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Machine Learning MRI

David S Knopman, Emily S Lundt, Terry M Therneau, Prashanthi Vemuri, Val J Lowe, Kejal Kantarci, Jeffrey L Gunter, Matthew L Senjem, Michelle M Mielke, Mary M Machulda, Bradley F Boeve, David T Jones, Jon Graff-Radford, Sabrina M Albertson, Christopher G Schwarz, Ronald C Petersen, Clifford R Jack
As more biomarkers for Alzheimer's disease and age-related brain conditions become available, more sophisticated analytic approaches are needed to take full advantage of the information they convey. Most work has been done using categorical approaches but the joint relationships of tau PET, amyloid PET and cortical thickness in their continuous distributions to cognition have been under-explored. We evaluated non-demented subjects over age 50 years in the Mayo Clinic Study of Aging, 2037 of whom had undergone 3 T MRI scan, 985 amyloid PET scan with 11C-Pittsburgh compound B (PIB) and MRI, and 577 PIB-PET, 18F-AV1451 flortaucipir PET and MRI...
February 12, 2019: Brain: a Journal of Neurology
Giacomo Bertolini, Emanuele La Corte, Domenico Aquino, Elena Greco, Zefferino Rossini, Andrea Cardia, Federico Nicolosi, Dario Bauer, Maria Grazia Bruzzone, Paolo Ferroli, Graziano Serrao
OBJECTIVE: Modern neuroanatomical education should be based on interdisciplinary methods that allow the understanding of the cerebral circuitry which is at the base of the structural connectivity. Ex-vivo MRI-guided dissection is an essential method for developing and refine the knowledge of complex three-dimensional brain anatomy and the mutual relationships between structures and architecture of the white matter bundles. The aim of this technical note is to present a new and innovative method of studying human brain white matter...
February 10, 2019: World Neurosurgery
Rose Dawn Bharath, Rajanikant Panda, Jeetu Raj, Sujas Bhardwaj, Sanjib Sinha, Ganne Chaitanya, Kenchaiah Raghavendra, Ravindranadh C Mundlamuri, Arivazhagan Arimappamagan, Malla Bhaskara Rao, Jamuna Rajeshwaran, Kandavel Thennarasu, Kaushik K Majumdar, Parthasarthy Satishchandra, Tapan K Gandhi
OBJECTIVES: Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state "epilepsy networks," we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE. METHODS: Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures...
February 8, 2019: European Radiology
Bino Varghese, Frank Chen, Darryl Hwang, Suzanne L Palmer, Andre Luis De Castro Abreu, Osamu Ukimura, Monish Aron, Manju Aron, Inderbir Gill, Vinay Duddalwar, Gaurav Pandey
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods...
February 7, 2019: Scientific Reports
Shuang Wu, Jin Meng, Qi Yu, Ping Li, Shen Fu
PURPOSE: Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas. METHODS: Eight classical machine learning methods were evaluated in terms of their stability and performance for pre-operative IDH genotype prediction. A total of 126 patients were enrolled for analysis. Overall, 704 radiomic features extracted from the pre-operative MRI images were analyzed...
February 4, 2019: Journal of Cancer Research and Clinical Oncology
Virginia Mato-Abad, Andrés Labiano-Fontcuberta, Santiago Rodríguez-Yáñez, Rafael García-Vázquez, Cristian R Munteanu, Javier Andrade-Garda, Angela Domingo-Santos, Victoria Galán Sánchez-Seco, Yolanda Aladro, Mª Luisa Martínez-Ginés, Lucía Ayuso, Julián Benito-León
INTRODUCTION: The unanticipated magnetic resonance imaging (MRI) detection in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named as radiologically isolated syndrome (RIS). As the difference between early MS (i.e., clinically isolated syndrome [CIS]) and RIS is the occurrence of a clinical event, it should be logical to improve detection of subclinical form without interfering with MRI as there are radiological diagnostic criteria for that...
February 3, 2019: European Journal of Neurology: the Official Journal of the European Federation of Neurological Societies
Ghazal Shafai-Erfani, Tonghe Wang, Yang Lei, Sibo Tian, Pretesh Patel, Ashesh B Jani, Walter J Curran, Tian Liu, Xiaofeng Yang
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a synthetic CT (SCT) from MRIs for patient setup and dose calculation. In this study, we aim to investigate the accuracy of dose calculation in prostate cancer radiotherapy using SCTs generated from MRIs using our learning-based method. We retrospectively investigated a total of 17 treatment plans from 10 patients, each having both planning CTs (pCT) and MRIs acquired before treatment...
January 31, 2019: Medical Dosimetry: Official Journal of the American Association of Medical Dosimetrists
Meng Liang, Zhengting Cai, Hongmei Zhang, Chencui Huang, Yankai Meng, Li Zhao, Dengfeng Li, Xiaohong Ma, Xinming Zhao
RATIONALE AND OBJECTIVES: To use machine learning-based magnetic resonance imaging radiomics to predict metachronous liver metastases (MLM) in patients with rectal cancer. MATERIALS AND METHODS: This study retrospectively analyzed 108 patients with rectal cancer (54 in MLM group and 54 in nonmetastases group). Feature selection were performed in the radiomic feature sets extracted from images of T2-weighted image (T2WI) and venous phase (VP) sequence respectively, and the combining feature set with 2058 radiomic features incorporating two sequences with the least absolute shrinkage and selection operator method...
January 30, 2019: Academic Radiology
Dong Nie, Junfeng Lu, Han Zhang, Ehsan Adeli, Jun Wang, Zhengda Yu, LuYan Liu, Qian Wang, Jinsong Wu, Dinggang Shen
High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages...
January 31, 2019: Scientific Reports
Kai Lønning, Patrick Putzky, Jan-Jakob Sonke, Liesbeth Reneman, Matthan W A Caan, Max Welling
Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learned from data. In clinical practice, both the underlying anatomy as well as image acquisition settings vary. For this reason, deep neural networks must be able to reapply what they learn across different measurement conditions. We propose to use Recurrent Inference Machines (RIM) as a framework for accelerated MRI reconstruction...
January 18, 2019: Medical Image Analysis
Takashi Matsubara, Tetsuo Tashiro, Kuniaki Uehara
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data for specific biomarkers of disorders. These studies have encountered the following dilemma: An end-to-end classification overfits to a small number of high-dimensional samples but unsupervised feature-extraction has the risk of extracting a signal of no interest...
January 28, 2019: IEEE Transactions on Bio-medical Engineering
Mark Wu, Satheesh Krishna, Rebecca E Thornhill, Trevor A Flood, Matthew D F McInnes, Nicola Schieda
BACKGROUND: The limitation of diagnosis of transition zone (TZ) prostate cancer (PCa) using subjective assessment of multiparametric (mp) MRI with PI-RADS v2 is related to overlapping features between cancers and stromal benign prostatic hyperplasia (BPH) nodules, particularly in small lesions. PURPOSE: To evaluate modeling of quantitative apparent diffusion coefficient (ADC), texture, and shape features using logistic regression (LR) and support vector machine (SVM) models for the diagnosis of transition zone PCa...
January 30, 2019: Journal of Magnetic Resonance Imaging: JMRI
Shuangkun Wang, Rongguo Zhang, Yufeng Deng, Kuan Chen, Dan Xiao, Peng Peng, Tao Jiang
Background: This study aimed to assess the feasibility of deep learning-based magnetic resonance imaging (MRI) in the prediction of smoking status. Methods: The head MRI 3D-T1WI images of 127 subjects (61 smokers and 66 non-smokers) were collected, and 176 image slices obtained for each subject. These subjects were 23-45 years old, and the smokers had at least 5 years of smoking experience. Approximate 25% of the subjects were randomly selected as the test set (15 smokers and 16 non-smokers), and the remaining subjects as the training set...
December 2018: Quantitative Imaging in Medicine and Surgery
Manon Ansart, Stéphane Epelbaum, Geoffroy Gagliardi, Olivier Colliot, Didier Dormont, Bruno Dubois, Harald Hampel, Stanley Durrleman
We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs...
January 30, 2019: Statistical Methods in Medical Research
Shi-Ting Feng, Yingmei Jia, Bing Liao, Bingsheng Huang, Qian Zhou, Xin Li, Kaikai Wei, Lili Chen, Bin Li, Wei Wang, Shuling Chen, Xiaofang He, Haibo Wang, Sui Peng, Ze-Bin Chen, Mimi Tang, Zhihang Chen, Yang Hou, Zhenwei Peng, Ming Kuang
OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS: This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients...
January 28, 2019: European Radiology
Agnieszka Pluta, Tomasz Wolak, Marta Sobańska, Natalia Gawron, Anna R Egbert, Bogna Szymańska, Andrzej Horban, Ewa Firląg-Burkacka, Przemysław Bieńkowski, Halina Sienkiewicz-Jarosz, Anna Ścińska-Bieńkowska, Adela Desowska, Mateusz Rusiniak, Bharat B Biswal, Stephen Rao, Robert Bornstein, Henryk Skarżyński, Emilia Łojek
OBJECTIVE: Findings on the influence of age and HIV on brain and cognition remain equivocal, particularly in aviremic subjects without other age or HIV-related comorbidities. We aimed to (a) examine the effect of HIV status and age on structural brain measurements and cognition, and (b) apply the machine learning technique to identify brain morphometric and cognitive features that are most discriminative between aviremic subjects with HIV on stable combination antiretroviral therapy (cART) and healthy controls...
January 28, 2019: Neuropsychology
Mostafa Mehdipour Ghazi, Mads Nielsen, Akshay Pai, M Jorge Cardoso, Marc Modat, Sébastien Ourselin, Lauge Sørensen
Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values...
January 12, 2019: Medical Image Analysis
Cutter A Lindbergh, Jinglei Lv, Yu Zhao, Catherine M Mewborn, Antonio N Puente, Douglas P Terry, Lisa M Renzi-Hammond, Billy R Hammond, Tianming Liu, L Stephen Miller
The carotenoids lutein (L) and zeaxanthin (Z) accumulate in retinal regions of the eye and have long been shown to benefit visual health. A growing literature suggests cognitive benefits as well, particularly in older adults. The present randomized controlled trial sought to investigate the effects of L and Z on brain function using resting state functional magnetic resonance imaging (fMRI). It was hypothesized that L and Z supplementation would (1) improve intra-network integrity of default mode network (DMN) and (2) reduce inter-network connectivity between DMN and other resting state networks...
January 24, 2019: Brain Imaging and Behavior
Cheng-Chia Lee, Huai-Che Yang, Chung-Jung Lin, Ching-Jen Chen, Hsiu-Mei Wu, Cheng-Ying Shiau, Wan-Yuo Guo, David Hung-Chi Pan, Kang-Du Liu, Wen-Yuh Chung, Syu-Jyun Peng
OBJECTIVE: To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidal component identification and quantification using unsupervised machine learning algorithm, and to evaluate the association between intervening nidal brain parenchyma and radiation-induced changes (RICs) after stereotactic radiosurgery (SRS). METHODS: Fully automated segmentation via unsupervised classification with fuzzy c-means clustering was used to analyze AVM nidus on T2-weighted magnetic resonance imaging...
January 21, 2019: World Neurosurgery
Sarfaraz Hussein, Pujan Kandel, Candice W Bolan, Michael B Wallace, Ulas Bagci
Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computeraided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D Convolutional Neural Network and Transfer Learning...
January 23, 2019: IEEE Transactions on Medical Imaging
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