A faster X-ray CT image reconstruction method using relaxed linearized algorithms.
problem Reduced X-ray dose while maintaining image quality in CT scans.
method Relaxed linearized augmented Lagrangian (AL) method with over-relaxation.
result The proposed method is about twice as fast as existing unrelaxed fast algorithms.
Deep learning improves 3D reconstruction from sparse X-ray views.
problem Sparse view CT reconstruction produces severe streaking artifacts.
method Proposes a deep learning architecture for 3D reconstruction from 9 views.
result Superior reconstruction performance confirmed with real data.
New CT image reconstruction method reduces X-ray dose while improving image quality.
problem Reducing X-ray dose in CT while maintaining image quality.
method Combines PWLS with learned sparsifying transform using alternating optimization and relaxed OS-LALM.
result Proposed method improves image quality for low dose levels compared to existing methods.
This paper explores deep learning for improving X-ray CT image reconstruction from undersampled data.
problem Improving image reconstruction from undersampled X-ray CT data.
method Analysis of classical and deep learning methods for solving inverse problems.
result Deep learning methods show promise in improving image quality from undersampled data.
Improved low-dose CT images with deep learning and framelet denoising.
problem Low-dose X-ray CT images often lack texture and detail.
method Proposed a wavelet residual network combining deep learning and framelet denoising.
result Significantly improved performance in preserving image detail.
The augmented Lagrangian (AL) method that solves convex optimization problems with linear constraints has drawn more attention recently in imaging applications due to its decomposable structure for composite cost functions and empirical fast convergence rate under weak conditions. However, for problems such as X-ray co…
Study geodesic X-ray transform and streaking artifacts on simple surfaces or spaces of constant curvature.
problem Streaking artifacts in CT images due to metal regions.
method Geodesic X-ray transform on nontrapping compact Riemannian manifolds with strictly convex boundaries.
result Streaking artifacts result from conormal singularities along common tangent geodesics.
Open dataset for machine learning with reduced high-angle artefacts.
problem High-angle artefacts in cone-beam CT data for machine learning.
method Open data collection of 42 walnuts with varied cone angles, combined for artefact reduction.
result Ground truth images from combined data for supervised learning.
New method reduces radiation dose in CT scans while improving image quality.
problem Reducing radiation dose in CT scans while maintaining image quality.
method Combines PWLS and ℓ1 prior with learned sparsifying transform and ADMM algorithm.
result Improves image quality compared to existing methods for sparse-view CT.
A cascaded CNN reduces artifacts in low-dose CT image denoising.
problem Reduce artifacts in denoised low-dose CT images.
method Cascaded training of CNNs on a dataset to iteratively improve denoising performance.
result The cascaded CNN approach significantly reduces artifacts in denoised CT images.
New deep learning methods improve CT image quality from few projections.
problem Sparse-view CT images suffer from streaking artifacts due to limited projections.
method Inspired by deep convolutional framelets, propose new U-Net variants that satisfy the frame condition.
result New U-Net variants provide better reconstruction performance for sparse-view CT.
Deep-learning method estimates bone 3D structure from X-ray images.
problem Estimating bone 3D structure from X-ray images.
method Triplet loss-trained neural network selecting closest 3D bone shape from predefined set.
result Average RMS distance of 1.08 mm between predicted and true shapes.
Deep learning improves ROI reconstruction in low-dose CT.
problem Severe cupping artifacts in standard analytic reconstruction.
method Proposes a deep learning architecture to remove null space signals from FBP reconstruction.
result Near-perfect reconstruction with 7-10 dB improvement in PSNR.
DEER network improves few-view breast CT image reconstruction efficiency and quality.
problem Efficient and high-quality few-view breast CT image reconstruction.
method Deep Efficient End-to-end Reconstruction (DEER) network with low model complexity.
result DEER network achieves competitive image quality with significantly fewer parameters compared to state-of-the-art methods.
New method reconstructs CT images from limited angles using neural networks.
problem High artifact reconstructions from limited angle CT scans.
method Implicit sinogram completion with 1D and 2D CNNs.
result Combined strategy outperforms competitive baselines.
New CycleGAN uses invertible generator for faster, less resource-intensive CT denoising.
problem Efficient unsupervised CT denoising without paired data.
method Single generator with wavelet residual domain, no discriminators, cycle consistency via invertible generator.
result Significantly improved denoising performance with faster training and less parameters.
Paper presents a deep learning method for CT image super-resolution.
problem Developing high-quality CT images with reduced radiation exposure.
method Generative adversarial network (GAN) with cycle consistency and residual learning constraints.
result The proposed method achieves accurate, efficient, and robust super-resolution of CT images.
Study identifies COVID-19 pneumonia from chest X-rays.
problem Identifying COVID-19 pneumonia from other types and healthy lungs using CXR images.
method Proposed a multi-class and hierarchical classification schema using CXR images, texture descriptors, and a pre-trained CNN model. Employed resampling algorithms and early/late fusion techniques.
result Achieved macro-avg F1-Score of 0.65 and F1-Score of 0.89 for COVID-19 identification in hierarchical classification scenario.
RODEO speeds up MRI and CT image reconstruction from sparse data.
problem Real-time reconstruction of dynamic medical images from limited data.
method Autoencoder with robust l1-norm cost function and Split Bregman method.
result Real-time image reconstruction with minimal quality loss.
Study assesses CNN model robustness to noise in low-cost CT scans.
problem Evaluate CNN model performance on noisy, artifact-prone low-cost CT images.
method Developed and tested a CNN model for head CT triage, varying tube current and projections.
result Model remains robust to reduced tube current and fewer projections, maintaining AUROC close to original.
New neural network reduces CT radiation, works for any ROI size.
problem CT ROI reconstruction suffers from cupping artifacts and high computation.
method Two neural networks: one learns ROI-specific artifacts, the other learns DBP inversion.
result New network outperforms existing methods for any ROI size.
Automates detection of electric devices in 3D x-ray images of luggage.
problem Detecting electric devices in cluttered 3D baggage images.
method Unpack, Predict, eXtract, Repack (UXPR) algorithm using segmentation and ensemble learning.
result System can accurately detect electric devices in 3D baggage images.
SUPER learning combines supervised and unsupervised methods for LDCT image reconstruction.
problem Low-dose CT image reconstruction challenges.
method Combines supervised and unsupervised learning methods.
result SUPER learning dramatically outperforms constituent methods.
Medical image reconstruction advances from sparse models to machine learning.
problem Improving image quality and reducing noise in medical imaging.
method Iterative reconstruction, modified data acquisition methods, and machine learning models.
result Machine learning methods show promise in improving image quality.
Ensemble classifier detects pneumonia patterns in chest CT images.
problem Manual and time-consuming diagnosis of pneumonia in chest CT images.
method Probabilistic Support Vector Machine (SVM) ensemble, kernel PCA, patch-based classification.
result 97.86% accuracy in pneumonia detection.
Neural Optimal Design of Experiments improves inverse problem solving efficiency.
problem Optimal experimental design in inverse problems.
method Jointly trains a reconstruction model and design variables in a single loop.
result Significantly reduces computational complexity and improves reconstruction accuracy.
Let $\cT$ be Teichmüller space of a closed surface of genus at least 2. For any point $c\in \cT$, we describe an action of the circle on $\cT\times \cT$, which limits to the earthquake flow when one of the parameters goes to a measured lamination in the Thurston boundary of $\cT$. This circle action shares some of the …
New method uses Gaussian process for limited-data CT reconstruction.
problem Reconstructing internal structures from limited x-ray projections.
method Gaussian process with basis function expansion for parameter estimation.
result Less sensitive to streak artifacts compared to filtered backprojection.
A new COVID-19 CT dataset helps develop AI diagnosis models.
problem Lack of publicly available COVID-19 CT datasets due to privacy issues.
method Built an open-sourced COVID-CT dataset and developed AI diagnosis methods.
result Developed AI diagnosis models achieving high accuracy and performance.
For a smooth manifold M we define the Teichmüller space $\cT(M)$ of all Riemannian metrics on M and the Teichmüller space $\cT^ε(M)$ of ε-pinched negatively curved metrics on M, where 0≤ε≤∞. We prove that if M is hyperbolic the natural inclusion $\cT^ε(M)\hookrightarrow\cT(M)$ is, in general, not…
Lecture notes on analysis tools for X-ray tomography.
problem Understanding X-ray tomography using mathematical analysis.
method Overview of analysis tools and ideas, minimal assumptions.
result Broad overview of analysis tools for X-ray tomography.
Enhances uncertainty estimation in medical image segmentation.
problem Frequency-related noise in medical imaging leads to biased uncertainty estimates.
method Extends MC-Dropout to the frequency domain for better uncertainty estimation.
result MC-Frequency Dropout improves calibration and uncertainty in semantic segmentation.
Sharp mapping properties and regularization for X-ray transform on disks of constant curvature.
problem Sharp mapping properties and regularization of X-ray transform.
method Derive functional relations and mapping properties using elliptic differential operators.
result Theoretical possibility of regularized inversions for X-ray transform.
CNNs trained on one hospital's x-rays perform poorly on x-rays from other hospitals.
problem Generalization of radiological deep learning models across different hospitals.
method Cross-sectional design using x-rays from three hospitals (NIH, Mount Sinai, Indiana).
result CNNs trained on one hospital's x-rays perform significantly worse on x-rays from other hospitals.
Regularity results for geodesic X-ray transform on nonsmooth manifolds
problem Geodesic X-ray transform on nonsmooth simple manifolds
method Symbol smoothing arguments and pseudodifferential operators with low regularity symbols
result Improved injectivity results for Lp functions Paper shows invertibility of tensor X-ray transform on certain manifolds.
problem Invertibility of tensor X-ray transform on asymptotically conic manifolds.
method Used 1-cusp pseudodifferential operator algebra and modified solenoidal gauge condition.
result Invertibility of tensor X-ray transform up to natural obstruction.
Local X-ray transform works well near boundaries in hyperbolic spaces.
problem Injectivity of X-ray transform near boundaries for hyperbolic metrics.
method Local injectivity proof for geodesic X-ray transform on asymptotically hyperbolic manifolds.
result Local injectivity near a boundary point for X-ray transform in dimensions 3 and higher, up to O(ρ5). Machine learning predicts x-ray pulse properties from XFEL parameters.
problem Characterizing XFEL pulses for sorting data due to large fluctuations.
method Applied machine learning to predict x-ray pulse properties using electron beam and x-ray parameters.
result Mean errors below 0.3 eV for photon energy and below 1.6 fs for delay between pulses at 530 eV.
Study characterizes X-ray transform kernel for periodic slabs and related manifolds.
problem Characterizing the kernel of X-ray transform for tensor fields on periodic slabs.
method Characterization of the kernel for L2-regular m-tensors on [0,1]imesTn. result Kernel characterization extends to more general manifolds, including the Möbius strip.
Model learns to focus on key areas of chest X-rays.
problem Detecting abnormalities in chest X-rays.
method Recurrent visual attention model using reinforcement learning.
result Model can focus on informative areas of X-rays.
Functions with constant geodesic X-ray transform are restricted to manifolds with specific geometrical properties.
problem Existence of functions with constant geodesic X-ray transform on manifolds.
method Analyzing the geometrical properties of manifolds based on the existence of such functions.
result Functions with constant geodesic X-ray transform impose specific geometrical restrictions on the manifold.
NIST's CTS Challenge evaluates speaker recognition using telephony data.
problem Improving speaker recognition accuracy using telephony data.
method Large-scale neural network architectures, extensive data augmentation, proprietary data, and fine-tuning.
result Remarkable improvements in speaker recognition performance.
Study shows X-ray transform injectivity for hyperbolic manifolds.
problem Recovering metrics from boundary measurements on hyperbolic manifolds.
method Injectivity of X-ray transform in several cases.
result Injectivity of X-ray transform proven for asymptotically hyperbolic manifolds.
RADNET achieves radiologist-level accuracy in CT scan hemorrhage detection.
problem Automated detection of brain hemorrhages in CT scans.
method RADNET uses a 3D context-aware deep learning model with attention mechanisms.
result RADNET achieves 81.82% accuracy in hemorrhage prediction, comparable to radiologists.
Study shows stability in X-ray transform on specific hyperbolic manifolds.
problem Stability of X-ray transform on asymptotically hyperbolic manifolds.
method Constructed a parametrix for the normal operator in 0-pseudodifferential calculus.
result Showed a stability estimate for the X-ray transform.
Study X-ray transform on Anosov manifolds with improved stability estimates.
problem Analyzing the geodesic X-ray transform on Anosov manifolds.
method Refined Livsic theorem for Anosov flows, new quantitative finite time Livsic theorem.
result New stability estimates for the X-ray transform.
Improved s-CT generation from MRI using Markov random field and NIG distributions.
problem Generating accurate substitute CT images from MRI for attenuation correction and dose planning.
method Introduced flexible mixture models with spatial dependency and NIG distributions. Used a stochastic EM gradient algorithm for efficient parameter estimation.
result Enhanced predictive quality of s-CT images, reducing mean absolute error by 17.9%.
Deep learning tackles X-ray noise without clean data.
problem Lack of clean X-ray images for deep learning denoising.
method Uses Stein's Unbiased Risk Estimator (SURE) to train a deep neural network.
result SURE-based approach effectively denoises X-ray images.