.L.E.[C. L. Edwin], Ho, Y.E.J.[Y. E. Janice], Sheykhahmad, F.R.[Fatima Rashid],
Implementation of convolutional neural network categorizers in coronary ischemia detection,
IJIST(31), No. 1, 2021, pp. 313-326.
DOI Link 2102
cardiac artery illness, convolutional neural networks categorizers, software-based detection BibRef

Su, R.[Ruisheng], Cornelissen, S.A.P.[Sandra A. P.], van der Sluijs, M.[Matthijs], van Es, A.C.G.M.[Adriaan C. G. M.], van Zwam, W.H.[Wim H.], Dippel, D.W.J.[Diederik W. J.], Lycklama, G.[Geert], van Doormaal, P.J.[Pieter Jan], Niessen, W.J.[Wiro J.], van der Lugt, A.[Aad], van Walsum, T.[Theo],
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients,
MedImg(40), No. 9, September 2021, pp. 2380-2391.
Biomedical imaging, Imaging, Visualization, Radiology, Motion segmentation, Image segmentation, Brain, Stroke, DSA, autoTICI, MR CLEAN Registry BibRef

Zhang, L.[Long], Zhu, C.[Chuang], Wu, Y.W.[Yue-Wei], Yang, Y.[Yang], Luo, Y.H.[Yi-Hao], Song, R.N.[Ruo-Ning], Liu, L.[Lian], Yang, J.[Jie],
SFCN: Symmetric feature comparison network for detecting ischemic stroke lesions on CT images,
IET-IPR(15), No. 12, 2021, pp. 2818-2832.
DOI Link 2109

Zhu, J.Y.[Jing-Yi], Liu, C.[Chao], Liu, Y.[Yan], Chen, J.B.[Jiang-Bo], Zhang, Y.[Yachao], Yao, K.[Kuanming], Wang, L.[Lidai],
Self-Fluence-Compensated Functional Photoacoustic Microscopy,
MedImg(40), No. 12, December 2021, pp. 3856-3866.
Optical imaging, Biomedical optical imaging, Optical attenuators, Optical scattering, Optical saturation, Adaptive optics, ischemic stroke BibRef

Guo, L., Nguyen-Trong, N., AI-Saffar, A., Stancombe, A., Bialkowski, K., Abbosh, A.,
Calibrated Frequency-Division Distorted Born Iterative Tomography for Real-Life Head Imaging,
MedImg(41), No. 5, May 2022, pp. 1087-1103.
Antenna measurements, Radio frequency, Transmitting antennas, Phantoms, Tomography, Microwave theory and techniques, stroke imaging BibRef

Zhao, B.[Bin], Liu, Z.Y.[Zhi-Yang], Liu, G.H.[Guo-Hua], Wu, M.R.[Meng-Ran], Cao, C.[Chen], Jin, S.[Song], Wu, H.[Hong], Ding, S.X.[Shu-Xue],
Combine unlabeled with labeled MR images to measure acute ischemic stroke lesion by stepwise learning,
IET-IPR(16), No. 14, 2022, pp. 3965-3976.
DOI Link 2212

Kaya, B.[Buket], Önal, M.[Muhammed],
A CNN transfer learning-based approach for segmentation and classification of brain stroke from noncontrast CT images,
IJIST(33), No. 4, 2023, pp. 1335-1352.
DOI Link 2307
brain stroke, clinical decision support system, computer tomography, convolution neural network, semantic segmentation BibRef

Umirzakova, S.[Sabina], Ahmad, S.[Shabir], Mardieva, S.[Sevara], Muksimova, S.[Shakhnoza], Whangbo, T.K.[Taeg Keun],
Deep learning-driven diagnosis: A multi-task approach for segmenting stroke and Bell's palsy,
PR(144), 2023, pp. 109866.
Elsevier DOI 2310
Segmentation, Face parsing, Early stroke detection, Bell's palsy detection BibRef

Wang, X.Y.[Xin-Ying], Yi, J.[Jian], Li, Y.[Yang],
Cerebral stroke classification based on fusion model of 3D EmbedConvNext and 3D Bi-LSTM network,
IJIST(33), No. 6, 2023, pp. 1944-1956.
DOI Link 2311
3D CNN, Bi-LSTM, ConvNeXt, deep learning, self-attention, stroke BibRef

Alshehri, F.[Fatima], Muhammad, G.[Ghulam],
A few-shot learning-based ischemic stroke segmentation system using weighted MRI fusion,
IVC(140), 2023, pp. 104865.
Elsevier DOI 2312
Ischemic stroke segmentation, MRI, Few-shot learning, Convolutional neural network BibRef

Kumar, S.[Shubham], Agarwal, A.[Arjun], Golla, S.[Satish], Tanamala, S.[Swetha], Upadhyay, U.[Ujjwal], Chattoraj, S.[Subhankar], Putha, P.[Preetham], Chilamkurthy, S.[Sasank],
Mind the Clot: Automated LVO Detection on CTA using Deep Learning,

Chennuri, S.[Saurav], Lai, S.[Sha], Billot, A.[Anne], Varkanitsa, M.[Maria], Braun, E.J.[Emily J.], Kiran, S.[Swathi], Venkataraman, A.[Archana], Konrad, J.[Janusz], Ishwar, P.[Prakash], Betke, M.[Margrit],
Fusion Approaches to Predict Post-stroke Aphasia Severity from Multimodal Neuroimaging Data,

Pleasure, M.[Mara], Redekop, E.[Ekaterina], Polson, J.S.[Jennifer S.], Zhang, H.Y.[Hao-Yue], Kaneko, N.[Naoki], Speier, W.[William], Arnold, C.W.[Corey W],
Pathology-Based Ischemic Stroke Etiology Classification via Clot Composition Guided Multiple Instance Learning,

Gomes, N.B.[Nicolas Barbosa], Yoshida, A.[Arissa], de Oliveira, G.C.[Guilherme Camargo], Roder, M.[Mateus], Papa, J.P.[Joăo Paulo],
Facial Point Graphs for Stroke Identification,
Springer DOI 2312

Upadhyay, U.[Ujjwal], Ranjan, M.[Mukul], Golla, S.[Satish], Tanamala, S.[Swetha], Sreenivas, P.[Preetham], Chilamkurthy, S.[Sasank], Pandian, J.[Jeyaraj], Tarpley, J.[Jason],
Deep-aspects: A Segmentation-assisted Model for Stroke Severity Measurement,
Springer DOI 2304

Wan, Q.[Qin], Kuang, Z.[Zhuo], Deng, X.[Xianbo], Yu, L.[Li],
BGSNet: Bidirectional-Guided Semi-3D Network for Prediction of Hematoma Expansion,
Training, Deep learning, Visualization, Solid modeling, Predictive models, Feature extraction, Prediction, Attention mechanism BibRef

Kalmutskiy, K.[Kirill], Tulupov, A.[Andrey], Berikov, V.[Vladimir],
Recognition of Tomographic Images in the Diagnosis of Stroke,
Springer DOI 2103

Bensalah, A.[Asma], Chen, J.[Jialuo], Fornés, A.[Alicia], Carmona-Duarte, C.[Cristina], Lladós, J.[Josep], Ferrer, M.Á.[Miguel Ángel],
Towards Stroke Patients' Upper-limb Automatic Motor Assessment Using Smartwatches,
Springer DOI 2103

Wang, Y., Wang, H., Chen, S., Katsaggelos, A.K., Martersteck, A., Higgins, J., Hill, V.B., Parrish, T.B.,
A 3D Cross-Hemisphere Neighborhood Difference Convnet for Chronic Stroke Lesion Segmentation,
stroke lesion segmentation, brain symmetry, convolutional neural networks BibRef

Ho, K.C., Scalzo, F., Sarma, K.V., El-Saden, S., Arnold, C.W.,
A temporal deep learning approach for MR perfusion parameter estimation in stroke,
Biological neural networks, Biological tissues, Convolution, Deconvolution, Estimation, Imaging, Parameter, estimation BibRef

Yahiaoui, A.F.Z., Bessaid, A.,
Segmentation of ischemic stroke area from CT brain images,
Band-pass filters BibRef

Wang, Y., Katsaggelos, A.K., Wang, X., Parrish, T.B.,
A deep symmetry convnet for stroke lesion segmentation,
Biological neural networks BibRef

Giacalone, M., Frindel, C., Robini, M., Rousseau, D.,
Interest of non-negativity constraint in perfusion DSC-MRI deconvolution for acute stroke,
biomedical MRI BibRef

O'Reilly, C.[Christian], Plamondon, R.[Rejean],
Looking for the brain stroke signature,
WWW Link. 1302

Mujumdar, S.[Shashank], Varma, R., Kishore, L.T.,
A novel framework for segmentation of stroke lesions in Diffusion Weighted MRI using multiple b-value data,
WWW Link. 1302

Scalzo, F.[Fabien], Hao, Q.[Qing], Alger, J.R.[Jeffrey R.], Hu, X.[Xiao], Liebeskind, D.S.[David S.],
Tissue Fate Prediction in Acute Ischemic Stroke Using Cuboid Models,
ISVC10(II: 292-301).
Springer DOI 1011

Scalzo, F.[Fabien], Hao, Q.[Qing], Walczak, A.M.[Alan M.], Hu, X.[Xiao], Hoi, Y.[Yiemeng], Hoffmann, K.R.[Kenneth R.], Liebeskind, D.S.[David S.],
Computational Hemodynamics in Intracranial Vessels Reconstructed from Biplane Angiograms,
ISVC10(III: 359-367).
Springer DOI 1011

Chang, T.C.[Tzyh-Chyang], Lee, J.D.[Jiann-Der], Huang, C.H.[Chung-Hsien], Wu, T.[Tony], Chen, C.J.[Chi-Jen], Wu, S.J.[Shwu-Jiuan],
The Diagnostic Application of Brain Image Processing and Analysis System for Ischemic Stroke,
ISVC06(II: 31-38).
Springer DOI 0611

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain Development Analysis, Infant Brain .

Last update:Jan 30, 2024 at 20:33:16