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IEEE Transactions on Medical Imaging

Neerav Dixit, Pascal P Stang, John M Pauly, Greig C Scott
Patients who have implanted medical devices with long conductive leads are often restricted from receiving MRI scans due to the danger of RF-induced heating near the lead tips. Phantom studies have shown that this heating varies significantly on a case-by-case basis, indicating that many patients with implanted devices can receive clinically useful MRI scans without harm. However, the difficulty of predicting RF-induced lead tip heating prior to scanning prevents numerous implant recipients from being scanned...
October 17, 2017: IEEE Transactions on Medical Imaging
Agisilaos Chartsias, Thomas Joyce, Mario Valerio Giuffrida, Sotirios A Tsaftaris
We propose a multi-input multi-output fully convolutional neural network model for MRI synthesis. The model is robust to missing data, as it benefits from, but does not require, additional input modalities. The model is trained end-to-end, and learns to embed all input modalities into a shared modalityinvariant latent space. These latent representations are then combined into a single fused representation, which is transformed into the target output modality with a learnt decoder. We avoid the need for curriculum learning by exploiting the fact that the various input modalities are highly correlated...
October 17, 2017: IEEE Transactions on Medical Imaging
Andreas Horneff, Michael Eder, Erich Hell, Johannes Ulrici, Jorg Felder, Volker Rasche, Jens Anders
Developing custom-built MR coils is a cumbersome task, in which an a priori prediction of the coils' SNR performance, their sensitivity pattern and their depth of penetration helps to greatly speed up the design process by reducing the required hardware manufacturing iterations. The simulationbased design flow presented in this paper takes the entire MR imaging process into account. That is, it includes all geometric and material properties of the coil and the phantom, the thermal noise as well as the target MR sequences...
October 17, 2017: IEEE Transactions on Medical Imaging
Grace J Gang, Jeffrey H Siewerdsen, J Webster Stayman
This work presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood (PL) reconstruction that maximizes a taskbased imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index (d0) throughout the image. The optimization algorithm alternates between FFM (represented by lowdimensional basis functions) and local regularization (including the regularization strength and directional penalty weights)...
October 16, 2017: IEEE Transactions on Medical Imaging
Enrico Pellegrini, Gavin Robertson, Tom MacGillivray, Jano van Hemert, Graeme Houston, Emanuele Trucco
The classification of blood vessels into arterioles and venules is a fundamental step in the automatic investigation of retinal biomarkers for systemic diseases. In this paper we present a novel technique for vessel classification on ultra-wide-fieldof- view images of the retinal fundus acquired with a scanning laser ophthalmoscope. To our best knowledge, this is the first time that a fully automated artery/vein classification technique for this type of retinal imaging with no manual intervention has been presented...
October 13, 2017: IEEE Transactions on Medical Imaging
Kyounghun Lee, Eung Je Woo, Jin Keun Seo
Electrical impedance tomography (EIT) provides functional images of an electrical conductivity distribution inside the human body. Since the 1980s, many potential clinical applications have arisen using inexpensive portable EIT devices. EIT acquires multiple trans-impedance measurements across the body from an array of surface electrodes around a chosen imaging slice. The conductivity image reconstruction from the measured data is a fundamentally ill-posed inverse problem notoriously vulnerable to measurement noise and artifacts...
October 13, 2017: IEEE Transactions on Medical Imaging
Jo Schlemper, Jose Caballero, Joseph V Hajnal, Anthony Price, Daniel Rueckert
Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling. Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed...
October 13, 2017: IEEE Transactions on Medical Imaging
Chung Chan, John Onofrey, Yiqiang Jian, Mary Germino, Xenophon Papademetris, Richard E Carson, Chi Liu
Respiratory motion during PET/CT imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this study, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-byevent (NR-EBE) respiratory motion compensated list-mode reconstruction algorithm. The proposed method consists of 2 components, the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation (NR-INTEX)...
October 10, 2017: IEEE Transactions on Medical Imaging
Inaki Rabanillo, Santiago Aja-Fernandez, Carlos Alberola-Lopez, Diego Hernando
Characterization of the noise distribution in MR images has multiple applications, including quality assurance and protocol optimization. Noise characterization is particularly important in the presence of parallel imaging acceleration with multi-coil acquisitions, where the noise distribution can contain severe spatial heterogeneities. If the parallel imaging reconstruction is a linear process, an accurate noise analysis can be carried out by taking into account the correlations between all the samples involved...
October 9, 2017: IEEE Transactions on Medical Imaging
T Speidel, J Paul, S Wundrak, V Rasche
MR imaging of short relaxation times spin systems has been a widely discussed topic with serious clinical applications and led to the emergence of fast imaging ultra-short echo-time sequences. Nevertheless, these sequences suffer from image blurring due to the related sampling point spread function and are highly prone to imaging artefacts arising from e.g. chemical shifts or magnetic susceptibilities. In this work, we present a concept of spherical quasi-random single-point imaging. The approach is highly accelerateable due to intrinsic undersampling properties and capable of strong metal artefact suppression...
October 9, 2017: IEEE Transactions on Medical Imaging
Jingyan Xu, Frederic Noo, Benjamin M W Tsui
We present a direct (noniterative) algorithm for one dimensional (1-D) quadratic data fitting with neighboring intensity differences penalized by the Huber function. Applications of such an algorithm include 1-D processing of medical signals, such as smoothing of tissue time concentration curves in kinetic data analysis or sinogram preprocessing, and using it as a subproblem solver for 2-D or 3-D image restoration and reconstruction. Dynamic programming (DP) was used to develop the direct algorithm. The problem was reformulated as a sequence of univariate optimization problems, for k = 1,…,N, where N is the number of data points...
October 5, 2017: IEEE Transactions on Medical Imaging
Ti Bai, Hao Yan, Xun Jia, Steve Jiang, Ge Wang, Xuanqin Mou
Despite the rapid developments of x-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this work, a sparse constraint based on the 3D dictionary is incorporated into a regularized iterative reconstruction framework, defining the 3DDL method. In addition, by analyzing the sparsity level curve associated with different regularization parameters, a new adaptive parameter selection strategy is proposed to facilitate our 3DDL method...
October 5, 2017: IEEE Transactions on Medical Imaging
Marc Filippi, Michel Desvignes, Eric Moisan
In dynamic planar imaging, extraction of signals specific to structures is complicated by structures superposition. Due to overlapping, signals extraction with classic regions of interest (ROIs) methods suffers from inaccuracy, as extracted signals are a mixture of targeted signals. Partial volume effect raises the same issue in dynamic tomography. Source separation methods such as factor analysis of dynamic sequences, have been developped to unmix such data. However the underlying problem is underdetermined and the model used is not relevant in the whole image...
October 4, 2017: IEEE Transactions on Medical Imaging
Pedro Costa, Adrian Galdran, Maria Ines Meyer, Meindert Niemeijer, Michael Abramoff, Ana Maria Mendonca, Aurelio Campilho
In medical image analysis applications, the availability of large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis...
October 2, 2017: IEEE Transactions on Medical Imaging
Caner Mercan, Selim Aksoy, Ezgi Mercan, Linda G Shapiro, Donald L Weaver, Joann G Elmore
Digital pathology has entered a new era with the availability of whole slide scanners that create high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significance have emerged as two new challenges. However, generalizability of the state-of-theart algorithms, whose accuracies were reported on carefully selected regions of interest (ROI) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown...
October 2, 2017: IEEE Transactions on Medical Imaging
Armin Rund, Christoph Stefan Aigner, Karl Kunisch, Rudolf Stollberger
Optimal control approaches have proved useful in designing RF pulses for large tip-angle applications. A typical challenge for optimal control design is the inclusion of constraints resulting from physiological or technical limitations, that assure the realizability of the optimized pulses. In this work we show how to treat such inequality constraints, in particular, amplitude constraints on the B1 field, the slice-selective gradient and its slew rate, as well as constraints on the slice profile accuracy. For the latter a pointwise profile error and additional phase constraints are prescribed...
October 2, 2017: IEEE Transactions on Medical Imaging
Roozbeh Shams, Yiming Xiao, Francois Hebert, Matthew Abramowitz, Rupert Brooks, Hassan Rivaz
Image guidance has become the standard of care for patient positioning in radiotherapy, where image registration is often a critical step to help manage patient motion. However, in practice, verification of registration quality is often adversely affected by difficulty in manual inspection of 3D images and time constraint, thus affecting the therapeutic outcome. Therefore, we proposed to employ both bootstrapping and the supervised learning methods of linear discriminant analysis and random forest to help robustly assess registration quality in ultrasound-guided radiotherapy...
September 28, 2017: IEEE Transactions on Medical Imaging
Yuanke Zhang, Junyan Rong, Hongbing Lu, Yuxiang Xing, Jing Meng
The valuable structure features in full-dose CT (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This study aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm (APFA), for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively...
September 27, 2017: IEEE Transactions on Medical Imaging
Fitsum Mesadi, Ertunc Erdil, Mujdat Cetin, Tolga Tasdizen
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape and appearance distributions, and the level set representation does not support construction of local priors. In this paper, we present novel appearance and shape models for image segmentation based on a differentiable implicit parametric shape representation called disjunctive normal shape model (DNSM)...
September 26, 2017: IEEE Transactions on Medical Imaging
Yuanwei Li, Chin Pang Ho, Matthieu Toulemonde, Navtej Chahal, Roxy Senior, Meng-Xing Tang
Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels...
September 26, 2017: IEEE Transactions on Medical Imaging
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