Read by QxMD icon Read

Machine Learning MRI

Sied Kebir, Manuel Weber, Lazaros Lazaridis, Cornelius Deuschl, Teresa Schmidt, Christoph Mönninghoff, Kathy Keyvani, Lale Umutlu, Daniela Pierscianek, Michael Forsting, Ulrich Sure, Martin Stuschke, Christoph Kleinschnitz, Björn Scheffler, Patrick M Colletti, Domenico Rubello, Christoph Rischpler, Martin Glas
PURPOSE: With the advent of the revised WHO classification from 2016, molecular features, including isocitrate dehydrogenase (IDH) mutation have become important in glioma subtyping. This pilot trial analyzed the potential for C-methionine (MET) PET/MRI in classifying glioma according to the revised WHO classification using a machine learning model. METHODS: Patients with newly diagnosed WHO grade II-IV glioma underwent preoperative MET-PET/MRI imaging. Patients were retrospectively divided into four groups: IDH wild-type glioblastoma (GBM), IDH wild-type grade II/III glioma (GII/III-IDHwt), IDH mutant grade II/III glioma with codeletion of 1p19q (GII/III-IDHmut1p19qcod) or without 1p19q-codeletion (GII/III-IDHmut1p19qnc)...
December 3, 2018: Clinical Nuclear Medicine
Yu Yan, Edward Somer, Vicente Grau
BACKGROUND: New PET tracers could have a substantial impact on the early diagnosis of Alzheimer's disease (AD), particularly if they are accompanied by optimised image analysis and machine learning methods. Fractal dimension (FD) analysis, a measure of shape complexity, has been proven useful in MRI but its application to fluorine-18 amyloid PET has not yet been demonstrated. Shannon entropy (SE) has also been proposed as a measure of image complexity in DTI imaging, but it is not yet widely used in radiology...
November 29, 2018: Nuclear Medicine Communications
Burak Kocak, Emine Sebnem Durmaz, Pinar Kadioglu, Ozge Polat Korkmaz, Nil Comunoglu, Necmettin Tanriover, Naci Kocer, Civan Islak, Osman Kizilkilic
OBJECTIVE: To investigate the value of machine learning (ML)-based high-dimensional quantitative texture analysis (qTA) on T2-weighted magnetic resonance imaging (MRI) in predicting response to somatostatin analogues (SA) in acromegaly patients with growth hormone (GH)-secreting pituitary macroadenoma, and to compare the qTA with quantitative and qualitative T2-weighted relative signal intensity (rSI) and immunohistochemical evaluation. METHODS: Forty-seven patients (24 responsive; 23 resistant patients to SA) were eligible for this retrospective study...
November 30, 2018: European Radiology
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
Haike Zhang, Esther Alberts, Viola Pongratz, Mark Mühlau, Claus Zimmer, Benedikt Wiestler, Paul Eichinger
Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible to predict the conversion from CIS to multiple sclerosis (MS) based on the baseline MRI scan by studying image features of these lesions. We analyzed 84 patients diagnosed with CIS from a prospective observational single center cohort. The patients were followed up for at least three years...
November 5, 2018: NeuroImage: Clinical
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
Nathaniel B Gunter, Christopher G Schwarz, Jonathan Graff-Radford, Jeffrey L Gunter, David T Jones, Neill R Graff-Radford, Ronald C Petersen, David S Knopman, Clifford R Jack
OBJECTIVE: Create an automated classifier for imaging characteristics of disproportionately enlarged subarachnoid space hydrocephalus (DESH), a neuroimaging phenotype of idiopathic normal pressure hydrocephalus (iNPH). METHODS: 1597 patients from the Mayo Clinic Study of Aging (MCSA) were reviewed for imaging characteristics of DESH. One core feature of DESH, the presence of tightened sulci in the high-convexities (THC), was used as a surrogate for the presence of DESH as the expert clinician-defined criterion on which the classifier was trained...
November 19, 2018: NeuroImage: Clinical
M Nakagawa, T Nakaura, T Namimoto, Y Iyama, M Kidoh, K Hirata, Y Nagayama, S Oda, F Sakamoto, S Shiraishi, Y Yamashita
AIM: To compare the performance of machine learning using multiparametric magnetic resonance imaging (mp-MRI) and positron-emission tomography (PET) to distinguish between uterine sarcoma and leiomyoma. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board and informed consent was waived. Sixty-seven consecutive patients with uterine sarcoma or leiomyoma who underwent pelvic 3 T MRI and PET were included. Of 67 patients, 11 had uterine sarcomas and 56 had leiomyomas...
November 21, 2018: Clinical Radiology
Yang Ding, Sabrina Suffren, Pierre Bellec, Gregory A Lodygensky
Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated identification tools. Clinical trials involving motion-prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect major artifacts among 2D neonatal MRI via supervised machine learning...
November 22, 2018: Human Brain Mapping
Mayssa Soussia, Islem Rekik
Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways...
2018: Frontiers in Neuroinformatics
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)
OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings...
November 1, 2018: Neurosurgical Focus
(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
Heiko Neeb, Jochen Schenk
OBJECTIVES: The current work investigates the performance of different multivariate supervised machine learning models to predict the presence or absence of multiple sclerosis (MS) based on features derived from quantitative MRI acquisitions. The performance of these models was evaluated for images which are significantly degraded due to subject motion, a problem which is often observed in clinical routine diagnostics. Finally, the difference between a true multivariate analysis and the corresponding univariate analysis based on single parameters alone was addressed...
November 12, 2018: Zeitschrift Für Medizinische Physik
C S Eke, E Jammeh, X Li, C Carroll, S Pearson, E Ifeachor
With the increasing number of people living with Alzheimer's disease (AD), there is a need for low-cost and easy to use methods to detect AD early to facilitate access to appropriate care pathways. Neuroimaging biomarkers (such as those based on PET and MRI) and biochemical biomarkers (such as those based on CSF) are recommended by international guidelines to facilitate diagnosis. However, neuroimaging is expensive and may not be widely available and CSF testing is invasive. Bloodbased biomarkers offer the potential for the development of a low-cost and more time efficient tool to detect AD to complement CSF and neuroimaging as blood is much easier to obtain...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Jordina Torrents-Barrena, Gemma Piella, Narcis Masoller, Eduard Gratacos, Elisenda Eixarch, Mario Ceresa, Miguel A Gonzalez Ballester
Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data reduces the high cost associated to medical image acquisition, as well as avoiding confidentiality and privacy issues, and consequently allows the creation of public data repositories for scientific purposes. Within the context of fetal imaging, we adopt an auto-encoder based Generative Adversarial Network for synthetic fetal MRI generation. The proposed architecture features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Ting Shen, Jiehui Jiang, Yupeng Li, Ping Wu, Chuantao Zuo, Zhuangzhi Yan
Prediction of Alzheimer's disease (AD) from Mild Cognitive Impairment (MCI) has become popular in recent years. Especially, deep learning technique has been used to extract high-quality features and for classification in this topic. Whether the patient would converse from MCI into AD is a particular evaluation criteria in clinics. However, there is no such a conversion prediction model in literature. Therefore, the purpose of this study is to propose a decision supporting model based on deep learning and machine learning to predict the conversion probability from MCI into AD within one year...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Vincent Chin-Hung Chen, Tung-Yeh Lin, Dah-Cherng Yeh, Jyh-Wen Chai, Jun-Cheng Weng
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo-brain. In this study, we aimed to construct machine-learning models to detect the subtle alternations of the brain in postchemotherapy BC patients. METHODS: Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study...
November 12, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Sameer H Halani, Safoora Yousefi, Jose Velazquez Vega, Michael R Rossi, Zheng Zhao, Fatemeh Amrollahi, Chad A Holder, Amelia Baxter-Stoltzfus, Jennifer Eschbacher, Brent Griffith, Jeffrey J Olson, Tao Jiang, Joseph R Yates, Charles G Eberhart, Laila M Poisson, Lee A D Cooper, Daniel J Brat
Oligodendrogliomas are diffusely infiltrative gliomas defined by IDH -mutation and co-deletion of 1p/19q. They have highly variable clinical courses, with survivals ranging from 6 months to over 20 years, but little is known regarding the pathways involved with their progression or optimal markers for stratifying risk. We utilized machine-learning approaches with genomic data from The Cancer Genome Atlas to objectively identify molecular factors associated with clinical outcomes of oligodendroglioma and extended these findings to study signaling pathways implicated in oncogenesis and clinical endpoints associated with glioma progression...
2018: NPJ Precision Oncology
D Collazos-Huertas, D Cárdenas-Peña, G Castellanos-Dominguez
The early detection of Alzheimer's disease and quantification of its progression poses multiple difficulties for machine learning algorithms. Two of the most relevant issues are related to missing data and results interpretability. To deal with both issues, we introduce a methodology to predict conversion of mild cognitive impairment patients to Alzheimer's from structural brain MRI volumes. First, we use morphological measures of each brain structure to build an instance-based feature mapping that copes with missed follow-up visits...
September 20, 2018: International Journal of Neural Systems
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"