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IEEE Transactions on Medical Imaging

Ram Iyer, Farzana Nasrin, Elton See, Steven Mathews
Optical coherence tomography (OCT) is a non-invasive imaging technique used to study and understand internal structures of biological tissues such as the anterior chamber of the human eye. An interesting problem is the reconstruction of the shape of the biological tissue from OCT images, that is not only a good fit of the data but also respects the smoothness properties observed in the images. A similar problem arises in Magnetic Resonance Imaging (MRI). We cast the problem as a penalized weighted least squares regression with a penalty on the magnitude of the second derivative (Laplacian) of the surface...
October 19, 2016: IEEE Transactions on Medical Imaging
Guy Medan, Naomi Shamul, Leo Joskowicz
We present a new method for rigid registration of CT datasets in 3D Radon space based on sparse sampling of scanning projections. The inputs are the two 3D Radon transforms of the CT scans, one densely sampled and the other sparsely sampled (limited number of scan angles/ranges). The output is the rigid transformation that best matches them. The method first finds the best matching between each projection direction vector in the sparse transform and the corresponding direction vector in the dense transform...
October 6, 2016: IEEE Transactions on Medical Imaging
Li Zhao, Weiying Dai, Salil Soman, David Hackney, Eric Wong, Philip Robson, David Alsop
Functional imaging provides hemodynamic and metabolic information and is increasingly being incorporated into clinical diagnostic and research studies. Typically functional images have reduced signal-to-noise ratio and spatial resolution compared to other non-functional cross sectional images obtained as part of a routine clinical protocol. We hypothesized that enhancing visualization and interpretation of functional images with anatomic information could provide preferable quality and superior diagnostic value...
October 6, 2016: IEEE Transactions on Medical Imaging
Giulia Matrone, Alessandro Ramalli, Alessandro Savoia, Piero Tortoli, Giovanni Magenes
Multi-Line Transmission (MLT) was recently demonstrated as a valuable tool to increase the frame rate of ultrasound images. In this approach, the multiple beams that are simultaneously transmitted may determine cross-talk artifacts that are typically reduced, although not eliminated, by the use of Tukey apodization on both transmission and reception apertures, which unfortunately worsens the image lateral resolution. In this paper we investigate the combination, and related performance, of Filtered-Delay Multiply And Sum (F-DMAS) beamforming with MLT for high frame-rate ultrasound imaging...
October 4, 2016: IEEE Transactions on Medical Imaging
Gopal Nataraj, Jon-Fredrick Nielsen, Jeffrey Fessler
Rapid, reliable quantification of MR relaxation parameters T1 and T2 is desirable for many clinical applications. Steady-state sequences such as Spoiled Gradient-Recalled Echo (SPGR) and Dual-Echo Steady-State (DESS) are fast and wellsuited for relaxometry because the signals they produce are quite sensitive to T1 and T2 variation. However, T1, T2 estimation with these sequences typically requires multiple scans with varied sets of acquisition parameters. This paper describes a systematic framework for selecting scan types (e...
October 4, 2016: IEEE Transactions on Medical Imaging
Gabriel Ramos-Llorden, Arnold J den Dekker, Gwendolyn Van Steenkiste, Ben Jeurissen, Floris Vanhevel, Johan Van Audekerke, Marleen Verhoye, Jan Sijbers
In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1- weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1-weighted images is crucial. Conventionally, the T1-weighted images are first registered based on a general-purpose registration metric, after which the T1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1 map using a unified estimation approach...
September 20, 2016: IEEE Transactions on Medical Imaging
Leyuan Fang, Shutao Li, David Cunefare, Sina Farsiu
We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance...
September 20, 2016: IEEE Transactions on Medical Imaging
Mirco Hess, Florian Buther, Klaus Schafers
Physiological motion combined with elongated scanning times in PET leads to image degradation and quantification errors. Correction approaches usually require 1-D signals that can be obtained with hardware-based or data-driven methods. Most of the latter are optimized or limited to capture internal motion along the superior-inferior (S-I) direction. In this work we present methods for also extracting anterior-posterior (A-P) motion from PET data and propose a set of novel weighting mechanisms that can be used to emphasize certain lines-ofresponse (LORs) for an increased sensitivity and better signalto- noise ratio (SNR)...
September 19, 2016: IEEE Transactions on Medical Imaging
Junseob Shin, Lianjie Huang
One of the major challenges in array-based medical ultrasound imaging is the image quality degradation caused by sidelobes and off-axis clutter, which is an inherent limitation of the conventional delay-and-sum (DAS) beamforming operating on a finite aperture. Ultrasound image quality is further degraded in imaging applications involving strong tissue attenuation and/or low transmit power. In order to effectively suppress acoustic clutter from off-axis targets and random noise in a robust manner, we introduce in this paper a new adaptive filtering technique called frequency-space (F-X) prediction filtering or FXPF, which was first developed in seismic imaging for random noise attenuation...
September 16, 2016: IEEE Transactions on Medical Imaging
Valery Vishnevskiy, Tobias Gass, Gabor Szekely, Christine Tanner, Orcun Goksel
Spatial regularization is essential in image registration, which is an ill-posed problem. Regularization can help to avoid both physically implausible displacement fields and local minima during optimization. Tikhonov regularization (squared ℓ 2 -norm) is unable to correctly represent non-smooth displacement fields, that can, for example, occur at sliding interfaces in the thorax and abdomen in image time-series during respiration. In this paper, isotropic Total Variation (TV) regularization is used to enable accurate registration near such interfaces...
September 16, 2016: IEEE Transactions on Medical Imaging
Peter Fischer, Thomas Pohl, Anthony Faranesh, Andreas Maier, Joachim Hornegger
Respiratory signals are required for image gating and motion compensation in minimally invasive interventions. In X-ray fluoroscopy, extraction of a respiratory signal can be challenging due to characteristics of interventional imaging, in particular injection of contrast agent and automatic exposure control. We present a novel method for respiratory signal extraction based on dimensionality reduction that can tolerate these events. Images are divided into patches of multiple sizes. Low-dimensional embeddings are generated for each patch using illumination-invariant kernel PCA...
September 16, 2016: IEEE Transactions on Medical Imaging
Nghia Nguyen, Richard Prager
We analyze the principles underlying minimum variance distortionless response (MVDR) beamforming in order to integrate it into a pixel-based algorithm. There is a challenge posed by the low echo signal-to-noise ratio (eSNR) when calculating beamformer contributions at pixels far away from the beam centreline. Together with the well-known scarcity of samples for covariance matrix estimation, this reduces the beamformer performance and degrades the image quality. To address this challenge, we implement the MVDR algorithm in two different ways...
September 15, 2016: IEEE Transactions on Medical Imaging
Ahmed Soliman, Fahmi Khalifa, Ahmed Elnakib, Mohamed Abou El-Ghar, Neal Dunlap, Brian Wang, Georgy Gimel'farb, Robert Keynton, Ayman El-Baz
To accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively...
September 12, 2016: IEEE Transactions on Medical Imaging
Frederic M Brochu, Joanna Brunker, James Joseph, Michal R Tomaszewski, Stefan Morscher, Sarah E Bohndiek
Optoacoustic tomography is a fast developing imaging modality, combining the high contrast available from optical excitation of tissue with the high resolution and penetration depth of ultrasound detection. Light is subject to both absorption and scattering when travelling through tissue; adequate knowledge of tissue optical properties and hence the spatial fluence distribution is required to create an optoacoustic image that is directly proportional to chromophore concentrations at all depths. Using data from a commercial multispectral optoacoustic tomography (MSOT) system, we implemented an iterative optimization for fluence correction based on a finite-element implementation of the delta-Eddington approximation to the Radiative Transfer Equation (RTE)...
September 8, 2016: IEEE Transactions on Medical Imaging
David Svoboda, Vladimir Ulman
The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations...
September 7, 2016: IEEE Transactions on Medical Imaging
Gilad Drozdov, Amir Rosenthal
In optoacoustic tomography, negatively focused transducers may be used for improving the tangential image resolution while preserving a high signal-to-noise ratio. Commonly, image reconstruction in such scenarios is facilitated by the use of the virtual-detector approach. Although the validity of this approach has been experimentally verified, it is based on an approximation whose effect on optoacoustic image reconstruction has not yet been studied. In this paper, we analyze the response of the negatively focused acoustic detectors in 2D in both time and frequency domain...
September 7, 2016: IEEE Transactions on Medical Imaging
Youyi Song, Ee-Leng Tan, Xudong Jiang, Jie-Zhi Cheng, Dong Ni, Siping Chen, Baiying Lei, Tianfu Wang
Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer...
September 7, 2016: IEEE Transactions on Medical Imaging
Zhiqian Chang, Ruoqiao Zhang, Jean-Baptiste Thibault, Debashish Pal, Lin Fu, Ken Sauer, Charles Bouman
An increasing number of X-ray CT procedures are being conducted with drastically reduced dosage, due at least in part to advances in statistical reconstruction methods that can deal more effectively with noise than can traditional techniques. As data become photon-limited, more detailed models are necessary to deal with count rates that drop to the levels of system electronic noise. We present two options for sinogram pre-treatment that can improve the performance of photonstarved measurements, with the intent of following with modelbased image reconstruction...
September 7, 2016: IEEE Transactions on Medical Imaging
Pengfei Song, Armando Manduca, Joshua Trzasko, Shigao Chen
Robust clutter filtering is essential for ultrasound small vessel imaging. Eigen-based clutter filtering techniques have recently shown great improvement in clutter rejection over conventional clutter filters in small animals. However, for in vivo human imaging, eigen-based clutter filtering can be challenging due to the complex spatially-varying tissue and noise characteristics. To address this challenge, we present a novel block-wise adaptive singular value decomposition (SVD) based clutter filtering technique...
September 2, 2016: IEEE Transactions on Medical Imaging
Likun Tan, Matthew McGarry, Elijah Van Houten, Ming Ji, Ligin Solamen, John Weaver, Keith Paulsen
We describe an efficient gradient computation for solving inverse problems arising in magnetic resonance elastography (MRE). The algorithm can be considered as a generalized 'adjoint method' based on a Lagrangian formulation. One requirement for the classic adjoint method is assurance of the self-adjoint property of the stiffness matrix in the elasticity problem. In this paper, we show this property is no longer a necessary condition in our algorithm, but the computational performance can be as efficient as the classic method, which involves only two forward solutions and is independent of the number of parameters to be estimated...
August 31, 2016: IEEE Transactions on Medical Imaging
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