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Found 14 papers, 4 papers with code
Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5.
Synthetic PET via Domain Translation of 3D MRI
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT.
Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics.
Physics-driven Deep Learning for PET/MRI
In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease.
Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstruction
Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling.
Utilizing the Structure of the Curvelet Transform with Compressed Sensing
The curvelet representation is approximately sparse; thus, it is a useful sparsifying transformation to be used with compressed sensing.
Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform
The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities.
Anisotropic field-of-views in radial imaging
One drawback is that standard implementations do not support anisotropic field-of-view (FOV) shapes, which are used to match the imaging parameters to the object or region-of-interest.
Predicting Generalization in Deep Learning via Local Measures of Distortion
We study generalization in deep learning by appealing to complexity measures originally developed in approximation and information theory.
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
In this manuscript, we present a generalization of several existing iterative model based algorithms.
Fast Variable Density Poisson-Disc Sample Generation with Directional Variation
We present a fast method for generating random samples according to a variable density Poisson-disc distribution.
Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery
Magnetic resonance imaging with hyperpolarized contrast agents can provide unprecedented \textit{in-vivo} measurements of metabolism, but yields images that are lower resolution than that achieved with proton anatomical imaging.
Utilizing the Wavelet Transform's Structure in Compressed Sensing
In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result.
Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions
We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory.
Medical Physics Image and Video Processing
