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IEEE Transactions on Medical Imaging

Huangsheng Pu, Peng Gao, Yang Liu, Junyan Rong, Feng Shi, Hongbing Lu
Cone beam X-ray luminescence computed tomography (CB-XLCT) is a promising imaging technique in studying the physiological and pathological processes in small animals. However, the dynamic bio-distributions of probes in small animal especially in adjacent targets are still hard to be captured directly from dynamic CB-XLCT. In this work, a 4D temporal-spatial reconstruction method based on principal component analysis (PCA) in the projection space is proposed for dynamic CB-XLCT. Firstly, projections of angles in each 3D frame are compressed to reduce the noises initially...
May 15, 2019: IEEE Transactions on Medical Imaging
Matthew R Morgan, Gregg E Trahey, William F Walker
Conventional B-Mode ultrasound imaging assumes targets consist of collections of point scatterers. Diffraction, however, presents a fundamental limit on a scanner's ability to resolve individual scatterers in most clinical imaging environments. Wellknown optics and ultrasound literature has characterized these diffuse scattering targets as spatially incoherent and statistically stationary. In this paper, we apply a piecewise-stationary statistical model to diffuse scattering targets, in which the covariance of backscattered echoes can be described as the linear superposition of constituent components corresponding to echoes from discrete spatial regions in the field...
May 15, 2019: IEEE Transactions on Medical Imaging
Zang-Hee Cho, Young-Don Son, Hang-Keun Kim, Dae-Hyuk Kwon, Yo-Han Joo, Jong Beom Ra, Yong Choi, Young-Bo Kim
Demands for in-vivo human molecular imaging with high resolution and high sensitivity in PET require several new design formulae. A classical problem of positron emission tomography (PET) design, however, was the trade-off between sensitivity and resolution. To satisfy both requirements, the brain-body convertible PET with wobbling and zooming is proposed. The features of this new proposed system are wobble sampling for high-resolution imaging and zooming mode for high sensitivity, especially for the brain dedicated imaging...
May 13, 2019: IEEE Transactions on Medical Imaging
Nasibeh Talebi, Ali Motie Nasrabadi, Iman Mohammad-Rezazadeh, Robert Coben
Quantifying causal (effective) interactions between different brain regions is very important in neuroscience research. Many of conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may oversimplify functions and dynamics of the brain. In the present study, we propose a causal relationship estimator called "nCREANN" (nonlinear Causal Relationship Estimation by Artificial Neural Network) that identifies both linear and nonlinear components of effective connectivity in the brain...
May 13, 2019: IEEE Transactions on Medical Imaging
Xiongbiao Luo, Hui-Qing Zeng, Ying Wan, Xiao-Bin Zhang, Yan-Ping Du, Terry M Peters
Endoscopic vision plays a significant role in minimally invasive surgical procedures. The visibility and maintenance of such direct in-situ vision is paramount not only for safety by preventing inadvertent injury, but also to improve precision and reduce operating time. Unfortunately, endoscopic vision is unavoidably degraded due to illumination variations during surgery. This work aims to restore or augment such degraded visualization and quantitatively evaluate it during robotic surgery. A multiscale bilateral-weighted retinex method is proposed to remove non-uniform and highly directional illumination and enhance surgical vision, while an objective noreference image visibility assessment method is defined in terms of sharpness, naturalness, and contrast, to quantitatively and objectively evaluate endoscopic visualization on surgical video sequences...
May 10, 2019: IEEE Transactions on Medical Imaging
Youyi Song, Lei Zhu, Jing Qin, Baiying Lei, Bin Sheng, Kup-Sze Choi
We present a novel approach to segmenting overlapping cytoplasm of cells in cervical smear images by leveraging adaptive shape priors extracted from cytoplasm's contour fragments and shape statistics. The main challenge of this task is that many occluded boundaries in cytoplasm clumps are extremely difficult to be identified, sometimes even visually indistinguishable. Given a clump where multiple cytoplasms overlap, our method starts by cutting its contour into a set of contour fragments. We then locate the corresponding contour fragments of each cytoplasm by a grouping process...
May 8, 2019: IEEE Transactions on Medical Imaging
Geng Chen, Bin Dong, Yong Zhang, Weili Lin, Dinggang Shen, Dinggang Shen, Pew-Thian Yap
Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naïvely can hence blur subtle structures and aggravate partial volume effects...
May 8, 2019: IEEE Transactions on Medical Imaging
Michaela Ruttorf, Stephanie Kristensen, Lothar R Schad, Jorge Almeida
Transcranial direct current stimulation (tDCS) is routinely used in basic and clinical research, but its efficacy has been challenged on a methodological, statistical and technical basis recently. The arguments against tDCS derive from insufficient understanding of how this technique interacts with brain processes physiologically. Because of its potential as a central tool in neuroscience, it is important to clarify whether tDCS affects neuronal activity. Here, we investigate influences of offline tDCS on network architecture measured by functional magnetic resonance imaging...
May 7, 2019: IEEE Transactions on Medical Imaging
Israel Almodovar-Rivera, Ranjan Maitra
Functional Magnetic Resonance Imaging is a noninvasive tool for studying cerebral function. Many factors challenge activation detection, especially in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated fast adaptive smoothing and thresholding (FAST) algorithm that uses smoothing and extreme value theory on correlated statistical parametric maps for thresholding. Performance on experiments spanning a range of low-signal settings is very encouraging...
May 7, 2019: IEEE Transactions on Medical Imaging
Mehran Pesteie, Purang Abolmaesumi, Robert N Rohling
Current deep supervised learning methods typically require large amounts of labeled data for training. Since there is a significant cost associated with clinical data acquisition and labeling, medical datasets used for training these models are relatively small in size. In this paper, we aim to alleviate this limitation by proposing a variational generative model along with an effective data augmentation approach that utilizes the generative model to synthesize data. In our approach, the model learns the probability distribution of image data conditioned on a latent variable and the corresponding labels...
May 6, 2019: IEEE Transactions on Medical Imaging
B Aubert, C Vazquez, T Cresson, S Parent, J De Guise
To date, 3D spine reconstruction from biplanar radiographs involves intensive user supervision and semiautomated methods that are time-consuming and not effective in clinical routine. This paper proposes a new, fast and automated 3D spine reconstruction method through which a realistic statistical shape model of the spine is fitted to images using convolutional neural networks (CNN). The CNNs automatically detect the anatomical landmarks controlling the spine model deformation through a hierarchical and gradual iterative process...
May 3, 2019: IEEE Transactions on Medical Imaging
Nima Torbati, Ahmad Ayatollahi, Parham Sadeghipour
Intravascular Ultrasound (IVUS) is a widely used interventional imaging technique for the assessment of atherosclerosis plaque. Due to pulsatile heart motions, transverse and longitudinal motions are observed during in-vivo pullbacks of IVUS sequences. These motion artifacts can mislead the volume-based data retrieved from IVUS studies and hinder the visualization of the vessel condition. To overcome this problem, a new fully automatic image-based gating algorithm was proposed in the current study. We utilized the phase information of the dual-tree complex wavelet transform (DT-CWT) coefficients to detect the motion of edge-like structures...
May 1, 2019: IEEE Transactions on Medical Imaging
Ji Ae Park, Kyeung Jun Kang, In Ok Ko, Kyo Chul Lee, Bup Kyung Choi, Nitish Katoch, Jin Woong Kim, Hyung Joong Kim, Oh In Kwon, Eung Je Woo
Radiation therapy (RT) has been widely used as a powerful treatment tool to address cancerous tissue because of its ability to control cell growth. Its ionizing radiation damages the DNA of cancerous tissues, leading to cell death. Medical imaging, however, still has limitations regarding the reliability of its assessment of tissue response and in predicting the treatment effect because of its inability to provide contrast information on the gradual, minute tissue changes after RT. A recently developed magnetic resonance (MR)-based conductivity imaging method may provide direct, highly sensitive information on this tissue response because its contrast mechanism is based on the concentration and mobility of ions in intracellular-and extracellular spaces...
April 29, 2019: IEEE Transactions on Medical Imaging
Qingjie Meng, Matthew Sinclair, Veronika Zimmer, Benjamin Hou, Martin Rajchl, Nicolas Toussaint, Ozan Oktay, Jo Schlemper, Alberto Gomez, James Housden, Jacqueline Matthew, Daniel Rueckert, Julia A Schnabel, Bernhard Kainz
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming...
April 25, 2019: IEEE Transactions on Medical Imaging
M Mirzaei, A Asif, H Rivaz
No abstract text is available yet for this article.
April 25, 2019: IEEE Transactions on Medical Imaging
Yi Wang, Haoran Dou, Xiaowei Hu, Lei Zhu, Lei Zhu, Xin Yang, Ming Xu, Jing Qin, Pheng-Ann Heng, Tianfu Wang, Dong Ni
Automatic prostate segmentation in transrectal ultrasound (TRUS) images is of essential importance for image-guided prostate interventions and treatment planning. However, developing such automatic solutions remains very challenging due to the missing/ambiguous boundary and inhomogeneous intensity distribution of the prostate in TRUS, as well as the large variability in prostate shapes. This paper develops a novel 3D deep neural network equipped with attention modules for better prostate segmentation in TRUS by fully exploiting the complementary information encoded in different layers of the convolutional neural network (CNN)...
April 25, 2019: IEEE Transactions on Medical Imaging
Tao Zhou, Mingxia Liu, Kim-Han Thung, Dinggang Shen
The fusion of complementary information contained in multi-modality data (e.g., Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and genetic data) has advanced the progress of automated Alzheimer's disease (AD) diagnosis. However, multi-modality based AD diagnostic models are often hindered by the missing data, i.e., not all the subjects have complete multi-modality data. One simple solution used by many previous studies is to discard samples with missing modalities. However, this significantly reduces the number of training samples, thus leading to a sub-optimal classification model...
April 25, 2019: IEEE Transactions on Medical Imaging
Zhuoran Jiang, Yingxuan Chen, Yawei Zhang, Yun Ge, Fang-Fang Yin, Lei Ren
Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this study, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled CBCT. For training, CBCT images were reconstructed using TV based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth...
April 23, 2019: IEEE Transactions on Medical Imaging
Hui Meng, Kun Wang, Yuan Gao, Yushen Jin, Xibo Ma, Jie Tian
Fluorescence molecular tomography (FMT), as a powerful imaging technique in preclinical research, can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labelled probe noninvasively. However, because of the light scattering effect and the ill-pose of inverse problem, it is challenging to develop an efficient reconstruction method, which can provide accurate location and morphology of the fluorescence distribution. In this research, we proposed a novel adaptive Gaussian weighted Laplace prior (AGWLP) regularization method, which assumed the variance of fluorescence intensity between any two voxels had a non-linear correlation with their Gaussian distance...
April 22, 2019: IEEE Transactions on Medical Imaging
Chelsea A S Dunning, Magdalena Bazalova-Carter
X-ray fluorescence CT (XFCT) has shown promise for molecular imaging of gold nanoparticles. To date, XFCT has been induced by kilovoltage photon beams due to the high photoelectric interaction probability. We compare K-shell and L-shell XFCT induced by photon, electron, and proton beams for two phantom sizes. A 2.5cm and 5.0cm-diameter phantom with four 5mm and 10mm vials, respectively, with gold-solutions of 0.1%-2% by weight were built in TOPAS, a GEANT4-based Monte Carlo simulation tool. The 2.5cm-phantom was imaged with XFCT induced by beams of 7...
April 22, 2019: IEEE Transactions on Medical Imaging
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