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Paper Citation Record · LEDGER

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.14320.

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pith.paper-citation-record.v1
2607.14320 v1

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measured 34 of 34 reference resolution

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34 of 34 outbound references displayed

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Outbound references

Observation 54ce908a-c9f2-406e-b029-4dbb8c466c41 · outbound

This paper cites The meaning of interior tomography,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction The meaning of interior tomography,

Reference 1

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Observation b2de6125-241b-4b09-84ba-a7d8b838ff82 · outbound

This paper cites Compressed sensing based interior tomography,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Compressed sensing based interior tomography,

Reference 2

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Observation c41d2450-affe-427d-b9bf-60bda602a926 · outbound

This paper cites Image reconstruction for sparse-view CT and interior CT: Introduction to compressed sensing and differentiated backprojection,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Image reconstruction for sparse-view CT and interior CT: Introduction to compressed sensing and differentiated backprojection,

Reference 3

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Observation 48dfadce-008f-4ff2-8286-ad46ce5280b6 · outbound

This paper cites A practical local tomography reconstruction algorithm based on a known sub-region,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction A practical local tomography reconstruction algorithm based on a known sub-region,

Reference 4

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Observation 7feceea0-0118-41fd-8469-8e830a23e779 · outbound

This paper cites an unresolved cited work.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Unresolved cited work

Reference 5

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Observation e3c302fa-19eb-43f4-9d81-46f104a6fe8f · outbound

This paper cites FBP and the interior problem in 2D tomography,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction FBP and the interior problem in 2D tomography,

Reference 6

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Observation bad260b6-903f-498d-baa7-babde7f5d0ef · outbound

This paper cites Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Simultaneous algebraic reconstruction technique (SART): A superior implementation of the ART algorithm,

Reference 7

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Observation 76b158a4-c8e5-4c51-8afe-d4e0631f2319 · outbound

This paper cites Ordered-subset simultaneous algebraic recon- struction techniques (OS-SART),.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Ordered-subset simultaneous algebraic recon- struction techniques (OS-SART),

Reference 8

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Observation ff9fe9fb-ee8b-4f62-a237-e166561a6613 · outbound

This paper cites Bone-induced streak artifact suppression in sparse-view CT image reconstruction,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Bone-induced streak artifact suppression in sparse-view CT image reconstruction,

Reference 9

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Observation ba142619-a358-466c-aaa5-7e56dc4f45be · outbound

This paper cites Artifact reduction methods for truncated projections in iterative breast tomosynthesis reconstruction,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Artifact reduction methods for truncated projections in iterative breast tomosynthesis reconstruction,

Reference 10

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Observation 82f6c35c-bd7a-4ee3-a387-33348f9ba8f7 · outbound

This paper cites A diffusion-based truncated projection artifact reduction method for iterative digital breast tomosynthesis reconstruction,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction A diffusion-based truncated projection artifact reduction method for iterative digital breast tomosynthesis reconstruction,

Reference 11

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Observation 2210b11e-da20-4d50-a8de-dc9734914dc1 · outbound

This paper cites Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,

Reference 12

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Observation 708dcd96-64ef-4880-979d-70d46470a9b5 · outbound

This paper cites Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,

Reference 13

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Observation 9563a035-ea2e-47d0-b9fc-2901efd68f98 · outbound

This paper cites Solving inverse problems using data-driven models,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Solving inverse problems using data-driven models,

Reference 14

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Observation 8bc7310b-6067-4f95-bd85-261e201c43de · outbound

This paper cites Low-dose ct reconstruction via edge-preserving total variation regularization,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Low-dose ct reconstruction via edge-preserving total variation regularization,

Reference 15

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Observation dd93c977-a54e-4838-bdef-d9e86070ab9b · outbound

This paper cites Deep Learning Interior Tomography for Region-of-Interest Reconstruction.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Deep Learning Interior Tomography for Region-of-Interest Reconstruction

Reference 16

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Observation 454a5d40-b638-46ac-ae71-d28dd30e6da1 · outbound

This paper cites One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection

Reference 17

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Observation abac39a3-4dd0-48df-b37a-4eabb6891f1a · outbound

This paper cites End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction End-to-End Deep Learning for Interior Tomography with Low-Dose X-ray CT

Reference 18

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Observation 563b7f37-2bf8-4390-9204-62c568df01d4 · outbound

This paper cites Solving inverse problems in medical imaging with score-based generative models,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Solving inverse problems in medical imaging with score-based generative models,

Reference 19

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Observation a07ffaf8-3ad4-421d-afc3-99b5e65cb074 · outbound

This paper cites DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction

Reference 20

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Observation e526e88d-a163-4735-a8b2-fecf6dc8e39e · outbound

This paper cites Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction

Reference 21

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This paper cites Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine

Reference 22

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This paper cites Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems

Reference 23

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Denoising Diffusion Probabilistic Models

Reference 24

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This paper cites Score-based generative modeling through stochastic differ- ential equations,.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Score-based generative modeling through stochastic differ- ential equations,

Reference 25

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Elucidating the design space of diffusion-based generative models,

Reference 26

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Poisson flow generative models,

Reference 27

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction PFGM++: Unlocking the potential of physics-inspired generative mod- els,

Reference 28

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Image quality assessment: From error visibility to structural similarity,

Reference 29

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction The unreasonable effectiveness of deep features as a perceptual metric,

Reference 30

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction A first-order primal-dual algorithm for convex problems with applications to imaging,

Reference 31

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Tigre v3: Efficient and easy to use iterative computed tomographic reconstruction toolbox for real datasets,

Reference 32

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FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction DM4CT: Benchmarking dif- fusion models for computed tomography reconstruction,

Reference 33

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This paper cites Available: https://doi.org/10.1088/2631-8695/adbb3a.

FORCE-Interior: A Poisson Flow Generative Prior for Interior Tomography Reconstruction Available: https://doi.org/10.1088/2631-8695/adbb3a

Reference 2025

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