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Deep Learning MRI

Alexander Selvikvåg Lundervold, Arvid Lundervold
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry...
December 12, 2018: Zeitschrift Für Medizinische Physik
Andrew M Blamire
Neurodegenerative disease is the umbrella term which refers to a range of clinical conditions causing degeneration of neurons within the central nervous system leading to loss of brain function and eventual death. The most prevalent of these is Alzheimer's disease (AD), which affects approximately 50 million people worldwide and is predicted to reach 75 million by 2030. Neurodegenerative diseases can only be fully diagnosed at post mortem by neuropathological assessment of the type and distribution of protein deposits which characterise each different condition, but there is a clear role for imaging technologies in aiding patient diagnoses in life...
October 2018: Progress in Nuclear Magnetic Resonance Spectroscopy
Christian Lucas, André Kemmling, Nassim Bouteldja, Linda F Aulmann, Amir Madany Mamlouk, Mattias P Heinrich
Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase. If infarct growth over time could be predicted accurately from functional acute imaging protocols together with advanced machine-learning based image analysis, the expected benefits of treatment options could be better weighted against potential risks. The quality of the outcome prediction by convolutional neural networks (CNNs) is so far limited, which indicates that even highly complex deep learning algorithms are not fully capable of directly learning physiological principles of tissue salvation through weak supervision due to a lack of data (e...
2018: Frontiers in Neurology
Kevin T Chen, Enhao Gong, Fabiola Bezerra de Carvalho Macruz, Junshen Xu, Athanasia Boumis, Mehdi Khalighi, Kathleen L Poston, Sharon J Sha, Michael D Greicius, Elizabeth Mormino, John M Pauly, Shyam Srinivas, Greg Zaharchuk
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [18 F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed...
December 11, 2018: Radiology
Yan Wang, Luping Zhou, Biting Yu, Lei Wang, Chen Zu, David S Lalush, Weili Lin, Xi Wu, Jiliu Zhou, Dinggang Shen
Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose one to reduce the radiation exposure. In this paper, we propose a 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the high-quality FDG PET image from the low-dose one with the accompanying MRI images that provide anatomical information...
November 29, 2018: IEEE Transactions on Medical Imaging
Ruba Alkadi, Fatma Taher, Ayman El-Baz, Naoufel Werghi
We address the problem of prostate lesion detection, localization, and segmentation in T2W magnetic resonance (MR) images. We train a deep convolutional encoder-decoder architecture to simultaneously segment the prostate, its anatomical structure, and the malignant lesions. To incorporate the 3D contextual spatial information provided by the MRI series, we propose a novel 3D sliding window approach, which preserves the 2D domain complexity while exploiting 3D information. Experiments on data from 19 patients provided for the public by the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) show that our approach outperforms traditional pattern recognition and machine learning approaches by a significant margin...
November 30, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Toru Higaki, Yuko Nakamura, Fuminari Tatsugami, Takeshi Nakaura, Kazuo Awai
Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as "noise and artifact reduction", "super resolution" and "image acquisition and reconstruction". For each category, we present and outline the features of some studies.
November 29, 2018: Japanese Journal of Radiology
Paolo Simoni, Merhan Ghassemi, Vinh Dat-Minh Le, Grammatina Boitsios
Ultrasound (US) allows a reliable examination of the brachial plexus except for the spinal nerve roots, located deep in the neuro-foramina, beyond the shadowing of the transverse processes of the vertebral bodies. All the other fascicles of the brachial plexus can be mapped by US from the roots of the spinal cervical nerves, from C5 to T1 to the branches at level of the axillary region. US can be considered as an alternative to Magnetic Resonance Imaging (MRI) when MRI is contraindicated, not readily available or in case of claustrophobia...
December 16, 2017: Journal of the Belgian Society of Radiology
Yang Yang, Lin-Feng Yan, Xin Zhang, Yu Han, Hai-Yan Nan, Yu-Chuan Hu, Bo Hu, Song-Lin Yan, Jin Zhang, Dong-Liang Cheng, Xiang-Wei Ge, Guang-Bin Cui, Di Zhao, Wen Wang
Background: Accurate glioma grading before surgery is of the utmost importance in treatment planning and prognosis prediction. But previous studies on magnetic resonance imaging (MRI) images were not effective enough. According to the remarkable performance of convolutional neural network (CNN) in medical domain, we hypothesized that a deep learning algorithm can achieve high accuracy in distinguishing the World Health Organization (WHO) low grade and high grade gliomas. Methods: One hundred and thirteen glioma patients were retrospectively included...
2018: Frontiers in Neuroscience
Nicholas Bien, Pranav Rajpurkar, Robyn L Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, Safwan Halabi, Evan Zucker, Gary Fanton, Derek F Amanatullah, Christopher F Beaulieu, Geoffrey M Riley, Russell J Stewart, Francis G Blankenberg, David B Larson, Ricky H Jones, Curtis P Langlotz, Andrew Y Ng, Matthew P Lungren
BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations...
November 2018: PLoS Medicine
Shangran Qiu, Gary H Chang, Marcello Panagia, Deepa M Gopal, Rhoda Au, Vijaya B Kolachalama
Introduction: Our aim was to investigate if the accuracy of diagnosing mild cognitive impairment (MCI) using the Mini-Mental State Examination (MMSE) and logical memory (LM) test could be enhanced by adding MRI data. Methods: Data of individuals with normal cognition and MCI were obtained from the National Alzheimer Coordinating Center database (n = 386). Deep learning models trained on MRI slices were combined to generate a fused MRI model using different voting techniques to predict normal cognition versus MCI...
2018: Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Xinran Zhong, Ruiming Cao, Sepideh Shakeri, Fabien Scalzo, Yeejin Lee, Dieter R Enzmann, Holden H Wu, Steven S Raman, Kyunghyun Sung
PURPOSE: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation. METHODS: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort...
November 20, 2018: Abdominal Radiology
Weiming Lin, Tong Tong, Qinquan Gao, Di Guo, Xiaofeng Du, Yonggui Yang, Gang Guo, Min Xiao, Min Du, Xiaobo Qu
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images...
2018: Frontiers in Neuroscience
(no author information available yet)
OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate cases and by poor interrater agreement. Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes. In this pilot study, the authors aimed to evaluate the utility of using deep learning to predict GTR after transsphenoidal surgery for pituitary adenoma...
November 1, 2018: Neurosurgical Focus
Miaoyun Zhao, Li Wang, Jiawei Chen, Dong Nie, Yulai Cong, Sahar Ahmad, Angela Ho, Peng Yuan, Steve H Fung, Hannah H Deng, James Xia, Dinggang Shen
Automatic segmentation of medical images finds abundant applications in clinical studies. Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning of craniomaxillofacial (CMF) surgeries as it shows clear bony structures. However, CT imaging poses radiation risks for the subjects being scanned. Alternatively, Magnetic Resonance Imaging (MRI) is considered to be safe and provides good visualization of the soft tissues, but the bony structures appear invisible from MRI. Therefore, the segmentation of bony structures from MRI is quite challenging...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Guannan Li, Mingxia Liu, Quansen Sun, Dinggang Shen, Li Wang
Currently there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavior observations at three or four years old. Since intervention efforts may miss a critical developmental window after 2 years old, it is significant to identify imaging-based biomarkers for early diagnosis of ASD. Although some methods using magnetic resonance imaging (MRI) for brain disease prediction have been proposed in the last decade, few of them were developed for predicting ASD in early age...
September 2018: Machine Learning in Medical Imaging
Chenjie Ge, Irene Yu-Hua Gu, Asgeir Store Jakola, Jie Yang
This paper addresses issues of brain tumor, glioma, grading from multi-sensor images. Different types of scanners (or sensors) like enhanced T1-MRI, T2-MRI and FLAIR, show different contrast and are sensitive to different brain tissues and fluid regions. Most existing works use 3D brain images from single sensor. In this paper, we propose a novel multistream deep Convolutional Neural Network (CNN) architecture that extracts and fuses the features from multiple sensors for glioma tumor grading/subcategory grading...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui
Mild traumatic brain injury is a growing public health problem with an estimated incidence of over 1.7 million people annually in US. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. This work aims to directly use diffusion MR images obtained within one month of trauma to detect injury, by incorporating deep learning techniques. To overcome the challenge due to limited training data, we describe each brain region using the bag of word representation, which specifies the distribution of representative patch patterns...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Mengya Yang, Peng Yang, Ahmed Elazab, Wen Hou, Xia Li, Tianfu Wang, Wenbin Zou, Baiying Lei
Alzheimer's disease (AD) is a neurodegenerative disease with an irreversible and progressive process. Close monitoring of AD is essential for making adjustments in the treatment plan. Since clinical scores can indicate the disease status effectively, the prediction of the scores based on the magnetic resonance imaging (MRI data is highly desirable. Different from previous studies at a single time point, we propose to build a model to explore the relationship between MRI data and scores, thereby predicting longitudinal scores at future time points from the corresponding MRI data...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Guang Yang, Jun Chen, Zhifan Gao, Heye Zhang, Hao Ni, Elsa Angelini, Raad Mohiaddin, Tom Wong, Jennifer Keegan, David Firmin
Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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