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IEEE Transactions on Medical Imaging

Arvind Balachandrasekaran, Vincent Magnotta, Mathews Jacob
We introduce a structured low rank matrix completion algorithm to recover a series of images from their undersampled measurements, where the signal along the parameter dimension at every pixel is described by a linear combination of exponentials. We exploit the exponential behavior of the signal at every pixel, along with the spatial smoothness of the exponential parameters to derive an annihilation relation in the Fourier domain. This relation translates to a low-rank property on a structured matrix constructed from the Fourier samples...
July 14, 2017: IEEE Transactions on Medical Imaging
Korbinian Mechlem, Sebastian Ehn, Thorsten Sellerer, Eva Braig, Daniela Munzel, Franz Pfeiffer, Peter B Noel
By acquiring tomographic measurements with several distinct photon energy spectra, spectral computed tomography (spectral CT) is able to provide additional material-specific information compared to conventional CT. This information enables the generation of material selective images, which have found various applications in medical imaging. However, material decomposition typically leads to noise amplification and a degradation of the signal-to-noise ratio. This is still a fundamental problem of spectral CT, especially for low-dose medical applications...
July 13, 2017: IEEE Transactions on Medical Imaging
Ilkay Oksuz, Anirban Mukhopadhyay, Rohan Dharmakumar, Sotirios A Tsaftaris
A fully automated 2D+time myocardial segmentation framework is proposed for Cardiac Magnetic Resonance (CMR) Blood-Oxygen-Level-Dependent (BOLD) datasets. Ischemia detection with CINE BOLD CMR relies on spatiotemporal patterns in myocardial intensity but these patterns also trouble supervised segmentation methods, the de-facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace...
July 12, 2017: IEEE Transactions on Medical Imaging
Michael D Ketcha, Tharindu De Silva, Runze Han, Ali Uneri, Joseph Goerres, Matthew Jacobson, Sebastian Vogt, Gerhard Kleinszig, Jeffrey H Siewerdsen
For image-guided procedures, the imaging task is often tied to the registration of intraoperative and preoperative images to a common coordinate system. While the accuracy of this registration is a vital factor in system performance, there is relatively little work that relates registration accuracy to image quality factors such as dose, noise, and spatial resolution. To create a theoretical model for such a relationship, we present a Fisher information approach to analyze registration performance in explicit dependence on the underlying image quality factors of image noise, spatial resolution, and signal power spectrum...
July 11, 2017: IEEE Transactions on Medical Imaging
Ling Zhang, Andreas Wahle, Zhi Chen, John J Lopez, Tomas Kovarnik, Milan Sonka
Features of high-risk coronary artery plaques prone to major adverse cardiac events (MACE) were identified by intravascular ultrasound (IVUS) virtual histology (VH). These plaque features are: Thin-cap fibroatheroma (TCFA), plaque burden PB≥70%, or minimal luminal area MLA≤4mm². Identification of arterial locations likely to later develop such high-risk plaques may help prevent MACE. We report a machine learning method for prediction of future high-risk coronary plaque locations and types in patients under statin therapy...
July 11, 2017: IEEE Transactions on Medical Imaging
Shile Qi, Vince D Calhoun, Theo G M van Erp, Juan Bustillo, Eswar Damaraju, Jessica A Turner, Yuhui Du, Jian Yang, Jiayu Chen, Qingbao Yu, Daniel H Mathalon, Judith M Ford, James Voyvodic, Bryon A Mueller, Aysenil Belger, Sarah McEwen, Steven G Potkin, Adrian Preda, Tianzi Jiang, Jing Sui
By exploiting cross-information among multiple imaging data, multimodal fusion has often been used to better understand brain diseases. However, most current fusion approaches are blind, without adopting any prior information. There is increasing interest to uncover the neurocognitive mapping of specific clinical measurements on enriched brain imaging data; hence, a supervised, goal-directed model that employs prior information as a reference to guide multimodal data fusion is much needed and becomes a natural option...
July 11, 2017: IEEE Transactions on Medical Imaging
Christian F Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P Fletcher, Sandra Smith, Lisa M Koch, Bernhard Kainz, Daniel Rueckert
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box...
July 11, 2017: IEEE Transactions on Medical Imaging
Zhipeng Jia, Xingyi Huang, Eric I-Chao Chang, Yan Xu
In this paper, we develop a new weakly-supervised learning algorithm to learn to segment cancerous regions in histopathology images. Our work is under a multiple instance learning framework (MIL) with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: (1) We build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCN) in which image-toimage weakly-supervised learning is performed...
July 7, 2017: IEEE Transactions on Medical Imaging
Ukash Nakarmi, Yanhua Wang, Jingyuan Lyu, Dong Liang, Leslie Ying
While many low rank and sparsity based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub- Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank (KLR) model generalizing the conventional low rank formulation...
July 5, 2017: IEEE Transactions on Medical Imaging
Peter Fischer, Anthony Faranesh, Thomas Pohl, Andreas Maier, Toby Rogers, Kanishka Ratnayaka, Robert Lederman, Joachim Hornegger
In X-ray fluoroscopy, static overlays are used to visualize soft tissue. We propose a system for cardiac and respiratory motion compensation of these overlays. It consists of a 3-D motion model created from real-time MR imaging. Multiple sagittal slices are acquired and retrospectively stacked to consistent 3-D volumes. Slice stacking considers cardiac information derived from the ECG and respiratory information extracted from the images. Additionally, temporal smoothness of the stacking is enhanced. Motion is estimated from the MR volumes using deformable 3-D/3-D registration...
July 4, 2017: IEEE Transactions on Medical Imaging
Mingyue Yu, Yang Li, Teng Ma, K Kirk Shung, Qifa Zhou
Intravascular ultrasound (IVUS) has been frequently used for coronary artery imaging clinically. More importantly, IVUS is the fundamental image modality for most advanced multimodality intravascular imaging techniques since it provides a more comprehensive picture of vessel anatomy on which other imaging data can be superimposed. However, image quality in the deeper region is poor because of the downgraded lateral resolution and contrast-to-noise ratio. In this study, we report on the application of an ultrasound beamforming method that combines virtual source synthetic aperture (VSSA) focusing and coherence factor weighting (CFW) to improve the IVUS image quality...
July 4, 2017: IEEE Transactions on Medical Imaging
Fangxu Xing, Jonghye Woo, Arnold D Gomez, Dzung L Pham, Philip V Bayly, Maureen Stone, Jerry L Prince
Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissue. It is challenging to obtain three-dimensional (3D) motion estimates due to a tradeoff between image slice density and acquisition time. Typically, interpolation methods are used either to combine two-dimensional motion extracted from sparse slice acquisitions into 3D motion or to construct a dense volume from sparse acquisitions before image registration methods are applied. This paper proposes a new phase-based 3D motion estimation technique that first computes harmonic phase volumes from interpolated tagged slices and then matches them using an image registration framework...
July 4, 2017: IEEE Transactions on Medical Imaging
Zixu Yan, Feng Chen, Dexing Kong
It is essential for physicians to obtain the accurate venous tree from abdominal CT angiography (CTA) series in order to carry out the preoperative planning and intraoperative navigation for hepatic surgery. In this process, one of the important tasks is to separate the given liver venous mask into its hepatic and portal parts. In this paper, we present a novel method for liver venous tree separation. The proposed method first concentrates on extracting potential vessel intersection points between hepatic and portal venous systems...
June 30, 2017: IEEE Transactions on Medical Imaging
Pascal Zille, Vince D Calhoun, Julia M Stephen, Tony W Wilson, Yu-Ping Wang
In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using adequate statistical tools. Here, we propose to rely on a generalized fused Lasso penalty, which allows us to make use of the entire dataset in order to estimate connectivity patterns that are either shared across classes, or specific to a given group...
June 29, 2017: IEEE Transactions on Medical Imaging
N Anantrasirichai, Wesley Hayes, Marco Allinovi, David Bull, Alin Achim
This paper presents a novel method for line restoration in speckle images. We address this as a sparse estimation problem using both convex and non-convex optimisation techniques based on the Radon transform and sparsity regularisation. This breaks into subproblems which are solved using the alternating direction method of multipliers (ADMM), thereby achieving line detection and deconvolution simultaneously. We include an additional deblurring step in the Radon domain via a total variation blind deconvolution to enhance line visualisation and to improve line recognition...
June 29, 2017: IEEE Transactions on Medical Imaging
Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is crucial for the accuracy of the entire brain analysis process. State-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry; therefore have limited success when these assumptions do not hold or image registration fails...
June 28, 2017: IEEE Transactions on Medical Imaging
Pascal Zille, Vince D Calhoun, Yu-Ping Wang
Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this work, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities...
June 28, 2017: IEEE Transactions on Medical Imaging
Marie Bieth, Loic Peter, Stephan G Nekolla, Matthias Eiber, Georg Langs, Markus Schwaiger, Bjoern Menze
Whole body oncological screening using CT images requires a good anatomical localisation of organs and of the skeleton. While a number of algorithms for multi-organ localisation have been presented, developing algorithms for a dense anatomical annotation of the whole skeleton, however, has not been addressed until now. Only methods for specialised applications, e.g., in spine imaging, have been previously described. In this work, we propose an approach for localising and annotating different parts of the human skeleton in CT images...
June 27, 2017: IEEE Transactions on Medical Imaging
Peter Mountney, Jonathan M Behar, Daniel Toth, Maria Panayiotou, Sabrina Reiml, Marie-Pierre Jolly, Rashed Karim, Li Zhang, Alexander Brost, Christopher A Rinaldi, Kawal Rhode
Patients with drug-refractory heart failure can greatly benefit from cardiac resynchronization therapy (CRT). A CRT device can resynchronize the contractions of the left ventricle (LV) leading to reduced mortality. Unfortunately 30- 50% of patients do not respond to treatment when assessed by objective criteria such as cardiac remodeling. A significant contributing factor is suboptimal placement of the LV lead. It has been shown that placing this lead away from scar and at the point of latest mechanical activation can improve response rates...
June 27, 2017: IEEE Transactions on Medical Imaging
M Mehdi Farhangi, Hichem Frigui, Albert Seow, Amir A Amini
SCoTS captures a sparse representation of shapes in an input image through a linear span of previously delineated shapes in a training repository. The model updates shape prior over level set iterations and captures variabilities in shapes by a sparse combination of the training data. The level set evolution is therefore driven by a data term as well as a term capturing valid prior shapes. During evolution, the shape prior influence is adjusted based on shape reconstruction, with the assigned weight determined from the degree of sparsity of the representation...
June 26, 2017: IEEE Transactions on Medical Imaging
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