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IEEE Transactions on Medical Imaging

Lu Ding, X Luis Dean Ben, Daniel Razansky
Optimal optoacoustic tomographic sampling is often hindered by the frequency-dependent directivity of ultrasound sensors, which can only be accounted for with an accurate three-dimensional (3D) model. Herein, we introduce a 3D modelbased reconstruction method applicable to optoacoustic imaging systems employing detection elements with arbitrary size and shape. The computational complexity and memory requirements are mitigated by introducing an efficient graphics processing unit (GPU)-based implementation of the iterative inversion...
May 12, 2017: IEEE Transactions on Medical Imaging
Xiongbiao Luo, A McLeod, Stephen Pautler, Christopher Schlachta, Terry Peters
Fogged surgical field visualization that is a common and potentially harmful problem can lead to inappropriate device use and incorrectly targeted tissue and increase surgical risks in endoscopic surgery. This work aims to remove fog or smoke on endoscopic video sequences to augment and maintain a direct and clear visualization of the operating field. A new visibilitydriven fusion defogging framework is proposed for surgical endoscopic video processing. This framework first recovers the visibility and enhances the contrast of hazy images...
May 11, 2017: IEEE Transactions on Medical Imaging
Tharindu De Silva, Derek Cool, Jing Yuan, Cesare Romagnoli, Jagath Samarabandu, Aaron Fenster, Aaron Ward
In magnetic resonance (MR)-targeted, 3D transrectal ultrasound (TRUS)-guided biopsy, prostate motion during the procedure increases the needle targeting error and limits the ability to accurately sample MR-suspicious tumour volumes. The robustness of 2D-3D registration methods for prostate motion compensation is impacted by local optima in the search space. In this paper, we analysed prostate motion characteristics and investigated methods to incorporate such knowledge into the registration optimization framework to improve robustness against local optima...
May 10, 2017: IEEE Transactions on Medical Imaging
Huazhu Fu, Yanwu Xu, Stephen Lin, Xiaoqin Zhang, Damon Wing Kee Wong, Jiang Liu, Alejandro Frangi, Mani Baskaran, Tin Aung
Angle-closure glaucoma is a major cause of irreversible visual impairment and can be identified by measuring the anterior chamber angle (ACA) of the eye. The ACA can be viewed clearly through Anterior Segment Optical Coherence Tomography (AS-OCT), but the imaging characteristics and the shapes and locations of major ocular structures can vary significantly among different AS-OCT modalities, thus complicating image analysis. To address this problem, we propose a datadriven approach for automatic AS-OCT structure segmentation, measurement and screening...
May 10, 2017: IEEE Transactions on Medical Imaging
Jian Wang, Roman Schaffert, Anja Borsdorf, Benno Heigl, Xiaolin Huang, Joachim Hornegger, Andreas Maier
In image-guided interventional procedures, live 2-D X-ray images can be augmented with preoperative 3-D CT or MRI images to provide planning landmarks and enhanced spatial perception. An accurate alignment between the 3-D and 2-D images is a prerequisite for fusion applications. This paper presents a dynamic rigid 2-D/3-D registration framework, which measures the local 3-D-to-2-D misalignment and efficiently constrains the update of both planar and non-planar 3-D rigid transformations using a novel point-to-plane correspondence model...
May 8, 2017: IEEE Transactions on Medical Imaging
Wolf-Dieter Vogl, Sebastian M Waldstein, Bianca S Gerendas, Ursula Schmidt-Erfurth, Georg Langs
Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients' response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and Random Forests based extra trees (ET)...
May 2, 2017: IEEE Transactions on Medical Imaging
Xingying Wang, Vipin Seetohul, Ruimin Chen, Ming Qian, Congzhi Wang, Zhihong Huang, Qifa Zhou, Hairong Zheng, Sandy Cochran, Weibao Qiu
Wireless capsule endoscopy has opened a new era by enabling remote diagnostic assessment of the gastrointestinal (GI) tract in a painless procedure. Video capsule endoscopy (VCE) is currently commercially available worldwide. However, it is limited to visualization of superficial tissue. Ultrasound (US) imaging is a complementary solution as it is capable of acquiring transmural information from the tissue wall. This paper presents a mechanical scanning device incorporating a high-frequency transducer specifically as a proof of concept for ultrasound capsule endoscopy (USCE), providing information that may usefully assist future research...
May 2, 2017: IEEE Transactions on Medical Imaging
Charles Tremblay-Darveau, Avinoam Bar-Zion, R Williams, Paul Sheeran, Laurent Milot, Thanasis Loupas, Dan Adam, Matthew Bruce, Peter Burns
While plane-wave imaging can improve the performance of power Doppler by enabling much longer ensembles than systems using focused beams, the long-ensemble averaging of the zero-lag autocorrelation R(0) estimates does not directly decrease the mean noise level, but only decreases its variance. Spatial variation of the noise due to the time-gain compensation and the received beamforming aperture ultimately limits sensitivity. In this paper, we demonstrate that the performance of power Doppler imaging can be improved by leveraging the higher lags of the autocorrelation (e...
April 28, 2017: IEEE Transactions on Medical Imaging
Abd-Krim Seghouane, Asif Iqbal
Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI datasets are however structured data matrices with notions of temporal smoothness in the column direction. This prior information which can be converted to a constraint of smoothness on the learned dictionary atoms has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information...
April 28, 2017: IEEE Transactions on Medical Imaging
Yi Yin, Oliver Sedlaczek, Benedikt Muller, Arne Warth, Margarita Gonzalez-Vallinas, Niels Grabe, Hans-Ulrich Kauczor, Kai Breuhahn, Irene Vignon-Clemental, Dirk Drasdo
Diffusion-weighted magnetic resonance imaging (DWI) is a key non-invasive imaging technique for cancer diagnosis and tumor treatment assessment, reflecting Brownian movement of water molecules in tissues. Since densely packed cells restrict molecule mobility, tumor tissues produce usually higher signal (a.k.a less attenuated signal) on isotropic maps compared to normal tissues. However, no general quantitative relation between DWI data and the cell density has been established. In order to link low-resolution clinical cross-sectional data with high-resolution histological information, we developed an image processing and analysis chain, which was used to study the correlation between the diffusion coefficient (D value) estimated from DWI and tumor cellularity from serial histological slides of a resected non-small cell lung cancer (NSCLC) tumor...
April 27, 2017: IEEE Transactions on Medical Imaging
Jwala Dhamala, Hermenegild Arevalo, John Sapp, Milan Horacek, Katherine Wu, Natalia Trayanova, Linwei Wang
To obtain a patient-specific cardiac electrophysiological (EP) model, it is important to estimate the threedimensionally distributed tissue properties of the myocardium. Ideally, the tissue property should be estimated at the resolution of the cardiac mesh. However, such high dimensional estimation face major challenges in identifiability and computation. Most existing works reduce this dimension by partitioning the cardiac mesh into a pre-defined set of segments. The resulting lowresolution solutions have a limited ability to represent the underlying heterogeneous tissue properties of varying sizes, locations and distributions...
April 25, 2017: IEEE Transactions on Medical Imaging
Taly Schmidt, Rina Barber, Emil Sidky
The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts...
April 24, 2017: IEEE Transactions on Medical Imaging
Sara Park, Jongseong Jang, Jeesu Kim, Young Soo Kim, Chulhong Kim
Imaging that fuses multiple modes has become a useful tool for diagnosis and therapeutic monitoring. As a next step, real-time fusion imaging has attracted interest as for a tool to guide surgery. One widespread fusion imaging technique in surgery combines real-time ultrasound (US) imaging and preacquired magnetic resonance (MR) imaging. However, US imaging visualizes only structural information with relatively low contrast. Here, we present a photoacoustic (PA), US, and MR fusion imaging system which integrates a clinical PA and US imaging system with an optical tracking-based navigation subsystem...
April 24, 2017: IEEE Transactions on Medical Imaging
Daniele Ravi, Himar Fabelo, Gustavo Marrero Callico, GuangZhong Yang
Recent advances in hyperspectral imaging have made it a promising solution for intra-operative tissue characterization, with the advantages of being non-contact, non-ionizing and non-invasive. Working with hyperspectral images in vivo, however, is not straightforward as the high dimensionality of the data makes real-time processing challenging. In this paper, a novel dimensionality reduction scheme and a new processing pipeline are introduced to obtain a detailed tumour classification map for intra-operative margin definition during brain surgery...
April 24, 2017: IEEE Transactions on Medical Imaging
Chumin Zhao, Jerzy Kanicki
High-resolution, low noise x-ray detectors based on CMOS active pixel sensor (APS) technology have demonstrated superior imaging performance for digital breast tomosynthesis (DBT). This paper presents a task-based model for a high resolution medical imaging system to evaluate its ability to detect simulated microcalcifications and masses as lesions for breast cancer. Three-dimensional (3D) cascaded system analysis for a 50 μm pixel pitch CMOS APS x-ray detector was integrated with an object task function, a medical imaging display model, and the human eye contrast sensitivity function to calculate the detectability index and area under the ROC curve (AUC)...
April 19, 2017: IEEE Transactions on Medical Imaging
Peter Muller, Jennifer Mueller, Michelle Mellenthin
A real-time implementation of Calderón's method for the reconstruction of a 2-D conductivity from electrical impedance tomography (EIT) data is presented in which domainspecific modeling is taken into account. This is the first implementation of Calderón's method that accounts for correct modeling of non-symmetric domain boundaries in image reconstruction. The domain-specific Calderón's method is derived and reconstructions from experimental tank data are presented, quantifying the distortion when correct modeling is not included in the reconstruction algorithm...
April 19, 2017: IEEE Transactions on Medical Imaging
Yading Yuan, Ming Chao, Yeh-Chi Lo
Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this article, we present a fully automatic method for skin lesion segmentation by leveraging a 19-layer deep convolutional neural networks (CNNs) that is trained end-to- end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data...
April 18, 2017: IEEE Transactions on Medical Imaging
Jacob Herrmann, Eric A Hoffman, David W Kaczka
We seek to use computed tomography (CT) to characterize regional lung parenchymal deformation during highfrequency and multi-frequency oscillatory ventilation. Periodic motion of thoracic structures results in artifacts of CT images obtained by standard reconstruction algorithms, especially for frequencies exceeding that of the X-ray source rotation. In this study, we propose an acquisition and reconstruction technique for high resolution imaging of the thorax during periodic motion. Our technique relies on phase-binning projections according to the frequency of subject motion relative to the scanner rotation, prior to volumetric reconstruction...
April 18, 2017: IEEE Transactions on Medical Imaging
Abolfazl Mehranian, Martin Belzunce, Claudia Prieto, Alexander Hammers, Andrew Reader
In this work we propose a generalized joint sparsity regularization prior and reconstruction framework for the syner-gistic reconstruction of PET and undersampled sensitivity en-coded (SENSE) MRI data with the aim of improving image quality beyond that obtained through conventional independent recon-structions. The proposed prior improves upon the joint total vari-ation (TV) using a non-convex potential function that assigns a rel-atively lower penalty for the PET and MR gradients whose mag-nitudes are jointly large, thus permitting the preservation and for-mation of common boundaries irrespective of their relative orien-tation...
April 18, 2017: IEEE Transactions on Medical Imaging
Felix Bragman, Jamie McClelland, Joseph Jacob, John Hurst, David Hawkes
A fully automated, unsupervised lobe segmentation algorithm is presented based on a probabilistic segmentation of the fissures and the simultaneous construction of a population model of the fissures. A two-class probabilistic segmentation segments the lung into candidate fissure voxels and the surrounding parenchyma. This was combined with anatomical information and a groupwise fissure prior to drive non-parametric surface fitting to obtain the final segmentation. The performance of our fissure segmentation was validated on 30 patients from the COPDGene cohort, achieving a high median F1-score of 0:90 and showed general insensitivity to filter parameters...
April 18, 2017: IEEE Transactions on Medical Imaging
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