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IEEE Transactions on Medical Imaging

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https://www.readbyqxmd.com/read/28644802/fully-non-linear-sp3-approximation-based-fluorescence-optical-tomography
#1
Naren Naik, Nishigandha Patil, Yamini Yadav, Jerry Eriksson, Asima Pradhan
In fluorescence optical tomography, many works in literature focus on the linear reconstruction problem to obtain the fluorescent yield or the linearized reconstruction problem to obtain the absorption coefficient. The nonlinear reconstruction problem, to reconstruct the fluorophore absorption coefficient, is of interest in imaging studies as it presents the possibility of better reconstructions owing to a more appropriate model. Accurate and computationally efficient forward models are also critical in the reconstruction process...
June 21, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28644801/magnetic-particle-imaging-for-quantification-of-vascular-stenoses-a-phantom-study
#2
S Herz, P Vogel, T Kampf, M A Ruckert, S Veldhoen, V C Behr, T A Bley
Magnetic particle imaging (MPI) is a promising new tomographic imaging method to detect the spatial distribution of superparamagnetic iron-oxide nanoparticles (SPIOs). The aim of this study was to investigate the potential of MPI to quantify artificial stenoses in vessel phantoms. Custom-made stenosis phantoms (length 40 mm; inner diameter 8 mm) with different degrees of stenosis (0 %, 25 %, 50 %, 75 % and 100 %) were scanned in a custom-built MPI scanner (in-plane resolution: ~ 1-1.5 mm, field of view: 65 × 29 × 29 mm3)...
June 21, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28644800/online-combination-of-epid-cherenkov-imaging-for-3d-dosimetry-in-a-liquid-phantom
#3
Petr Bruza, Jacqueline M Andreozzi, David J Gladstone, Lesley A Jarvis, Joerg Rottmann, Brian W Pogue
Online acquisition of Cherenkov and portal imaging data was combined with a reconstruction scheme called EC3D, providing a full 3D dosimetry of megavoltage X-ray beams in a water tank. The methodology was demonstrated and quantified in a single static beam. Furthermore, the dynamics and visualization of the 3D dose reconstruction were demonstrated with a volumetric modulated arc therapy (VMAT) plan for TG-119 C-Shape geometry. The developed algorithm combines depth dose information, provided by Cherenkov images, with the lateral dose distribution, provided by the electronic portal imaging device (EPID)...
June 20, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28644799/hybrid-cs-dmri-periodic-time-variant-subsampling-and-omnidirectional-total-variation-based-reconstruction
#4
Yipeng Liu, Shan Wu, Xiaolin Huang, Bing Chen, Ce Zhu
Compressive sensing (CS) has been used to accelerate dynamic magnetic resonance imaging (DMRI). Currently, the online CS-DMRI is faster, whereas the offline CS-DMRI provides higher accuracy for image reconstruction. To achieve good image reconstruction performance in terms of both speed and accuracy, we propose a hybrid CS-DMRI method using periodic timevariant subsampling for different frames. In each period, there is one reference frame that is sampled at a higher subsampling ratio. The two nearby reference frames with good reconstruction quality can be used to provide rough predictions of the other frames between them...
June 20, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641249/eit-imaging-regularization-based-on-spectral-graph-wavelets
#5
Bo Gong, Benjamin Schullcke, Sabine Krueger-Ziolek, Marko Vauhkonen, Gerhard Wolf, Ullrich Mueller-Lisse, Knut Moeller
The objective of Electrical Impedance Tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite element method framework. In previous studies, standard sparse regularization was used for difference EIT to achieve a sparse solution. However, regarding element-wise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise...
June 16, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641248/method-for-simulating-dose-reduction-in-digital-breast-tomosynthesis
#6
Lucas R Borges, Igor Guerrero, Predrag R Bakic, Alessandro Foi, Andrew D A Maidment, Marcelo A C Vieira
This work proposes a new method of simulating dose reduction in digital breast tomosynthesis (DBT), starting from a clinical image acquired with a standard radiation dose. It considers both signal-dependent quantum and signal-independent electronic noise. Furthermore, the method accounts for pixel crosstalk, which causes the noise to be frequency-dependent, thus increasing the simulation accuracy. For an objective assessment, simulated and real images were compared in terms of noise standard deviation, signal-to-noise ratio (SNR) and normalized noise power spectrum (NNPS)...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28641247/modeling-task-fmri-data-via-deep-convolutional-autoencoder
#7
Heng Huang, Xintao Hu, Yu Zhao, Milad Makkie, Qinglin Dong, Shijie Zhao, Lei Guo, Tianming Liu
Task-based fMRI (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is challenging due to at least two problems: the lack of the ground truth of underlying neural activity and the highly complex intrinsic structure of tfMRI data. To better understand brain networks based on fMRI data, data-driven approaches have been proposed, for instance, Independent Component Analysis (ICA) and Sparse Dictionary Learning (SDL). However, both ICA and SDL only build shallow models, and they are under the strong assumption that original fMRI signal could be linearly decomposed into time series components with their corresponding spatial maps...
June 15, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28622671/low-dose-ct-with-a-residual-encoder-decoder-convolutional-neural-network-red-cnn
#8
Hu Chen, Yi Zhang, Mannudeep K Kalra, Feng Lin, Yang Chen, Peixo Liao, Jiliu Zhou, Ge Wang
Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details...
June 13, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28622670/adaptive-clutter-demodulation-for-non-contrast-ultrasound-perfusion-imaging
#9
Jaime Tierney, Crystal Coolbaugh, Theodore Towse, Brett Byram
Conventional Doppler ultrasound is useful for visualizing fast blood flow in large resolvable vessels. However, frame rate and tissue clutter caused by movement of the patient or sonographer make visualizing slow flow with ultrasound difficult. Patient and sonographer motion causes spectral broadening of the clutter signal, which limits ultrasound's sensitivity to velocities greater than 5-10mm/s for typical clinical imaging frequencies. To address this, we propose a clutter filtering technique that may increase the sensitivity of Doppler measurements to at least as low as 0...
June 13, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28613165/lossless-compression-of-medical-images-using-3d-predictors
#10
Luis Lucas, Nuno Rodrigues, Luis Cruz, Sergio Faria
This paper describes a highly efficient method for lossless compression of volumetric sets of medical images, such as CTs or MRIs. The proposed method, referred to as 3D-MRP, is based on the principle of minimum rate predictors (MRP), which is one of the state-of-the-art lossless compression technologies, presented in the data compression literature. The main features of the proposed method include the use of 3D predictors, 3D-block octree partitioning and classification, volume-based optimisation and support for 16 bit-depth images...
June 9, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28613164/model-based-generation-of-large-databases-of-cardiac-images-synthesis-of-pathological-cine-mr-sequences-from-real-healthy-cases
#11
Nicolas Duchateau, Maxime Sermesant, Herve Delingette, Nicholas Ayache
Collecting large databases of annotated medical images is crucial for the validation and testing of feature extraction, statistical analysis and machine learning algorithms. Recent advances in cardiac electromechanical modeling and image synthesis provided a framework to generate synthetic images based on realistic mesh simulations. Nonetheless, their potential to augment an existing database with large amounts of synthetic cases requires further investigation. We build upon these works and propose a revised scheme for synthesizing pathological cardiac sequences from real healthy sequences...
June 9, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28600242/advanced-fast-3d-electromagnetic-solver-for-microwave-tomography-imaging
#12
Nikolai Simonov, Bo-Ra Kim, Kwang-Jae Lee, Soon-Ik Jeon, Seong-Ho Son
This paper describes a fast forward electromagnetic solver (FFS) for the image reconstruction algorithm of our microwave tomography (MT) system. Our apparatus is a preclinical prototype of a biomedical imaging system, designed for the purpose of early breast cancer detection. It operates in the 3-6 GHz frequency band using a circular array of probe antennas immersed in a matching liquid; it produces image reconstructions of the permittivity and conductivity profiles of the breast under examination. Our reconstruction algorithm solves the electromagnetic inverse problem and takes into account the real electromagnetic properties of the probe antenna array as well as the influence of the patient's body and that of the upper metal screen sheet...
June 7, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28600241/automated-quantitative-bone-analysis-in-in-vivo-x-ray-micro-computed-tomography-%C3%A2%C2%B5ct
#13
Ali Behrooz, Peet Kask, Jeff Meganck, Joshua Kempner
Measurement and analysis of bone morphometry in 3D micro-computed tomography volumes using automated image processing and analysis improves the accuracy, consistency, reproducibility, and speed of preclinical osteological research studies. Automating segmentation and separation of individual bones in 3D micro-computed tomography volumes of murine models presents significant challenges considering partial volume effects and joints with thin spacing, i.e., 50 to 100 μm. In this paper, novel hybrid splitting filters are presented to overcome the challenge of automated bone separation...
June 6, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28600240/neuron-segmentation-with-high-level-biological-priors
#14
Niko Krasowski, Thorsten Beier, Graham Knott, Ullrich Koethe, Fred Hamprecht, Anna Kreshuk
We present a novel approach to the problem of neuron segmentation in image volumes acquired by Electron Microscopy. Existing methods such as agglomerative or correlation clustering rely solely on boundary evidence and have problems where such evidence is lacking (e.g. incomplete staining) or ambiguous (e.g. co-located cell and mitochondria membranes). We investigate if these difficulties can be overcome by means of sparse region appearance cues that differentiate between pre- and postsynaptic neuron segments in mammalian neural tissue...
June 6, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28613163/sinogram-blurring-matrix-estimation-from-point-sources-measurements-with-rank-one-approximation-for-fully-3d-pet
#15
Kuang Gong, Jian Zhou, Michel Tohme, Martin Judenhofer, Yongfeng Yang, Jinyi Qi
An accurate system matrix is essential in PET for reconstructing high quality images. To reduce storage size and image reconstruction time, we factor the system matrix into a product of a geometry projection matrix and a sinogram blurring matrix. The geometric projection matrix is computed analytically and the sinogram blurring matrix is estimated from point source measurements. Previously we have estimated a two dimensional (2D) blurring matrix for a preclinical PET scanner. The 2D blurring matrix only considers blurring effects within a transaxial sinogram and does not compensate for intersinogram blurring effects...
June 2, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28574348/direct-multitype-cardiac-indices-estimation-via-joint-representation-and-regression-learning
#16
Wufeng Xue, Ali Islam, Mousumi Bhaduri, Shuo Li
Cardiac indices estimation is of great importance during identification and diagnosis of cardiac disease in clinical routine. However, estimation of multitype cardiac indices with consistently reliable and high accuracy is still a great challenge due to the high variability of cardiac structures and complexity of temporal dynamics in cardiac MR sequences. While efforts have been devoted into cardiac volumes estimation through feature engineering followed by a independent regression model, these methods suffer from the vulnerable feature representation and incompatible regression model...
May 26, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28574347/anisotropic-discrete-dual-tree-wavelet-transform-for-improved-classification-of-trabecular-bone
#17
Hind Oulhaj, Mohammed Rziza, Aouatif Amine, Hechmi Toumi, Eric Lespessailles, Mohammed El Hassouni, Rachid Jennane
This paper deals with a new Anisotropic Discrete Dual-Tree Wavelet Transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional Discrete Dual-Tree Wavelet Transform (DDTWT) by using the anisotropic basis functions associated with the Hyperbolic Wavelet Transform (HWT) instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The Generalized Gaussian Distribution (GGD) is used to model the distribution of the sub-band coefficients...
May 26, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28574346/generative-adversarial-networks-for-noise-reduction-in-low-dose-ct
#18
Jelmer M Wolterink, Tim Leiner, Max A Viergever, Ivana Isgum
Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxel-wise loss minimization. An adversarial discriminator CNN was simultaneously trained to distinguish the output of the generator from routinedose CT images. The performance of this discriminator was used as an adversarial loss for the generator...
May 26, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28574345/noncontact-3-dimensional-speckle-contrast-diffuse-correlation-tomography-of-tissue-blood-flow-distribution
#19
Chong Huang, Daniel Irwin, Mingjun Zhao, Shang Yu, Nneamaka Agochukwu, Lesley Wong, Guoqiang Yu
Recent advancements in near-infrared diffuse correlation techniques and instrumentation have opened the path for versatile deep tissue microvasculature blood flow imaging systems. Despite this progress there remains a need for a completely noncontact, noninvasive device with high translatability from small/testing (animal) to large/target (human) subjects with trivial application on both. Accordingly, we discuss our newly developed setup which meets this demand, termed noncontact speckle contrast diffuse correlation tomography (nc_scDCT)...
May 26, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28574344/a-framework-for-the-generation-of-realistic-synthetic-cardiac-ultrasound-and-magnetic-resonance-imaging-sequences-from-the-same-virtual-patients
#20
Yitian Zhou, Sophie Giffard-Roisin, Mathieu De Craene, Sorina Camarasu-Pop, Jan D'hooge, Martino Alessandrini, Denis Friboulet, Maxime Sermesant, Olivier Bernard
The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases including both ventricular dyssynchrony and myocardial ischemia...
May 25, 2017: IEEE Transactions on Medical Imaging
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