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Radon Inversion via Deep Learning

https://doi.org/10.1109/tmi.2020.2964266

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Outgoing claims: the papers this work cites, builds on, or argues with.

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CLEAR: Comprehensive Learning Enabled Adversarial Reconstruction for Subtle Structure Enhanced Low-Dose CT Imaging

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A 3D Tubular Flux Model for Centerline Extraction in Neuron Volumetric Images

inferred← citeshttps://doi.org/10.1109/tmi.2022.3167809openalex-snapshot

Self-Training Strategy Based on Finite Element Method for Adaptive Bioluminescence Tomography Reconstruction

inferred← citeshttps://doi.org/10.1109/tmi.2022.3175529openalex-snapshot

Sam’s Net: A Self-Augmented Multistage Deep-Learning Network for End-to-End Reconstruction of Limited Angle CT

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Noise Characteristics Modeled Unsupervised Network for Robust CT Image Reconstruction

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Unsharp Structure Guided Filtering for Self-Supervised Low-Dose CT Imaging

inferred← citeshttps://doi.org/10.1109/tmi.2023.3313421openalex-snapshot

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction

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AIRPORT: A Data Consistency Constrained Deep Temporal Extrapolation Method To Improve Temporal Resolution In Contrast Enhanced CT Imaging

inferred← citeshttps://doi.org/10.1109/tmi.2023.3347258openalex-snapshot

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution

inferred← citeshttps://doi.org/10.1109/tmi.2024.3405024openalex-snapshot

Cross-Domain Denoising for Low-Dose Multi-Frame Spiral Computed Tomography

inferred← citeshttps://doi.org/10.1109/tmi.2024.3475516openalex-snapshot

Solving Zero-Shot Sparse-View CT Reconstruction With Variational Score Solver

inferred← citeshttps://doi.org/10.1109/tmi.2025.3541491openalex-snapshot

CT-SDM: A Sampling Diffusion Model for Sparse-View CT Reconstruction Across Various Sampling Rates

inferred← citeshttps://doi.org/10.1109/tmi.2025.3575071openalex-snapshot

GLIMPSE: Generalized Locality for Scalable and Robust CT

inferred← citeshttps://doi.org/10.1109/tmi.2026.3669463openalex-snapshot

Tomographic Foundation Model—FORCE: Flow-Oriented Reconstruction Conditioning Engine

inferred← citeshttps://doi.org/10.1109/tmi.2026.3674948openalex-snapshot

DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction

inferred← citeshttps://doi.org/10.1109/trpms.2021.3083361openalex-snapshot

Noise-Generating-Mechanism-Driven Unsupervised Learning for Low-Dose CT Sinogram Recovery

inferred← citeshttps://doi.org/10.1109/trpms.2021.3107454openalex-snapshot

Artificial Intelligence in Radiation Therapy

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Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer

inferred← citeshttps://doi.org/10.1109/trpms.2023.3316349openalex-snapshot

A Review of Deep Learning CT Reconstruction From Incomplete Projection Data

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Noise-Generating Mechanism-Driven Implicit Diffusion Prior for Low-Dose CT Sinogram Recovery

inferred← citeshttps://doi.org/10.1109/trpms.2025.3571281openalex-snapshot

Cross-Domain Reconstruction Network Incorporating Sinogram Sinusoidal-Structure Transformer Denoiser and UNet for Low-Dose/Low-Count Sinograms

inferred← citeshttps://doi.org/10.1109/trpms.2025.3574209openalex-snapshot

Towards Unified CT Reconstruction: Federated Metadata Learning With Personalized Condition-Modulated iRadonMAP

inferred← citeshttps://doi.org/10.1109/tsp.2023.3330267openalex-snapshot

A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems

inferred← citeshttps://doi.org/10.1111/sapm.70053openalex-snapshot

Augmented Total Variation Regularization in Radon‐Type Inverse Problems

inferred← citeshttps://doi.org/10.1117/1.jbo.28.2.026004openalex-snapshot

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module

inferred← citeshttps://doi.org/10.1145/3503161.3548204openalex-snapshot

Learning Projection Views for Sparse-View CT Reconstruction

inferred← citeshttps://doi.org/10.1145/3528223.3530121openalex-snapshot

NeAT

inferred← citeshttps://doi.org/10.1145/3746027.3758233openalex-snapshot

MORE: Multi-Organ Medical Image REconstruction Dataset

inferred← citeshttps://doi.org/10.1148/radiol.221257openalex-snapshot

Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects

inferred← citeshttps://doi.org/10.1177/08953996251319187openalex-snapshot

Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction

inferred← citeshttps://doi.org/10.1186/s42492-022-00127-yopenalex-snapshot

Deep learning tomographic reconstruction through hierarchical decomposition of domain transforms

inferred← citeshttps://doi.org/10.12122/j.issn.1673-4254.2024.02.16openalex-snapshot

[A low- dose CT reconstruction algorithm across different scanners based on federated feature learning].

inferred← citeshttps://doi.org/10.1364/boe.549880openalex-snapshot

Towards real-time diffuse optical tomography with a handheld scanning probe

inferred← citeshttps://doi.org/10.1364/oe.460208openalex-snapshot

Physics-assisted generative adversarial network for X-ray tomography

inferred← citeshttps://doi.org/10.1364/oe.532255openalex-snapshot

INeAT: an artifact-suppressed and resolution-enhanced computed tomography through iterative neural adaptive tomography

inferred← citeshttps://doi.org/10.21037/qims-23-768openalex-snapshot

Transferring U-Net between low-dose CT denoising tasks: a validation study with varied spatial resolutions

inferred← citeshttps://doi.org/10.3389/fnume.2026.1885873openalex-snapshot

Dual-domain transformer-based learned primal-dual reconstruction for PET imaging

inferred← citeshttps://doi.org/10.3390/a16060270openalex-snapshot

CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction

inferred← citeshttps://doi.org/10.3390/bioengineering11070646openalex-snapshot

HEAL: High-Frequency Enhanced and Attention-Guided Learning Network for Sparse-View CT Reconstruction

inferred← citeshttps://doi.org/10.3390/jimaging7030044openalex-snapshot

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications

inferred← citeshttps://doi.org/10.3390/math10040579openalex-snapshot

A Generalized Construction Model for CT Projection-Wise Filters on the SDBP Technique

inferred← citeshttps://doi.org/10.3390/math14193501openalex-snapshot

From the Radon Transform to Deep Learning in Medical Image Reconstruction

inferred← citeshttps://doi.org/10.34133/cbsystems.0376openalex-snapshot

Federated Metadata-Constrained iRadonMAP Framework with Mutual Learning for All-in-One Computed Tomography Imaging

inferred← citeshttps://doi.org/10.3788/lop212853openalex-snapshot

基于全广义变分约束加权最小二乘的低剂量计算机断层重建方法

inferred← citeshttps://doi.org/10.3788/lop230488openalex-snapshot

基于深度学习的稀疏或有限角度CT重建方法研究综述

inferred← citeshttps://doi.org/10.48550/arxiv.2006.06270openalex-snapshot

Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction

inferred← citeshttps://doi.org/10.48550/arxiv.2008.01846openalex-snapshot

Stabilizing Deep Tomographic Reconstruction

inferred← citeshttps://doi.org/10.48550/arxiv.2009.01782openalex-snapshot

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

inferred← citeshttps://doi.org/10.48550/arxiv.2105.14130openalex-snapshot

3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions

inferred← citeshttps://doi.org/10.48550/arxiv.2106.09834openalex-snapshot

AI-Enabled Ultra-Low-Dose CT Reconstruction

inferred← citeshttps://doi.org/10.6009/jjrt.2024-2309openalex-snapshot

[CT] 5. Various CT Image Reconstruction Methods Applying Deep Learning

inferred← citeshttps://doi.org/10.70675/96d4a1e2za7eaz438bza08bzec4325c1ffe0openalex-snapshot

Role of deep learning into three-dimensional reconstruction for breast tomosynthesis

inferred← citeshttps://doi.org/10.70675/c4fdfb62z9c63z48a0z953az4b95c4bc1dddopenalex-snapshot

Récupération de phase basée sur l'apprentissage profond pour l'imagerie à contraste de phase par rayons X

inferred← citeshttps://doi.org/10.7557/18.5676openalex-snapshot

A Tomographic Reconstruction Method using Coordinate-based Neural Network with Spatial Regularization

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