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IEEE Transactions on Medical Imaging

Xiaofei Du, Thomas Kurmann, Ping-Lin Chang, Maximilian Allan, Sebastien Ourselin, Raphael Sznitman, John D Kelly, Danail Stoyanov
Instrument detection, pose estimation, and tracking in surgical videos are an important vision component for computer-assisted interventions. While significant advances have been made in recent years, articulation detection is still a major challenge. In this paper, we propose a deep neural network for articulated multi-instrument 2-D pose estimation, which is trained on detailed annotations of endoscopic and microscopic data sets. Our model is formed by a fully convolutional detection-regression network. Joints and associations between joint pairs in our instrument model are located by the detection subnetwork and are subsequently refined through a regression subnetwork...
May 2018: IEEE Transactions on Medical Imaging
Haofu Liao, Addisu Mesfin, Jiebo Luo
Automatic vertebrae identification and localization from arbitrary computed tomography (CT) images is challenging. Vertebrae usually share similar morphological appearance. Because of pathology and the arbitrary field-of-view of CT scans, one can hardly rely on the existence of some anchor vertebrae or parametric methods to model the appearance and shape. To solve the problem, we argue that: 1) one should make use of the short-range contextual information, such as the presence of some nearby organs (if any), to roughly estimate the target vertebrae; and 2) due to the unique anatomic structure of the spine column, vertebrae have fixed sequential order, which provides the important long-range contextual information to further calibrate the results...
May 2018: IEEE Transactions on Medical Imaging
Melissa W Haskell, Stephen F Cauley, Lawrence L Wald
We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search...
May 2018: IEEE Transactions on Medical Imaging
Limin Zhang, Shudong Jiang, Yan Zhao, Jinchao Feng, Brian W Pogue, Keith D Paulsen
An approach using direct regularization from co-registered dynamic contrast enhanced magnetic reson- ance images was used to reconstruct near-infrared spectral tomography patient images, which does not need image segmentation. 20 patients with mammography/ultrasound confirmed breast abnormalities were involved in this paper, and the resulting images indicated that tumor total hemoglobin concentration contrast differentiated malignant from benign cases (p-value = 0.021). The approach prod- uced reconstructed images, which significantly reduced surface artifacts near the source-detector locations (p-value = 4...
May 2018: IEEE Transactions on Medical Imaging
Denis Fortun, Paul Guichard, Virginie Hamel, Carlos Oscar S Sorzano, Niccolo Banterle, Pierre Gonczy, Michael Unser
The imaging of proteins within macromolecular complexes has been limited by the low axial resolution of optical microscopes. To overcome this problem, we propose a novel computational reconstruction method that yields isotropic resolution in fluorescence imaging. The guiding principle is to reconstruct a single volume from the observations of multiple rotated particles. Our new operational framework detects particles, estimates their orientation, and reconstructs the final volume. The main challenge comes from the absence of initial template and a priori knowledge about the orientations...
May 2018: IEEE Transactions on Medical Imaging
Biao Cai, Pascal Zille, Julia M Stephen, Tony W Wilson, Vince D Calhoun, Yu Ping Wang
Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) time series, especially during resting state periods, provides a powerful tool to assess human brain functional architecture in health, disease, and developmental states. Recently, the focus of connectivity analysis has shifted toward the subnetworks of the brain, which reveals co-activating patterns over time. Most prior works produced a dense set of high-dimensional vectors, which are hard to interpret. In addition, their estimations to a large extent were based on an implicit assumption of spatial and temporal stationarity throughout the fMRI scanning session...
May 2018: IEEE Transactions on Medical Imaging
Daniel C Mellema, Pengfei Song, Randall R Kinnick, Joshua D Trzasko, Matthew W Urban, James F Greenleaf, Armando Manduca, Shigao Chen
Shear wave elastography methods are able to accurately measure tissue stiffness, allowing these techniques to monitor the progression of hepatic fibrosis. While many methods rely on acoustic radiation force to generate shear waves for 2-D imaging, probe oscillation shear wave elastography (PROSE) provides an alternative approach by generating shear waves through continuous vibration of the ultrasound probe while simultaneously detecting the resulting motion. The generated shear wave field in in vivo liver is complicated, and the amplitude and quality of these shear waves can be influenced by the placement of the vibrating probe...
May 2018: IEEE Transactions on Medical Imaging
M Allan, S Ourselin, D J Hawkes, J D Kelly, D Stoyanov
Estimating the 3-D pose of instruments is an important part of robotic minimally invasive surgery for automation of basic procedures as well as providing safety features, such as virtual fixtures. Image-based methods of 3-D pose estimation provide a non-invasive low cost solution compared with methods that incorporate external tracking systems. In this paper, we extend our recent work in estimating rigid 3-D pose with silhouette and optical flow-based features to incorporate the articulated degrees-of-freedom (DOFs) of robotic instruments within a gradient-based optimization framework...
May 2018: IEEE Transactions on Medical Imaging
Marcel Straub, Volkmar Schulz
Magnetic particle imaging (MPI) is a novel tomographic imaging technique, which visualizes the distribution of a magnetic nanoparticle-based tracer material. However, reconstructed MPI images often suffer from an insufficiently compensated image background caused by rapid non-deterministic changes in the background signal of the imaging device. In particular, the signal-to-background ratio (SBR) of the images is reduced for lower tracer concentrations or longer acquisitions. The state-of-the-art procedure in MPI is to frequently measure the background signal during the sample measurement...
May 2018: IEEE Transactions on Medical Imaging
Md Tauhidul Islam, Anuj Chaudhry, Songyuan Tang, Ennio Tasciotti, Raffaella Righetti
Ultrasound poroelastography aims at assessing the poroelastic behavior of biological tissues via estimation of the local temporal axial strains and effective Poisson's ratios (EPR). Currently, reliable estimation of EPR using ultrasound is a challenging task due to the limited quality of lateral strain estimation. In this paper, we propose a new two-step EPR estimation technique based on dynamic programming elastography (DPE) and Horn-Schunck (HS) optical flow estimation. In the proposed method, DPE is used to estimate the integer axial and lateral displacements while HS is used to obtain subsample axial and lateral displacements from the motion-compensated pre-compressed and post-compressed radio frequency data...
May 2018: IEEE Transactions on Medical Imaging
K C Santosh, Sameer Antani
Our primary motivator is the need for screening HIV+ populations in resource-constrained regions for exposure to Tuberculosis, using posteroanterior chest radiographs (CXRs). The proposed method is motivated by the observation that radiological examinations routinely conduct bilateral comparisons of the lung field. In addition, the abnormal CXRs tend to exhibit changes in the lung shape, size, and content (textures), and in overall, reflection symmetry between them. We analyze the lung region symmetry using multi-scale shape features, and edge plus texture features...
May 2018: IEEE Transactions on Medical Imaging
Hailong He, Andreas Buehler, Dmitry Bozhko, Xiaohua Jian, Yaoyao Cui, Vasilis Ntziachristos
Optoacoustic (photoacoustic) endoscopy has shown potential to reveal complementary contrast to optical endoscopy methods, indicating clinical relevance. However operational parameters for accurate optoacoustic endoscopy must be specified for optimal performance. Recent support from the EU Horizon 2020 program ESOTRAC to develop a next-generation optoacoustic esophageal endoscope directs the interrogation of the optimal frequency required for accurate implementation. We simulated the frequency response of the esophagus wall and then validated the simulation results with experimental measurements of pig esophagus...
May 2018: IEEE Transactions on Medical Imaging
Ling Dai, Ruogu Fang, Huating Li, Xuhong Hou, Bin Sheng, Qiang Wu, Weiping Jia
Timely detection and treatment of microaneurysms is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting microaneurysms in fundus images is a highly challenging task due to the low image contrast, misleading cues of other red lesions, and the large variation of imaging conditions. Existing methods tend to fail in face of the large intra-class variation and small inter-class variations for microaneurysm detection in fundus images. Recently, hybrid text/image mining computer-aided diagnosis systems have emerged to offer a promise of bridging the semantic gap between images and diagnostic information...
May 2018: IEEE Transactions on Medical Imaging
Tao Feng, Jizhe Wang, Youjun Sun, Wentao Zhu, Yun Dong, Hongdi Li
The goal is to develop an adaptive center-of-mass (COM)-based approach for device-less respiratory gating of list-mode positron emission tomography (PET) data. Our method contains two steps. The first is to automatically extract an optimized respiratory motion signal from the list-mode data during acquisition. The respiratory motion signal was calculated by tracking the location of COM within a volume of interest (VOI). The signal prominence (SP) was calculated based on Fourier analysis of the signal. The VOI was adaptively optimized to maximize SP...
May 2018: IEEE Transactions on Medical Imaging
Zhiwei Wang, Chaoyue Liu, Danpeng Cheng, Liang Wang, Xin Yang, Kwang-Ting Cheng
Automated methods for detecting clinically significant (CS) prostate cancer (PCa) in multi-parameter magnetic resonance images (mp-MRI) are of high demand. Existing methods typically employ several separate steps, each of which is optimized individually without considering the error tolerance of other steps. As a result, they could either involve unnecessary computational cost or suffer from errors accumulated over steps. In this paper, we present an automated CS PCa detection system, where all steps are optimized jointly in an end-to-end trainable deep neural network...
May 2018: IEEE Transactions on Medical Imaging
Yueming Jin, Qi Dou, Hao Chen, Lequan Yu, Jing Qin, Chi-Wing Fu, Pheng-Ann Heng
We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet seamlessly integrates a convolutional neural network (CNN) and a recurrent neural network (RNN) to form a novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos...
May 2018: IEEE Transactions on Medical Imaging
Satyananda Kashyap, Honghai Zhang, Karan Rao, Milan Sonka
A fully automated knee magnetic resonance imaging (MRI) segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance of cartilage regions and their boundaries. A neighborhood approximation forest is used first to provide contextual feature to the second-level RF classifier that also considers local features and produces location-specific costs for the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework...
May 2018: IEEE Transactions on Medical Imaging
Tian Mou, Jian Huang, Finbarr O'Sullivan
The basic emission process associated with positron emission tomography (PET) imaging is Poisson in nature. Reconstructed images inherit some aspects of this-regional variability is typically proportional to the regional mean. Iterative reconstruction using expectation-maximization (EM), widely used in clinical imaging now, imposes positivity constraints that impact noise properties. This paper is motivated by the analysis of data from a physical phantom study of a PET/CT scanner in routine clinical use. Both traditional filtered back-projection (FBP) and EM reconstructions of the images are considered...
May 2018: IEEE Transactions on Medical Imaging
David F C Hsu, David L Freese, Paul D Reynolds, Derek R Innes, Craig S Levin
We are developing a 1-mm3 resolution, high-sensitivity positron emission tomography (PET) system for loco-regional cancer imaging. The completed system will comprise two cm detector panels and contain 4 608 position sensitive avalanche photodiodes (PSAPDs) coupled to arrays of mm3 LYSO crystal elements for a total of 294 912 crystal elements. For the first time, this paper summarizes the design and reports the performance of a significant portion of the final clinical PET system, comprising 1 536 PSAPDs, 98 304 crystal elements, and an active field-of-view (FOV) of cm...
April 2018: IEEE Transactions on Medical Imaging
Steven Tilley, Matthew Jacobson, Qian Cao, Michael Brehler, Alejandro Sisniega, Wojciech Zbijewski, J Webster Stayman
We present a novel reconstruction algorithm based on a general cone-beam CT forward model, which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized-Likelihood objective function, which incorporates models of blur and correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared with deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur...
April 2018: IEEE Transactions on Medical Imaging
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