. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DEC...
There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral reso...
Limited angle reconstruction is a typical ill-posed problem in computed tomography (CT). Given incomplete projection data, images reconstructed by conventional analytical algorithms and iterative methods suffer from severe structural distortions and ...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Sep 27, 2022
High-frame-rate ultrasound imaging uses unfocused transmissions to insonify an entire imaging view for each transmit event, thereby enabling frame rates over 1000 frames per second (fps). At these high frame rates, it is naturally challenging to real...
Conventional noise reduction algorithms have been used in image processing for a very long time, but recently, deep learning-based algorithms have been shown to significantly reduce the noise in CT images. In this paper, a comparison of CT noise redu...
AJR. American journal of roentgenology
Sep 21, 2022
Iterative reconstruction (IR) techniques are susceptible to contrast-dependent spatial resolution, limiting overall radiation dose reduction potential. Deep learning image reconstruction (DLIR) may mitigate this limitation. The purpose of our study...
PURPOSE: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction.
. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning me...
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationshi...
BACKGROUND: Two deep learning image reconstruction (DLIR) techniques from two different computed tomography (CT) vendors have recently been introduced into clinical practice.
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