Read by QxMD icon Read

IEEE Transactions on Medical Imaging

Jarrod Collins, Jared Weis, Jon Heiselman, Logan Clements, Amber Simpson, Willam Jarnagin, Michael Miga
In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n=13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods...
February 13, 2017: IEEE Transactions on Medical Imaging
Alan Miranda, Steven Staelens, Sigrid Stroobants, Jeroen Verhaeghe
To avoid the confounding effects of anesthesia and immobilization stress in rat brain positron emission tomography (PET), motion tracking based unrestrained awake rat brain imaging is being developed. In this work we propose a fast and accurate rat head motion tracking method based on small PET point sources. PET point sources (3-4) attached to the rat's head are tracked in image space using 15-32 ms time frames. Our point source tracking (PST) method was validated using a manually moved microDerenzo phantom that was simultaneously tracked with an optical tracker (OT) for comparison...
February 13, 2017: IEEE Transactions on Medical Imaging
Chenxi Hu, Stanley Reeves, Dana Peters, Donald Twieg
R 2 mapping is a useful tool in blood-oxygen-level dependent (BOLD) fMRI due to its quantitative nature. However, like T 2 -weighted imaging, standard R 2 mapping based on multiecho EPI suffers from geometric distortion, due to strong offresonance near the air-tissue interface. Joint mapping of R 2 and off-resonance can correct the geometric distortion and is less susceptible to motion artifacts. Single-shot joint mapping of R 2 and off-resonance is possible with a rosette trajectory due to its frequent sampling of the k-space center...
February 13, 2017: IEEE Transactions on Medical Imaging
Bulat Ibragimov, Robert Korez, Bostjan Likar, Franjo Pernus, Lei Xing, Tomaz Vrtovec
Computerized segmentation of pathological structures in medical images is challenging, as, in addition to unclear image boundaries, image artifacts and traces of surgical activities, the shape of pathological structures may be very different from the shape of normal structures. Even if a sufficient number of pathological training samples are collected, statistical shape modeling cannot always capture shape features of pathological samples as they may be suppressed by shape features of a considerably larger number of healthy samples...
February 13, 2017: IEEE Transactions on Medical Imaging
Maria Kuklisova Murgasova, Georgia Lockwood-Estrin, Rita G Nunes, Shaihan Malik, Mary Rutherford, Daniel Rueckert, Joseph Hajnal
Geometric distortion induced by the main B0 field disrupts the consistency of fetal EPI data, on which diffusion and functional MRI is based. In this paper we present a novel data-driven method for simultaneous motion and distortion correction of fetal EPI. A motion-corrected and reconstructed T2 weighted ssFSE volume is used as a model of undistorted fetal brain anatomy. Our algorithm interleaves two registration steps: estimation of fetal motion parameters by aligning EPI slices to the model; and deformable registration of EPI slices to slices simulated from the undistorted model to estimate the distortion field...
February 9, 2017: IEEE Transactions on Medical Imaging
Nadine Gdaniec, Matthias Schluter, Martin Hofmann, Michael Kaul, Kannan Krishnan, Alexander Schlafer, Tobias Knopp
The temporal resolution of the tomographic imaging method magnetic particle imaging (MPI) is remarkably high. The spatial resolution is degraded for measured voltage signal with low signal-to-noise ratio, because the regularization in the image reconstruction step needs to be increased for system-matrix approaches and for deconvolution steps in x-space approaches. To improve the signal-to-noise ratio, block-wise averaging of the signal over time can be advantageous. However, since block-wise averaging decreases the temporal resolution, it prevents resolving the motion...
February 9, 2017: IEEE Transactions on Medical Imaging
Jelena Novosel, Koenraad Vermeer, Jan H de Jong, Ziyuan Wang, Lucas Van Vliet
Accurate quantification of retinal structures in 3D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level sets framework. In the new approach, lesions are modelled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers thereby allowing the approach to handle local intensity variations...
February 8, 2017: IEEE Transactions on Medical Imaging
Jelena Novosel, Suzanne Yzer, Koenraad Vermeer, Lucas Van Vliet
Extraction of image-based biomarkers, such as the presence, visibility or thickness of a certain layer, from 3D optical coherence tomography data provides relevant clinical information. We present a method to simultaneously determine the number of visible layers in the outer retina and segment them. The method is based on a model selection approach with special attention given to the balance between the quality of a fit and model complexity. This will ensure that a more complex model is selected only if this is sufficiently supported by the data...
February 8, 2017: IEEE Transactions on Medical Imaging
Alessandro Arduino, Luca Zilberti, Mario Chiampi, Oriano Bottauscio
CSI-EPT is a recently developed technique for the electric properties tomography, that recovers the electric properties distribution starting from measurements performed by magnetic resonance imaging scanners. This method is an optimal control approach based on the contrast source inversion technique, which distinguishes itself from other electric properties tomography techniques for its capability to recover also the local specific absorption rate distribution, essential for online dosimetry. Up to now, CSI-EPT has only been described in terms of integral equations, limiting its applicability to homogeneous unbounded background...
February 8, 2017: IEEE Transactions on Medical Imaging
Duygu Sarikaya, Jason Corso, Khurshid Guru
Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos...
February 8, 2017: IEEE Transactions on Medical Imaging
Evan Phillips, Paolo Di Achille, Matthew Bersi, Jay Humphrey, Craig Goergen
A multi-modality imaging based modeling approach was used to study complex unsteady hemodynamics and lesion growth in a dissecting abdominal aortic aneurysm model. We combined in vivo ultrasound (geometry and flow) and in vitro optical coherence tomography (geometry) to obtain the high resolution needed to construct detailed hemodynamic simulations over large portions of the murine vasculature, which include fine geometric complexities. We illustrate this approach for a spectrum of dissecting abdominal aortic aneurysms induced in male apolipoprotein E-null mice by high-dose angiotensin II infusion...
February 6, 2017: IEEE Transactions on Medical Imaging
Jorge Bernal, Nima Tajbakhsh, F Javier Sanchez, Bogdan J Matuszewski, Hao Chen, Lequan Yu, Quentin Angermann, Olivier Romain, Bjorn Rustad, Ilangko Balasingham, Konstantin Pogorelov, Sungbin Choi, Quentin Debard, L Maier-Hein, Stefanie Speidel, Danail Stoyanov, Patrick Brandao, Henry Cordova, Cristina Sanchez-Montes, Suryakanth R Gurudu, Gloria Fernandez-Esparrach, Xavier Dray, Jianming Liang, Aymeric Histace
Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis...
February 2, 2017: IEEE Transactions on Medical Imaging
Hongbo Guo, Xiaowei He, Muhan Liu, Zeyu Zhang, Zhenhua Hu, Jie Tian
Cerenkov luminescence tomography (CLT) provides a novel technique for three-dimensional (3D) noninvasive detection of radiopharmaceuticals in living subjects. However, because of the severe scattering of Cerenkov light, the reconstruction accuracy and stability of CLT is still unsatisfied. In this study, a modified weight multispectral CLT (wmCLT) reconstruction strategy was developed which split the Cerenkov radiation (CR) spectrum into several sub-spectral bands and weighted the subspectral results to obtain the final result...
February 2, 2017: IEEE Transactions on Medical Imaging
Andres Saucedo, Stamatios Lefkimmiatis, Novena Rangwala, Kyunghyun Sung
This paper presents and analyzes an alternative formulation of the locally low-rank (LLR) regularization framework for magnetic resonance image (MRI) reconstruction. Generally, LLR-based MRI reconstruction techniques operate by dividing the underlying image into a collection of matrices formed from image patches. Each of these matrices is assumed to have low rank due to the inherent correlations among the data, whether along the coil, temporal, or multi-contrast dimensions. LLR regularization has been successful for various MRI applications such as parallel imaging and accelerated quantitative parameter mapping...
January 26, 2017: IEEE Transactions on Medical Imaging
Satoshi Kondo, Kazuya Takagi, Mutsumi Nishida, Takahito Iwai, Yusuke Kudo, Kouji Ogawa, Toshiya Kamiyama, Hitoshi Shibuya, Kaoru Kahata, Chikara Shimizu
This study proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions (FLLs) using the contrast agent Sonazoid R ⃝ . This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatocellular carcinoma (HCC), or metastatic liver tumor using support vector machines (SVM) with a combination of selected optimal features...
January 26, 2017: IEEE Transactions on Medical Imaging
Adam van Niekerk, Andre van der Kouwe, Ernesta Meintjes
In MRI brain imaging, subject motion limits obtainable image clarity. Due to the hardware layout of an MRI scanner, gradient excitations can be used to rapidly detect position. Orientation, however, is more difficult to detect and is commonly calculated by comparing the position measurements of multiple spatially constrained points to a reference dataset. The result is increased size of the apparatus the subject must wear, which can influence the imaging workflow. In optical based methods marker attachment sites are limited due to the line of sight requirement between the camera and marker, and an external reference frame is introduced...
January 25, 2017: IEEE Transactions on Medical Imaging
Samuel Kadoury, William Mandel, Marjolaine Roy-Beaudry, Marie-Lyne Nault, Stefan Parent
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the data. We propose a discriminative probabilistic manifold embedding where locally linear mappings transform data points from high-dimensional space to corresponding lowdimensional coordinates. A discriminant adjacency matrix is constructed to maximize the separation between progressive and non-progressive groups of patients diagnosed with scoliosis, while minimizing the distance in latent variables belonging to the same class...
January 23, 2017: IEEE Transactions on Medical Imaging
Yuanyuan Jia, Ali Gholipour, Zhongshi He, Simon Warfield
In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution 3D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multi-frame super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the high-resolution slices and the low-resolution sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of over-complete dictionaries that are used in a sparse land local model to upsample the input scans...
January 23, 2017: IEEE Transactions on Medical Imaging
Xin Chen, Muhammad Usman, Christian Baumgartner, Daniel Balfour, Paul Marsden, Andrew Reader, Claudia Prieto, Andrew Peter King
We present a novel retrospective self-gating method based on manifold alignment (MA), which enables reconstruction of free-breathing, high spatial and temporal resolution abdominal MRI sequences. Based on a radial golden-angle (RGA) acquisition trajectory, our method enables a multi-dimensional self-gating signal to be extracted from the k-space data for more accurate motion representation. The k-space radial profiles are evenly divided into a number of overlapping groups based on their radial angles. MA is then used to simultaneously learn and align the low dimensional manifolds of all groups, and embed them into a common manifold...
January 20, 2017: IEEE Transactions on Medical Imaging
Juan Wang, Huanjun Ding, FateMeh Azamian, Brian Zhou, Carlos Iribarren, Sabee Molloi, Pierre Baldi
Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected in mammograms, can be useful risk markers associated with the disease. We investigate the feasibility of automated and accurate detection of BACs in mammograms for risk assessment of coronary artery disease. We develop a twelve-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixelwise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists...
January 19, 2017: IEEE Transactions on Medical Imaging
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"