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Found 41 papers, 4 papers with code
ProxNF: Neural Field Proximal Training for High-Resolution 4D Dynamic Image Reconstruction
Neural fields can address the twin challenges of data incompleteness and computational burden by exploiting underlying redundancies in these spatiotemporal objects.
Technical Note: An Efficient Implementation of the Spherical Radon Transform with Cylindrical Apertures
The spherical Radon transform (SRT) is an integral transform that maps a function to its integrals over concentric spherical shells centered at specified sensor locations.
Attention-Based CNN-BiLSTM for Sleep State Classification of Spatiotemporal Wide-Field Calcium Imaging Data
Comparison with Existing Method: On a 3-hour WFCI recording, the CNN-BiLSTM achieved a kappa of 0. 67, comparable to a kappa of 0. 65 corresponding to the human EEG/EMG-based scoring.
Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography
Deep learning-based image-to-image learned reconstruction (IILR) methods are being investigated as scalable and computationally efficient alternatives.
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics
Aim: To improve the efficiency of endoscopic screening, we proposed a novel end-expandable endoscopic optical fiber probe for larger field of visualization and employed a deep learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability.
Spatiotemporal Image Reconstruction to Enable High-Frame Rate Dynamic Photoacoustic Tomography with Rotating-Gantry Volumetric Imagers
Aim: The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy.
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work.
AmbientFlow: Invertible generative models from incomplete, noisy measurements
In applications such as computed imaging, it is often difficult to acquire such data due to requirements such as long acquisition time or high radiation dose, while acquiring noisy or partially observed measurements of these objects is more feasible.
Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography
Once trained, the CNN can perform real-time FWI image reconstruction from USCT waveform data.
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning
A series of out-of-distribution tests further confirmed the generality of our framework.
High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction.
Ideal Observer Computation by Use of Markov-Chain Monte Carlo with Generative Adversarial Networks
Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task.
A Test Statistic Estimation-based Approach for Establishing Self-interpretable CNN-based Binary Classifiers
The model is trained to estimate the test statistic of the given trained black-box deep binary classifier to maintain a similar accuracy.
On the impact of incorporating task-information in learning-based image denoising
The task-component was designed to measure the performance of a numerical observer (NO) on a signal detection task.
Investigating the robustness of a learning-based method for quantitative phase retrieval from propagation-based x-ray phase contrast measurements under laboratory conditions
In this work, a LBM was assessed for its applicability under practical scenarios by evaluating its robustness and generalizability under typical experimental variations.
A Memory-Efficient Dynamic Image Reconstruction Method using Neural Fields
Computational and memory requirements are particularly burdensome for three-dimensional dynamic imaging applications requiring high resolution in both space and time.
Assessing the ability of generative adversarial networks to learn canonical medical image statistics
In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment.
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging
However, procedures for establishing stochastic image models (SIMs) using GANs remain generic and do not address specific issues relevant to medical imaging.
Application of DatasetGAN in medical imaging: preliminary studies
Generative adversarial networks (GANs) have been widely investigated for many potential applications in medical imaging.
Prior image-based medical image reconstruction using a style-based generative adversarial network
Discrepancy between the sought-after and prior images is measured in the disentangled latent-space, and is used to regularize the inverse problem in the form of constraints on specific styles of the disentangled latent-space.
Mining the manifolds of deep generative models for multiple data-consistent solutions of ill-posed tomographic imaging problems
In this work, a new empirical sampling method is proposed that computes multiple solutions of a tomographic inverse problem that are consistent with the same acquired measurement data.
A Method for Evaluating Deep Generative Models of Images via Assessing the Reproduction of High-order Spatial Context
We designed several stochastic context models (SCMs) of distinct image features that can be recovered after generation by a trained GAN.
A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods
Specifically, a hybrid approach is developed that combines a multi-task convolutional neural network and a Markov-Chain Monte Carlo (MCMC) method in order to approximate the IO for detection-estimation tasks.
Impact of deep learning-based image super-resolution on binary signal detection
The presented study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.
Learning stochastic object models from medical imaging measurements by use of advanced ambient generative adversarial networks
AmbientGANs established using the proposed training procedure are systematically validated in a controlled way using computer-simulated magnetic resonance imaging (MRI) data corresponding to a stylized imaging system.
Assessing the Impact of Deep Neural Network-based Image Denoising on Binary Signal Detection Tasks
The performance of the ideal observer (IO) and common linear numerical observers are quantified and detection efficiencies are computed to assess the impact of the denoising operation on task performance.
Prior Image-Constrained Reconstruction using Style-Based Generative Models
Obtaining a useful estimate of an object from highly incomplete imaging measurements remains a holy grail of imaging science.
Advancing the AmbientGAN for learning stochastic object models
Medical imaging systems are commonly assessed and optimized by use of objective-measures of image quality (IQ) that quantify the performance of an observer at specific tasks.
On hallucinations in tomographic image reconstruction
The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
Deeply-Supervised Density Regression for Automatic Cell Counting in Microscopy Images
In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images.
Compressible Latent-Space Invertible Networks for Generative Model-Constrained Image Reconstruction
To circumvent this problem, in this work, a framework for reconstructing images from incomplete measurements is proposed that is formulated in the latent space of invertible neural network-based generative models.
Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.
Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve.
Approximating the Hotelling Observer with Autoencoder-Learned Efficient Channels for Binary Signal Detection Tasks
Overall, AEs are demonstrated to be competitive with state-of-the-art methods for generating efficient channels for the HO and can have superior performance on small datasets.
Learning Numerical Observers using Unsupervised Domain Adaptation
In this study, we propose and investigate the use of an adversarial domain adaptation method to mitigate the deleterious effects of domain shift between simulated and experimental image data for deep learning-based numerical observers (DL-NOs) that are trained on simulated images but applied to experimental ones.
Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
Medical image reconstruction is typically an ill-posed inverse problem.
Markov-Chain Monte Carlo Approximation of the Ideal Observer using Generative Adversarial Networks
To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed.
Progressively-Growing AmbientGANs For Learning Stochastic Object Models From Imaging Measurements
However, because medical imaging systems record imaging measurements that are noisy and indirect representations of object properties, GANs cannot be directly applied to establish stochastic models of objects to-be-imaged.
Approximating the Ideal Observer and Hotelling Observer for binary signal detection tasks by use of supervised learning methods
For binary signal detection tasks, the Bayesian Ideal Observer (IO) sets an upper limit of observer performance and has been advocated for use in optimizing medical imaging systems and data-acquisition designs.
Reconstruction-Aware Imaging System Ranking by use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference
In this work, a sparsity-driven observer (SDO) that can be employed to optimize hardware by use of a stochastic object model describing object sparsity is described and investigated.
Deep Learning-Guided Image Reconstruction from Incomplete Data
The CNN is trained to encode high level information about the class of images being imaged; this information is utilized to mitigate artifacts in intermediate images produced by use of an iterative method.
