Read by QxMD icon Read

IEEE Transactions on Medical Imaging

Ioannis Androulakis, Marguerite E Faure, Ricardo J P Budde, Theo van Walsum
Cardiac computed tomography (CT) is a valuable tool for functional mechanical heart valve (MHV) assessment. An important aspect of bileaflet MHV assessment is evaluation and measurement of leaflet opening and closing angles. Performed manually however, it is a laborious and time consuming task. In this paper, we propose an automated approach for bileaflet MHV leaflet angle computation. This method consists of four steps. After a one click selection of the MHV region on an axial image, an automatic MHV extraction using thresholding and connected component analysis based on voxel intensities is performed...
September 19, 2018: IEEE Transactions on Medical Imaging
Koen Salvo, Michel Defrise
Maximum Likelihood Expectation-Maximization (MLEM) is a popular algorithm to reconstruct the activity image in Positron Emission Tomography (PET). This paper introduces a 'fundamental equality' for the MLEM complete data from which two key properties easily follow that allows us to: (i) prove in an elegant and compact way the convergence of MLEM for a forward model with fixed background (i.e., counts such as random and scatter coincidences); and (ii) generalize this proof for the MLEM-3 algorithm. Moreover we give necessary and sufficient conditions for the solution to be unique...
September 18, 2018: IEEE Transactions on Medical Imaging
Nora Ouzir, Adrian Basarab, Olivier Lairez, Jean-Yves Tourneret
This paper introduces a robust 2D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spatial smoothness and sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced using robust weighting functions for the data fidelity term as well as for the spatial and sparse regularizations...
September 18, 2018: IEEE Transactions on Medical Imaging
Jonghye Woo, Jerry L Prince, Maureen Stone, Fangxu Xing, Arnold D Gomez, Jordan R Green, Christopher J Hartnick, Thomas J Brady, Timothy G Reese, Van J Wedeen, Georges El Fakhri
Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-Magnetic Resonance Imaging (MRI) sheds light on the mechanisms of normal and pathological muscle coordination patterns, yielding improvement in surgical planning, treatment, or rehabilitation procedures...
September 18, 2018: IEEE Transactions on Medical Imaging
Mark He, Suzanne L Baker, Vyoma D Shah, Samuel N Lockhart, William J Jagust
The 18F-AV-1451 PET tracer binds to tau, an Alzheimer's Disease (AD) biomarker. The standardized uptake value ratio (SUVR) 80-100 min window is widely used to quantify tau binding, although 18F-AV-1451 continues increasing relative to a reference region in regions with tau deposition. Left uncorrected, acquisition time inaccuracies can lead to errors from -4% to 6% in 20-min SUVR measurements in subjects with Alzheimer's Disease. In 40 subjects with scans from 75-115 min following 18F-AV-1451 injection, we created 20-min reconstructions (4×5 min) of start-times ranging from 75-85 min, as proxies of offset scans and calculated the mean in regions of interest (ROIs)...
September 17, 2018: IEEE Transactions on Medical Imaging
Jiehan Hong, Min Su, Yanyan Yu, Zhiqiang Zhang, Rong Liu, Yaocai Huang, Peitian Mu, Hairong Zheng, Weibao Qiu
Both the morphological anatomy and functional parameters such as flow speed of the artery provide valuable information for the evaluation of cardiovascular diseases. Direct measurement of the arterial wall can be achieved by intravascular optical/ultrasound imaging methods, however no functional data are acquired with these methods. Fractional flow reserve (FFR) and Doppler wire have been used to assess the blood flow information, but do not provide cross-sectional images of the artery. This study is the first to design and fabricate a dual mode imaging catheter that contains a forward-looking ultrasonic transducer and a side-looking ultrasonic transducer together in one catheter...
September 12, 2018: IEEE Transactions on Medical Imaging
Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Jaewon Yang, Youngho Seo, Georges El Fakhri, Jinyi Qi, Quanzheng Li
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-patient information. An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool...
September 12, 2018: IEEE Transactions on Medical Imaging
Guobao Wang
Current clinical dynamic PET has an effective temporal resolution of 5-10 seconds, which can be adequate for traditional compartmental modeling but is inadequate for exploiting the benefit of more advanced tracer kinetic modeling for characterization of diseases (e.g., cancer and heart disease). There is a need to improve dynamic PET to allow fine temporal sampling of 1-2 seconds. However, reconstruction of these shorttime frames from tomographic data is extremely challenging as the count level of each frame is very low and high noise presents in both spatial and temporal domains...
September 12, 2018: IEEE Transactions on Medical Imaging
Mohamed A Naser, Diego R T Sampaio, Nina M Munoz, Cayla A Wood, Trevor M Mitcham, Wolfgang Stefan, Konstantin V Sokolov, Theo Z Pavan, Rony Avritscher, Richard R Bouchard
As photoacoustic (PA) imaging makes its way into the clinic, accuracy of PA-based metrics becomes increasingly important. To address this need, a method combining finite-element-based local fluence correction (LFC) with signal-to-noise-ratio (SNR) regularization was developed and validated to accurately estimate oxygen saturation (SO2) in tissue. With data from a Vevo LAZR system, performance of our LFC approach was assessed in ex vivo blood targets (37.6% - 99.6% SO2) and in vivo rat arteries. Estimation error of absolute SO2 and change in SO2 reduced from 10...
September 10, 2018: IEEE Transactions on Medical Imaging
R Gradl, K S Morgan, M Dierolf, C Jud, L Hehn, B Gunther, W Moller, D Kutschke, L Yang, T Stoeger, D Pfeiffer, B Gleich, K Achterhold, O Schmid, F Pfeiffer
X-ray grating interferometry is a powerful emerging tool in biomedical imaging, providing access to three complementary image modalities. In addition to the conventional attenuation modality, interferometry provides a phase modality that visualises soft tissue structures, and a dark-field modality that relates to the number and size of sub-resolution scattering objects. A particularly strong dark-field signal originates from the alveoli or air sacs in the lung. Dark-field lung radiographs in animal models have already shown increased sensitivity in diagnosing lung diseases such as lung cancer or emphysema, compared to conventional x-ray chest radiography...
September 6, 2018: IEEE Transactions on Medical Imaging
Gijs van Tulder, Marleen de Bruijne
Machine learning algorithms can have difficulties adapting to data from different sources, for example from different imaging modalities. We present and analyze three techniques for unsupervised cross-modality feature learning, using a shared autoencoder-like convolutional network that learns a common representation from multi-modal data. We investigate a form of feature normalization, a learning objective that minimizes crossmodality differences, and modality dropout, in which the network is trained with varying subsets of modalities...
September 6, 2018: IEEE Transactions on Medical Imaging
Tao Feng, Jizhe Wang, Yun Dong, Jun Zhao, Hongdi Li
Compared to external device based approaches, a data-driven gating technique in PET imaging is advantageous as it does not require additional hardware or procedure. Currently, data-driven cardiac gating is less studied than respiratory gating. The aim of this study is to develop a robust data-driven cardiac gating approach for clinical application. First, the central location of the heart is obtained from the corresponding CT image. A cylinder-shaped volume of interest (VOI) centered at the central location of the heart is used to confine cardiac signal calculation...
September 6, 2018: IEEE Transactions on Medical Imaging
Daniela M Zoller, Thomas A W Bolton, Fikret Isik Karahanoglu, Stephan Eliez, Marie Schaer, Dimitri Van De Ville
Functional magnetic resonance imaging (fMRI) is a non-invasive tomographic imaging modality that has provided insights into systems-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-activation pattern (iCAP) approach is one such approach that relies on the detection of timepoints with significant transient activity to subsequently retrieve spatially and temporally overlapping large-scale brain networks. To recover temporal profiles of the iCAPs for further time-resolved analysis, spatial patterns are fitted back to the activity-inducing signals...
September 4, 2018: IEEE Transactions on Medical Imaging
Jingxin Liu, Bolei Xu, Chi Zheng, Yuanhao Gong, Jon Garibaldi, Daniele Soria, Andew Green, Ian O Ellis, Wenbin Zou, Guoping Qiu
One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies...
September 3, 2018: IEEE Transactions on Medical Imaging
Yingying Zhu, Xiaofeng Zhu, Minjeong Kim, Jin Yan, Daniel Kaufer, Guorong Wu
Hyper-graph techniques have been widely investigated in computer vision and medical imaging applications, showing superior performance for modeling complex subject-wise relationships and sufficient flexibility to deal with missing data from multi-modal neuroimaging data. Existing hyper-graph methods, however, are inadequate for two reasons. Firstly, representations are generated only from the observed imaging data, a process that is completely independent of the subsequent data label inference/classification step...
August 31, 2018: IEEE Transactions on Medical Imaging
John A Onofrey, Lawrence H Staib, Xenophon Papademetris
Accurate segmentation of the brain surface in postsurgical CT images is critical for image-guided neurosurgical procedures in epilepsy patients. Following surgical implantation of intra-cranial electrodes, surgeons require accurate registration of the post-implantation CT images to pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. One way to perform the registration is via surface matching. The key challenge in this setup is the CT segmentation, where extraction of the cortical surface is difficult due to missing parts of the skull and artifacts introduced from the electrodes...
August 30, 2018: IEEE Transactions on Medical Imaging
Kelei He, Xiaohuan Cao, Yinghuan Shi, Dong Nie, Yang Gao, Dinggang Shen
Accurate segmentation of pelvic organs (i.e., prostate, bladder and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning based method, with a novel distinctive curve guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, the first stage is for fast and robust organ detection in the raw CT images...
August 30, 2018: IEEE Transactions on Medical Imaging
Sourav Pramanik, Debapriya Banik, Debotosh Bhattacharjee, Mita Nasipuri, Mrinal Kanti Bhowmik, Gautam Majumdar
Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in a TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the high-intensity-pixels and the centroid-knowledge of SRs (SCH-CS), is proposed to approximately locate the SRs, whose contours are later used as the initial evolving curves of the level set method (LSM)...
August 29, 2018: IEEE Transactions on Medical Imaging
Ting Yang, Steven M Pogwizd, Gregory P Walcott, Long Yu, Bin He
The aim of this study is to develop and evaluate a novel imaging method (SSF, Spatial gradient Sparse in Frequency domain) for the reconstruction of activation sequences of ventricular arrhythmia from noninvasive body surface potential map (BSPM) measurements. We formulated and solved the electrocardiographic inverse problem in the frequency domain, and the activation time was encoded in the phase information of the imaging solution. A cellular automaton heart model was used to generate focal ventricular tachycardia (VT)...
August 23, 2018: IEEE Transactions on Medical Imaging
Adrian V Dalca, Katherine L Bouman, William T Freeman, Natalia S Rost, Mert R Sabuncu, Polina Golland
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains...
August 22, 2018: 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"