Comprehensive CT Evaluation in Acute Ischemic Stroke: Impact on Diagnosis and Treatment Decisions by Askell Löve, Roger Siemund, Gunnar Andsberg, Mats Cronqvist, Stig Holtås, Isabella Björkman-Burtscher

“…In some cases CT angiography revealed the cause of stroke while CT perfusion located and graded the perfusion defect with reasonable accuracy, confirmed by follow-up MR-diffusion. In the presented cases findings of the comprehensive CT examination influenced the treatment strategy. …”
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Brain Tumor Detection Using a Deep CNN Model by Sonia Ben Brahim, Samia Dardouri, Ridha Bouallegue

“…Our study involves the application of a deep convolutional neural network (DCNN) to diagnose brain tumors from MR images. …”
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Crossed Aphasia. I: A Case-Study with Purely Deep Lesion by M. Laiacona, C. Stangalino, L. Lorenzi

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A survey of MRI-based brain tissue segmentation using deep learning by Liang Wu, Shirui Wang, Jun Liu, Lixia Hou, Na Li, Fei Su, Xi Yang, Weizhao Lu, Jianfeng Qiu, Ming Zhang, Li Song

“…This survey examines both deep learning and MRI, providing an overview of the latest advances in fetal, infant, and adult brain tissue segmentation techniques based on deep learning. …”
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Importancia del procesamiento de imágenes en radioterapia desde la perspectiva del análisis de riesgo by Antonio Torres Valle, Rodolfo Alfonso Laguardia, José de Jesús Rivero Oliva

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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

“…We propose seven MRs combined with different statistical methods to statistically verify whether the program under test adheres to the relation(s) specified in the MR(s). We further use mutation testing techniques to show the usefulness of the proposed approach in the healthcare space and test two CNN-based deep learning models (used for pneumonia detection among patients). …”
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Photocatalytic Decomposition of Methyl Red Dye by Using Nanosized Zinc Oxide Deposited on Glass Beads in Various pH and Various Atmosphere by H. R. Ebrahimi, M. Modrek

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A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI by M Nisha, T Kannan, K Sivasankari

“…The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. …”
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Flexible and cost-effective deep learning for accelerated multi-parametric relaxometry using phase-cycled bSSFP by Florian Birk, Lucas Mahler, Julius Steiglechner, Qi Wang, Klaus Scheffler, Rahel Heule

“…In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phase-cycled balanced steady-state free precession (pc-bSSFP) imaging. …”
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Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction by Yimeng Kang, Wenjing Li, Qingqing Lv, Qiuying Tao, Jieping Sun, Jinghan Dang, Xiaoyu Niu, Zijun Liu, Shujian Li, Zanxia Zhang, Kaiyu Wang, Baohong Wen, Jingliang Cheng, Yong Zhang, Weijian Wang

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Mixed reality infrastructure based on deep learning medical image segmentation and 3D visualization for bone tumors using DCU-Net by Kun Wang, Yong Han, Yuguang Ye, Yusi Chen, Daxin Zhu, Yifeng Huang, Ying Huang, Yijie Chen, Jianshe Shi, Bijiao Ding, Jianlong Huang

“…The MR system based on deep learning and three-dimensional visualization technology has great potential in the diagnosis and treatment of bone tumors, and is expected to promote clinical practice and improve efficacy.…”
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A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging by Luca Vellini, Flaviovincenzo Quaranta, Sebastiano Menna, Elisa Pilloni, Francesco Catucci, Jacopo Lenkowicz, Claudio Votta, Michele Aquilano, Andrea D’Aviero, Martina Iezzi, Francesco Preziosi, Alessia Re, Althea Boschetti, Danila Piccari, Antonio Piras, Carmela Di Dio, Alessandro Bombini, Gian Carlo Mattiucci, Davide Cusumano

“…This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, an area not yet explored. …”
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Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis by Felix Kubicka, Qinxuan Tan, Tom Meyer, Dominik Nickel, Elisabeth Weiland, Moritz Wagner, Stephan Rodrigo Marticorena Garcia

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An ecosystem approach to the evaluation and impact analysis of heterogeneous preventive and/or early interventions projects for veterans and first responders in seven countries by S. Lukersmith, L. Salvador-Carulla, C. Woods, T. Niyonsenga, M.R. Gutierrez-Colosia, I. Mohanty, C.R. Garcia-Alonso, D. Diaz-Milanes, J.A. Salinas-Perez, R. Davey, A. Aryani

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Assisted Parkinsonism Diagnosis Using Multimodal MRI—The Role of Clinical Insights by Tobias Meindl, Alexander Hapfelmeier, Tobias Mantel, Angela Jochim, Jonas Deppe, Silke Zwirner, Jan S. Kirschke, Yong Li, Bernhard Haslinger

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Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework by Aobo Wang, Tianyi Wang, Xingyu Liu, Xingyu Liu, Xingyu Liu, Ning Fan, Shuo Yuan, Peng Du, Congying Zou, Ruiyuan Chen, Yu Xi, Zhao Gu, Hongxing Song, Qi Fei, Yiling Zhang, Yiling Zhang, Lei Zang

“…BackgroundThe high prevalence of low back pain has led to an increasing demand for the analysis of lumbar magnetic resonance (MR) images. This study aimed to develop and evaluate a deep-learning-assisted automated system for diagnosing and grading lumbar intervertebral disc degeneration based on lumbar T2-weighted sagittal and axial MR images.MethodsThis study included a total of 472 patients who underwent lumbar MR scans between January 2021 and November 2023, with 420 in the internal dataset and 52 in the external dataset. …”
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Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas by Valeria Cerina, Chiara Benedetta Rui, Andrea Di Cristofori, Davide Ferlito, Giorgio Carrabba, Carlo Giussani, Gianpaolo Basso, Elisabetta De Bernardi

“…Abstract An assessment scheme is proposed to evaluate GBM gross tumor core and T2-FLAIR hyper-intensity segmentations on preoperative multicentric MR images as a function of tumor morphology and MRI characteristics. 74 gross tumor core and T2-FLAIR hyper-intensity BraTS-Toolkit and DeepBraTumIA automatic segmentations, and 42 gross tumor core neurosurgeon manual segmentations were accordingly evaluated. …”
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Responsible research in health disparities using the Adolescent Brain Cognitive DevelopmentSM (ABCD) study by MR Gonzalez, C Cardenas-Iniguez, DE Linares, S Wonnum, K Bagot, EJ White, A Cuan, S DiMatteo, YD Akiel, P Lindsley, JC Harris, E Perez-Amparan, TD Powell, Comité Organizador Latino de City Heights (COLCH), G Dowling, D Alkire, WK Thompson, TM Murray

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Frameworks for the design and reporting of anaesthesia interventions in perioperative clinical trials by Karen D. Coulman, Lucy Elliott, Natalie S. Blencowe, Joyce Yeung, Leila Rooshenas, Robert J. Hinchliffe, Ronelle Mouton, Eric Albrecht, David Bosanquet, Kariem El-Boghdadly, Gudrun Kunst, Mandeep Phull, Helen Galley, Ben Gibbison, Suzanne Harrogate, Anna Simpson, Jasmeet Soar, Michael Gillies, Mr James Glasbey, Sina Grape, David Hewson, Simon Howell, Louise Savic, Alan MacFarlane, Brendan McGrath, Ciara O'Donnell, Jon Silversides, Katrina Pirie, Andrew Toner

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Exploring variation in the six-month review for stroke survivors: a national survey of current practice in England by Rich Holmes, Suzanne Ackerley, Rebecca J Fisher, Louise A Connell

“…Abstract Background The Six-Month Review (6MR) was introduced in the United Kingdom to provide a holistic, systematic review of the ongoing needs faced by stroke survivors. …”
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