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Machine Learning MRI

Shamel Fahmi, Frank F J Simonis, Momen Abayazid
BACKGROUND: Respiratory-induced motion (RIM) causes uncertainties in localizing hepatic lesions, which could lead to inaccurate targeting during interventions. One approach to mitigate the problem is respiratory motion estimation (RME), in which the liver motion is estimated by measuring external signals called surrogates. METHODS: A learning-based approach has been developed and validated to estimate the RIM of hepatic lesions. External markers placed on the human's abdomen were chosen as surrogates...
August 15, 2018: International Journal of Medical Robotics + Computer Assisted Surgery: MRCAS
Chang-Cun Pan, Jia Liu, Jie Tang, Xin Chen, Fang Chen, Yu-Liang Wu, Yi-Bo Geng, Cheng Xu, Xinran Zhang, Zhen Wu, Pei-Yi Gao, Jun-Ting Zhang, Hai Yan, Hongen Liao, Li-Wei Zhang
BACKGROUND: H3K27M is the most frequent mutation in brainstem gliomas (BSGs), and it has great significance in the differential diagnosis, prognostic prediction and treatment strategy selection of BSGs. There has been a lack of reliable noninvasive methods capable of accurately predicting H3K27M mutations in BSGs. METHODS: A total of 151 patients with newly diagnosed BSGs were included in this retrospective study. The H3K27M mutation status was obtained by whole-exome, whole-genome or Sanger's sequencing...
August 7, 2018: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
Dong-Dong Xiao, Peng-Fei Yan, Yu-Xuan Wang, Mohamed Saied Osman, Hong-Yang Zhao
OBJECTIVES: To investigate the diagnostic value of magnetic resonance imaging (MRI)-based 3D texture and shape features in the differentiation of glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL). PATIENTS AND METHODS: A total of eighty-two patients, including sixty patients with GBM and twenty-two patients with PCNSL were followed up retrospectively from January 2012 to September 2017. MRI-based 3D texture and shape analysis were performed to evaluate the detectable differences between the two malignancies...
August 2, 2018: Clinical Neurology and Neurosurgery
Yiping Lu, Li Liu, Shihai Luan, Ji Xiong, Daoying Geng, Bo Yin
OBJECTIVES: The preoperative prediction of the WHO grade of a meningioma is important for further treatment plans. This study aimed to assess whether texture analysis (TA) based on apparent diffusion coefficient (ADC) maps could non-invasively classify meningiomas accurately using tree classifiers. METHODS: A pathology database was reviewed to identify meningioma patients who underwent tumour resection in our hospital with preoperative routine MRI scanning and diffusion-weighted imaging (DWI) between January 2011 and August 2017...
August 7, 2018: European Radiology
Oh Young Bang, Jong-Won Chung, Jeong Pyo Son, Wi-Sun Ryu, Dong-Eog Kim, Woo-Keun Seo, Gyeong-Moon Kim, Yoon-Chul Kim
Revascularization therapies have been established as the treatment mainstay for acute ischemic stroke. However, a substantial number of patients are either ineligible for revascularization therapy, or the treatment fails or is futile. At present, non-contrast computed tomography is the first-line neuroimaging modality for patients with acute stroke. The use of magnetic resonance imaging (MRI) to predict the response to early revascularization therapy and to identify patients for delayed treatment is desirable...
2018: Frontiers in Neurology
Hailong Li, Nehal A Parikh, Lili He
Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence...
2018: Frontiers in Neuroscience
Chen Qin, Joseph V Hajnal, Daniel Rueckert, Jo Schlemper, Jose Caballero, Anthony N Price
Accelerating the data acquisition of dynamic magnetic resonance imaging (MRI) leads to a challenging ill-posed inverse problem, which has received great interest from both the signal processing and machine learning communities over the last decades. The key ingredient to the problem is how to exploit the temporal correlations of the MR sequence to resolve aliasing artefacts. Traditionally, such observation led to a formulation of an optimisation problem, which was solved using iterative algorithms. Recently, however, deep learning based-approaches have gained significant popularity due to their ability to solve general inverse problems...
August 6, 2018: IEEE Transactions on Medical Imaging
Yuheng Chen, Wenna Duan, Parshant Sehrawat, Vaibhav Chauhan, Freddy J Alfaro, Anna Gavrieli, Xingye Qiao, Vera Novak, Weiying Dai
BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity...
August 5, 2018: Journal of Magnetic Resonance Imaging: JMRI
K Egger, M Rijntjes
The differential diagnosis of atypical dementia remains difficult. The use of positron emission tomography (PET) still represents the gold standard for imaging diagnostics. According to the current evidence, however, magnetic resonance imaging (MRI) is almost equal to fluorodeoxyglucose (FDG)-PET, but only when using new big data and machine learning methods. In cases of atypical dementia, especially in younger patients and for follow-up, MRI is preferable to computed tomography (CT). In the clinical routine, promising MRI procedures are e...
August 3, 2018: Der Nervenarzt
David Bonekamp, Simon Kohl, Manuel Wiesenfarth, Patrick Schelb, Jan Philipp Radtke, Michael Götz, Philipp Kickingereder, Kaneschka Yaqubi, Bertram Hitthaler, Nils Gählert, Tristan Anselm Kuder, Fenja Deister, Martin Freitag, Markus Hohenfellner, Boris A Hadaschik, Heinz-Peter Schlemmer, Klaus H Maier-Hein
Purpose To compare biparametric contrast-free radiomic machine learning (RML), mean apparent diffusion coefficient (ADC), and radiologist assessment for characterization of prostate lesions detected during prospective MRI interpretation. Materials and Methods This single-institution study included 316 men (mean age ± standard deviation, 64.0 years ± 7.8) with an indication for MRI-transrectal US fusion biopsy between May 2015 and September 2016 (training cohort, 183 patients; test cohort, 133 patients). Lesions identified by prospective clinical readings were manually segmented for mean ADC and radiomics analysis...
July 31, 2018: Radiology
Peter L Choyke
No abstract text is available yet for this article.
July 31, 2018: Radiology
Sergey M Plis, Md Faijul Amin, Adam Chekroud, Devon Hjelm, Eswar Damaraju, Hyo Jong Lee, Juan R Bustillo, KyungHyun Cho, Godfrey D Pearlson, Vince D Calhoun
This work presents a novel approach to finding linkage/association between multimodal brain imaging data, such as structural MRI (sMRI) and functional MRI (fMRI). Motivated by the machine translation domain, we employ a deep learning model, and consider two different imaging views of the same brain like two different languages conveying some common facts. That analogy enables finding linkages between two modalities. The proposed translation-based fusion model contains a computing layer that learns "alignments" (or links) between dynamic connectivity features from fMRI data and static gray matter patterns from sMRI data...
July 25, 2018: NeuroImage
Fan Liang, Pengjiang Qian, Kuan-Hao Su, Atallah Baydoun, Asha Leisser, Steven Van Hedent, Jung-Wen Kuo, Kaifa Zhao, Parag Parikh, Yonggang Lu, Bryan J Traughber, Raymond F Muzic
BACKGROUND: Manual contouring remains the most laborious task in radiation therapy planning and is a major barrier to implementing routine Magnetic Resonance Imaging (MRI) Guided Adaptive Radiation Therapy (MR-ART). To address this, we propose a new artificial intelligence-based, auto-contouring method for abdominal MR-ART modeled after human brain cognition for manual contouring. METHODS/MATERIALS: Our algorithm is based on two types of information flow, i.e. top-down and bottom-up...
July 24, 2018: Artificial Intelligence in Medicine
Sebastian Moguilner, Adolfo M García, Ezequiel Mikulan, Eugenia Hesse, Indira García-Cordero, Margherita Melloni, Sabrina Cervetto, Cecilia Serrano, Eduar Herrera, Pablo Reyes, Diana Matallana, Facundo Manes, Agustín Ibáñez, Lucas Sedeño
The search for biomarkers of neurodegenerative diseases via fMRI functional connectivity (FC) research has yielded inconsistent results. Yet, most FC studies are blind to non-linear brain dynamics. To circumvent this limitation, we developed a "weighted Symbolic Dependence Metric" (wSDM) measure. Using symbolic transforms, we factor in local and global temporal features of the BOLD signal to weigh a robust copula-based dependence measure by symbolic similarity, capturing both linear and non-linear associations...
July 25, 2018: Scientific Reports
Rui Zhao, Xinxin Zhang, Yuanqiang Zhu, Ningbo Fei, Jinbo Sun, Peng Liu, Xuejuan Yang, Wei Qin
Sleep deprivation (SD) impairs the ability of response inhibition. However, few studies have explored the quantitative prediction of performance impairment using Magnetic Resonance Imaging (MRI) data. In this study, structural MRI data were used to predict the change in response inhibition performance (ΔSSRT) measured by a stop-signal task (SST) after 24 h of SD in 52 normal young subjects. For each subject, T1-weighted MRI data were acquired and the gray matter (GM) volumes were calculated using voxel-based morphometry (VBM) analysis...
2018: Frontiers in Human Neuroscience
Farnaz Hoseini, Asadollah Shahbahrami, Peyman Bayat
Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function...
July 23, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
T Küstner, S Gatidis, A Liebgott, M Schwartz, L Mauch, P Martirosian, H Schmidt, N F Schwenzer, K Nikolaou, F Bamberg, B Yang, F Schick
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest...
July 21, 2018: Magnetic Resonance Imaging
Dmitry Petrov, Boris A Gutman, Shih-Hua Julie Yu, Theo G M van Erp, Jessica A Turner, Lianne Schmaal, Dick Veltman, Lei Wang, Kathryn Alpert, Dmitry Isaev, Artemis Zavaliangos-Petropulu, Christopher R K Ching, Vince Calhoun, David Glahn, Theodore D Satterthwaite, Ole Andreas Andreasen, Stefan Borgwardt, Fleur Howells, Nynke Groenewold, Aristotle Voineskos, Joaquim Radua, Steven G Potkin, Benedicto Crespo-Facorro, Diana Tordesillas-Gutiérrez, Li Shen, Irina Lebedeva, Gianfranco Spalletta, Gary Donohoe, Peter Kochunov, Pedro G P Rosa, Anthony James, Udo Dannlowski, Bernhard T Baune, André Aleman, Ian H Gotlib, Henrik Walter, Martin Walter, Jair C Soares, Stefan Ehrlich, Ruben C Gur, N Trung Doan, Ingrid Agartz, Lars T Westlye, Fabienne Harrisberger, Anita Riecher-Rössler, Anne Uhlmann, Dan J Stein, Erin W Dickie, Edith Pomarol-Clotet, Paola Fuentes-Claramonte, Erick Jorge Canales-Rodríguez, Raymond Salvador, Alexander J Huang, Roberto Roiz-Santiañez, Shan Cong, Alexander Tomyshev, Fabrizio Piras, Daniela Vecchio, Nerisa Banaj, Valentina Ciullo, Elliot Hong, Geraldo Busatto, Marcus V Zanetti, Mauricio H Serpa, Simon Cervenka, Sinead Kelly, Dominik Grotegerd, Matthew D Sacchet, Ilya M Veer, Meng Li, Mon-Ju Wu, Benson Irungu, Esther Walton, Paul M Thompson
As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality...
September 2017: Machine Learning in Medical Imaging
Ashirbani Saha, Michael R Harowicz, Lars J Grimm, Connie E Kim, Sujata V Ghate, Ruth Walsh, Maciej A Mazurowski
BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. METHODS: We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67)...
July 23, 2018: British Journal of Cancer
Rhodri Cusack, Conor J Wild, Leire Zubiaurre-Elorza, Annika C Linke
From their second year, infants typically begin to show rapid acquisition of receptive and expressive language. Here, we ask why these language skills do not begin to develop earlier. One evolutionary hypothesis is that infants are born when many brains systems are immature and not yet functioning, including those critical to language, because human infants have large have a large head and their mother's pelvis size is limited, necessitating an early birth. An alternative proposal, inspired by discoveries in machine learning, is that the language systems are mature enough to function but need auditory experience to develop effective representations of speech, before the language functions that manifest in behaviour can emerge...
May 9, 2018: Hearing Research
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