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Deep Learning MRI

Matteo Maspero, Mark H F Savenije, Anna M Dinkla, Peter R Seevinck, Martijn P W Intven, Ina M Juergenliemk-Schulz, Linda G W Kerkmeijer, Cornelis A T Van den Berg
To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic-CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images to be used for accurate MR-based dose calculations in the entire pelvis.
 A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation...
August 15, 2018: Physics in Medicine and Biology
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
Fang Liu, Zhaoye Zhou, Alexey Samsonov, Donna Blankenbaker, Will Larison, Andrew Kanarek, Kevin Lian, Shivkumar Kambhampati, Richard Kijowski
Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method...
July 31, 2018: Radiology
Sen Liang, Rongguo Zhang, Dayang Liang, Tianci Song, Tao Ai, Chen Xia, Liming Xia, Yan Wang
Non-invasive prediction of isocitrate dehydrogenase ( IDH ) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images...
July 30, 2018: Genes
Md Faijul Amin, Sergey M Plis, 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
Morteza Mardani, Enhao Gong, Joseph Y Cheng, Shreyas S Vasanawala, Greg Zaharchuk, Lei Xing, John M Pauly
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require trade offs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ1/ℓ2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold...
July 23, 2018: IEEE Transactions on Medical Imaging
Feiyu Chen, Valentina Taviani, Itzik Malkiel, Joseph Y Cheng, Jonathan I Tamir, Jamil Shaikh, Stephanie T Chang, Christopher J Hardy, John M Pauly, Shreyas S Vasanawala
Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017...
July 24, 2018: Radiology
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
Olivier Bernard, Alain Lalande, Clement Zotti, Frederick Cervenansky, Xin Yang, Pheng-Ann Heng, Irem Cetin, Karim Lekadir, Oscar Camara, Miguel Angel Gonzalez Ballester, Gerard Sanroma, Sandy Napel, Steffen Petersen, Georgios Tziritas, Elias Grinias, Mahendra Khened, Varghese Alex Kollerathu, Ganapathy Krishnamurthi, Marc-Michel Rohe, Xavier Pennec, Maxime Sermesant, Fabian Isensee, Paul Jager, Klaus H Maier-Hein, Chrisitan F Baumgartner, Lisa M Koch, Jelmer M Wolterink, Ivana Isgum, Yeonggul Jang, Yoonmi Hong, Jay Patravali, Shubham Jain, Olivier Humbert, Pierre-Marc Jodoin
Delineation of the left ventricular cavity, myocardium and right ventricle from cardiac magnetic resonance images (multi-slice 2D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly-available and fully-annotated dataset for the purpose of Cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts...
May 17, 2018: IEEE Transactions on Medical Imaging
Mingxia Liu, Jun Zhang, Dong Nie, Pew-Thian Yap, Dinggang Shen
Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest (ROIs) defined by experts. In addition, because of the possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis...
January 10, 2018: IEEE Journal of Biomedical and Health Informatics
Qiao Zheng, Herve Delingette, Nicolas Duchateau, Nicholas Ayache
We propose a method based on deep learning to perform cardiac segmentation on short axis MRI image stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. 3D-consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from UK Biobank...
March 29, 2018: IEEE Transactions on Medical Imaging
Jun Shi, Zheng Li, Shihui Ying, Chaofeng Wang, Qingping Liu, Qi Zhang, Pingkun Yan
Spatial resolution is a critical imaging parameter in magnetic resonance imaging (MRI). The image super-resolution (SR) is an effective and cost efficient alternative technique to improve the spatial resolution of MR images. Over the past several years, the convolutional neural networks (CNN)-based SR methods have achieved state-of-the-art performance. However, CNNs with very deep network structures usually suffer from the problems of degradation and diminishing feature reuse, which add difficulty to network training and degenerate the transmission capability of details for SR...
June 4, 2018: IEEE Journal of Biomedical and Health Informatics
Guotai Wang, Maria A Zuluaga, Wenqi Li, Rosalind Pratt, Premal A Patel, Michael Aertsen, Tom Doel, Anna L Divid, Jan Deprest, Sebastien Ourselin, Tom Vercauteren
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art automatic segmentation methods. However, fully automatic results may still need to be refined to become accurate and robust enough for clinical use. We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy...
June 1, 2018: IEEE Transactions on Pattern Analysis and Machine Intelligence
Mohamed Shehata, Fahmi Khalifa, Ahmed Soliman, Mohammed Ghazal, Fatma Taher, Mohamed Abou El-Ghar, Amy Dwyer, Georgy Gimel'farb, Robert Keynton, Ayman El-Baz
OBJECTIVE: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. METHODS: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) MRI data...
June 25, 2018: IEEE Transactions on Bio-medical Engineering
Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great benefit by estimating a desired imaging modality without incurring an actual scan. In this paper, we propose a generative adversarial approach to address this challenging problem. Specifically, we train a fully convolutional network (FCN) to generate a target image given a source image...
March 9, 2018: IEEE Transactions on Bio-medical Engineering
Dongwook Lee, Jaejun Yoo, Sungho Tak, Jong Ye
Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. The deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation...
April 2, 2018: IEEE Transactions on Bio-medical Engineering
Ricardo Pizarro, Haz-Edine Assemlal, Dante De Nigris, Colm Elliott, Samson Antel, Douglas Arnold, Amir Shmuel
Neuroimaging science has seen a recent explosion in dataset size driving the need to develop database management with efficient processing pipelines. Multi-center neuroimaging databases consistently receive magnetic resonance imaging (MRI) data with unlabeled or incorrectly labeled contrast. There is a need to automatically identify the contrast of MRI scans to save database-managing facilities valuable resources spent by trained technicians required for visual inspection. We developed a deep learning (DL) algorithm with convolution neural network architecture to automatically infer the contrast of MRI scans based on the image intensity of multiple slices...
June 29, 2018: Neuroinformatics
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