Paper Citation Record · LEDGER

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2506.03183.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.03183 v1

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measured 39 of 39 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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39 of 39 outbound references displayed

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Outbound references

Observation 76633da9-8e3f-4b1c-8fa7-fddaee5082b4 · outbound

This paper cites Learning a variational network for reconstruction of accelerated MRI data,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Learning a variational network for reconstruction of accelerated MRI data,

Reference 1

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This paper cites A deep cascade of convolutional neural networks for dynamic MR image reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study A deep cascade of convolutional neural networks for dynamic MR image reconstruction,

Reference 2

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This paper cites MoDL: Model-based deep learning architecture for inverse problems,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study MoDL: Model-based deep learning architecture for inverse problems,

Reference 3

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This paper cites Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues,

Reference 4

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This paper cites Dense recurrent neural networks for accelerated MRI: History- cognizant unrolling of optimization algorithms,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Dense recurrent neural networks for accelerated MRI: History- cognizant unrolling of optimization algorithms,

Reference 5

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Observation 087b1a39-b708-45a6-8c2a-cbef2cba7e48 · outbound

This paper cites Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging,

Reference 6

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Observation cee73627-4133-436d-a5c2-2a76fc201481 · outbound

This paper cites Advancing machine learning for MR image recon- struction with an open competition: Overview of the 2019 fastMRI challenge,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Advancing machine learning for MR image recon- struction with an open competition: Overview of the 2019 fastMRI challenge,

Reference 7

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This paper cites Results of the 2020 fastMRI challenge for machine learning MR image reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Results of the 2020 fastMRI challenge for machine learning MR image reconstruction,

Reference 8

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Observation 961dce3c-6ee2-4bdb-bfe5-ddc2e2ea576d · outbound

This paper cites Akc ¸akaya, M.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Akc ¸akaya, M

Reference 9

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Observation df8869f8-22fe-4609-bd16-d7ea5e829d92 · outbound

This paper cites Self-supervised physics-guided deep learning recon- struction for high-resolution 3D LGE CMR,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Self-supervised physics-guided deep learning recon- struction for high-resolution 3D LGE CMR,

Reference 10

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Observation 5727c8de-39be-463a-8042-70db2749552d · outbound

This paper cites High-quality 0.5 mm isotropic fMRI: Random matrix theory meets physics-driven deep learning,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study High-quality 0.5 mm isotropic fMRI: Random matrix theory meets physics-driven deep learning,

Reference 11

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Observation f87ce692-c8eb-4c8c-876f-cb2cb9920bb3 · outbound

This paper cites Highly-accelerated high-resolution multi-echo fMRI using self-supervised physics-driven deep learning reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Highly-accelerated high-resolution multi-echo fMRI using self-supervised physics-driven deep learning reconstruction,

Reference 12

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Observation d9fc617e-988d-4914-909a-5163a4e7b08e · outbound

This paper cites Non-cartesian self-supervised physics-driven deep learn- ing reconstruction for highly-accelerated multi-echo spiral fMRI,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Non-cartesian self-supervised physics-driven deep learn- ing reconstruction for highly-accelerated multi-echo spiral fMRI,

Reference 13

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Observation e6865f07-e2a8-4591-854d-29405e8e1a2a · outbound

This paper cites 20-fold accelerated 7T fMRI using referenceless self-supervised deep learning reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study 20-fold accelerated 7T fMRI using referenceless self-supervised deep learning reconstruction,

Reference 14

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Observation 86f1ef46-b7cf-465f-b8ff-c6b5310ef54c · outbound

This paper cites Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging,

Reference 15

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Observation d3dca6af-a5fb-4cad-8e70-15826593349d · outbound

This paper cites SENSE: Sensitivity encoding for fast MRI,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study SENSE: Sensitivity encoding for fast MRI,

Reference 16

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Observation cac79b1a-6162-4f0b-9599-6b397212e2e9 · outbound

This paper cites Automatic compilation of diverse CNNs onto high-performance FPGA accelerators,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Automatic compilation of diverse CNNs onto high-performance FPGA accelerators,

Reference 17

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This paper cites ALAMO: FPGA acceleration of deep learning algorithms with a modularized RTL compiler,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study ALAMO: FPGA acceleration of deep learning algorithms with a modularized RTL compiler,

Reference 18

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This paper cites Caffeine: Toward uniformed representation and acceleration for deep convolutional neural networks,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Caffeine: Toward uniformed representation and acceleration for deep convolutional neural networks,

Reference 19

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Observation 66d10c55-1694-42ce-b670-5b2b096ff5eb · outbound

This paper cites Algorithm-hardware co-optimization for energy-efficient drone detec- tion on resource-constrained FPGA,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Algorithm-hardware co-optimization for energy-efficient drone detec- tion on resource-constrained FPGA,

Reference 20

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Observation 6c92b04a-0b99-4cd2-a4bd-b519b911c5e0 · outbound

This paper cites FPGA acceleration of GCN in light of the symmetry of graph adjacency matrix,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study FPGA acceleration of GCN in light of the symmetry of graph adjacency matrix,

Reference 21

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Observation fdcacc14-2053-44e5-b29d-78ed5bf8fc94 · outbound

This paper cites Array compression for mri with large coil arrays,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Array compression for mri with large coil arrays,

Reference 22

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Observation 1606a790-4e4a-4923-b441-b1b88c013ed6 · outbound

This paper cites The WU-Minn Human Connectome Project: An overview,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study The WU-Minn Human Connectome Project: An overview,

Reference 23

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Observation fad9f4eb-ab63-4403-bfdc-c18bf8b4e5a1 · outbound

This paper cites Optimization methods for magnetic resonance image reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Optimization methods for magnetic resonance image reconstruction,

Reference 24

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Observation f99bc936-6426-4a79-8705-ea9d9f502a3c · outbound

This paper cites Self-supervised learning of physics-guided recon- struction neural networks without fully sampled reference data,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Self-supervised learning of physics-guided recon- struction neural networks without fully sampled reference data,

Reference 25

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Observation ea5f831a-1ebc-4aa2-b052-3e9864c46200 · outbound

This paper cites Multi-mask self-supervised learning for physics- guided neural networks in highly accelerated magnetic resonance imag- ing,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Multi-mask self-supervised learning for physics- guided neural networks in highly accelerated magnetic resonance imag- ing,

Reference 26

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Observation 635157dd-976b-4992-900f-c25a02f64b43 · outbound

This paper cites Zero-shot self- supervised learning for MRI reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Zero-shot self- supervised learning for MRI reconstruction,

Reference 27

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Observation 24f6a813-bda2-4f5d-bd61-92583aa36a3b · outbound

This paper cites Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Unsupervised deep learning methods for biological image reconstruction and enhancement: An overview from a signal processing perspective,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:43.437634Z

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Observation cb55cfb8-c94b-4b69-b6c9-2d8a5bc986f2 · outbound

This paper cites Cycle-consistent self- supervised learning for improved highly-accelerated MRI reconstruc- tion,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Cycle-consistent self- supervised learning for improved highly-accelerated MRI reconstruc- tion,

Reference 29

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Observation 5119eb6a-7c1e-4a1d-b715-81fcfd563170 · outbound

This paper cites A convex compressibility- inspired unsupervised loss function for physics-driven deep learning reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study A convex compressibility- inspired unsupervised loss function for physics-driven deep learning reconstruction,

Reference 30

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This paper cites Sparsity-driven parallel imaging consistency for improved self-supervised MRI reconstruction,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Sparsity-driven parallel imaging consistency for improved self-supervised MRI reconstruction,

Reference 31

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Observation 75dd6be3-2f5a-4dbf-ba9e-1cfa8a9d2170 · outbound

This paper cites Revisitingℓ 1-wavelet compressed-sensing MRI in the era of deep learning,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Revisitingℓ 1-wavelet compressed-sensing MRI in the era of deep learning,

Reference 32

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Observation 3d30a81f-c438-4fe5-b68a-df51246e8dca · outbound

This paper cites Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR,

Reference 33

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This paper cites NTIRE 2017 challenge on single image super-resolution: Methods and results,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study NTIRE 2017 challenge on single image super-resolution: Methods and results,

Reference 34

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This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Smoothquant: Accurate and efficient post-training quantization for large language models,

Reference 35

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This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 36

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Observation f5fc0c95-0087-4f23-b26a-12295cc2d410 · outbound

This paper cites fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Reference 37

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This paper cites fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study fastMRI: a publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning,

Reference 38

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Observation fc83801d-23f1-4123-9984-292272aaa7c7 · outbound

This paper cites Advances in sensitivity encoding with arbitrary k-space trajectories,.

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study Advances in sensitivity encoding with arbitrary k-space trajectories,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:37:41.629760Z

Source-reported events for the cited work

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Pith citing papers

No inbound Pith citation observations are available.