Volume 39, Issue 12

Wireless capsule endoscopy (WCE) is a novel imaging tool that allows noninvasive visualization of the entire gastrointestinal (GI) tract without causing discomfort to patients. Convolutional neural networks (CNNs), though perform favorably against traditional machine learning methods, show limited capacity in WCE image classification due to the small lesions and background interference. To overcom...

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Fetal brain magnetic resonance imaging (MRI) offers exquisite images of the developing brain but is not suitable for second-trimester anomaly screening, for which ultrasound (US) is employed. Although expert sonographers are adept at reading US images, MR images which closely resemble anatomical images are much easier for non-experts to interpret. Thus in this article we propose to generate MR-lik...

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The objectives were to develop a novel three-dimensional technology for imaging naturally occurring shear wave (SW) propagation, demonstrate feasibility on human volunteers and quantify SW velocity in different propagation directions. Imaging of natural SWs generated by valve closures has emerged to obtain a direct measurement of cardiac stiffness. Recently, natural SW velocity was assessed in two...

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This paper describes a novel approach of detecting different stages of Alzheimer’s disease (AD) and imaging beta-amyloid plaques and tau tangles in the brain using RF sensors. Dielectric measurements were obtained from grey matter and white matter regions of brain tissues with severe AD pathology at a frequency range of 200 MHz to 3 GHz using a vector network analyzer and dielectric probe. Computa...

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Delay-and-sum (DAS) beamforming is unable to identify individual scatterers when their density is so high that their point spread functions overlap. This paper proposes a convolutional neural network (CNN)-based method to detect and localize high-density scatterers, some of which are closer than the resolution limit of delay-andsum (DAS) beamforming. A CNN was designed to take radio frequency chan...

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Chromosome enumeration is an essential but tedious procedure in karyotyping analysis. To automate the enumeration process, we develop a chromosome enumeration framework, DeepACEv2, based on the region based object detection scheme. The framework is developed following three steps. Firstly, we take the classical ResNet-101 as the backbone and attach the Feature Pyramid Network (FPN) to the backbone...

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Metal artifacts commonly appear in computed tomography (CT) images of the patient body with metal implants and can affect disease diagnosis. Known deep learning and traditional metal trace restoring methods did not effectively restore details and sinogram consistency information in X-ray CT sinograms, hence often causing considerable secondary artifacts in CT images. In this paper, we propose a ne...

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X-ray fluorescence computed tomography (XFCT) with nanoparticles (NPs) as contrast agents shows potential for molecular biomedical imaging with higher spatial resolution than present methods. To date the technique has been demonstrated on phantoms and mice, however, parameters such as radiation dose, exposure times and sensitivity have not yet allowed for high-spatial-resolution in vivo longitudin...

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Compactness, among several others, is one unique and very attractive feature of a scanning fiber-optic two-photon endomicroscope. To increase the scanning area and the total number of resolvable pixels (i.e., the imaging throughput), it typically requires a longer cantilever which, however, leads to a much undesired, reduced scanning speed (and thus imaging frame rate). Herein we introduce a new d...

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This work proposes a novel shape-driven reconstruction approach for difference electrical impedance tomography (EIT). In the proposed approach, the reconstruction problem is formulated as a shape reconstruction problem and solved via an explicit and geometrical methodology, where the geometry of the embedded inclusions is represented by a shape and topology description function (STDF). To incorpor...

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Automating the classification of camera-obtained microscopic images of White Blood Cells (WBCs) and related cell subtypes has assumed importance since it aids the laborious manual process of review and diagnosis. Several State-Of-The-Art (SOTA) methods developed using Deep Convolutional Neural Networks suffer from the problem of domain shift - severe performance degradation when they are tested on...

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Collagen fibers in biological tissues have a complex 3D organization containing rich information linked to tissue mechanical properties and are affected by mutations that lead to diseases. Quantitative assessment of this 3D collagen fiber organization could help to develop reliable biomechanical models and understand tissue structure-function relationships, which impact diagnosis and treatment of ...

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We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we ...

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Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse ${k}$ -space data. Conventional methods typically optimize an energy function, producing restoration of hi...

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Reconstruction of neuronal populations from ultra-scale optical microscopy (OM) images is essential to investigate neuronal circuits and brain mechanisms. The noises, low contrast, huge memory requirement, and high computational cost pose significant challenges in the neuronal population reconstruction. Recently, many studies have been conducted to extract neuron signals using deep neural networks...

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Despite the benefits of mammography investigations, some studies have shown that X-ray exposure from the mammography screening itself can statistically cause breast cancer in a small fraction of women. Therefore, a dose reduction in mammography is desirable. At the same time, there is a demand for a higher spatial resolution in mammographic imaging. The most promising way to achieve these goals is...

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With each heartbeat, periodic variations in arterial blood pressure are transmitted along the vasculature, resulting in localized deformations of the arterial wall and its surrounding tissue. Quantification of such motions may help understand various cerebrovascular conditions, yet it has proven technically challenging thus far. We introduce a new image processing algorithm called amplified Flow (...

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Automatic rib fracture recognition from chest X-ray images is clinically important yet challenging due to weak saliency of fractures. Weakly Supervised Learning (WSL) models recognize fractures by learning from large-scale image-level labels. In WSL, Class Activation Maps (CAMs) are considered to provide spatial interpretations on classification decisions. However, the high-responding regions, nam...

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Material decomposition in X-ray imaging uses the energy-dependence of attenuation to digitally decompose an object into specific constituent materials, generally at the cost of enhanced image noise. Propagation-based X-ray phase-contrast imaging is a developing technique that can be used to reduce image noise, in particular from weakly attenuating objects. In this paper, we combine spectral phase-...

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We designed a dedicated multi-detector multi-pinhole brain SPECT scanner to generate images of higher quality compared to general-purpose systems. The system, AdaptiSPECT-C, is intended to adapt its sensitivity-resolution trade-off by varying its aperture configurations allowing both high-sensitivity dynamic and high-spatial-resolution static imaging. The current system design consists of 23 detec...

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The phased-array radio frequency (RF) coil plays a vital role in magnetic resonance-guided focused ultrasound (MRgFUS) neuromodulation studies, where accurate brain functional stimulations and neural circuit observations are required. Although various designs of phased-array coils have been reported, few are suitable for ultrasound stimulations. In this study, an MRgFUS neuromodulation system comp...

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Ultrasound vascular strain imaging has shown its potential to interrogate the motion of the vessel wall induced by the cardiac pulsation for predicting plaque instability. In this study, a sparse model strain estimator (SMSE) is proposed to reconstruct a dense strain field at a high resolution, with no spatial derivatives, and a high computation efficiency. This sparse model utilizes the highly-co...

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Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D context), in this paper, we novelly identify the wide variation in the ratio between intra- and inter-slice resolutions as a crucial obstacle to the perf...

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Neurosurgery targets in the thalamus can be challenging to identify during transcranial MRI-guided focused ultrasound (MRgFUS) thermal ablation due to poor image quality. They also neighbor structures that can result in side effects if damaged. Here we demonstrate that the phase data obtained during MRgFUS for MR temperature imaging (MRTI) contains anatomic information that could be useful in guid...

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Complex decorrelation-based OCT angiography (OCTA) has the potential for monitoring hemodynamic activities in a label-free, high-resolution, and quantitative manner. To improve the measurement dynamic range and uncertainty of blood flow, an adaptive spatial-temporal (ST) kernel was proposed for decorrelation estimation and it was validated through a theoretical simulation and experimental measurem...

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