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Machine Learning MRI

Fan Li, Manhua Liu
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Most existing methods extract the hand-craft features after image processing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups...
October 2, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Eyal Lotan, Rajan Jain, Narges Razavian, Girish M Fatterpekar, Yvonne W Lui
OBJECTIVE: Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION: We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making...
October 17, 2018: AJR. American Journal of Roentgenology
Elizabeth Hope Cain, Ashirbani Saha, Michael R Harowicz, Jeffrey R Marks, P Kelly Marcom, Maciej A Mazurowski
PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients. METHODS: Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI...
October 16, 2018: Breast Cancer Research and Treatment
Fuk Hay Tang, Jasmine L C Chan, Bill K L Chan
This study proposes an accurate method in assessing chronological age of the adolescents using a machine learning approach using MRI images. We also examined the value of MRI with Tanner-Whitehouse 3 (TW3) method in assessing skeletal maturity. Seventy-nine 12-17-year-old healthy Hong Kong Chinese adolescents were recruited. The left hand and wrist region were scanned by a dedicated skeletal MRI scanner. T1-weighted three-dimensional coronal view images for the left hand and wrist region were acquired. Independent maturity indicators such as subject body height, body weight, bone marrow composition intensity quantified by MRI, and TW3 skeletal age were included for artificial neural network (ANN) analysis...
October 15, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Mustafa R Bashir, Tanya Wolfson, Anthony C Gamst, Kathryn J Fowler, Michael Ohliger, Shetal N Shah, Adina Alazraki, Andrew T Trout, Cynthia Behling, Daniela S Allende, Rohit Loomba, Arun Sanyal, Jeffrey Schwimmer, Joel E Lavine, Wei Shen, James Tonascia, Mark L Van Natta, Adrija Mamidipalli, Jonathan Hooker, Kris V Kowdley, Michael S Middleton, Claude B Sirlin
BACKGROUND: The liver R2* value is widely used as a measure of liver iron but may be confounded by the presence of hepatic steatosis and other covariates. PURPOSE: To identify the most influential covariates for liver R2* values in patients with nonalcoholic fatty liver disease (NAFLD). STUDY TYPE: Retrospective analysis of prospectively acquired data. POPULATION: Baseline data from 204 subjects enrolled in NAFLD/NASH (nonalcoholic steatohepatitis) treatment trials...
October 14, 2018: Journal of Magnetic Resonance Imaging: JMRI
Hao Yang, Junran Zhang, Qihong Liu, Yi Wang
BACKGROUND: Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number of migraineurs do not receive the accurate diagnosis when using traditional diagnostic criteria based on the guidelines of the International Headache Society...
October 11, 2018: Biomedical Engineering Online
Mohtaram Nematollahi, Mahdie Jajroudi, Farshid Arbabi, Amir Azarhomayoun, Zohreh Azimifar
Background: Machine learning is a type of artificial intelligence which aims to improve machine with the ability of extracting knowledge from the environment. Glioblastoma multiforme (GBM) is one of the most common and aggressive primary malignant brain tumors in adults. Due to a low rate of survival in patients with these tumors, machine learning can help physicians for better decision-making. The aim of this paper is to develop a machine learning model for predicting the survival rate of patients with GBM based on clinical features and magnetic resonance imaging (MRI)...
July 2018: Asian Journal of Neurosurgery
Bun Yamagata, Takashi Itahashi, Junya Fujino, Haruhisa Ohta, Motoaki Nakamura, Nobumasa Kato, Masaru Mimura, Ryu-Ichiro Hashimoto, Yuta Aoki
Endophenotype refers to a measurable and heritable component between genetics and diagnosis, and the same endophenotype is present in both individuals with a diagnosis and their unaffected siblings. Determination of the neural correlates of an endophenotype and diagnosis is important in autism spectrum disorder (ASD). However, prior studies enrolling individuals with ASD and their unaffected siblings have generally included only one group of typically developing (TD) subjects; they have not accounted for differences between TD siblings...
October 2, 2018: Brain Imaging and Behavior
Joshua M Goldenberg, Julio Cárdenas-Rodríguez, Mark D Pagel
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T1 relaxation, CEST, and DCE MRI. METHODS: The T1 relaxation time constants, % CEST at five saturation frequencies, and vascular permeability constants from DCE MRI were measured from Hs 766 T, MIA PaCa-2, and SU.86.86 pancreatic tumor models. We used each of these measurements as predictors for machine learning classifier algorithms...
September 17, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Rosaleena Mohanty, Anita M Sinha, Alexander B Remsik, Keith C Dodd, Brittany M Young, Tyler Jacobson, Matthew McMillan, Jaclyn Thoma, Hemali Advani, Veena A Nair, Theresa J Kang, Kristin Caldera, Dorothy F Edwards, Justin C Williams, Vivek Prabhakaran
The primary goal of this work was to apply data-driven machine learning regression to assess if resting state functional connectivity (rs-FC) could estimate measures of behavioral domains in stroke subjects who completed brain-computer interface (BCI) intervention for motor rehabilitation. The study cohort consisted of 20 chronic-stage stroke subjects exhibiting persistent upper-extremity motor deficits who received the intervention using a closed-loop neurofeedback BCI device. Over the course of this intervention, resting state functional MRI scans were collected at four distinct time points: namely, pre-intervention, mid-intervention, post-intervention and 1-month after completion of intervention...
2018: Frontiers in Neuroscience
Mark D Shen, Christine W Nordahl, Deana D Li, Aaron Lee, Kathleen Angkustsiri, Robert W Emerson, Sally J Rogers, Sally Ozonoff, David G Amaral
BACKGROUND: We previously showed, in two separate cohorts, that high-risk infants who were later diagnosed with autism spectrum disorder had abnormally high extra-axial cerebrospinal fluid (CSF) volume from age 6-24 months. The presence of increased extra-axial CSF volume preceded the onset of behavioural symptoms of autism and was predictive of a later diagnosis of autism spectrum disorder. In this study, we aimed to establish whether increased extra-axial CSF volume is found in a large, independent sample of children diagnosed with autism spectrum disorder, whether extra-axial CSF remains abnormally increased beyond infancy, and whether it is present in both normal-risk and high-risk children with autism...
September 27, 2018: Lancet Psychiatry
Roberta Vasta, Antonio Cerasa, Alessia Sarica, Emanuele Bartolini, Iolanda Martino, Francesco Mari, Tiziana Metitieri, Aldo Quattrone, Antonio Gambardella, Renzo Guerrini, Angelo Labate
Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a definite neurobiological substrate. In this multicenter cross-sectional study, we applied a multivariate classification algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level...
September 28, 2018: Epilepsy & Behavior: E&B
Mathilde Giacalone, Pejman Rasti, Noelie Debs, Carole Frindel, Tae-Hee Cho, Emmanuel Grenier, David Rousseau
We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel...
September 23, 2018: Medical Image Analysis
Tilo Kircher, Markus Wöhr, Igor Nenadic, Rainer Schwarting, Gerhard Schratt, Judith Alferink, Carsten Culmsee, Holger Garn, Tim Hahn, Bertram Müller-Myhsok, Astrid Dempfle, Maik Hahmann, Andreas Jansen, Petra Pfefferle, Harald Renz, Marcella Rietschel, Stephanie H Witt, Markus Nöthen, Axel Krug, Udo Dannlowski
Genetic (G) and environmental (E) factors are involved in the etiology and course of the major psychoses (MP), i.e. major depressive disorder (MDD), bipolar disorder (BD), schizoaffective disorder (SZA) and schizophrenia (SZ). The neurobiological correlates by which these predispositions exert their influence on brain structure, function and course of illness are poorly understood. In the FOR2107 consortium, animal models and humans are investigated. A human cohort of MP patients, healthy subjects at genetic and/or environmental risk, and control subjects (N = 2500) has been established...
September 28, 2018: European Archives of Psychiatry and Clinical Neuroscience
Mara Ten Kate, Alberto Redolfi, Enrico Peira, Isabelle Bos, Stephanie J Vos, Rik Vandenberghe, Silvy Gabel, Jolien Schaeverbeke, Philip Scheltens, Olivier Blin, Jill C Richardson, Regis Bordet, Anders Wallin, Carl Eckerstrom, José Luis Molinuevo, Sebastiaan Engelborghs, Christine Van Broeckhoven, Pablo Martinez-Lage, Julius Popp, Magdalini Tsolaki, Frans R J Verhey, Alison L Baird, Cristina Legido-Quigley, Lars Bertram, Valerija Dobricic, Henrik Zetterberg, Simon Lovestone, Johannes Streffer, Silvia Bianchetti, Gerald P Novak, Jerome Revillard, Mark F Gordon, Zhiyong Xie, Viktor Wottschel, Giovanni Frisoni, Pieter Jelle Visser, Frederik Barkhof
BACKGROUND: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66...
September 27, 2018: Alzheimer's Research & Therapy
Uran Ferizi, Harrison Besser, Pirro Hysi, Joseph Jacobs, Chamith S Rajapakse, Cheng Chen, Punam K Saha, Stephen Honig, Gregory Chang
BACKGROUND: A current challenge in osteoporosis is identifying patients at risk of bone fracture. PURPOSE: To identify the machine learning classifiers that predict best osteoporotic bone fractures and, from the data, to highlight the imaging features and the anatomical regions that contribute most to prediction performance. STUDY TYPE: Prospective (cross-sectional) case-control study. POPULATION: Thirty-two women with prior fragility bone fractures, of mean age = 61...
September 25, 2018: Journal of Magnetic Resonance Imaging: JMRI
Reyhaneh Nosrati, Abraam Soliman, Habib Safigholi, Masoud Hashemi, Matthew Wronski, Gerard Morton, Ana Pejović-Milić, Greg Stanisz, William Y Song
BACKGROUND AND PURPOSE: Permanent seed brachytherapy is an established treatment option for localized prostate cancer. Currently, post-implant dosimetry is performed on CT images despite challenging target delineation due to limited soft tissue contrast. This work aims to develop an MRI-only workflow for post-implant dosimetry of prostate brachytherapy seeds. MATERIAL AND METHODS: A prostate mimicking phantom containing twenty stranded I-125 dummy seeds and calcifications was constructed...
September 19, 2018: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
A de Pierrefeu, T Löfstedt, C Laidi, F Hadj-Selem, J Bourgin, T Hajek, F Spaniel, M Kolenic, P Ciuciu, N Hamdani, M Leboyer, T Fovet, R Jardri, J Houenou, E Duchesnay
OBJECTIVE: Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect...
September 21, 2018: Acta Psychiatrica Scandinavica
Mariana Zurita, Cristian Montalba, Tomás Labbé, Juan Pablo Cruz, Josué Dalboni da Rocha, Cristián Tejos, Ethel Ciampi, Claudia Cárcamo, Ranganatha Sitaram, Sergio Uribe
Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients...
September 4, 2018: NeuroImage: Clinical
Richard McKinley, Fan Hung, Roland Wiest, David S Liebeskind, Fabien Scalzo
Background: Dynamic susceptibility contrast (DSC) MR perfusion is a frequently-used technique for neurovascular imaging. The progress of a bolus of contrast agent through the tissue of the brain is imaged via a series of T2* -weighted MRI scans. Clinically relevant parameters such as blood flow and Tmax can be calculated by deconvolving the contrast-time curves with the bolus shape (arterial input function). In acute stroke, for instance, these parameters may help distinguish between the likely salvageable tissue and irreversibly damaged infarct core...
2018: Frontiers in Neurology
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