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Deep Learning MRI

Dimitrios Ataloglou, Anastasios Dimou, Dimitrios Zarpalas, Petros Daras
Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by a combination of Replace and Refine networks...
March 15, 2019: Neuroinformatics
Benjamin Rohaut, Kevin W Doyle, Alexandra S Reynolds, Kay Igwe, Caroline Couch, Adu Matory, Batool Rizvi, David Roh, Angela Velazquez, Murad Megjhani, Soojin Park, Sachin Agarwal, Christine M Mauro, Gen Li, Andrey Eliseyev, Vincent Perlbarg, Sander Connolly, Adam M Brickman, Jan Claassen
The purpose of this study was to determine the significance of deep structural lesions for impairment of consciousness following hemorrhagic stroke and recovery at ICU discharge. Our study focused on deep lesions that previously were implicated in studies of disorders of consciousness. We analyzed MRI measures obtained within the first week of the bleed and command following throughout the ICU stay. A machine learning approach was applied to identify MRI findings that best predicted the level consciousness...
March 12, 2019: Scientific Reports
Sampurna Biswas, Hemant K Aggarwal, Mathews Jacob
PURPOSE: To introduce a novel framework to combine deep-learned priors along with complementary image regularization penalties to reconstruct free breathing & ungated cardiac MRI data from highly undersampled multi-channel measurements. METHODS: Image recovery is formulated as an optimization problem, where the cost function is the sum of data consistency term, convolutional neural network (CNN) denoising prior, and SmooThness regularization on manifolds (SToRM) prior that exploits the manifold structure of images in the dataset...
March 12, 2019: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Peter D Chang, Tony T Wong, Michael J Rasiej
Deep learning for MRI detection of sports injuries poses unique challenges. To address these difficulties, this study examines the feasibility and incremental benefit of several customized network architectures in evaluation of complete anterior cruciate ligament (ACL) tears. Two hundred sixty patients, ages 18-40, were identified in a retrospective review of knee MRIs obtained from September 2013 to March 2016. Half of the cases demonstrated a complete ACL tear (624 slices), the other half a normal ACL (3520 slices)...
March 11, 2019: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Brent van der Heyden, Patrick Wohlfahrt, Daniëlle B P Eekers, Christian Richter, Karin Terhaag, Esther G C Troost, Frank Verhaegen
In radiotherapy, computed tomography (CT) datasets are mostly used for radiation treatment planning to achieve a high-conformal tumor coverage while optimally sparing healthy tissue surrounding the tumor, referred to as organs-at-risk (OARs). Based on CT scan and/or magnetic resonance images, OARs have to be manually delineated by clinicians, which is one of the most time-consuming tasks in the clinical workflow. Recent multi-atlas (MA) or deep-learning (DL) based methods aim to improve the clinical routine by an automatic segmentation of OARs on a CT dataset...
March 11, 2019: Scientific Reports
P Korfiatis, B Erickson
This paper describes state-of-the-art methods for molecular biomarker prediction utilising magnetic resonance imaging. This review paper covers both classical machine learning approaches and deep learning approaches to identifying the predictive features and to perform the actual prediction. In particular, there have been substantial advances in recent years in predicting molecular markers for diffuse gliomas. There are few examples of molecular marker prediction for other brain tumours. Deep learning has contributed significantly to these advances, but suffers from challenges in identifying the features used to make predictions...
March 5, 2019: Clinical Radiology
Chih-Chieh Liu, Jinyi Qi
PET images often suffer poor signal-to-noise ratio (SNR). Our objective is to improve the SNR of PET images using a deep neural network (DNN) model and MRI images without requiring any higher SNR PET images in training.
 Our proposed DNN model consists of three modified U-Nets (3U-net). The PET training input data and targets were reconstructed using filtered-backprojection (FBP) and maximum likelihood expectation maximization (MLEM), respectively. FBP reconstruction was used because of its computational efficiency so that the trained network not only removes noise, but also accelerates image reconstruction...
March 7, 2019: Physics in Medicine and Biology
Tatyana Ivanovska, Thomas G Jentschke, Amro Daboul, Katrin Hegenscheid, Henry Völzke, Florentin Wörgötter
PURPOSE: The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging data, which contain strong artifacts including intensity inhomogeneities. METHODS: We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction, breast volume segmentation, nipple extraction, and fibroglandular tissue segmentation. For the segmentation steps, a well-known deep learning architecture is employed...
March 6, 2019: International Journal of Computer Assisted Radiology and Surgery
Zhenglun Kong, Ting Li, Junyi Luo, Shengpu Xu
Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies, or other novel imaging technologies. In addition, image segmentation also provides detailed structural description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation methods. Here, we first use some preprocessing methods such as wavelet denoising to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) on 5 MRI head image datasets...
2019: Journal of Healthcare Engineering
Mumtaz Hussain Soomro, Matteo Coppotelli, Silvia Conforto, Maurizio Schmid, Gaetano Giunta, Lorenzo Del Secco, Emanuele Neri, Damiano Caruso, Marco Rengo, Andrea Laghi
The main goal of this work is to automatically segment colorectal tumors in 3D T2-weighted (T2w) MRI with reasonable accuracy. For such a purpose, a novel deep learning-based algorithm suited for volumetric colorectal tumor segmentation is proposed. The proposed CNN architecture, based on densely connected neural network, contains multiscale dense interconnectivity between layers of fine and coarse scales, thus leveraging multiscale contextual information in the network to get better flow of information throughout the network...
2019: Journal of Healthcare Engineering
Ning Lang, Yang Zhang, Enlong Zhang, Jiahui Zhang, Daniel Chow, Peter Chang, Hon J Yu, Huishu Yuan, Min-Ying Su
PURPOSE: To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers using radiomics and deep learning, compared to traditional hot-spot ROI analysis. METHODS: In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE) sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung; 31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics...
February 28, 2019: Magnetic Resonance Imaging
Jessica B Girault, Brent C Munsell, Danaële Puechmaille, Barbara D Goldman, Juan C Prieto, Martin Styner, John H Gilmore
Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth...
February 27, 2019: NeuroImage
Sheeba J Sujit, Ivan Coronado, Arash Kamali, Ponnada A Narayana, Refaat E Gabr
BACKGROUND: Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. PURPOSE: To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). STUDY TYPE: Retrospective. POPULATION: The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database...
February 27, 2019: Journal of Magnetic Resonance Imaging: JMRI
R Thillaikkarasi, S Saravanan
The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy tissue but the malignant tumor can affect the neighboring tissues of brain that can lead to the death of person. An early detection of brain tumor can be required to protect the survival of patients. Usually, the brain tumor is detected using MRI scanning method. However, the radiologists are not providing the effective tumor segmentation in MRI image due to the irregular shape of tumors and position of tumor in the brain...
February 27, 2019: Journal of Medical Systems
Riddhish Bhalodia, Shireen Y Elhabian, Ladislav Kavan, Ross T Whitaker
Statistical shape modeling is an important tool to characterize variation in anatomical morphology. Typical shapes of interest are measured using 3D imaging and a subsequent pipeline of registration, segmentation, and some extraction of shape features or projections onto some lower-dimensional shape space, which facilitates subsequent statistical analysis. Many methods for constructing compact shape representations have been proposed, but are often impractical due to the sequence of image preprocessing operations, which involve significant parameter tuning, manual delineation, and/or quality control by the users...
September 2018: Shape in Medical Imaging: International Workshop, ShapeMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings
Muhammad Naveed Iqbal Qureshi, Seungjun Ryu, Joonyoung Song, Kun Ho Lee, Boreom Lee
Purpose : To perform automatic assessment of dementia severity using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI) data. Method : We divided 133 Alzheimer's disease (AD) patients with clinical dementia rating (CDR) scores from 0.5 to 3 into two groups based on dementia severity; the groups with very mild/mild (CDR: 0.5-1) and moderate to severe (CDR: 2-3) dementia consisted of 77 and 56 subjects, respectively. We used rs-fMRI to extract functional connectivity features, calculated using independent component analysis (ICA), and performed automated severity classification with three-dimensional convolutional neural networks (3D-CNNs) based on deep learning...
2019: Frontiers in Aging Neuroscience
Moritz Zaiss, Anagha Deshmane, Mark Schuppert, Kai Herz, Felix Glang, Philipp Ehses, Tobias Lindig, Benjamin Bender, Ulrike Ernemann, Klaus Scheffler
PURPOSE: To determine the feasibility of employing the prior knowledge of well-separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z-spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach trained with 3 T and 9.4 T CEST spectral data from brains of the same subjects. METHODS: Highly spectrally resolved Z-spectra from the same volunteer were acquired by 3D-snapshot CEST MRI at 3 T and 9.4 T at low saturation power of B1 = 0...
February 25, 2019: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
King Chung Ho, William Speier, Haoyue Zhang, Fabien Scalzo, Suzie El-Saden, Corey W Arnold
Current clinical practice relies on clinical history to determine the time since stroke onset (TSS). Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options such as thrombolysis. Patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this work, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences...
February 25, 2019: IEEE Transactions on Medical Imaging
Ghalib A Bello, Timothy J W Dawes, Jinming Duan, Carlo Biffi, Antonio de Marvao, Luke S G E Howard, J Simon R Gibbs, Martin R Wilkins, Stuart A Cook, Daniel Rueckert, Declan P O'Regan
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors...
February 11, 2019: Nature Machine Intelligence
Suzan Vreemann, Mehmet U Dalmis, Peter Bult, Nico Karssemeijer, Mireille J M Broeders, Albert Gubern-Mérida, Ritse M Mann
OBJECTIVES: The purpose of this study is to evaluate the predictive value of the amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE), measured at baseline on breast MRI, for breast cancer development and risk of false-positive findings in women at increased risk for breast cancer. METHODS: Negative baseline MRI scans of 1533 women participating in a screening program for women at increased risk for breast cancer between January 1, 2003, and January 1, 2014, were selected...
February 22, 2019: European Radiology
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