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IEEE Transactions on Medical Imaging

Elias Pavlatos, Hong Chen, Keyton Clayson, Xueliang Pan, Jun Liu
Imaging corneal biomechanical changes or abnormalities is important for better clinical diagnosis and treatment of corneal diseases. We propose a novel ultrasound-based method, called ocular pulse elastography (OPE), to image corneal deformation during the naturally occurring ocular pulse. Experiments on animal and human donor eyes, as well as synthetic radiofrequency (RF) data, were used to evaluate the efficacy of the OPE method. Using very high-frequency ultrasound (center frequency = 55 MHz), correlation-based speckle tracking yielded an accuracy of less than 10% error for axial tissue displacements of or above...
February 2018: IEEE Transactions on Medical Imaging
Ben Cassidy, F DuBois Bowman, Caroline Rae, Victor Solo
There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales...
February 2018: IEEE Transactions on Medical Imaging
Ling Zhang, Le Lu, Ronald M Summers, Electron Kebebew, Jianhua Yao
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build the predictive models for tumor growth. In this paper, we investigate the possibility of using deep convolutional neural networks to directly represent and learn the cell invasion and mass-effect, and to predict the subsequent involvement regions of a tumor. The invasion network learns the cell invasion from information related to metabolic rate, cell density, and tumor boundary derived from multimodal imaging data...
February 2018: IEEE Transactions on Medical Imaging
Markus Zimmermann, Zaheer Abbas, Krzysztof Dzieciol, N Jon Shah
A new reconstruction method, coined MIRAGE, is presented for accurate, fast, and robust parameter mapping of multiple-echo gradient-echo (MEGE) imaging, the basis sequence of novel quantitative magnetic resonance imaging techniques such as water content and susceptibility mapping. Assuming that the temporal signal can be modeled as a sum of damped complex exponentials, MIRAGE performs model-based reconstruction of undersampled data by minimizing the rank of local Hankel matrices. It further incorporates multi-channel information and spatial prior knowledge...
February 2018: IEEE Transactions on Medical Imaging
Nikolas Lessmann, Bram van Ginneken, Majd Zreik, Pim A de Jong, Bob D de Vos, Max A Viergever, Ivana Isgum
Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates...
February 2018: IEEE Transactions on Medical Imaging
Valur T Olafsson, Douglas C Noll, Jeffrey A Fessler
Penalized least-squares iterative image reconstruction algorithms used for spatial resolution-limited imaging, such as functional magnetic resonance imaging (fMRI), commonly use a quadratic roughness penalty to regularize the reconstructed images. When used for complex-valued images, the conventional roughness penalty regularizes the real and imaginary parts equally. However, these imaging methods sometimes benefit from separate penalties for each part. The spatial smoothness from the roughness penalty on the reconstructed image is dictated by the regularization parameter(s)...
February 2018: IEEE Transactions on Medical Imaging
Georg Schramm, Martin Holler, Ahmadreza Rezaei, Kathleen Vunckx, Florian Knoll, Kristian Bredies, Fernando Boada, Johan Nuyts
In this article, we evaluate Parallel Level Sets (PLS) and Bowsher's method as segmentation-free anatomical priors for regularized brain positron emission tomography (PET) reconstruction. We derive the proximity operators for two PLS priors and use the EM-TV algorithm in combination with the first order primal-dual algorithm by Chambolle and Pock to solve the non-smooth optimization problem for PET reconstruction with PLS regularization. In addition, we compare the performance of two PLS versions against the symmetric and asymmetric Bowsher priors with quadratic and relative difference penalty function...
February 2018: IEEE Transactions on Medical Imaging
Amar V Nasrulloh, Chris G Willcocks, Philip T G Jackson, Caspar Geenen, Maged S Habib, David H W Steel, Boguslaw Obara
Macular holes are blinding conditions, where a hole develops in the central part of retina, resulting in reduced central vision. The prognosis and treatment options are related to a number of variables, including the macular hole size and shape. High-resolution spectral domain optical coherence tomography allows precise imaging of the macular hole geometry in three dimensions, but the measurement of these by human observers is time-consuming and prone to high inter- and intra-observer variability, being characteristically measured in 2-D rather than 3-D...
February 2018: IEEE Transactions on Medical Imaging
John S H Baxter, Zahra Hosseini, Terry M Peters, Maria Drangova
Sensitivity to phase deviations in MRI forms the basis of a variety of techniques, including magnetic susceptibility weighted imaging and chemical shift imaging. Current phase processing techniques fall into two families: those which process the complex image data with magnitude and phase coupled, and phase unwrapping-based techniques that first linearize the phase topology across the image. However, issues, such as low signal and the existence of phase poles, can lead both methods to experience error. Cyclic continuous max-flow (CCMF) phase processing uses primal-dual-variational optimization over a cylindrical manifold, which represent the inherent topology of phase images, increasing its robustness to these issues...
February 2018: IEEE Transactions on Medical Imaging
Evan Levine, Brian Hargreaves
In high-dimensional magnetic resonance imaging applications, time-consuming, sequential acquisition of data samples in the spatial frequency domain ( -space) can often be accelerated by accounting for dependencies in linear reconstruction, at the cost of noise amplification that depends on the sampling pattern. Common examples are support-constrained, parallel, and dynamic MRI, and -space sampling strategies are primarily driven by image-domain metrics that are expensive to compute for arbitrary sampling patterns...
February 2018: IEEE Transactions on Medical Imaging
Juan F P J Abascal, Manuel Desco, Juan Parra-Robles
Diffusion MRI data are generally acquired using hyperpolarized gases during patient breath-hold, which yields a compromise between achievable image resolution, lung coverage, and number of -values. In this paper, we propose a novel method that accelerates the acquisition of diffusion MRI data by undersampling in both the spatial and -value dimensions and incorporating knowledge about signal decay into the reconstruction (SIDER). SIDER is compared with total variation (TV) reconstruction by assessing its effect on both the recovery of ventilation images and the estimated mean alveolar dimensions (MADs)...
February 2018: IEEE Transactions on Medical Imaging
Paris D L Flood, Scott A Banks
This paper describes an automated method for registering 3-D models of metallic knee implants to single-plane radiographic images. We develop a multistage approach that identifies the correct pose by matching altered dilations of an edge-detected image with the silhouette of an implant model. The location of the similarity function's minimum is found using a novel optimization routine that combines the Dividing Rectangles algorithm with properties of the registration metric. Depending on the implant type (tibial or femoral), this technique reliably converges under maximum displacements of approximately 25 to 55 millimeters for translation components and 25° to 55° for Euler angles...
January 2018: IEEE Transactions on Medical Imaging
Dongwoon Hyun, Lotfi Abou-Elkacem, Valerie A Perez, Sayan Mullick Chowdhury, Juergen K Willmann, Jeremy J Dahl
Ultrasound molecular imaging (USMI) is accomplished by detecting microbubble (MB) contrast agents that have bound to specific biomarkers, and can be used for a variety of imaging applications, such as the early detection of cancer. USMI has been widely utilized in preclinical imaging in mice; however, USMI in humans can be challenging because of the low concentration of bound MBs and the signal degradation caused by the presence of heterogenous soft tissue between the transducer and the lesion. Short-lag spatial coherence (SLSC) beamforming has been proposed as a robust technique that is less affected by poor signal quality than standard delay-and-sum (DAS) beamforming...
January 2018: IEEE Transactions on Medical Imaging
Thomas A W Bolton, Anjali Tarun, Virginie Sterpenich, Sophie Schwartz, Dimitri Van De Ville
Functional magnetic resonance imaging (fMRI) provides a window on the human brain at work. Spontaneous brain activity measured during resting-state has already provided many insights into brain function. In particular, recent interest in dynamic interactions between brain regions has increased the need for more advanced modeling tools. Here, we deploy a recent fMRI deconvolution technique to express resting-state temporal fluctuations as a combination of large-scale functional network activity profiles. Then, building upon a novel sparse coupled hidden Markov model (SCHMM) framework, we parameterised their temporal evolution as a mix between intrinsic dynamics, and a restricted set of cross-network modulatory couplings extracted in data-driven manner...
January 2018: IEEE Transactions on Medical Imaging
Liang Liu, Xinxin Li, Kai Xiang, Jing Wang, Shan Tan
Cone-beam computed tomography (CBCT) has been widely used in radiation therapy. For accurate patient setup and treatment target localization, it is important to obtain high-quality reconstruction images. The total variation (TV) penalty has shown the state-of-the-art performance in suppressing noise and preserving edges for statistical iterative image reconstruction, but it sometimes leads to the so-called staircase effect. In this paper, we proposed to use a new family of penalties-the Hessian Schatten (HS) penalties-for the CBCT reconstruction...
December 2017: IEEE Transactions on Medical Imaging
Donghyeon Lee, Jiseoc Lee, Hyoyi Kim, Taewon Lee, Jeongtae Soh, Miran Park, Changhwan Kim, Yeon Ju Lee, Seungryong Cho
A single-scan dual-energy low-dose cone-beam CT (CBCT) imaging technique that exploits a multi-slit filter is proposed in this paper. The multi-slit filter installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data...
December 2017: IEEE Transactions on Medical Imaging
Weikang Zhang, Ying Song, Yi Chen, Jingchen Ma, Jianqi Sun, Jun Zhao
Repeated CT scans are known to increase the risk of cancer; thus, it is paradoxical to use multiple follow-up CT scans to monitor the development of a lung nodule and conduct early treatment of the nodule. In the case of a solitary lung nodule, regional scanning and region of interest (ROI) reconstruction are likely to restore the internal area at the nodule. A limited-range few-view CT is proposed in this paper for lung nodule follow-ups with extremely reduced X-radiation. For a planned scanning of an ROI, where a solitary lung nodule is positioned, a limited-range few-view CT can be employed, and thus, less tissue is exposed to X-radiation per view...
December 2017: IEEE Transactions on Medical Imaging
Qi Xie, Dong Zeng, Qian Zhao, Deyu Meng, Zongben Xu, Zhengrong Liang, Jianhua Ma
Computed tomography (CT) image recovery from low-mAs acquisitions without adequate treatment is always severely degraded due to a number of physical factors. In this paper, we formulate the low-dose CT sinogram preprocessing as a standard maximum a posteriori (MAP) estimation, which takes full consideration of the statistical properties of the two intrinsic noise sources in low-dose CT, i.e., the X-ray photon statistics and the electronic noise background. In addition, instead of using a general image prior as found in the traditional sinogram recovery models, we design a new prior formulation to more rationally encode the piecewise-linear configurations underlying a sinogram than previously used ones, like the TV prior term...
December 2017: IEEE Transactions on Medical Imaging
Andrey Makeev, Stephen J Glick
Iodinated contrast-enhanced X-ray imaging of the breast has been studied with various modalities, including full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT. Contrast imaging with breast CT has a number of advantages over FFDM and DBT, including the lack of breast compression, and generation of fully isotropic 3-D reconstructions. Nonetheless, for breast CT to be considered as a viable tool for routine clinical use, it would be desirable to reduce radiation dose...
December 2017: IEEE Transactions on Medical Imaging
Ilkay Oksuz, Anirban Mukhopadhyay, Rohan Dharmakumar, Sotirios A Tsaftaris
A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal 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...
November 2017: IEEE Transactions on Medical Imaging
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