4,656 results match your criteria IEEE Transactions on Medical Imaging[Journal]


Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.

IEEE Trans Med Imaging 2019 Feb 8. Epub 2019 Feb 8.

Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions within OCT images are utilized to guide the CNN to achieve more accurate classification. Read More

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http://dx.doi.org/10.1109/TMI.2019.2898414DOI Listing
February 2019

Generative Adversarial Networks for Facilitating Stain-Independent Supervised & Unsupervised Segmentation: A Study on Kidney Histology.

IEEE Trans Med Imaging 2019 Feb 14. Epub 2019 Feb 14.

A major challenge in the field of segmentation in digital pathology is given by the high effort for manual data annotations in combination with many sources introducing variability in the image domain. This requires methods that are able to cope with variability without requiring to annotate a large amount of samples for each characteristic. In this paper, we develop approaches based on adversarial models for image- to-image translation relying on unpaired training. Read More

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https://ieeexplore.ieee.org/document/8642295/
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http://dx.doi.org/10.1109/TMI.2019.2899364DOI Listing
February 2019
5 Reads

Deep Learning for Fast and Spatially-Constrained Tissue Quantification from Highly-Accelerated Data in Magnetic Resonance Fingerprinting.

IEEE Trans Med Imaging 2019 Feb 13. Epub 2019 Feb 13.

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this work is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with fewer sampling data. Read More

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http://dx.doi.org/10.1109/TMI.2019.2899328DOI Listing
February 2019
3.390 Impact Factor

Model-based Chemical Exchange Saturation Transfer MRI for Robust z-Spectrum Analysis.

IEEE Trans Med Imaging 2019 Feb 12. Epub 2019 Feb 12.

This work introduces a novel, model-based chemical exchange saturation transfer (CEST) MRI, in which asymmetric spectra of interest are directly estimated from complete or incomplete measurements by incorporating subspace-based spectral signal decomposition into the measurement model of CEST MRI for robust z-spectrum analysis. Spectral signals are decomposed into symmetric and asymmetric components. The symmetric component, which varies smoothly, is delineated by linear superposition of a finite set of vectors in a basis trained from simulated (Lorentzian) signal vectors augmented with datadriven signal vectors, while the asymmetric component is to be inherently lower than or equal to zero due to saturation transfer phenomena. Read More

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http://dx.doi.org/10.1109/TMI.2019.2898672DOI Listing
February 2019

Total Variation Regularization of Pose Signals with an Application to 3D Freehand Ultrasound.

IEEE Trans Med Imaging 2019 Feb 11. Epub 2019 Feb 11.

Three-dimensional freehand imaging techniques are gaining wider adoption due to their ?exibility and cost ef?ciency. Typical examples for such a combination of a tracking system with an imaging device are freehand SPECT or freehand 3D ultrasound. However, the quality of the resulting image data is heavily dependent on the skill of the human operator and on the level of noise of the tracking data. Read More

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http://dx.doi.org/10.1109/TMI.2019.2898480DOI Listing
February 2019

Pulsed Excitation in Magnetic Particle Imaging.

IEEE Trans Med Imaging 2019 Feb 11. Epub 2019 Feb 11.

Magnetic Particle Imaging (MPI) is a promising new tracer-based imaging modality. The steady-state, nonlinear magnetization physics most fundamental to MPI typically predicts improving resolution with increasing tracer magnetic core size. For larger tracers, and given typical excitation slew rates, this steady-state prediction is compromised by dynamic processes that induce a significant secondary blur and prevent us from achieving high resolution using larger tracers. Read More

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http://dx.doi.org/10.1109/TMI.2019.2898202DOI Listing
February 2019

An efficient preconditioner for stochastic gradient descent optimization of image registration.

IEEE Trans Med Imaging 2019 Feb 11. Epub 2019 Feb 11.

Stochastic gradient descent (SGD) is commonly used to solve (parametric) image registration problems. In case of badly scaled problems, SGD however only exhibits sublinear convergence properties. In this paper we propose an efficient preconditioner estimation method to improve the convergence rate of SGD. Read More

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http://dx.doi.org/10.1109/TMI.2019.2897943DOI Listing
February 2019

Mapping biological current densities with Ultrafast Acoustoelectric Imaging: application to the beating rat heart.

IEEE Trans Med Imaging 2019 Feb 7. Epub 2019 Feb 7.

Ultrafast Acoustoelectric Imaging (UAI) is a novel method for the mapping of biological current densities, which may improve the diagnosis and monitoring of cardiac activation diseases such as arrhythmias. This work evaluates the feasibility of performing UAI in beating rat hearts. A previously described system based on a 256-channel ultrasound (US) research platform fitted with a 5-MHz linear array was used for simultaneous UAI, ultrafast B-mode, and electrocardiogram (ECG) recordings. Read More

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http://dx.doi.org/10.1109/TMI.2019.2898090DOI Listing
February 2019

The Impulse Response of Negatively Focused Spherical Ultrasound Detectors and its Effect on Tomographic Optoacoustic Reconstruction.

IEEE Trans Med Imaging 2019 Feb 5. Epub 2019 Feb 5.

In optoacoustic tomography, negatively focused detectors have been shown to improve the tangential image resolution without sacrificing sensitivity. Since no exact inversion formulae exist for optoacoustic image reconstruction with negatively focused detectors, image reconstruction in such cases is based on using the virtual-detector approximation, in which it is assumed that the response of the negatively focused detector is identical, up to a constant time delay, to that of a point-like detector positioned in the detector's center of curvature. In this work, we analyze the response of negatively focused spherical ultrasound detectors in three dimensions and demonstrate how their properties affect the optoacoustic reconstruction. Read More

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http://dx.doi.org/10.1109/TMI.2019.2897588DOI Listing
February 2019

VoxelMorph: A Learning Framework for Deformable Medical Image Registration.

IEEE Trans Med Imaging 2019 Feb 4. Epub 2019 Feb 4.

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. Read More

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http://dx.doi.org/10.1109/TMI.2019.2897538DOI Listing
February 2019

Learning a Probabilistic Model for Diffeomorphic Registration.

IEEE Trans Med Imaging 2019 Feb 4. Epub 2019 Feb 4.

We propose to learn a low-dimensional probabilistic deformation model from data which can be used for registration and the analysis of deformations. The latent variable model maps similar deformations close to each other in an encoding space. It enables to compare deformations, generate normal or pathological deformations for any new image or to transport deformations from one image pair to any other image. Read More

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http://dx.doi.org/10.1109/TMI.2019.2897112DOI Listing
February 2019

Robust non-rigid motion compensation of free-breathing myocardial perfusion MRI data.

IEEE Trans Med Imaging 2019 Feb 1. Epub 2019 Feb 1.

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast enhanced (DCE-) magnetic resonance imaging (MRI) data using tracer-kinetic modelling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. Read More

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https://ieeexplore.ieee.org/document/8632981/
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http://dx.doi.org/10.1109/TMI.2019.2897044DOI Listing
February 2019
3 Reads

OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI.

IEEE Trans Med Imaging 2019 Feb 1. Epub 2019 Feb 1.

This paper introduces a new estimationtheoretic framework for experiment design in the context of MR image reconstruction under sparsity constraints. The new framework is called OEDIPUS (Oracle-based Experiment Design for Imaging Parsimoniously Under Sparsity constraints), and is based on combining the constrained Cramér-Rao bound with classical experiment design techniques. Compared to popular random sampling approaches, OEDIPUS is fully deterministic and automatically tailors the sampling pattern to the specific imaging context of interest (i. Read More

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http://dx.doi.org/10.1109/TMI.2019.2896180DOI Listing
February 2019

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge.

IEEE Trans Med Imaging 2019 Feb;38(2):550-560

Automated detection of cancer metastases in lymph nodes has the potential to improve the assessment of prognosis for patients. To enable fair comparison between the algorithms for this purpose, we set up the CAMELYON17 challenge in conjunction with the IEEE International Symposium on Biomedical Imaging 2017 Conference in Melbourne. Over 300 participants registered on the challenge website, of which 23 teams submitted a total of 37 algorithms before the initial deadline. Read More

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http://dx.doi.org/10.1109/TMI.2018.2867350DOI Listing
February 2019

Recalibrating Fully Convolutional Networks With Spatial and Channel "Squeeze and Excitation" Blocks.

IEEE Trans Med Imaging 2019 Feb;38(2):540-549

In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve the state-of-the-art performance. Architectural innovations of F-CNNs have mainly been on improving spatial encoding or network connectivity to aid gradient flow. In this paper, we aim toward an alternate direction of recalibrating the learned feature maps adaptively, boosting meaningful features while suppressing weak ones. Read More

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http://dx.doi.org/10.1109/TMI.2018.2867261DOI Listing
February 2019

Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.

IEEE Trans Med Imaging 2019 Feb;38(2):515-524

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. Read More

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http://dx.doi.org/10.1109/TMI.2018.2866845DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364320PMC
February 2019

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map.

IEEE Trans Med Imaging 2019 Feb;38(2):448-459

The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the development of interpretable models, the detection and segmentation of cell nuclei is of the utmost importance. In this paper, we describe a new method to automatically segment nuclei from Haematoxylin and Eosin (H&E) stained histopathology data with fully convolutional networks. Read More

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http://dx.doi.org/10.1109/TMI.2018.2865709DOI Listing
February 2019

Fast System Calibration with Coded Calibration Scenes for Magnetic Particle Imaging.

IEEE Trans Med Imaging 2019 Jan 31. Epub 2019 Jan 31.

Magnetic particle imaging (MPI) is a relatively new medical imaging modality, which detects the nonlinear response of magnetic nanoparticles (MNPs) that are exposed to external magnetic fields. The system matrix (SM) method for MPI image reconstruction requires a time consuming system calibration scan prior to image acquisition, where a single MNP sample is measured at each voxel position in the field-of-view (FOV). The scanned sample has the maximum size of a voxel so that the calibration measurements have relatively poor signal-to-noise ratio (SNR). Read More

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http://dx.doi.org/10.1109/TMI.2019.2896289DOI Listing
January 2019

Deformable Image Registration Using Functions of Bounded Deformation.

IEEE Trans Med Imaging 2019 Jan 31. Epub 2019 Jan 31.

Deformable image registration is a widely used technique in the field of computer vision and medical image processing. Basically, the task of deformable image registration is to find the displacement field between the moving image and the fixed image. Many variational models are proposed for deformable image registration, under the assumption that the displacement field is continuous and smooth. Read More

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http://dx.doi.org/10.1109/TMI.2019.2896170DOI Listing
January 2019

Robust Single-shot T 2 Mapping via Multiple Overlapping-Echo Acquisition and Deep Neural Network.

IEEE Trans Med Imaging 2019 Jan 31. Epub 2019 Jan 31.

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time consuming and sensitive to motion artifacts. A single-shot quantitative T2 mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T2 mapping in milliseconds. Read More

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https://ieeexplore.ieee.org/document/8630865/
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http://dx.doi.org/10.1109/TMI.2019.2896085DOI Listing
January 2019
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Manhattan distance based adaptive 3D transform-domain collaborative filtering for laser speckle imaging of blood flow.

IEEE Trans Med Imaging 2019 Jan 29. Epub 2019 Jan 29.

Laser speckle contrast imaging (LSCI) is a full-field, noncontact imaging technology for mapping blood flow with high spatio-temporal resolution, in which the speckle contrast can be estimated either in spatial domain or temporal domain. Temporal laser speckle contrast imaging (tLSCI) provides higher spatial resolution than spatial domain does. However, when the number of sampling frames is limited, it is difficult to obtain accurate blood flow velocity owing to the significant statistical noise. Read More

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http://dx.doi.org/10.1109/TMI.2019.2896007DOI Listing
January 2019
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Ea-GANs: Edge-aware Generative Adversarial Networks for Cross-modality MR Image Synthesis.

IEEE Trans Med Imaging 2019 Jan 29. Epub 2019 Jan 29.

Magnetic resonance imaging (MRI) is a widely used medical imaging protocol that can be configured to provide different contrast between the tissues in human body. By setting different scanning parameters, each MR imaging modality reflects the unique visual characteristic of scanned body part, benefiting the subsequent analysis from multiple perspectives. To utilise the complementary information from multiple imaging modalities, cross-modality MR image synthesis has aroused increasing research interest recently. Read More

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https://ieeexplore.ieee.org/document/8629301/
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http://dx.doi.org/10.1109/TMI.2019.2895894DOI Listing
January 2019
3 Reads

Monitoring acute stroke progression: multi-parametric OCT imaging of cortical perfusion, flow, and tissue scattering in a mouse model of permanent focal ischemia.

IEEE Trans Med Imaging 2019 Jan 31. Epub 2019 Jan 31.

Cerebral ischemic stroke causes injury to brain tissue characterized by a complex cascade of neuronal and vascular events. Imaging during early stages of its development allows prediction of tissue infarction and penumbra, so that optimal intervention can be determined in order to salvage brain function impairment. Therefore, there is a critical need for novel imaging techniques that can characterize brain injury in the earliest phases of ischemic stroke. Read More

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http://dx.doi.org/10.1109/TMI.2019.2895779DOI Listing
January 2019
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Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection.

IEEE Trans Med Imaging 2019 Jan 25. Epub 2019 Jan 25.

Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. Read More

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http://dx.doi.org/10.1109/TMI.2019.2895318DOI Listing
January 2019

Capacitively Coupled Electrical Impedance Tomography (CCEIT) for Brain Imaging.

IEEE Trans Med Imaging 2019 Jan 25. Epub 2019 Jan 25.

Electrical impedance tomography (EIT) is considered as a potential candidate for brain stroke imaging due to its compactness and potential use in bedside and emergency settings. The electrode-skin contact impedance and low conductivity of skull pose some practical challenges to EIT head imaging. This work studies the application of capacitively coupled electrical impedance tomography (CCEIT) in brain imaging for the first time. Read More

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http://dx.doi.org/10.1109/TMI.2019.2895035DOI Listing
January 2019
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A robust method to estimate the time constant of elastographic parameters.

IEEE Trans Med Imaging 2019 Jan 24. Epub 2019 Jan 24.

Novel viscoelastic and poroelastic elastography techniques rely on the accurate estimation of the temporal behavior of the axial or lateral strains and related parameters. From the temporal curve of the elastographic parameter of interest, the time constant (TC) is estimated using analytical models and curve-fitting techniques such as Levenberg-Marquardt (LM), Nelder-Mead (NM) and trust-region reflective (TR). In this paper, we propose a new technique named variable projection (VP) to estimate accurately and robustly the TC and steady state value of the elastographic parameter of interest from its temporal curve. Read More

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https://ieeexplore.ieee.org/document/8625607/
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http://dx.doi.org/10.1109/TMI.2019.2894782DOI Listing
January 2019
4 Reads

Fast Robust Dejitter and Interslice Discontinuity Removal in MRI Phase Acquisitions: Application to Magnetic Resonance Elastography.

IEEE Trans Med Imaging 2019 Jan 25. Epub 2019 Jan 25.

MRI phase contrast imaging methods that assemble slice-wise acquisitions into volumes can contain interslice phase discontinuities (IPDs) over the course of the scan from sources including unavoidable physiological activity. In magnetic resonance elastography (MRE) this can alter wavelength and tissue stiffness estimates, invalidating the analysis. We first model this behavior as jitter along the z-axis of the phase of 3D complex-valued wave volumes. Read More

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http://dx.doi.org/10.1109/TMI.2019.2893369DOI Listing
January 2019

Multi-Site Harmonization of Diffusion MRI Data via Method of Moments.

IEEE Trans Med Imaging 2019 Jan 24. Epub 2019 Jan 24.

Diffusion MRI is a powerful tool for non-invasive probing of brain tissue microstructure. Recent multi-center efforts in acquisition and analysis of diffusion MRI data significantly increase sample sizes and hence improve sensitivity and reliability in detecting subtle changes associated with development, aging, and diseases. However, discrepancies resulting from different scanner vendors, acquisition protocols, and image reconstruction algorithms can cause data incompatibility across imaging centers. Read More

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http://dx.doi.org/10.1109/TMI.2019.2895020DOI Listing
January 2019
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Efficient Multiple Organ Localization in CT Image using 3D Region Proposal Network.

IEEE Trans Med Imaging 2019 Jan 24. Epub 2019 Jan 24.

Organ localization is an essential preprocessing step for many medical image analysis tasks such as image registration, organ segmentation and lesion detection. In this work, we propose an efficient method for multiple organ localization in CT image using 3D region proposal network. Compared with other convolutional neural network based methods that successively detect the target organs in all slices to assemble the final 3D bounding box, our method is fully implemented in 3D manner, thus can take full advantages of the spatial context information in CT image to perform efficient organ localization with only one prediction. Read More

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https://ieeexplore.ieee.org/document/8625393/
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http://dx.doi.org/10.1109/TMI.2019.2894854DOI Listing
January 2019
2 Reads

A Universal Intensity Standardization Method Based on a Many-to-one Weak-paired Cycle Generative Adversarial Network for Magnetic Resonance Images.

IEEE Trans Med Imaging 2019 Jan 24. Epub 2019 Jan 24.

In magnetic resonance imaging (MRI), different imaging settings lead to various intensity distributions for a specific imaging object, which brings huge diversity to data-driven medical applications. To standardize the intensity distribution of magnetic resonance (MR) images from multiple centers and multiple machines using one model, a cycle generative adversarial network (CycleGAN)-based framework is proposed. It utilizes a unified forward generative adversarial network (GAN) path and multiple independent backward GAN paths to transform images in different groups into a single reference one. Read More

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http://dx.doi.org/10.1109/TMI.2019.2894692DOI Listing
January 2019
2 Reads

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches.

IEEE Trans Med Imaging 2019 Jan 23. Epub 2019 Jan 23.

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computeraided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this study, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Read More

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http://dx.doi.org/10.1109/TMI.2019.2894349DOI Listing
January 2019
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Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach.

IEEE Trans Med Imaging 2019 Jan 23. Epub 2019 Jan 23.

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. Read More

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http://dx.doi.org/10.1109/TMI.2019.2894322DOI Listing
January 2019
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Extraction of time-varying spatio-temporal networks using parameter-tuned constrained IVA.

IEEE Trans Med Imaging 2019 Jan 23. Epub 2019 Jan 23.

Dynamic functional connectivity (dFC) analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spatio-temporal networks. Read More

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https://ieeexplore.ieee.org/document/8624617/
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http://dx.doi.org/10.1109/TMI.2019.2893651DOI Listing
January 2019
3 Reads

Motion quantification and automated correction in clinical RSOM.

IEEE Trans Med Imaging 2019 Jan 24. Epub 2019 Jan 24.

Raster-scan optoacoustic mesoscopy (RSOM) offers high-resolution, non-invasive insights into skin pathophysiology, which holds promise for disease diagnosis and monitoring in dermatology and other fields. However, RSOM is quite vulnerable to vertical motion of the skin, which can depend on the part of the body being imaged. Motion correction algorithms have already been proposed, but they are not fully automated, they depend on anatomical segmentation pre-processing steps that might not be performed successfully, and they are not site-specific. Read More

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http://dx.doi.org/10.1109/TMI.2018.2883154DOI Listing
January 2019

A Multiscale Ray-Shooting Model for Termination Detection of Tree-Like Structures in Biomedical Images.

IEEE Trans Med Imaging 2019 Jan 15. Epub 2019 Jan 15.

Digital reconstruction (tracing) of tree-like structures, such as neurons, retinal blood vessels and bronchi, from volumetric images and 2D images is very important to biomedical research. Many existing reconstruction algorithms rely on a set of good seed points. 2D or 3D terminations are good candidates for such seed points. Read More

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http://dx.doi.org/10.1109/TMI.2019.2893117DOI Listing
January 2019
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Motion Correction in Optical Resolution Photoacoustic Microscopy.

IEEE Trans Med Imaging 2019 Jan 15. Epub 2019 Jan 15.

In this study, we are proposing a novel motion correction algorithm for high-resolution OR-PAM imaging. Our algorithm combines a modified demons-based tracking approach with a newly developed multi-scale vascular feature matching method (MSVFMM) to track motion between adjacent B-scan images without needing any reference object. We first applied this algorithm to correct motion artifacts within one three-dimensional (3D) data segment of rat iris obtained with OR-PAM imaging. Read More

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http://dx.doi.org/10.1109/TMI.2019.2893021DOI Listing
January 2019

Attention Residual Learning for Skin Lesion Classification.

IEEE Trans Med Imaging 2019 Jan 21. Epub 2019 Jan 21.

Automated skin lesion classification in dermoscopy images is an essential way to improve the diagnostic performance and reduce melanoma deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs in many image classification tasks, accurate classification of skin lesions remains challenging due to the insufficiency of training data, inter-class similarity, intra-class variation, and lack of the ability to focus on semantically meaningful lesion parts. To address these issues, we propose an attention residual learning convolutional neural network (ARL-CNN) model for skin lesion classification in dermoscopy images, which is composed of multiple ARL blocks, a global average pooling layer, and a classification layer. Read More

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http://dx.doi.org/10.1109/TMI.2019.2893944DOI Listing
January 2019

Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI.

IEEE Trans Med Imaging 2019 Jan 14. Epub 2019 Jan 14.

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. Read More

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http://dx.doi.org/10.1109/TMI.2019.2892378DOI Listing
January 2019

A wireless radio frequency triggered acquisition device (WRAD) for self-synchronised measurements of the rate of change of the MRI gradient vector field for motion tracking.

IEEE Trans Med Imaging 2019 Jan 10. Epub 2019 Jan 10.

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. Read More

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http://dx.doi.org/10.1109/TMI.2019.2891774DOI Listing
January 2019

PET Counting Response Variability Depending on Tumor Location, Activity, and Patient Obesity: A Feasibility Study of Solitary Pulmonary Nodule Using Monte Carlo.

IEEE Trans Med Imaging 2019 Jan 10. Epub 2019 Jan 10.

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. Read More

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http://dx.doi.org/10.1109/TMI.2019.2891578DOI Listing
January 2019
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Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection.

IEEE Trans Med Imaging 2019 Jan 7. Epub 2019 Jan 7.

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. Read More

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http://dx.doi.org/10.1109/TMI.2019.2891305DOI Listing
January 2019

A feasibility study of extracting tissue textures from a previous full-dose CT database as prior knowledge for Bayesian reconstruction of current low-dose CT images.

IEEE Trans Med Imaging 2019 Jan 3. Epub 2019 Jan 3.

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. Read More

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https://ieeexplore.ieee.org/document/8600348/
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http://dx.doi.org/10.1109/TMI.2018.2890788DOI Listing
January 2019
5 Reads

Automatic Pathological Lung Segmentation in Low-dose CT Image using Eigenspace Sparse Shape Composition.

IEEE Trans Med Imaging 2019 Jan 1. Epub 2019 Jan 1.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2890510DOI Listing
January 2019
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Increasing Accuracy of Optimal Surfaces Using Min-marginal Energies.

IEEE Trans Med Imaging 2019 Jan 1. Epub 2019 Jan 1.

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. Read More

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https://ieeexplore.ieee.org/document/8599009/
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http://dx.doi.org/10.1109/TMI.2018.2890386DOI Listing
January 2019
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Design and Demonstration of a Configurable Imaging Platform for Combined Laser, Ultrasound, and Elasticity Imaging.

IEEE Trans Med Imaging 2018 Dec 27. Epub 2018 Dec 27.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2889736DOI Listing
December 2018

Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction.

IEEE Trans Med Imaging 2018 Dec 24. Epub 2018 Dec 24.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2889314DOI Listing
December 2018

Automatic Resonance Frequency Retuning of Stretchable Liquid Metal Receive Coil for Magnetic Resonance Imaging.

IEEE Trans Med Imaging 2018 Dec 20. Epub 2018 Dec 20.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2888959DOI Listing
December 2018

Cardiac Phase Detection in Echocardiograms with Densely Gated Recurrent Neural Networks and Global Extrema Loss.

IEEE Trans Med Imaging 2018 Dec 24. Epub 2018 Dec 24.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2888807DOI Listing
December 2018
3.390 Impact Factor

Efficient 3D low-discrepancy k-space sampling using highly adaptable Seiffert Spirals.

IEEE Trans Med Imaging 2018 Dec 20. Epub 2018 Dec 20.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2888695DOI Listing
December 2018

PET Image Reconstruction Using Deep Image Prior.

IEEE Trans Med Imaging 2018 Dec 19. Epub 2018 Dec 19.

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. Read More

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http://dx.doi.org/10.1109/TMI.2018.2888491DOI Listing
December 2018