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IEEE Transactions on Medical Imaging

L Kerem Senel, Toygan Kilic, Alper Gungor, Emre Kopanoglu, H Emre Guven, Emine U Saritas, Aykut Koc, Tolga Cukur
A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can degrade reconstruction quality or reduce scan efficiency...
January 14, 2019: IEEE Transactions on Medical Imaging
Adam van Niekerk, Ernesta Meintjes, Andre van der Kouwe
In this work we present a device that is capable of wireless synchronisation to the MRI pulse sequence time frame with sub-microsecond precision. This is achieved by detecting radio frequency pulses in the parent pulse sequence using a small resonant circuit. The device incorporates a 3-axis pickup coil, constructed using conventional printed circuit board (PCB) manufacturing techniques, to measure the rate of change of the gradient waveforms with respect to time. Using Maxwell-s equations, assuming negligible rates of change of curl and divergence, a model of the expected gradient derivative (slew) vector field is presented...
January 10, 2019: IEEE Transactions on Medical Imaging
L Theodorakis, G Loudos, V Prassopoulos, C Kappas, I Tsougos, P Georgoulias
We aim to investigate the counting response variations of Positron Emission Tomography (PET) scanners with different detector configurations in the presence of Solitary Pulmonary Nodule (SPN). Using experimentally validated Monte Carlo simulations, the counting performance of four different scanner models with varying tumor activity, location, and patient obesity is represented using NECR (Noise Equivalent Count Rate). NECR is a well-established quantitative metric which has positive correlation with clinically perceived image quality...
January 10, 2019: IEEE Transactions on Medical Imaging
Huangjing Lin, Hao Chen, Simon Graham, Qi Dou, Nasir Rajpoot, Pheng-Ann Heng
Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from wholeslide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists' workload and simultaneously reduce misdiagnosis rate. However, automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size...
January 7, 2019: IEEE Transactions on Medical Imaging
Yongfeng Gao, Zhengrong Liang, William H Moore, Hao Zhang, Marc J Pomeroy, John A Ferretti, Thomas V Bilfinger, Jianhua Ma, Hongbing Lu
Markov random field (MRF) has been widely used to incorporate a priori knowledge as penalty or regularizer to preserve edge sharpness while smoothing the region enclosed by the edge for pieces-wise smooth image reconstruction. In our earlier study, we proposed a type of MRF reconstruction method for low-dose CT (LdCT) scans using tissue-specific textures extracted from the same patient's previous full-dose CT (FdCT) scans as prior knowledge. It showed advantages in clinical applications. This study aims to remove the constraint of using previous data of the same patient...
January 3, 2019: IEEE Transactions on Medical Imaging
Geng Chen, Dehui Xiang, Bin Zhang, Haihong Tian, Xiaoling Yang, Fei Shi, Weifang Zhu, Bei Tian, Xinjian Chen
Segmentation of lungs with severe pathology is a nontrivial problem in clinical application. Due to complex structures, pathological changes, individual differences and low image quality, accurate lung segmentation in clinical 3D CT images is still a challenging task. To overcome these problems, a novel dictionary-based approach is introduced to automatically segment pathological lungs in 3D low-dose CT images. Sparse shape composition is integrated with eigenvector space shape prior model, called eigenspace sparse shape composition, to reduce local shape reconstruction error caused by weak and misleading appearance prior information...
January 1, 2019: IEEE Transactions on Medical Imaging
Jens Petersen, Andres M Arias-Lorza, Raghavendra Selvan, Daniel Bos, Aad van der Lugt, Jesper H Pedersen, Mads Nielsen, Marleen de Bruijne
Optimal surface methods are a class of graph cut methods posing surface estimation as an n-ary ordered labeling problem. They are used in medical imaging to find interacting and layered surfaces optimally and in low order polynomial time. Representing continuous surfaces with discrete sets of labels, however, leads to discretization errors and, if graph representations are made dense, excessive memory usage. Limiting memory usage and computation time of graph cut methods are important and graphs that locally adapt to the problem has been proposed as a solution...
January 1, 2019: IEEE Transactions on Medical Imaging
Heechul Yoon, Yiying I Zhu, Steven K Yarmoska, Stanislav Y Emelianov
This paper introduces a configurable combined laser, ultrasound, and elasticity (CLUE) imaging platform. The CLUE platform enables imaging sequences capable of simultaneously providing quantitative acoustic, optical, and mechanical contrast for comprehensive diagnosis and monitoring of complex diseases, such as cancer. The CLUE imaging platform was developed on a Verasonics ultrasound scanner integrated with a pulsed laser, and it was designed to be modular and scalable to allow researchers to create their own specific imaging sequences efficiently...
December 27, 2018: IEEE Transactions on Medical Imaging
Jaya Prakash, Dween Sanny, Sandeep Kumar Kalva, Manojit Pramanik, Phaneendra K Yalavarthy
Photoacoustic tomography involves reconstructing the initial pressure rise distribution from the measured acoustic boundary data. The recovery of the initial pressure rise distribution tends to be an ill-posed problem in presence of noise and when limited independent data is available, necessitating regularization. The standard regularization schemes include, Tikhonov, ℓ1-norm, and total-variation. These regularization schemes weigh the singular values equally irrespective of the noise level present in the data...
December 24, 2018: IEEE Transactions on Medical Imaging
Fatemeh Taheri Dezaki, Zhibin Liao, Christina Luong, Hany Girgis, Neeraj Dhungel, Amir H Abdi, Delaram Behnami, Ken Gin, Robert Rohling, Purang Abolmaesumi, Teresa Tsang
Accurate detection of end-systolic (ES) and enddiastolic (ED) frames in an echocardiographic cine series can be a difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem, and propose several deep learning-based architectures that minimize a novel global extrema structured loss function to localize the ED and ES frames...
December 24, 2018: IEEE Transactions on Medical Imaging
Andreas Mehmann, Christian Vogt, Matija Varga, Andreas Port, Jonas Reber, Josip Marjanovic, Klaas Paul Pruessmann, Benjamin Sporrer, Qiuting Huang, Gerhard Troster
Stretchable magnetic resonance (MR) receive coils show shifts in their resonance frequency when stretched. An in-field receiver measures the frequency response of a stretchable coil. The receiver and coil are designed to operate at 128MHz for a 3T MR scanner. Based on the measured frequency response, we are able to detect the changes of the resonance frequency of the coil. We show a proportional-integral-derivative (PID) controller that tracks the changes in resonance frequency and retunes the stretchable coil...
December 20, 2018: IEEE Transactions on Medical Imaging
T Speidel, P Metze, V Rasche
The overall duration of acquiring a Nyquist sampled 3D dataset can be significantly shortened by enhancing the efficiency of k-space sampling. This can be achieved by increasing the coverage of k-space for every trajectory interleave. Further acceleration is possible by making use of advantageous undersampling properties. In this work, a versatile 3D centre-out k-space trajectory, based on Jacobian elliptic functions (Seiffert's spiral) is presented. The trajectory leads to a lowdiscrepancy coverage of k-space using a considerably reduced number of read-outs compared to other approaches...
December 20, 2018: IEEE Transactions on Medical Imaging
Kuang Gong, Ciprian Catana, Jinyi Qi, Quanzheng Li
Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. This is especially true for medical image reconstruction problems, where raw data are needed. Inspired by the deep image prior framework, in this work we proposed a personalized network training method where no prior training pairs are needed, but only the patient' own prior information...
December 19, 2018: IEEE Transactions on Medical Imaging
Kerem C Tezcan, Christian F Baumgartner, Roger Luechinger, Klaas P Pruessmann, Ender Konukoglu
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly...
December 17, 2018: IEEE Transactions on Medical Imaging
Zhiqian Chang, Dong Hye Ye, Somesh Srivastava, Jean-Baptiste Thibault, Ken Sauer, Charles Bouman
High-attenuation materials pose significant challenges to computed tomographic (CT) imaging. Formed of high mass-density and high atomic number elements, they cause more severe beam hardening and scattering artifacts than do waterlike materials. Pre-corrected line-integral density measurements are no longer linearly proportional to the path lengths, leading to reconstructed image suffering from streaking artifacts extending from metal, often along highest-density directions. In this paper, a novel prior-based iterative approach is proposed to reduce metal artifacts...
December 14, 2018: IEEE Transactions on Medical Imaging
Ehsan Abadi, Brian Harrawood, Shobhit Sharma, Anuj Kapadia, William P Segars, Ehsan Samei
The purpose of this study was to develop a CT simulation platform that is 1) compatible with voxel-based computational phantoms, 2) capable of modeling the geometry and physics of commercial CT scanners, and 3) computationally efficient. Such a simulation platform is designed to enable the virtual evaluation and optimization of CT protocols and parameters for achieving a targeted image quality while reducing radiation dose. Given a voxelized computational phantom and a parameter file describing the desired scanner and protocol, the developed platform DukeSim calculates projection images using a combination of ray-tracing and Monte Carlo techniques...
December 12, 2018: IEEE Transactions on Medical Imaging
F Kucharczak, F Ben Bouallegue, O Strauss, D Mariano-Goulart
In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that relies on direct estimation of confidence intervals for each reconstructed value...
December 12, 2018: IEEE Transactions on Medical Imaging
Yue Hu, Xiaohan Liu, Mathews Jacob
Recent theory of mapping an image into a structured low-rank Toeplitz or Hankel matrix has become an effective method to restore images. In this paper, we introduce a generalized structured low-rank algorithm to recover images from their undersampled Fourier coefficients using infimal convolution regularizations. The image is modeled as the superposition of a piecewise constant component and a piecewise linear component. The Fourier coefficients of each component satisfy an annihilation relation, which results in a structured Toeplitz matrix, respectively...
December 11, 2018: IEEE Transactions on Medical Imaging
Anita Karsa, Karin Shmueli
Recent Magnetic Resonance Imaging (MRI) techniques, such as Quantitative magnetic Susceptibility Mapping (QSM), employ the signal phase to reveal disease-related changes in tissue composition including iron or calcium content. The MRI phase is also routinely used in functional and diffusion MRI for distortion correction. However, phase images are wrapped into a range of 2π radians. PRELUDE is the gold standard method for robust, spatial, 3-dimensional, MRI phase unwrapping. Unfortunately, PRELUDE's computation time can reach 15 minutes for a severely wrapped brain image and nearly 10 hours to unwrap a full head-and-neck image on a standard PC...
December 11, 2018: IEEE Transactions on Medical Imaging
Ethan K Murphy, Joseph Skinner, Maria Martucci, Seward B Rutkove, Ryan J Halter
This study establishes for the first time that a coupled Ultrasound (US) and Electrical Impedance Tomography (EIT) system can serve as a non-invasive, spatially-localized approach to extract clinically-relevant muscle properties. The US/EIT system represents a potential enhancement to Electrical Impedance Myography (EIM), which has shown promise as a non-invasive technology that may have important clinical use in indicating neuromuscular disease status and as a diagnostic tool. A 2.5D EIT algorithm evaluated on simulation, measured phantoms, and measured patient data was studied to evaluate US/EIT's ability to distinguish different aspects of muscle tissue...
December 10, 2018: IEEE Transactions on Medical Imaging
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