IEEE Transactions on Medical Imaging

We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in Echo Planar Imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time (TE). Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements.... View full abstract»

We introduce a framework for the reconstruction of the amplitude, phase and polarisation of an optical vector-field using measurements acquired by an imaging device characterised by an integral transform with an unknown spatially-variant kernel. By incorporating effective regularisation terms, this new approach is able to recover an optical vector-field with respect to an arbitrary representation ... View full abstract»

Due to peripheral nerve stimulation the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger field of view, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation vo... View full abstract»

In this study, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In step 1, we propose a dee... View full abstract»

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network (R-CNN) framework for multitask prediction using a Epithelial Network ... View full abstract»

The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in the literature. In this paper, we explore this relationship by investigating the mapping between morphological features of the cerebral cortex and cog... View full abstract»

Ultrasound shear wave elastography is an imaging modality for noninvasive evaluation of tissue mechanical properties. However, many current techniques overestimate lesions dimension or shape especially when small inclusions are taken into account. In this study we propose a new method called local phase velocity based imaging (LPVI) as an alternative technique to measure tissue elasticity. Two sep... View full abstract»

One of the most important and error-prone tasks in biological image analysis is the segmentation of touching or overlapping cells. Particularly for optical microscopy, including transmitted light and confocal fluorescence microscopy, there is often no consistent discriminative information to separate cells that touch or overlap. It is desired to partition touching foreground pixels into cells usin... View full abstract»

We present a three-dimensional bioimage analysis workflow to quantitatively analyze single, actin-stained cells with filopodial protrusions of diverse structural and temporal attributes, such as number, length, thickness, level of branching, and lifetime, in time-lapse confocal microscopy image data. Our workflow makes use of convolutional neural networks trained using real as well as synthetic im... View full abstract»

Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in q-space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial b-value dependence of the signal. Applications in motion correction and outlier rejection therefore r... View full abstract»

Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in a single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for MR fingerprinting. Specifically, we describe a discrete-time dynamic system to model spin dynamics, and derive an estimation-theoretic bound... View full abstract»

We propose a flexible form-fitting MRI receiver coil array assembled by individual coil modules. This design targets MRI applications requiring a receiver array conforming to the anatomy of various shapes or sizes. Coil modules in our proposed array were arranged with gaps between them. Each coil module had a circumferential shielding structure stacked on top of the coil. Together they achieve rob... View full abstract»

We present a dynamic atlas composed of neuromelanin-enhanced magnetic resonance brain images of 40 healthy subjects. The performance of this atlas is evaluated on the fully automated segmentation of two paired neuromelanin-rich brainstem healthy structures: the substantia nigra pars compacta and the locus coeruleus. We show that our dynamic atlas requires in average 60% less images, and therefore ... View full abstract»

Compared to traditional CT, dual energy CT (DECT) has the capability to improve material differentiation and reduce beam hardening artifacts, thus has wide prospects of applications. In this paper, by linearizing the polychromatic projections with their first order Taylor expansions, we derive an iterative reconstruction method for DECT. The method updates the basis material images by adding the r... View full abstract»

Non-Cartesian trajectories are advantageous in the collection and reconstruction of dynamic MR images. Like many other fast imaging methods, the reconstruction technique k-t BLAST has been extended to non-Cartesian k-t BLAST through a Conjugate Gradient based approach. However, it is necessary to transform and grid the data to a Cartesian domain as part of the reconstruction, a process which can b... View full abstract»

Probe-based confocal laser endomicroscopy (pCLE) is an emerging tool for epithelial cancer diagnosis, which enables in-vivo microscopic imaging during endoscopic procedures and facilitates the development of automatic recognition algorithms to identify the status of tissues. In this paper, we propose a transfer recurrent feature learning (TRFL) framework for classification tasks on pCLE videos. At... View full abstract»

We propose a framework for localization and classification of masses in breast ultrasound (BUS) images. We have experimentally found that training convolutional neural network based mass detectors with large, weakly annotated datasets presents a non-trivial problem, while overfitting may occur with those trained with small, strongly annotated datasets. To overcome these problems, we use a weakly a... View full abstract»

Cardiac computed tomography (CT) is a valuable tool for functional mechanical heart valve (MHV) assessment. An important aspect of bileaflet MHV assessment is evaluation and measurement of leaflet opening and closing angles. Performed manually however, it is a laborious and time consuming task. In this paper, we propose an automated approach for bileaflet MHV leaflet angle computation. This method... View full abstract»

This paper introduces a robust 2D cardiac motion estimation method. The problem is formulated as an energy minimization with an optical flow-based data fidelity term and two regularization terms imposing spatial smoothness and sparsity of the motion field in an appropriate cardiac motion dictionary. Robustness to outliers, such as imaging artefacts and anatomical motion boundaries, is introduced u... View full abstract»

Muscle coordination patterns of lingual behaviors are synergies generated by deforming local muscle groups in a variety of ways. Functional units are functional muscle groups of local structural elements within the tongue that compress, expand, and move in a cohesive and consistent manner. Identifying the functional units using tagged-Magnetic Resonance Imaging (MRI) sheds light on the mechanisms ... View full abstract»

Maximum Likelihood Expectation-Maximization (MLEM) is a popular algorithm to reconstruct the activity image in Positron Emission Tomography (PET). This paper introduces a ‘fundamental equality’ for the MLEM complete data from which two key properties easily follow that allows us to: (i) prove in an elegant and compact way the convergence of MLEM for a forward model with fixed background (i.e., cou... View full abstract»

Percutaneous coronary intervention (PCI) is the prevalent treatment for coronary artery disease, with hundreds of thousands of stents implanted annually. Computational studies have demonstrated the role of biomechanics in the failure of vascular stents, but clinical studies is this area are limited by a lack of understanding of the deployed stent geometry, which is required to accurately model and... View full abstract»

In this work we developed a deep convolutional neural network (CNN) for classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multistage transfer learning approach that utilized data from similar auxiliary domains for intermediate-stage fine-tuning. Breast imaging data from DBT and digitized screen-film mammography (SFM), digital mammography (DM) totaling 4,03... View full abstract»

The 18F-AV-1451 PET tracer binds to tau, an Alzheimer’s Disease (AD) biomarker. The standardized uptake value ratio (SUVR) 80-100 min window is widely used to quantify tau binding, although 18F-AV-1451 continues increasing relative to a reference region in regions with tau deposition. Left uncorrected, acquisition time inaccuracies can lead to errors from -4% to 6% in 20-min SUVR measurements in s... View full abstract»

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve PET image quality by using the existing inter-... View full abstract»